Electronics and Electrical Engineering

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ELECTRONICS AND ELECTRICAL ENGINEERING


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PROCEEDINGS OF THE 2014 ASIA-PACIFIC CONFERENCE ON ELECTRONICS ANDELECTRICAL ENGINEERING (EEEC 2014, SHANGHAI, CHINA, 27–28 DECEMBER 2014)

Electronics and ElectricalEngineering

Editor

Alan ZhaoShanghai Jiao Tong University, China


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Electronics and Electrical Engineering – Zhao (ed.) © 2015 Taylor & Francis Group, London, ISBN 978-1-138-02809-8

Table of contents

Foreword IXOrganizing Committee XI

Fuzzy based direct torque and flux control of induction motor drives 1C. Ning

Coordinated frequency control of thermal units with DC system in a wind-thermal-bundled system transmitted by High Voltage Direct Current (HVDC) line 7 J. Dang, Y. Tang, M.C. Song, J. Ning & X. Fu

A biomedical system combined fuzzy algorithm for telemedicine applications 13 P.L. Peng, P.Z. Chen, C.Y. Pan, G.J. Jong & B.H. Lin

A preventive control method for overload in a multi-source grid 17Z.W. Zhang, F. Xue, Y. Zhou, X.F. Song & L. Zhou

Effects of thermal annealing on the tungsten/lanthanum oxide interface 23 H. Wong, J.Q. Zhang, K. Kakushima, H. Iwai, J. Zhang & H. Jin

A study on the capacity optimization and allocation of wind/solar/diesel and energystorage hybrid micro-grid systems 27 J.G. Zhang, P.Y. Liu & H. Zhang

A neural network Proportional-Integral-Differential (PID) control based on a geneticalgorithm for a coupled-tank system 33

Y.S. Li & H.X. Li

Research into the reactive power compensation of a new dual buck non-isolated grid inverter 39 P. Sun, Y. Xie, Y. Fang, L.J. Huang & Y. Yao

Modelling condition monitoring inspection intervals 45 A. Raza & V. Ulansky

A coordinated voltage control strategy for a Doubly-Fed Induction Generator (DFIG) wind farm system 53 J.J. Zhao, X.G. Hu, X. Lv & X.H. Zhang

SOM-based intrusion detection for SCADA systems 57 H. Wei, H. Chen, Y.G. Guo, G. Jing & J.H. Tao

Nonlinear and adaptive backstepping speed tracking control of a permanent magnetsynchronous motor despite all parameter uncertainties and load torque disturbance variation 63 H.I. Eskikurt & M. Karabacak

The convolution theorem associated with fractional wavelet transform 71Y.Y. Lu, B.Z. Li & Y.H. Chen

Robustness testing method for intelligent electronic devices 75 H.T. Jiang, Y. Yang, W. Huang & Y.J. Guo

Design and analysis of quasi-optics for a millimeter wave imaging system 83 N.N. Wang, J.H. Qiu, Y. Zhang, Y. Zhang, P.Y. Zhang, H. Zong & L.Y. Xiao

A simply fabricated hybrid type metal based electrode for application in supercapacitors 87S.C. Lee, U.M. Patil & S.C. Jun

Joint scheduling based on users’ correlation in MU-CoMP 89Y.F. Wang & D.L. Wang

A monitoring system of harmonic additional loss from distribution transformers 95Z. Liu, Y. Liu, Q.Z. Cao & Z.L. Zhang

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A performance evaluation of higher order modulation considering Error Vector Magnitude (EVM)in a Long Term Evolution (LTE) advanced downlink 101 X.S. Liu & Z.G. Wen

Research on the control method of the driving system for the in-wheel driven range-extended electric vehicle 107S.T. Wang & X. Zhang

Research on TSP based two phases path planning method for sweep coverage of a mobilewireless sensor network 113Z.Y. Zhang, W.L. Wang, Q.S. Fang & H.M. Cheng

A monitor method of a distribution transformer’s harmonic wave compatible to its loss 117 D. Yu, Y. Zhao, Y. Zhang, Y. Tan, J.M. Zhang & Z.L. Zhang

Identification of a gas-solid two-phase flow regime based on an electrostatic sensor and Hilbert–Huang Transform (HHT) 123 J.X. Hu, X.H. Yao & T. Yan

A fast multilevel reconstruction method of depth maps based on Block Compressive Sensing 129T. Fan & G.Z. Wang

Dynamic modelling with validation for PEM fuel cell systems 135

Y. Chen, H. Wang, B. Huang & Y. Zhou

A highly sensitive new label-free bio-sensing platform using radio wave signal analysis,assisted by magnetic beads 143 J.H. Ji, K.S. Shin, Y.K. Ji & S.C. Jun

Noise analysis and suppression for an infrared focal plane array CMOS readout circuits 147 P.Y. Liu, J.L. Jiang & C.F. Wang

Speaker recognition performance improvement by enhanced feature extraction of vocalsource signals 151 J. Kang, Y. Kim & S. Jeong

Online detection and disturbance source location of low frequency oscillation 155

J. Luo, F.Z. Wang, C.W. Zhou & B.J. Wen

A soft-start Pulse Frequency Modulation-controlled boost converter for low-power applications 161 M.C. Lee, M.C. Hsieh & T.I. Tsai

Thermal analysis of phase change processes in aquifer soils 167 D. Enescu, H.G. Coanda, O. Nedelcu, C.I. Salisteanu & E.O. Virjoghe

The development of a slotted waveguide array antenna and a pulse generator for air surveillance radar 177 M. Wahab, D. Ruhiyat, I. Wijaya, F. Darwis & Y.P. Saputera

A harmonic model of an orthogonal core controllable reactor by magnetic circuit method 181W.S. Gu & H. Wang

A risk assessment model of power system cascading failure, considering the impact of ambient temperature 185 B.R. Zhou, R.R. Li, L.F. Cheng, P.Y. Di, L. Guan, S. Wang & X.C. Chen

Target speech detection using Gaussian mixture modeling of frequency bandwise power ratio for GSC-based beamforming 191 J. Lim, H. Jang, S. Jeong & Y. Kim

A compressive sampling method for signals with unknown spectral supports 195 E. Yang, X. Yan, K.Y. Qin, F. Li & B. Chen

Design and analysis of SSDC (Subsynchronous Damping Controller) for the Hulun Buir coal base plant transmission system 201G.S. Li, S.M. Han, X.D. Yu, S.W. Xiao & X.H. Xian

Stage division and damage degree of cascading failure 205 X.Q. Yan, F. Xue, Y. Zhou & X.F. Song

The design of a highly reliable management algorithm for a space-borne solid state recorder 211S. Li, Q. Song, Y. Zhu & J.S. An

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A patrol scheme improvement for disconnectors based on a logistic regression analysis 215 J.S. Li, Y.H. Zhu & Z.Q. Zhao

Progress on an energy storage system based on a modular multilevel converter 219 B. Ren, C. Liu, Y.H. Xu, C. Yuan, S.Y. Li & T. Wu

Robust fall detection based on particle flow for intelligent surveillance 225C.Q. Zhang & Y.P. Guan

An IEC 61850 based coordinated control architecture for a PV-storage microgrid 231 H.Y. Huang, F.J. Peng, X.Y. Huang, A.D. Xu, J.Y. Lei, L. Yu & Z. Shen

The design of an IED for a high voltage switch operating mechanism based on IEC 61850 237Z.Q. Liu & X.R. Li

A profile of charging/discharging loads on the grid due to electric vehicles under different price mechanisms 241 M.Y. Li & B. Zou

Algorithm design of the routing and spectrum allocation in OFDM-based software defined optical networks 247S. Liu, X.M. Li & D.Z. Zhao

The impact of Negative Bias Temperature Instability (NBTI) effect on D flip-flop 253 J.L. Yan, X.J. Li & Y.L. Shi

The electrical property of a three dimensional graphene composite for sensor applications 259 M.S. Nam, I. Shakery, J.H. Ji & C.J. Seong

A method of automatically generating power flow data files of BPA software for atransmission expansion planning project 261 B. Zhou, T. Wang, L. Guan, Q. Zhao, Y.T. Lv & L.F. Cheng

The analysis of training schemes for new staff members from substation operation and maintenance departments 267Y.T. Jiang, Y.B. Ren, X.H. Zhou, L. Mu, Y. Jiang & H.K. Liu

Research of source-grid-load coordinated operation and its evaluation indexes in ADN 271W. Liu, M.X. Zhao, H. Hui, C. Ye & S.H. Miao

Progress on the applications of cascaded H-bridges with energy storage systems and wind power integrated into the grid 275S.Y. Li, T. Wu, Y.S. Han, W. Cao & Y.H. Xu

Aluminium alloy plate flaw sizing by multipath detection 281 D.B. Guo, X.Z. Shen & L. Wang

An equilibrium algorithm clarity for the network coverage and power control in wirelesssensor networks 285 L. Zhu, C.X. Fan, Z.G. Wen, Y. Li & Z.Y. Zhai

Three-layer architecture based urban photovoltaic (PV) monitoring system for high-density,multipoint, and distributed PV generation 289 H. Gang, P. Qiu & D.C. He

Modelling and estimation of harmonic emissions for Distributed Generation (DG) 293 L.F. Li, N.H. Yu, J. Hu & X.P. Zhang

Mechanism and inhibiting methods for cogging torque ripples in Permanent Magnet Motors 299 H. Zhang, G.Y. Li & Z. Geng

Content-weighted and temporal pooling video quality assessment 305 F. Pan, C.F. Li, X.J. Wu & Y.W. Ju

A filter structure designing for an EAS system 311 M. Lin & J.L. Jiang

A mini-system design based on MSP430F249 317 M.M. Yang, Y.M. Tian & H.W. Wang

A control system for the speed position of a DC motor 321V.V. Ciucur

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Intelligent wireless image transmission system 325 M.M. Zhang, J.Y. Li & M.F. Wang

Model predictive control for a class of nonlinear systems via parameter adaptation 329C.X. Zhang, W. Zhang & D.W. Zhang

Control strategy of BESS for wind power dispatch based on variable control interval 333T. Lei, W.L. Chai, W.Y. Chen & X. Cai

A study of the maintenance of transformer using a cost-effectiveness optimization model 339 L.J. Guo & S.M. Tao

A new decision support system for a power grid enterprise overseas investment 343 L. Tuo & Z. Yi

A coal mine video surveillance system based on the Nios II soft-core processor 347 P.J. Wei & L.L. Shi

Research on a mechanism for measuring force in material moulding and the features of itsmeasuring circuits 351 H.P. An, Z.Y. Rui & R.F. Wang

Author index 355

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Foreword

The purpose of EEEC-14 is promoting the creativity of the Chinese nation in the scope of Electronics and Electrical Engineering. Electronics, as well as Electrical Engineering are always the companion of Electrical and Electronics.

When we were collecting the papers for the Conference on Electronics and Electrical Engineering, the inter-esting things were that a number of authors were quite keen to look for the same chance to make public thetheoretical works of intellectual creativity or formal results of scientific research or practice, written by thosewho are their former class mates, campus fellows, friends, relatives, colleagues and cooperators. It is really achance for us to organize a chance for our smart intellectuals to expose, exchange and confidently approve eachother and the value of their arduous work.

Electronics is one of the most important matters of our life and with us as ever we have existed, unlike

computer or information technology but unfortunately we have never known all Electronics we have used around us. Nevertheless, we have been working very hard to discover new sources of energy we are in need of and striving non-stop for new synthetic stuffs or man-made matters. High demand has pushed our scholars,experts and professionals to continue the mission, not only for the materials themselves but non-perilous to thelife and environment as well, causing the least hazard to the world. We appreciate our authors consciously toinvolve into that mission.

Asia-Pacific Electronics and Electrical Engineering Conference was scheduled to hold in Shanghai fromDecember 27–28, 2014; experts and scholars, a group of the authors and other related people have attended theconference with apparent interest; we are expecting a full success of the conference.

We appreciate those who responded to our proposal and submitted their papers, especially those whose papershave been selected for the conference EEEC-14, the sponsors who have provided their valuable and professionalsuggestions and instructions and the scholars and professors who have spent their efforts as peer reviewers.

Thank you!Samson Yu

December 1, 2014

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Organizing Committee

General Chairs

Prof. Soloman Kim, Shanghai Jiao Tong University, China

Technical Program Committee

Dr. Yuh-Huei Shyu, Tamkang University, Taiwan, ChinaDr. Yudi Gondokaryono, Institute of Teknologi Bandung, IndonesiaDr. Mohd Nazri Ismail, Universiti Kebangsaan, MalaysiaDr. Yongfeng Fang, Xidian University, China

Dr. Funian Li, Wuhan University of Science and Technology, ChinaDr. Gabriel Alungbe, University of Cincinnati, USADr. Mingsi Su, Lanzhou University, ChinaDr. V. L. Manekar, S. V. National Institute of Technology, IndiaProf. Xinge You, Huazhong University of Science and Technology, ChinaProf. Yuwen Chen, Shenyang Pharmaceutical University, ChinaProf. Jikang Jian, Xinjiang University, ChinaProf. Ling Song, Guangxi University, ChinaProf. Shaohua Teng, Nanjing University, ChinaProf. Jinyuan Jia, Tongji University, ChinaProf. Huailin Shu, Guangzhou University, ChinaProf. Yibin He, Wuhan Institute of Technology, China

Prof. Qiuling Tang, Guangxi University, ChinaProf. Qingfeng Chen, Guangxi University, ChinaProf. Lianming Wang, Northeast Normal University, ChinaProf. Lei Guo, Beihang University, ChinaProf. Zongtian Liu, Shanghai University, ChinaProf. Yimin Chen, Shanghai University, ChinaProf. Xiaoping Wei, China University of Mining and Technology, ChinaProf. Xiaodong Wang, North China Electric Power University, ChinaProf. Jianning Ding, Jiangsu University, ChinaProf. Xiaodong Jiang, School of Electrical Engineering & Automation of Tianjin University, ChinaProf. Jinan Gu, Jiangsu University, ChinaProf. Xueping Zhang, Henan University of Technology, China

Prof. Yingkui Gu, Jiangxi University of Science and Technology, ChinaProf. Shengyong Chen, Zhejiang University of Technology, ChinaProf. Qinghua You, Shanghai Maritime University, ChinaProf. Bintuan Wang, Beijing Jiaotong University, ChinaProf. Pengjian Shang, Beijing Jiaotong University, ChinaProf. Yiquan Wu, Nanjing University of Aeronautics and Astronautics, ChinaProf. Hongyong Zhao, Nanjing University of Aeronautics and Astronautics, ChinaProf. Gang Ren, Southeast University, ChinaProf. Jianning Ding, Nanjing Normal University, ChinaProf. Chen Peng, Nanjing Normal University, ChinaProf. Huajie Yin, South China University of Technology, ChinaProf. Yuhui Zhou, Bejing Jiaotong University, ChinaProf. Zhongjun Wang, Wuhan University of Technology, ChinaProf. Zhongqiang Wu, Yanshan University, ChinaProf. Wenguang Jiang, Yanshan University, ChinaProf. Fuchun Liu, Guangdong University of Technology, ChinaProf. Kangshun Li, South China Agricultural University, China

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Prof. Jie Ling, Guangdong University of Technology, ChinaProf. Lin Yang, Shanghai Jiaotong University, ChinaProf. Xinfan Feng, Jinan University, ChinaProf. Zongfu Hu, Tongji University, ChinaProf. Wanyang Dai, Nanjing University, China

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Fuzzy based direct torque and flux control of induction motor drives

Chuang Ning Professional Engineering Electrical Design, Perth, Australia

ABSTRACT: This paper investigates direct torque and flux control of an induction motor drive based on theFuzzy Logic (FL) control technique. Direct torque and flux control has become a widely acceptable alternativeto field oriented control The hysteresis-band controller for the stator flux and the electro-magnetic torque wasdesigned using a Fuzzy Logic System. (FLS) in MATLAB. Simulation results show that the direct torque and flux control using an FL approach performs very fast dynamic response and has a simple structure which makesit to be more popularly used in the industry.

1 INTRODUCTION

Induction Motor (IM) drives may be classified intotwo main control strategies. Scalar control, of the IMvoltage magnitude and frequency, is one of ac driveswhich produces good steady-state performance but poor dynamic response. There is an inherent couplingeffect using the scalar control because both torqueand flux are the functions of voltage or current and frequency. This results in sluggish response and is

prone to instability because of 5th order harmonics.However, vector control (VC) decouples these effects.A second IM drive method can be either the field

oriented control (FOC) or the direct torque and fluxcontrol (DTFC or DTC). The principle used in thesedrive methods is to asymptotically decouple the rotor speed from the rotor flux in vector controlled ac motor drives. The two most commonly used methods of the vector control are direct vector control and indi-rect vector control. Control with field orientation mayeither refer to the rotor field, or to the stator field,where each method has own merits [1].

Direct torque and flux control is also denoted asdirect self-control (DSC) which is introduced for voltage-fed pulse-width-modulation (PWM) inverter drives.This technique was claimed to have nearly com- parable performance with vector-controlled drives [2].Another significance mentioned in [3] is that DTFCdoes not rely on machine parameters, such as theinductances and permanent-magnet flux linkage. Con-sequently, a number of research approaches have been proposed for a wide range of industrial applicationswhere [3] is proposed for direct-drive PMSG wind turbines.

If an IM is being operated under its steady state,the three-phase drive can be easily presented as justone-phase because all the variables on the IM aresinusoidal, balanced and symmetrical. However, if theoperation requires dealing with dynamics of motor speeds or varying torque demands in a sudden change,

the motor voltages and currents are no longer in a sinu-soidal waveform. Hence, the IM drive scheme usingvector control or direct torque and field control isable to provide faster transient responses due to thesedynamics.

This paper is arranged as follows. Section 2lists the notations used in this paper. Section 3 explainsthe induction motor dynamics. Section 4 describes thedirect torque and flux control. Section 5 presents thedesign of the fuzzy logic controller. Section 6 illus-

trates the performance of the controller in the simula-tion. Specific conclusions are drawn in Section 7.

2 NOMENCLATURE

d e-q e synchronous reference frame direct, quadratureaxesd s-q s stationary reference framedirect, quadratureaxesusd ; usq stator voltagesisd ; isq stator currentsurd ; urq rotor voltages

ird ; irq rotor currentsψs; ψr stator, rotor flux vector R s; R r stator, rotor resistanceLs; Lr stator, rotor self-inductanceLm magnetizing inductanceσ resultant leakage constantωe; ωr synchronous, rotor speed P number of motor pole pairsJ Total inertiaTe; TL electromagnetic, load torque

3 INDUCTION MOTOR DYNAMICS

3.1 Machine model in arbitrary reference frames

In a three-phase ac machine, there are three main refer-ence frames of motion, which could be used to model

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Figure 1. Reference frame of motion in an ac machine.

Figure 2. Decoupling between rotor flux and torque.

its three main regions of operation. These are the sta-tionary reference frame for startup, the synchronousreference frame for equilibrium motion, and the rotor reference frame for changing speeds by accelerationor deceleration. The two commonly employed coordi-nate transformations with induction machines are thestationary and the synchronous reference frames as

shown in Fig. 1.In special reference frames, the expression for the

electromagnetic torque of the smooth-air-gap machineis similar to the expression for the torque of a sepa-rately excited dc machine.

These mathematical transformations of rotor ABCvariables to rotor d-q variables, which are knownas Park Transformation [4], can facilitate under-standing of the variation of the mutual inductance between the stator and the rotor under differing rotationconditions.

All the transformation equations from ds – qs frame

to de – qe frame, and vice verse, remain the same as inan induction motor. The complex stator current spacevectors sq and isd are defined as:

A dc motor-like electromechanical model can bederived for an ideal vector-controlled drive in the d-q co-ordinate. One of the advantages of the separatelyexcited dc motor of being able to decouple the fluxcontrol and the torque control that is thereby opened up. Fig. 2 shows this concept that the rotating vectorsare orthogonal, but decoupled.

3.2 Modeling of an induction motor in d–qco-ordinate

The mathematical model [5] of an induction motor ind–q reference frame can be writtenas the stator voltagedifferential equations (2):

And the rotor voltage differential equations (3):

These linked fluxes of stator and rotor are as follows:

Since the direction in d-axis is aligned with the rotor flux this means that the q-axis component of the rotor flux space vector is always zero.

The slip frequency ω sl can be calculated from thereference values of stator current components aredefined in the rotor flux oriented reference frame asfollows:

We can calculate the rotor speed from the relationωr =ωe −ωsl, since ωsl and ωe can be determined as [6]:

Here τ r is the rotor time constant denoted asτ r = Lr

Rr ,

and σ is a resultant leakage constant defined as:

2

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The state equations of a linear model for theinduction motor dynamics can be obtained as:

However, induction motors belong to a class of multi-variable nonlinear systems which could lead tothe control task to be rather complicated due to tounknown disturbances (load torque) and changes invalues of parameters during its operation. Such a chal-lenge have been stated in hybrid vector control or DTFC induction motor drives [6]–[10].

The electromagnetic torque equation can be

written as:

Thus, the magnitude of the torque is

where θ sr = θ s − θ r is the angle between the stator fluxand the rotor flux.

If the rotor flux remains constant and the stator flux is incrementally changed by the stator voltageV−s×t with the corresponding angle change of θ sr , the incremental torque is then obtained as:

The mechanical speed of the rotor in terms of theload constants can be computed as:

4 STUDY ON DIRECT TORQUE AND FLUXCONTROL

4.1 Space vector modulation of 3-phase voltage source in-verter with DTFC

During the IM drive operation, a control strategy for the voltage-fed space vector pulse-width-modulation(SVPWM) may be required with direct torque and field control. The structure of a typical 3-phase power inverter is shown in Fig. 3, where V A, V B and VC are

Figure 3. Basic scheme of 3-phase inverter connected to anAC motor.

Figure 4. SVPWM voltage vectors

Figure 5. (a) Two-level stator flux hysteresis-band. (b)Three-level stator torque hysteresis-band.

the voltages applied to the star-connected motor wind-ings, and where V DC is the continuous inverter inputvoltage.

The SVPWM method of generating the pulsed signals fits the above requirements and minimizesthe harmonic contents. The inverter voltage vectors provide eight possible combinations for the switchcommands. These eight switch combinations deter-mine eight phase voltage configurations. SVPWMsupplies the ac motor with the desired phase voltages.

A diagram in Fig. 4 depicts these combinations.

4.2 Control strategy of DTFC

The magnitudes of command stator flux ψ̂∗ s and T ∗

e arecompared with the respective estimated values, and theerrors are processed through the hysteresis-band (HB)controllers, as shown in Fig. 5.

The circular trajectory of the command flux

vector ψ̂∗ with the hysteresis-band rotates in an anti-clockwise direction. The flux hysteresis-band con-troller has two levels of digital output according to

the following relations [2]:

3

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Figure 6. Block diagram of direct torque and field control.

Table 1. Switching table of inverter voltage vectors.

Hψ HTe S1 S2 S3 S4 S5 S6

1 1 V2 V3 V4 V5 V6 V10 V0 V7 V0 V7 V0 V7−1 V6 V1 V2 V3 V4 V5

−1 1 V3 V4 V5 V6 V1 V20 V7 V0 V7 V0 V7 V0−1 V5 V6 V1 V2 V3 V4

Also, a torque controller proportional gain is chosen

by G = 1.5. The overall control of the DTFC is shownin Fig. 6.

When the inverter voltage sequence V1 −V6 is properly selected as shown in Fig. 4, the stator fluxrotates at the desired synchronous speed within thespecified band.As the stator resistance is small enoughto be neglected, we may consider that the stator fluxmonotonically follows the stator voltage at each steptime t.

Thus, changing the stator flux space vector can beaccomplished by changing the stator voltage duringa desired period of time which can be expressed as

follows:

Depending on the sector that the voltage referencein Fig. 4, two adjacent vectors are chosen. The binaryrepresentations of two adjacent basic vectors differ inonly one bit from 000 to 111.This means the switching pattern moves from one vector to the adjacent one. Thetwo vectors are time weighted in a sample period T to produce the desired output voltage to the inverter.

Table 1 applies the selected voltage vectors,which essentially affects both the flux and torquesimultaneously.

Table 2. Flux and torque variations due to applied voltagevectors.

Table 2 shows the effect of the voltage vectors onthe stator flux and the electromagnetic torque, whichthe arrows indicate the magnitudes and directions [2].

5 DESIGN OF THE FUZZY LOGICCONTROLLER

5.1 Structure of fuzzy control in DTFC

Fuzzy logic has been widely applied in power elec-tronic systems. Recently developed approaches inDTFC [11]–[13] have been proven to be more robustand improved performance for dynamic responses and static disturbance rejections using fuzzy logic con-trol. [11] also designs a hysteresis-band controller inDTFC using a fuzzy logic method, but their fuzzy con-troller appears not precise enough because of the less

membership functions being chosen. Now a fuzzy logic controller is considered for the

stator flux ψ̂∗ and torque T ∗ of the DTFC inductionmotor drive.The fuzzy inference system (FIS) onsistsof a formulation of the mapping from a given input setof E and CE to an output set using FL Mamdany typemethod in this study.

According to the switching table of the inverter volt-age vectors, the triangular membership functions (MF)of the seven linguistic terms for each of the two inputse( pu) and ce( pu) are defined in per unit values. du( pu)is the output of the fuzzy inference system. Here, e( pu)

is selected as the flux error ψ̂∗

− ψ̂ s for the difference between the command stator flux and the actual stator flux ψ s, and ce( pu) is the rate of change of

d dt ψ̂ s.

The represented linguistic variables in the fuzzy rulematrix are:

NB= negative big NM= negative medium NS=negative small Z= zeroPS= positive small PM = positive mediumPB= positive big

The general considerations in the design of the proposed fuzzy logic controller for this DTFC are:

1) If both e( pu) and ce( pu) are zero, then maintainthe present control setting du( pu)= 0 (V 0/V 7).

2) If e( pu) is not zero but is approaching to this valueat a satisfactory rate, then maintain the presentcontrol setting of the voltage vector.

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Table 3. Fuzzy logic rule matrix.

3) If e( pu) is growing, then change the control signaldu( pu) depending on the magnitude and sign of e( pu) and ce( pu) to force e( pu) towards zero.

Here, GE is a respective scale factor to convert thefuzzy input and output variables into as per unit.

The suitable fuzzy rules are selected for the DTFC,such that the variations of the fuzzy output du( pu)

depend on the required inverter voltage vectors aslisted in Table 2. This yields a 49-rule structure asshown in Table 3.

5.2 Fuzzy logic control implementation in MATLAB

The Fuzzy Logic Toolbox in MATLAB provides avery comprehensive user friendly environment to han-dle high-level engineering issues. Fig. 7 illustrates theMATLAB FIS editor for implementing this DTFCsystem.

The variation of the developed electromagnetic

torque can also be obtained from the equations (10),(11) and (12). Once obtained the flux error, thetorque error and the angle θ sr can be achieved inMATLAB/Simulink as follows in Fig. 8.

6 SIMULATION RESULTS

6.1 Performance of the DTFC drive

Once the fuzzy algorithm had been developed, the per-formance test of the DTFC drive was carried out ona fairly large 150 kW induction motor in simulation.The rotor speed was set at 500 rpm and the stator flux was set to 0:8 Wb. The motor was 80% loaded by960 Nm at 1:5 s after running.

Fig. 9 shows the stable flux locus and the stator current responding to the load. Fig. 10 illustrates the

Figure 7. Membership functions of the fuzzy logic con-troller (a) Input u(e), (b) Input u(ce), (c) Output u(du).

Figure 8. Fuzzy hysteresis-band controller in MATLAB/Simulink.

Figure 9. (a) Stator d-qaxis flux. (b) Stator current responseto the load change.

deviation responses from the rotor speed and the elec-tromagnetic torque respectively due to the load changeat t= 1:5 s.

It can be observed that the motor reaches the desired speed in 0:6 s.The rotor speed deviation affected by theload is 28 rpm to yield a speed error about 5:6% instan-taneously. The torque response following the load isconsiderably fast with less ripples. The steady-statethree-phase current of the motor is plotted in Fig. 11,which shows that it is smoothly balanced with lessdistortion.

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Figure 10. (a) Rotor speed deviation vs the reference rotor speed. (b) Electromagnetic torque vs the reference torque.

Figure 11. The steady-state three-phase current of themotor.

Table 4. The induction motor parameter values.

R s 14.85× 10−3 P 2 poles

Rr 9.295× 10−3 V rated 415V

L s 0.3027× 10−3 H f 50Hz

Lr 0.3027×

10

−3

H |

J 3.1kgm

2

Lm 10.46× 10−3 H B 0.08Nms

The simulated induction motorparametersare givenin Table 4.

We may note that one significant advantage of theDTFC drive is reasonably easy to be implemented numerically and conceptually less complicated indesign, because of the absence of the vector trans-formations from the d-q synchronous frame to the d-q stationary frame such as the FOC method. No feed- back current control is required for the DTFC drive,

but the motor seems to draw the higher current.

7 CONCLUSION

DTFC offers superior induction motor drive perfor-mances by effectively combining the accurate and fast flux and torque control. The fuzzy logic based direct torque and flux control of induction motor drivesappears quite simple and robust. The power inverter operational control plays an important key role in mod-ern power electronics motor drives and the DTFCdrive

has made its cost effective due to its simple controlstructure.

The DTFC technique combined with the FLS tech-nique has demonstrated a fast electromagnetic torqueresponse when the motor is 80% loaded and thestator magnetic flux can be kept upon the desired flux-band during the operation. Also, a good track-ing of the motor speed under the load change has beenverified.

REFERENCES

[1] J. Holtz S nsorl sscontrol of induction motor drives.Proceedings of the IEEE.,Vol.90,pp.1359–1394,2002.

[2] B. K. Bos Modem power electronics and AC driv s.Prentice Hall 2002.

[3] Z. Zhang, Y. Zhao, V. Qiao and L. Qu A discrete-timdirect-torU and fluxcontrol for direct-drive PMSGwind turbin. IEEE Industry Applications Conference, 1–8,2013.

[4] R.J. Lee, P. Pillay and R.G. Harley. D. Q refrence framesfor the simulation of induction motors. Electric Power

Systems Research, Vol. 8, pp. 15–26, 1984/1985.[5] J. Holtz. On the spatial propagation of transient mag-

netic fields in AC machin s. IEEE Transactions onIndustry Applications, Vol. 32(4), pp. 927–937, 1996.

[6] B. K. Bose, M. G. SimB D. R.Cr hus K. R ash karaand R.Martin. Speed sensorl ss hybrid vector controlled induction motor driv Industry Applications Coference,Vol. 1, pp. 137–143, 1995.

[7] P. Ttin n and M. Surandra. The next generation motor control method. DTC direιt torqu ιontrolIEEE Interna-tional Coernce on PowerElectronics. Drives and EnergySystems for Industrial Growth, Vol. 1, pp. 37–43, 1996.

[8] A.M rabet M. Ouhrouch and R. T. Bui Nonlin prediιtiv

control with disturbance obs rver for induction motor drive. IEEE International Symposium on IndustrialElectronics, Vol. 1, pp. 86–91, 2006.

[9] A. Merabet. H.Arioui and M. Ouhrouche Cascaded pre-dictive controll :rd ign for sp d ιontrol and load toU r tion of induιtion motor. American Control Conference,Vol.1, pp. 1139–1144, 2008.

[10] Y.S. Lai V.K. Wang and Y.C. Ch n Novel swi hing tchniqu for reducing the speed ripple of AC drives withdirect torque control. IEEE Transactions on IndustrialElectronics, Vol. 51(4), pp. 768–775, 2004.

[11] A.Lokriti Y. Zidani and S. Doubabi Fuzzy logic con-trol contribution to the dir ωttor u and fluxιontrol of an induction maιhin Interna – tional Conference on

Multimedia Co uting and Systems (ICMCS), pp. 1–6,2011.

[12] T. RameshA. K. Panda and S. S. Kumar 1)’pe-1 and type-2 fuzzy logi’ι p d ιontroll r based high performancdir ωt torqu and flux controlled induction motor drive.Annual IEEE India Conference (INDICONp) p. 1–6,2013.

[13] T. Ramesh A. K. Panda and S. S. Kumar Sliding-mode and fuzzy logtιcontrol based MRAS sp d stima-tors for s nsorl ss dir ωt torU and flux control of aninduction motor drive. Annual IEEE India Conference(INDICON), pp. 1–6, 2013.

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Coordinated frequency control of thermal units with DC system

in a wind-thermal-bundled system transmitted

by High Voltage Direct Current (HVDC) line

Jie DangCentral China Power Dispatching and Communication Center, Wuhan, China

Yi Tang, Mengchen Song & Jia NingSchool of Electrical Engineering, Southeast University, Nanjing, China

Xiangyun FuState Grid Jiangsu Electric Power Company, Lianyungang Power Supply Company, Lianyungang, China

ABSTRACT: The wind-thermal-bundled power system transmitted by High Voltage Direct Current (HVDC)line has been an important development mode for large-scale wind power bases in northwest of China nowadays.In terms of the frequency stability problem of sending-end system caused by wind power fluctuation and somefaults, the necessity for HVDC participating in frequency regulation is put forward. In this paper, the coordinated frequency control strategies of thermal units and HVDC link are proposed. Two cases are considered: thefluctuation of wind power and sending-end system faults. Simulations are carried out based on an equivalentwind-thermal-bundled power system to verify the effectiveness of the control strategies.

1 INTRODUCTION

Large amounts of wind power need to be transmit-ted to load centres by long transmission lines, becauseten million kilowatt level wind bases are distributed inthe northwest, the north, and the northeast of Chinawhich are far from the load centres. However, it isalmost impossible to transmit wind power alone to theload centres over a long distance due to the fluctuationof wind power. Nowadays, the developmental patternof wind-thermal-bundled power systems transmitted by HVDC transmission lines has been proposed, thusmaking full use of wind resources and mitigatingthe effects of fluctuations in wind power on receiver systems (Guo et al. 2012).

Large-scale wind power integration has a series of impacts on power system stability (Doherty et al. 2010,Chen et al. 2011). Research into the respect of fre-quency control focuses primarily on two points. One point is about wind turbine generation and wind power base research into wind turbine generators’ participa-tion in the system’s frequency regulation, for examplesof the analysis of frequency regulation capability of wind turbine (Zhang et al. 2012, Conroy et al. 2008),and inertia control of wind turbine (Keung et al. 2009,Miao et al. 2010). The other point is about power grid

control research into wind power’s impacts on fre-quency regulation and reserve (Wu et al. 2011), active power control strategies (Li et al. 2013) and an optimalgenerating unit tripping scheme during transmission(Chen et al. 2013).

An HVDC transmission system has multiple oper-

ation patterns and is usually applied to improving thestability of the AC-DC system. HVDC modulation participating in system frequency regulation is alwaysapplied in island operating mode of the system whosesending terminals are thermal power units (Chen et al.2013). A variable structure DC power modulation and an optimal coordinated control strategy is proposed for restraining AC system frequency oscillation caused bya sudden change of load (Zhu & Luo, 2012). Consid-ering the fluctuation of wind power, researchers (Zhuet al. 2013) put forward a coordinated control strategyon the basis of a DC system tracking the fluctuationin wind power and this strategy reduced the switchingtimes of the HVDCs’tap-changers and AC filters by aDC step control.

As a new development mode of power transmis-sion, the wind-thermal-bundled power system withAC/DC transmission lines is different from either thedistributed development pattern in Europe or the exist-ing development pattern of wind power in China. It isa typical structure of a large power supply, long trans-mission lines, and a weak power grid and its frequencycontrol strategy remains to be further researched. Inthis paper, the importance of DC system participatingin the frequency regulation of a sending-end system is

discussed. The coordinated frequency control strategyof thermal generator units and DC system is proposed for solving wind power fluctuation problemsand sending-end systems’ failure problems. At theend of this paper, the proposed strategy is applied in

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Figure 1. Wind-thermal-bundled system with HVDC link.

a simulation case based on the equivalent northwestsystem with wind-thermal-bundled generation and theresults show the validity of the proposed strategy.

2 INTRODUCTION OF THE STUDIED SYSTEMAND ITS FREQUENCYREGULATION METHODS

2.1 The system studied

The power system studied is shown in Figure 1.The wind-thermal-bundled power system transmitted by a DC line has two typical topologies: a wind-thermal-bundle islanded system with HVDC link (seeFigure 1a) and a wind-thermal-bundle interconnected system with HVDC link (see Figure 1b). In an islanded system, the sending end consists of only wind farmsand a thermal power plant. While in an interconnected system, the power source should be connected with theAC system.

In this paper, an equivalent system of WTB shownin Figure 1 is established. In this system, the totalinstallation capacity of WTB system is 3 GW and theactual transmission power is 2 GW. The wind power base and the thermal generation base are connected to a converter station by 750 kV transmission lines.The converter station transmits 2 GW output with a

±800 kV HVDC transmission system and the type of transmission line is a double loop. When working inthe interconnected mode, the sending-end is connected to the AC system by 750 kV double loop AC transmis-sion lines.A thermal generator is modelled on a 6 order synchronous generator model, with an excitation sys-tem and a speed control system in this case. The wind farm consists of GE1.5MW type doubly-fed inductiongenerators (DFIGs).

2.2 Existing problem

The active power output of wind turbine generatorsvaries with wind energy. Nowadays wind turbine gen-erators do not participate in frequency regulation inChina. Thus, when the system’s frequency changes,wind turbine generators could not response to it, which

provides no help to stabilize the system’s frequency.Moreover, due to the small inertia of the wind turbine,the whole system’s inertia decreases with some syn-chronous generators replace by wind turbine genera-tors. The frequency regulation capability significantlydeclined because the ratio of the installed capacity of the wind turbine generators is continuously increas-

ing. This situation aggravates the frequency regulating burden of the sending-end system. In addition, rely-ing solely on thermal power units, it is not possible toresponse to fast and large frequency changes. There-fore, it is necessary to study other frequency regulationstrategies.

Frequency fluctuation is severe in a wind-thermal- bundled islanded system because in this pattern load frequency regulation effect does not exist. In a wind-thermal-bundled interconnected system, if the trans-mission power is large, the short-circuit current of thesending system is small. In this situation, the transmis-sion system is recognized as a weak AC system whicheasily suffers from the influence of wind power fluc-tuation, wind farm failure, and disturbances of the ACsystem.

2.3 Frequency regulation methods in sending-end system

2.3.1 Primary Frequency Regulation (PFR) of thermal units

Primary frequency regulation means the function thatthe generator control system struggles to make theactive power reach a new equilibrium and keeps the

system’s frequency in an acceptable range by rais-ing or reducing the generating power automaticallyaccording to changes in the system’s frequency.

Several parameters like the dead band ε and the lim-ited range will affect the primary frequency regulationability of thermal units.

The governor dead band ε must be set up in a rea-sonable range. If the dead band is too small, even slightfrequency deviations will cause the governor to react.If the dead band is too large, it will affect the effetenessof primary frequency regulation because the governor does not response to large power shortages.

The limited range of PFR means the maximumcontrollable frequency range that generator units canachieve and this parameter determines the regulatingquantity of governor.

According to the guidelines for the power system’soperation, the dead band of the thermalgenerator units, based on electro hydraulic the turbine control system,is usually within±0.033 Hz (±2r/min) and the limited range is usually at 6%.

2.3.2 DC modulationA notable advantage of DC transmission systems over those of AC transmission systems is their fast control-lability. Therefore, the active power of a DC system can be regulated during disturbances, in order to improvethe stability of the system.

In this paper, DC frequency modulation and DCemergency power control are considered.

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Figure 2. Schematic diagram of the DC frequencymodulation controller.

Figure 3. DC active power curve of DC power modulation.

The configuration and parameters of a DC fre-quency modulation controller is shown in Figure 2.The input signal is the frequency deviation derived from an AC line, through a differential link, filter-ing link, pre-guided compensation link, notch filteringlink, amplification link and limiter, getting the outputsignal P MOD. Where ω is the frequency deviation of the AC system; T d is the time constant in a differen-tial link; T f is the time constant in the filter; ε is theguide compensation factor; A\ B\C \ D are parameters

of notch filter; K is the gain of the controller; P max and P min are the upper limit and lower limit of the controller respectively.

DC emergency power control means that the output of the DC power is artificially changed accordingto the rules shown in Figure 3. In Figure 3, t 1 repre-sents the start time of theraising/reducing power, and t 2represents the end time of the raising/reducing power, K represents the modulation rate and the formularelationship of DC power is:

3 COORDINATED FREQUENCY CONTROLSTRATEGY WITH WIND POWER FLUCTUATION

3.1 Coordinated frequency control philosophy and strategy

In consideration of wind speed fluctuation, the key of frequency regulation strategy is the coordination and cooperation between PFR and DC frequency modula-tion. Coordinated control strategy can be divided into

two modes according to regulated quantity and actionsequence:

Mode 1: the DC system works as an auxiliaryof the thermal generator unit when regulatingfrequencies.

Figure 4. Coordinated frequency control strategies under normal conditions.

Table 1. Parameters of three control strategies

Thermal units DC modulationControl

Strategies dead band limitation Td/s limitation

Strategy 1 0.033 Hz 6% 20 10%Strategy 2 0.1 Hz 5% 10 20%Strategy 3 0.033 Hz 6% – –

Mode 2: the thermal generator unit works as an auxil-iary of the DC system when regulating frequencies.

Flow charts of two modes are shown in Figure 4.Considering wind speed fluctuation, DC frequency

modulation controller is used to participate in fre-quency regulation. The two control modes above can be realized by setting different thermal generator units’governor dead band ε and derivative time constant T d of DC frequency modulation. For example, if gover-nor dead band ε is small and T

d is large, it can realize

the control objective of Mode 1. Similarly, if the gov-ernor dead band ε is large and T d is small, it canrealize the control objective of Mode 2. A simulationcase is set up to compare the control effect of twomodes for a wind-thermal-bundled islanded systemand a wind-thermal-bundled interconnected systemseparately.

3.2 Wind-thermal-bundled islanded system

Strategies are tested in WTB islanded system based onthe system shown in Figure 1a. In order to compare and analyse, three control strategies are tested in the simu-lation case: Mode 1, Mode 2, andPFR only. Parametersof these three strategies are shown in Table 1.

It assumes that a gust wind whose max speed is1 m/s happens at 10 s and lasts 10 s. Figure 5 shows

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Figure 5. Comparison of frequency curve of sending-end bus.

Table 2. The results of three control strategies.

Control Maximum frequencystrategies deviation/Hz Steady state

Strategy 1 0.23 better Strategy 2 0.193 oscillationStrategy 3 0.331 better

the changes of sending-end bus frequency with threecontrol strategies.

When only PFR worked, the maximum deviationof system frequency reached 0.34 Hz. Compared withMode 1, Mode 2 helped lower the maximum devia-tion of system frequency effectively but its stabilized frequency value was worse and frequency fluctuationhappened in the pattern of island operation condition.In all, both the resulting curve of frequency regula-tion and the new stabilized frequency value was better when Mod e 1 was applied in the simulation system.

Table 2 shows the results of simulation case withthree different strategies. Overall, it is reasonable tochoose Mode 1 as frequency control strategy in thewind-thermal-bundled islanded pattern with HVDCtransmission lines.

3.3 Wind-thermal-bundled interconnected system

Wind-thermal-bundled interconnected systemis estab-lished based on Figure 1b. The proposed strategiesare separately tested in this simulation case. Param-eters are the same with Table 1 and Figure 6 show thesimulation results.

Table 3 shows the results of the simulation case withthree control strategies.

When only relying on PFR, the deviation of sys-tem frequency is the largest and the frequency restored well. Apparently the result of Mode 1 is better thanthat of Mode 2 owing to a lower system frequencydeviation and a higher frequency restoration value. Inall, Mode 1 is very suitable for wind-thermal-bundled interconnected pattern with HVDC transmission lines.

Table 3. The results of three control strategies.

Control Maximum frequency Steady statestrategies deviation/Hz value/Hz

Strategy 1 0.23 50.01Strategy 2 0.26 49.96Strategy 3 0.34 50.02

Figure 6. Comparison of sending-end bus frequency curve.

To sum up, in the condition of wind power fluctua-tion, Mode 1 is the most competitive control strategyfor both wind-thermal-bundled islanded pattern and interconnected pattern.

4 COORDINATED FREQUENCY CONTROLSTRATEGY UNDER FAULT CONDITIONS

4.1 Coordinated frequency control philosophy and strategy

When power disturbance occurs in the power grid, suchas the input/excision of load, cut off generators, wind farms getting off, and HVDC monopole block, etc.,the sudden change in power will lead to imbalance

between generation and load. If the DC system oper-ates at constant power mode or constant current mode,the thermal generators will increase or decrease itselectromagnetic power according to its frequency reg-ulation characteristic in order to achieve a dynamic balance. If the response speed of the regulation sys-tem is slow or the generator has reached to its upper limit, the system will appear as a power persistentdisequilibrium, leading to the collapse of the wholesystem eventually.

The coordinated control strategy under fault con-ditions is as follows. Under normal circumstances,DC transmission lines operate at a fixed power mode.When a fault occurs in the sending-endAC system, thefault signal is detected by the fault detecting deviceand the DC emergency power modulation is started.The active power changed can be obtained by off-line calculation and online match, as well as dispatch

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Figure 7. Coordinated frequency control strategy under fault conditions.

instructions by dispatchers. At the same time, thermalunits assist frequency regulation. The specific control block diagram is shown in Figure 7.

4.2 Simulation

Simulations are carried out on wind-thermal-bundled islanded and interconnected systems based on Figure 1to verify the control strategies. At 20 second, parts of wind farms in sending system are out of operation,

with a loss of power of 327 MW. Comparison is madeamong the three methods: 1) No DC modulation; 2)DC frequency modulation; 3) DC emergency power modulation. The results are shown in Figures 8 and 9.

In wind-thermal-bundled islanded system, due tothe serious power imbalance in the DC rectifier caused by a fault, sending-end bus frequency decreased fast.Primary frequency regulation by thermal units can-not satisfy the frequency regulation requirements. DCfrequency modulation can effectively avoid frequencydeclining when faults occurred, while the stable valuecannot meet the standards. DC emergency power mod-ulation can effectively regulate the system’s frequencyand make the frequency return to the required value.In addition, the former two ways are differential reg-ulations, which need AGC or dispatcher to adjust thetransmission power in order to keep the frequency atthe specified value.

Figure 8. Sending-end bus frequency of wind-thermal- bundled islanded system.

Figure 9. Sending-end bus frequency of wind-thermal- bundled interconnected system.

In wind-thermal-bundled interconnected systems,without a DC modulation, the sending-end bus fre-quency exceeds the prescribed value at fault momentsand DC frequency modulation can effectively allevi-ate the frequency drop at fault times. However, thecontroller can only react to changed signals, so the fre-

quency values can reach to specified values after thefaults; when DC emergency power modulation con-troller is adopted, it can quickly adjust the DC active power to alleviate the imbalance. Therefore, the recov-ery of system frequency is at its optimum when usingDC emergency power modulation.

To sum up, under sending-end system faults, DCemergency power modulation should be adopted in wind-thermal-bundled islanded systems or wind-thermal-bundled interconnected systems, taking ther-mal units as an auxiliary frequency regulation.

5 CONCLUSIONS

This paper proposed coordinated frequency controlstrategies between thermal units and HVDC system

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to cope with frequency stability problems in wind-thermal-bundled sending-end system. The coordinated strategies are suitable fortwo cases: wind power fluctu-ation and sending-end system faults. The conclusionsare as follows:

1) For wind power fluctuation, primary frequencyregulation is the main regulation method, tak-

ing HVDC system as an auxiliary frequencyregulation mode.

2) For sending-end system faults, HVDC active power modulation is first started, taking thermalgenerators as an auxiliary frequency regulationmode.

This method is more suitable for the receiving-end system is strong while the sending-end system is rel-atively weak, such as in the three northern areas of China. Furthermore, the specific parameters of thestrategy should be set according to the actual grid structure.

ACKNOWLEDGEMENT

This study was supported by State Key Laboratoryof Alternate Electrical Power System with RenewableEnergy Sources (Grant No. LAPS14017).

REFERENCES

[1] X. Guo, S. Ma, and H. Shen, et al, “HVDC grid connection schemes and system stability control strate-gies for large-scale wind power,” Automatic of ElectricPower Systems, vol. 36, no. 22, pp. 107–115, 2012.(in Chinese).

[2] R. Doherty, A. Mullane, G. Lalor, D. J. Burke,A. Bryson, and M. O’Malley, “An assessment of theimpact of wind generation on system frequencycontrol,” IEEE Trans. Power Syst., vol. 25, no. I,

pp. 452–460, Feb. 2010.[3] Z. Chen, Y. Chen, and Z. Xing, et al, “A control strat-

egy of active power intelligent control system for largecluster of wind farms part two: Coordination control for shared transmission of wind power and thermal power,”Automation of Electric Power Systems, vol. 35, no. 21,

pp. 12–15, 2011. (in Chinese).

[4] Z. S. Zhang, Y. Z. Sun, J. Lin, and G. J. Li, “Coor-dinated frequency regulation by doubly fed inductiongenerator-based wind power plants,” IET Renew. Power Gen., vol. 6, no. 1, pp. 38–47, Jan. 2012.

[5] J. F. Conroy, and R. Watson, “Frequency responsecapabil-ity of full converter wind turbine generators incomparison to conventional generation,” IEEE Trans.Power Systems, vol. 23, no. 2, pp. 649–656, May. 2008.

[6] P. K. Keung, P. Li, H. Banakar, and B. T. Ooi, “Kineticenergy of wind-turbine generators for system frequencysupport,” IEEE Trans. Energy Systems, vol. 24, no. 1,

pp. 270–287, Feb. 2009.[7] Z. Miao, L. Fan and D. Osborn, et al. Wind farms

with HVDC delivery in inertial response and primaryfrequency-cy control [J]. Energy Conversion, IEEETransactions on, 2010, 25(4): 1171–1178.

[8] C. Wu, “Research on the influence of wind power on power balancing and reserve capacity,” East ChinaElectric Power, vol. 39, no. 6, pp. 993–996, 2011 (inChinese).

[9] Q. Li, T. Liu, and X. Li, “A new optimized dispatch method for power grid connected with large-scale

wind farms,” Power System Technology, vol. 37, no. 3, pp. 733–739, 2013. (in Chinese).

[10] S. Chen, H. Chen and X. Tang et al, “Genera-tor Tripping Control to Uphold Transient Stabilityof Power Grid Out-wards Transmitting Thermal-Generated Power Bundled With Wind Power,” Power System Technology, vol. 37, no. 2, pp. 515–519, 2013.(in Chinese).

[11] Y. Chen, Z. Cheng, and K. Zhang, et al, “Frequencyregulation strategy for islanding operation of HVDC,”Proceedings of the CSEE, vol. 33, no. 4, pp. 96–102,2013. (in Chinese)

[12] H. Zhu, and L. Luo, “Improving frequency stability

of parallel AC-DC hybrid systems by power modula-tion strategy of HVDC link,” Proceedings of the CSEE,vol. 32, no. 16, pp. 36–43, 2012 (in Chinese).

[13] Y. Zhu, P. Dong and G. Xie, et al, “Real-Time Sim-ulation of UHVDC Cooperative Control Suitable toLarge-Scale Wind Farms,” Power System Technology,vol. 37, no.7, pp. 1814–1819, 2013. (in Chinese).

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A biomedical system combined fuzzy algorithm

for telemedicine applications

Peng-Liang Peng, Pin-Zhang Chen, Chien-Yuan Pan, Gwo-Jia Jong & Bow-Han Lin National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan, R.O.C

ABSTRACT: In order to reduce the number of patients in hospitals for health examination as those sufferingfrom chronic diseases among an aging population gradually increases, we hope that doctors can still do healthdetection by a long-distance home care service. We have proposed a biomedical information network platformwhich integrates Wi-Fi and Radio Frequency Identification (RFID) systems in this paper. With the medicalinstruments of Wireless Sensor Network (WSN) chips, the technology of Zigbee, and a medical decision-making

system, we established a low-noise region to collect data. After constructing an interconnection between cloud servers and medical systems, users can scan these historical medical records through the website platform and readily grasp the physical condition of patients and save medical resources.

1 INTRODUCTION

With the improvement of living standards and healthliteracy, people increase their emphasis on physical

health. With the condition of medical staff shortages,we transmit the measured physiological data to thecloud server by way of wireless transmission and cre-ate a database. In order to reduce the waste of medicalresources and occupational time, medical personnelcan monitor the patients’ condition with a web plat-form in order that patients who really need medicalcare can have proper care. Therefore, it is worth dis-cussing and developing important issues to constructa biomedical health system that is adapted for homeadoption.

This paper proposes a method of conductingrecords and monitoring data through the integrationof biomedical devices, wireless sensor networks and cloud servers [1]. a) It obtains the users’physiologicalinformation by using wireless transmission technol-ogy. The use of RFID distinguishes each individualand receives their physiological information. b) Next,it transmits this information to cloud databases, inorder to build a web platform where patient and physi-cians can observe their condition in the pipeline of information. When it shows abnormal physiologicalcondition of patients, clinics will inform the patient back to the hospital through the network platform with preliminary diagnosis and the physician’s professionalassessment. Historical physiological data is recorded in the web platform to facilitate the physician in asystem of long-term follow up history and completethe goal of long-term care for the chronically ill and tracking them.

2 METHODLOGY

As shown in Figure 1, physicians keep control-ling patients’ physiological information through auto-

matic monitoring systems management platforms.The patients measured biomedical information is sent over the internet to cloud servers for data preservation. All physiological information previously stored measure-ments will always be in the medical record databasefor monitoring and information [2] and for setting thestandard values for the measured values (such as dias-tolic, systolic, mean arterial pressure). If the measured data is too low or too high, the system is going to auto-matically highlight. Achieving the goal of effective prevention is better than treatment.

Figure 1. Schematic platform.

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Figure 2. Blood pressure chart in a cloud network platform.

3 SYSTEM ARCHITECTURE

Figure 2 is the block diagram of the system’s archi-tecture. It can mainly be divided into the transmitter end, the transmission medium, and the receiving end of the three blocks. The following three parts were madefor this introduction.

3.1 Transmitter end

There is a RFID sensor in each sphygmomanome-ter. By way of a wireless mesh network physiologicalinformation is sent to the cloud servers via Zig- bee modules. With the use of the massive storage

space in the cloud, recording and saving masses of physiological information becomes viable and pro-vides important information as a reference for doctorsduring diagnosis.

3.2 Wireless sphygmomanometer

Using the RFID’s recognition function, it is possible tolower tremendously the chances of having a mistakein recording a patients’ history. The user will place theidentification card on the reader, and it will take the physiological data and save them in the cloud server

in an according IP. Therefore, it will be convenientfor doctors to monitor the patients. As shown in Fig-ure 3, the time displayed on the instrument will also be saved in the cloud server to prevent any medicalconflict related issues in the future.

3.3 Wireless sensor network

WSN combined wireless internet technology, sensors,data recording instrument and information technologywill be useful in health and medical attention care. Itcan also be used in ecological monitoring, business,home automation, and in different more special fields.

This paper is using Zigbee module [4], [5] to showthe great advantages of transmitting data wirelessly.The great advantages are the low cost of energy,its light weight, high sensitivity, and system com- patibility. Compared to cable transmission, wireless

Figure 3. The sphygmomanometer’s actual measurementchart.

Figure 4. The medical records databases schema diagram.

transmission lowers labor costs and improves theconvenience for users and enjoys energy saving.

3.4 Cloud servers

Taking a large amount of data transmission into con-sideration, we are planning to use the cloud server tohandle enormous physiological data usage. Using thehigh speed internet, we are able to connect serversaround the world to form a highly efficient data stor-age system, the “Cloud Main Frame.” More than oneserver will be operating at the same time, therefore,even when disconnection occurs during transmission,webpages lagging, due to the system crashing, can still be avoided.

3.5 Received end

The definition of a receiver in this paper is a casesdatabase. Its construction is shown in Figure 4. Ana-lyzing and comparing based on a users’ physiologicaldata to determine patients’ state of health, doctorscan understand patients’ health condition and makeinstantaneous diagnostic and treatment decisions. Notto mention that the cases database is growing day byday, which means the reliability for this comparisonincreases relatively.

Medical decision-making systemThe cases database contains all kinds of patients’ physiological information, personal information, and history of blood pressure, etc.

As shown in Figure 5, we build a medical decision-making system combined with a fuzzy algorithm. Dataanalysis is done according to data storage and fuzzycontrol rules.

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Figure 5. Fuzzy medical decision-making system’s archi-tectural diagram.

Figure 6. The block diagram of a fuzzy decision.

As shown in Figure 6, we used fuzzyfication to

measure physiological information about patients. Next, we compare the reference which are def ined byfuzzy rules and use defuzzification to get the valueof the users’ mean arterial pressures.

The output is a mean arterial pressure that usessystolic and diastolic blood pressure in medicaldecision-making.

The following table is a fuzzy defined rule base.

There are ten samples in Table 1. After increasingsamples to more than one hundred, the output of the interval can be done in greater detail with theenhancement of the number of samples. Relatively,the complexity will also increase.

In order to get exact output values, we added thealgorithm of defuzzification at the system’s end. Thesteps are as following [6], [7], [8], [9]:

Order reduction of the Type-2 membership values by computing upper MF yr by equation (2):

yr upper MF of output; i is a variable value changed from 1 to N ; N is number of non-zero membershipfunction values created by firing the rules; f i is thefuzzy value of input of i; y ir is the mean value of theoutput upper MF.

Then, compute lower MF yl by equation (3):

yl is the lower MF of output; yil is the mean value of

the output lower MF

Table 1. Defined rule bases in fuzzy algorithm.

Weight SBP DBP Number Age Sex (kg) (mmHg) (mmHg)

1 19 Female 55 105.00 72.332 23 Female 55 120.33 78.673 24 Female 60 79.33 50.00

4 28 Male 68 114.00 71.335 34 Female 65 108.00 71.336 38 Female 77 122.67 74.007 48 Male 80 124.00 85.678 51 Female 70 110.00 78.679 59 Female 75 156.33 89.3310 64 Female 92 121.33 84.67

Figure 7. The home page of web platform.

Compute the output of IT2FS by using centre of sums as a defuzzification method as in equation (4):

y is the output of the fuzzy system.

4 EXPERIMENTAL RESULTS

The experimental results are presented by a web plat-

form. As shown in Figure 7, the home pages includethe newest medical information and the connectionwith each hospital. The processes are fuzzification,an inference system, a database, a rule base and defuzzification, as shown in Figure 1.

Next, users enter their personal account and pass-words to view historical information stored in thecloud server which stored patients’ medical informa-tion. As shown in Figure 8. We choose line charts to present our experimental results, as shown in Figure9. Among the advantages are the fact that doctors and patients can scan these physiological information con-veniently. We could notice at any time if one of thedata has anomalies. Besides, we added a function to prevent any written modification in order to protect therights of both sides and avoid having medical malprac-tice disputes in the future. Neither patients nor clinicsare entitled to tamper with any relevant information in

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Figure 8. The user interface of supervision.

Figure 9. Blood pressure measurement line charts.

the biomedical platform. If having access to the net-work, users could check their own health information.This significantly enhances the users’convenience and

flexibility.

5 CONCLUSIONS

We have proposed WSD and RFID technology applica-tions in this paper. Under the Zigbee transmission, theinputs are diastolic and systolic. The output is called mean arterial pressure. In accordance with the defini-tion of normal blood pressure to determine the current physical condition of patients, doctors can evaluateand save diagnostic timing. Through the cloud con-

cept, patients can view their own medical records.The aging phenomonon is more severe, relatively, the probability of getting chronics is higher. The medi-cal resources are required urgently. In order to reduce

medical resources consumption, the medical web plat-form can achieve effective solutions. In the future, thesystem will increase the number of users in the pro-cess. We would like to realize this public convenienceand avoid waste of medical resources.

REFERENCES[1] Janghorbani, A., Arasteh, Abdollah, Moradi, M.H.,

“Application of local linear neuro-fuzzy model in pre-diction of mean arterial blood pressure time series”,Biomedical Engineering (ICBME), 2010 17th IranianConference of, pp. 1–4, Nov. 2010.

[2] L. Constantinescu, Jinman Kim, D.D. Feng, “SparkMed:A Framework for Dynamic Integration of MultimediaMedical Data into Distributed m-Health Systems”, Infor-mation Technology in Biomedicine, IEEE Transactionson, pp. 40–52, Jan. 2012.

[3] Po Yang, Wenyan Wu, Moniri, M., Chibelushi, C.C.,“Efficient Object Localization Using Sparsely Dis-

tributed Passive RFID Tags”, Industrial Electronics,IEEE Transactions on, pp. 5914–5924, Dec. 2013.

[4] Dhaka, H., Jain, A., Verma, K. “Impact of Coordinator Mobility on the throughput in a Zigbee Mesh Net-works”. Advance Computing Conference (IACC), IEEE2nd International, pp. 279–284, Feb. 2010.

[5] Zhou Yiming, Yang Xianglong, Guo Xishan, ZhouMingang, Wang Liren, “A Design of Greenhouse Moni-toring & Control System Based on ZigBee Wireless Sen-sor Network”, Wireless Communications, Networkingand Mobile Computing. WiCom. International Confer-ence, pp. 2563–2567, Sept. 2007.

[6] Al-Jaafreh, M.O., Al-Jumaily, A.A. “Type-2 FuzzySystem Based Blood Pressure Parameters Estimation”,Modeling & Simulation, 2008. AICMS 08. Second AsiaInternational Conference on, pp. 953–958, May 2008.

[7] Morsi, I., Abd El Gawad, Y.Z., “Fuzzy logic in heart rateand blood pressure measureing system”, Sensors Appli-cations Symposium (SAS), 2013 IEEE, pp. 19–21, Feb.2013.

[8] Janghorbani, A., Arasteh, Abdollah, Moradi, M.H.,“Application of local linear neuro-fuzzy model in pre-diction of mean arterial blood pressure time series”,Biomedical Engineering (ICBME), 2010 17th IranianConference of, pp. 3–4, Nov. 2010.

[9] Chin-Teng Lin, Shing-Hong Liu, Jia-Jung Wang, Zu-ChiWen, “Reduction of interference in oscillometric arterial

blood pressure measurement using fuzzy logic”, Biomed-ical Engineering, IEEE Transactions on, pp. 432–441,April 2003.

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A preventive control method for overload in a multi-source grid

Zaiwei Zhang Hohai University, Nanjing, China

Feng Xue, Ye Zhou, Xiaofang Song & Ling Zhou Nari Technology Development Limited Company, Nanjing, China

ABSTRACT: The power system structure hasgradually becomecomplicated dueto the integration of renewableenergy sources, which gives preventive control measures more alternatives. The problem becomes how to choosethe optimal preventive control strategy and this has become a focus of research. This paper proposes a preventivecontrol method under overload conditions in a multi-source grid. The proposed method formulates a control

performance index, based on control sensitivity, and also takes the characteristics of renewable energy sourcesand the control cost of different types of units into consideration. The objective function is to minimize thecontrol cost and post-control overload risk. In the end, the viability of the method is demonstrated through thesimulation of the IEEE 39-bus test system.

1 INTRODUCTION

With rapidly developing generational technologies of renewable energy (wind, photovoltaic, etc.),power sys-tems face a high level of penetration from renewable

energy sources and have gradually formed an inter-active complex multi-source grid, such as is found inmost areas in northwest China (Gansu, Qinghai, etc.).However, the intermittence of wind and photovoltaicenergy brings many uncertainties. Hence, we are faced with how to integrate the maximum amount of inter-mittent energy and effectively control various types of power sources at the same time. This has become anurgent issue to resolve.

Taking Gansu power grid as an example, there coex-ists many types of power generation, such as wind power, photovoltaic (PV) power, hydroelectric power,

thermal power, and gas turbine generation. From theaspect of safety, if there occurs a N-1 or N-2 faultin the tie line between the power sending and thereceiving end, and the power flow in it is relativelylarge, the remaining transmission lines or transform-ers would easily become overloaded unless there are preventive measures, which may also lead to seriousconsequences like cascading failures. So in order toeliminate the overload, effective overload preventivecontrol strategies must be developed through regulat-ing the power output of the generator units at power sending end. On the other hand, from the aspect of

economy, renewable (wind/PV) generator units could start/stop much faster and cost less than conventionalthermal units. However, the individual wind/PV power plant has a relatively smaller capacity to regulate. If the overload problem is too large that we need to shed

large amounts of power, and the control cost could be even higher due to the regulation of a plurality of wind/PV power stations. So, this control dilemma hasto be resolved.

The conventional overload preventive control mea-

sures are based on whether the power flow resultsexceed the safe operational constraints or not. If yes,the power output of relevant generation units should beregulated or the reactive power compensation deviceshould be adjusted. If it cannot reach a safe operationallimit, the next step is to carry out load reduction or other measures of control. The objective is to maintainthe system’s stability margin under normal operationalconditions and under contingencies as well as throughthe minimum of control costs. According to the dif-ferent control methods, the cost and the priority aredifferent too. Among them, the load-shedding is the

last choice when there is no alternative, the cost of which is also the highest. Preventive control priorityshould be given to the active power redistribution of relevant generators, and then there are the other meansof control. Hence, in this paper we only discuss thecontrol strategy which involves the regulation of therelevant power outputs.

Basically, two categories of approach could be used considering overload preventive control: the optimal programming approach and the sensitivity approach. Normally the optimal programming approach has acomplete mathematical model including the optimiza-

tion objective and all kinds of constraints, whichensures the safety of the power system but may lead to convergence problem as so many devices need to be regulated. In contrast, the sensitivity approachhas no iteration and focuses on objectives like the

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minimum amount of regulation or the minimum number of devices to be regulated, which enhance its practicability in actual practice. Deng et al. (1999) proposed earlier an active power control strategy, based on the sensitivity approach, and this was proposed toensure the minimum regulation of the amount of output power. Zhang & Wu adopted in 2012 a control

assessment index based on a sensitivity analysis of the active power in a transmission line and the active power in a generator bus. It determined the regulationorder according to the sensitivity ranking results. Theobjective was to minimize the amount of regulation, but the intermittence and the control costs for differentrenewable energy sources were not taken into consid-eration. Then Song et al. (2013) added the control costof different generator types on the basis of Zhang &Wu. In addition, the concept of overall sensitivity wasdeveloped by Cheng et al. (2011), so that the power regulations could be determined according to the over-all judgment of the overloaded lines or lines close tooverload. Moreover, Liu et al. (2008) established anoverall performance index algorithm to calculate theoptimal emergency control strategy.

Therefore, this paper proposes a preventive con-trol method for overload elimination in a multi-sourcegrid. Preventive control is relatively undemanding interms of speed. So, this paper focuses on an economic-oriented method, which also meets the safety require-ments. Based on control sensitivity, the effectivecontrol capacity is calculated, and the control per-formance index is formulated. After considering thecontrol cost of each generator type, the overload elim-

ination is tackled while keeping the system within theminimum control cost and post-control risks.

The paper is divided as follows. Section 2 presentsthe application of sensitivity in preventive control.Section 3 develops a complete model and procedure of overload preventive control. Lastly, Section 4 presentsverification and comparison results using the IEEE39-bus test system.

2 AN OVERLOAD PREVENTIVE CONTROLMODEL

2.1 Application of sensitivity in preventive control

Putting forward the concept of sensitivity is to quantifythe effect of control measures on the system stability.So the sensitivity analysis has a significant impact onsafety and stability assessments and preventive con-trol of power systems. It can determine the variationtrend of a controllable variable with respect to another corresponding variable. Then giving priority rankingfor all available control strategies, so as to realize theunity of safety and economy.

For an individual generator unit, we introduce

the sensitivity of control effect S ij , which can quan-tify the influence of the power control amount of uniti on the overload line j :

wh

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