Australian Journal of Basic and Applied Sciences, 10(15) October 2016, Pages: 261-271

AUSTRALIAN JOURNAL OF BASIC AND

APPLIED SCIENCES

ISSN:1991-8178 EISSN: 2309-8414 Journal home page: www.ajbasweb.com

Open Access Journal Published BY AENSI Publication © 2016 AENSI Publisher All rights reserved This work is licensed under the Creative Commons Attribution International License (CC BY). http://creativecommons.org/licenses/by/4.0/

To Cite This Article: R. Sakthivel and Dr. M. Arun., Thermal Power System Stabilizer Design Using H∞ Robust Technique Based On

Enhance ABC Optimal Power System. Aust. J. Basic & Appl. Sci., 10(15): 261-271, 2016

Thermal Power System Stabilizer Design Using H∞ Robust Technique

Based On Enhance ABC Optimal Power System

1R. Sakthivel and 2Dr. M. Arun

1R. Sakthivel, Assistant Professor, Department of Electrical Engineering, FEAT, Annamalai University, Annamalai Nagar-608002, Chidambaram, Tamil Nadu, India. 2Dr. M. Arun, Assistant Professor, Department of Electrical Engineering, FEAT, Annamalai University, Annamalai Nagar-608002,

Chidambaram, Tamil Nadu, India.

Address For Correspondence: R. Sakthivel, Assistant Professor, Department of Electrical Engineering, FEAT, Annamalai University, Annamalai Nagar-608002,

Chidambaram, Tamil Nadu, India. E

A R T I C L E I N F O A B S T R A C T

Article history:

Received 26 August 2016 Accepted 10 October 2016

Published 18 October 2016

Keywords:

PSS, RPSS, CPSS, H∞, Enhanced ABC

As power systems are Complex, very large and geographically distributed, it is difficult

to solve the low frequency oscillation problems. Therefore, it is most vital to implement a robust power system stabilizer with efficient optimization method. This paper

proposes a method which deals with thermal power system stability under deregulated

environment. In this work, a robust thermal power system stabilizer is implemented to meet the objective of frequency stability. In order to stabilize the tie-line power and

frequency oscillations effectively, an optimization technique of Enhanced Artificial Bee

Colony Algorithm is analyzed. The robust thermal power system stabilizer (RPSS) is designed using enhanced ABC to implement the controllers for dynamical systems in

electrical engineering. The results of proposed PSS with H∞ optimization and the

Conventional power system stabilizer (CPSS) are compared. An experimental results show that in comparison with other techniques, enhanced ABC is more effective to

produce desired response.

INTRODUCTION

Power System Stabilizers (PSS) are the most familiar and efficient devices to damp the power system

oscillations produced by interruptions. The transient stability of a system can be enhanced by providing suitably

tuned power system stabilizers on selected generators to provide damping to critical oscillatory modes. Properly

tuned Power System Stabilizers (PSS) will initiate a component of electrical torque in phase with generator rotor

speed deviations consequential in damping of low frequency power oscillations in which the generators are

involving. The input to stabilizer signal may be one of the locally accessible signals such as changes in rotor

speed, accelerating power, rotor frequency, electrical power output of generator or any other suitable signal.

This stabilizing signal is remunerated for phase and gain to result in suitable component of electrical torque that

results in damping of rotor oscillations and thereby increase power transmission and generation capabilities.

Constantly increasing complexity of electric power systems has enhanced interests in evolving superior

methodologies for Power System Stabilizers (PSS). Dynamic stability transient and considerations are among

the main disputes in the reliable and effective power system operations. Low Frequency Oscillation (LFO)

modes have been observed when power systems are interconnected by weak tie lines. The LFO mode, with

weak damping, is also known as electromechanical oscillation mode, and it generally occurs in the frequency

range of 0.1 to 2 Hz. PSSs are the very effective devices for damp out these oscillations.

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Australian Journal of Basic and Applied Sciences, 10(15) October 2016, Pages: 261-271

In Soliman (2014), a simple analytical method is suggested to compute the set of three terms of a stabilizing

PSSs. Therefore, PID controller based stabilization of the interval plant and PSS based phase lead compensator

is dealt using generalized Kharitonov’s theorem. Furthermore, sufficient and necessary constraints to

characterize the efficient stabilizing three term controllers are derived by implementing the Routh–Hurwitz

criterion to all of segment/vertex plants.

In Abdul-Ghaffar et al., (2013), a design of a SMIB power system for the stability enhancement with PID-

PSS has been implemented, in which, Hybrid Particle Swarm-Bacteria Forging Optimization (PSBFO)

technique is used to optimize its parameters. A PID based on real coded GA is developed in Duman and Ozturk

(2010) to improve power system dynamic, in which the parameters of the proposed stabilizer are corrected by

using real coded GA. In Ramya and Selvi (2015), a dynamic simulator is developed to simulate a synchronous

power plant for practical application. To verify the control devices using Real Time Interface in virtual

environments, the SMIB model is executed in DSP(Digital Signal Processor) of dSPACE hardware, which is a

platform for real time simulation. Yang proposed a capable metaheuristic BA. The author suggests that the BA

have superior performance over GA and PSO Yang (2010). Thermal energy refers to the energy present in a

system by feature of its temperature. The average translational kinetic energy possessed by free particles in

thermodynamic equilibrium may also be mentioned to as the thermal energy per particle.

Thermal energy is the portion of the thermodynamic or core energy of a system that is responsible for the

temperature of the system. The thermal energy of a system measures with its size and is therefore an extensive

property. It is not a state function of the system unless the system has been built so that all changes in internal

energy are due to thermal energy changes, as a result of heat transfer. Otherwise thermal energy is dependent on

the way by which the system achieved its temperature. Thermal energy can be converted into and out of other

types of energy, and is not generally a conserved quantity.

In the conventional PSS, the normal controller circuit is used. It balances the load coming from the thermal

power plant. Here the value of Kp, Ki and Kd value are constant and the output will be the normal output. While

using H∞ technique, value of Kp, Ki and Kd value are constant and the output as like as normal PSS output but

compared to normal PSS, H∞ technique output will be better.

In this, a thermal based power system stabilizer in deregulated environment is proposed. The proposed

enhanced ABC optimization technique is compared with conventional PSS and PSS with H∞ optimization

technique.

Literature Of Review:

Surveyed on automatic generation control in power systems are discussed. Several configurations of power

systems such as single area thermal system, single area hydro system and Multi area interconnected are

addressed. The various control techniques used in several configurations also studied (Kamali and

WahidaBanu, 2013)

Performance of three area reheat thermal power system is enhanced by using SMES energy storage unit and

response is related with and without considering energy storage unit in all areas. Damping oscillations, settling

time, peak overshoot are improved, when associated to the response of system without considering SMES unit.

General PI controller gain values are enhanced using Integral Time Absolute Error (ITAE) performance index

criterion (Jagatheesan et al., 2014).

Analyzed on dynamic performance of Load Frequency Control (LFC) of three area interconnected

hydrothermal reheat power system by using Artificial Intelligent and PI Controller. In the proposed scheme,

control methodology established using conventional PI controller, Fuzzy Logic controller (FLC) and Artificial

Neural Network (ANN) for three area interconnected hydro-thermal reheat power system (Surya Prakash and.

Sinha, 2010).

A new technique fuzzy logic PI controller is intended for automatic load frequency control of

interconnected power systems. The controller performances Fuzzy logic PI method is in work for a Load

Frequency Control for Generation of Interconnected Power System. The proposed controller can lever the non-

linearity and at the same time faster than other conventional controllers. The effectiveness of the projected

controller in increasing the damping of local and inter area modes of oscillation is established in a two area

interconnected power system (Ramanand Kashyap et al., 2013).

Proposed an Automatic Generation Control (AGC) of interconnected two area Hydro-Thermal System

using usual integral and fuzzy logic controllers. Effects of different number of inputs and triangular membership

functions for fuzzy logic controller on dynamic responses have been discovered. 1% step load perturbation has

been considered happening either in individual area or occurring simultaneously in all the areas (AshisTripathya

et al., 2012).

In (Akshay Kumar et al., 2015), a system based on Sliding mode control (SMC) is developed for the control

of TCSC and PSS to enhance the dynamic stability of the single machine infinite bus’s (SMIB). Generally, a

Sliding mode controller can give good performance in presence of both disturbance like unknown nonlinear

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Australian Journal of Basic and Applied Sciences, 10(15) October 2016, Pages: 261-271

function and uncertainties. Kanthalakshmi Srinivasan et al., (2014) developed a Fuzzy based Sliding Mode

Control PSS to improve the stability and dynamic response of the power system under fault conditions.

A modern robust load frequency controller for two area interconnected power system is offered to quench

the deviations in frequency and tie line power due to various load disturbances. The dynamic model of the

interconnected power system is established without the integral control. The area control error is also not

involved. The frequency and derivatives are zero under regular operation and after the disturbance effects are

expired. Then the problem is reorganized as the problem of state transfer from the initial steady state to final

steady state without oscillations in less time (VenkataPrasanth and Jayaram Kumar, 2009).

Optimal proportional-plus-integral controller with Particle swarm optimization is intended for load

frequency control of a two area thermal power system. The model is determined an optimization problem and a

diminishing the error function is derived for improving the performance of convergence to the solution. To

enhance the parameters of the PI controller, fuzzy logic technique and the particle swarm optimization algorithm

are used (Amitesh Kumar et al., 2014).

Load Frequency Control (LFC) of isolated two-area and single area re-heat inter-connected thermal power

system has been carried out by the classical controllers and performed on the system at 1% step load

perturbation in nominal loading conditions. Responses of deviation in tie-line power , deviation in frequencies

and choosing the optimum controller gain values to have well dynamic responses of the system have been

plotted, keeping in view the characteristics such as rise time, settling time, oscillations & peak overshoot (2014).

A proportional-integral-derivative controller (PID controller) is a control loop feedback

mechanism (controller) generally used in industrial control systems. A PID controller computes an error value

as the difference between a measured process variable and a exact set point. The controller efforts to minimize

the error by adjusting the process through the utilization of a manipulated variable.

The PID controller algorithm comprises three separate constant parameters, and is accordingly sometimes

known as three-term control: the proportional, the integral and derivative values, denoted P, I, and .Generally

put, these values can be interpreted in terms of time: P depends on the present error, I on the accumulation

of past errors, and D is a future error predictions, based on current rate of change. The weighted sum of these

three actions is used to regulate the process via a control element such as damper, the power supplied to a

heating element or a position of a control valve. For a discrete time case, the term PSD, for proportional-

summation-derivative, is frequently used.

Some applications may need using only one or two terms to provide the appropriate system control. This is

attained by setting the other parameters to zero. A PID controller will be called a PI, PD, P or I controller

without the respective control actions. PI controllers are fairly common, since derivative action is delicate to

measurement noise, whereas the non-appearance of an integral term may prevent the system from reaching its

target value due to the control action.

The PID control scheme is named after its three correcting conditions whose sum comprises the

manipulated variable (MV). The proportional, derivative and integral terms are summed to determine the output

of the PID controller. The final equation of the PID algorithm is:

𝑢(𝑡) = 𝑀𝑉(𝑡) = 𝐾𝑝𝑒(𝑡) + 𝐾𝑖 ∫ 𝑒(𝜏)𝑑𝜏 + 𝐾𝑑𝑑

𝑑𝑡𝑒(𝑡)

𝑡

0

Where

Kp: Proportional gain, a tuning parameter

Ki: Integral gain, a tuning parameter

Kd: Derivative gain, a tuning parameter

e: Error =SP-PV

t: Time or instantaneous time (the present)

𝜏: Variable of integration; takes on values from time 0 to the present t.

Equivalently, the transfer function in the Laplace Domain of the PID controller is

𝐿(𝑠) = 𝐾𝑝 +𝐾𝑖

𝑠⁄ + 𝐾𝑑𝑠

Where S: complex number frequency

H∞ Robust Design Technique Based On Enhance Abc Optimal Power System Stabilizer:

H∞

H∞ methods are used to synthesize controllers attaining stabilization with guaranteed performance. To

use H∞ methods, a control designer expresses the problem as a mathematical optimization problem and then

finds the controller that resolves this optimization. H∞ techniques have the benefit over classical control

techniques in that they are eagerly applicable to multivariate system problems with cross-coupling between

channels. But non-linear constraints such as saturation are generally not well-handled.

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Australian Journal of Basic and Applied Sciences, 10(15) October 2016, Pages: 261-271

Artificial Bee Colony (ABC):

In ABC algorithm, the processes of the exploration and exploitation contrast with each other, so the two

abilities should be well balanced for attaining good optimization performance. Owing to the search form of

ABC algorithm, a candidate solution would be generated by moving the preceding one towards another solution

selected randomly from the population. The ABC algorithm has previously proved to be a very effectual

technique for solving global optimization. ABC is not only a high performance optimizer which is very simple

to understand and implement. Yet, ABC could be slow to converge and sometimes trap in a local optimal

solution.

Enhanced Artificial Bee Colony Algorithm:

Fig. 1: Flow chart for Enhanced Artificial Bee Colony algorithm

In order to further enhance the performances of ABC, three main changes are made by introducing the best-

so-far solution, inertia weight and acceleration coefficients to alter the search process. In addition, the search

form of ABC is good at exploration but poor at exploitation. Therefore, to improve the exploitation, the

modification forms of the employed bees and the onlooker ones are different in the second acceleration

coefficient.

In initialization, EABC similar to ABC starts by associating all employed bees with arbitrarily generated

food sources. After initialization, the food source populations are subject to repetitive cycles of the search

processes of the employed bees, the onlooker bees and the scout bees. The main differentiation between EABC

and ABC is how bees get the candidate solutions. In EABC, an employed bee initially works out three new

solutions by three different solution search equations, and then selects and identifies the best one as the

candidate solution. Here, due to calculating the candidate solution before the employed bee choose where they

should go to discover, the process of calculating new food position is called ‘predict’. Subsequent to the bees

‘predict’ new candidate solution by three different solution search equations, they pick the best one from the

three solutions as the candidate solution. If the fitness values of the candidate solution is superior than the best

fitness value achieved so far, then the employed bee’s moves to this new food source and synchronously leaves

the old one, or else it remains the prior food source in its mind. If all employed bees have completed this

process, they share the fitness information with the onlookers, each of which prefers a food source according to

probability. As in the case of the employed bee, an onlooker ‘predicts’ three alteration on the position in her

memory, and then chooses the best one as the candidate source and verifies the fitness value of the candidate

source. Offering that the fitness value of the candidate source is better than that of the preceding one, the bee

would memorize the new position and forget the old one. In EABC, the three solution search equations are

265 R. Sakthivel and Dr. M. Arun, 2016

Australian Journal of Basic and Applied Sciences, 10(15) October 2016, Pages: 261-271

separately calculated, but influence each other by the selected best solution. Overall the EABC has inherited the

clever sides of the other three algorithms.

Modelling Of Thermal Power System:

The power system taken in this study is modeled as a single synchronous generator connected through a

parallel transmission line to a very large network approximated by an infinite bus (SMIB) based on thermal

units. The state variables measured here be speed deviation and power system acceleration. Let Pm and Pc

signifies the mechanical and electrical power respectively. The developed Simulink model of the thermal power

system is shown in the following figure

Fig. 2: Block diagram of the proposed SMIB power system

Fig. 3: Simulink model of with (RPSS) and (CPSS) control for the PSS

A speed limiter is a governor utilized to bound the top speed of a vehicle. For some classes of vehicle and in

some jurisdictions they are a statutory requirement, for some other vehicles the manufacturer offers a non-

statutory system which may be fixed or programmable by the driver. A steam boiler component in which heat is

applied to intermediate pressure steam, which has given up some of its energy in extension through the high

pressure turbine.

A turbine is a rotatory mechanical device that extracts energy from a fluid flow and translates it into

useful work. A turbine is a machine with at least one moving part termed as rotor assembly, which is a shaft or

drum with attachment of blades. Moving fluid performances on the blades so that they move and impart

rotational energy to the rotor.

266 R. Sakthivel and Dr. M. Arun, 2016

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Simulation Results:

An optimization method is examined here for PID gains setting. Response of active power, terminal

voltage, stator angle voltage, speed deviation was observed. Comparison of the robust optimization technique

enhanced ABC with the conventional PSS and PSS with H∞ technique show that optimization technique can

attain excellent robustness, while the design process used in much simpler.

In this thermal PSS design, the robustness of PSS should be evaluated in different loading conditions and

different operating conditions. The change of operating conditions corresponds to the changes of transmission

line parameters and the active and reactive powers.

The quantitative results of the comparison of the static and dynamic performances (Static error, damping

coefficients, peak time and settling time) with CPSS, thermal PSS (RPSS) and with Horch et al.,(2014) and

Sakthivel and Arun (2016) of the different parameters are shown in table I and II. The proposed Table I and

Table II clearly show the efficiency of the proposed method in comparison with CPSS. Comparing the results of

the system it can be directly identified that very large developments of static and dynamic performances of the

system with the RPSS in comparison with the application of the CPSS and also outperforms over Horch et

al.,(2014) and Sakthivel and Arun (2016). Simulation results demonstrate the good damping performance of the

robust designed thermal PSS with enhanced ABC. Results show that enhanced ABC, an optimization method is

more effective to damp out oscillations.

I.Under-exited mode x=0.5 , y=0.85 , z=0.1802

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II. Nominale mode x=0.3, y=0.85, z=0.1102:

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Australian Journal of Basic and Applied Sciences, 10(15) October 2016, Pages: 261-271

c) Stator Angle Voltage:

III. Over-excited mode x=0.2 ,y=0.85, z=0.6760:

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Table I: Damping Coefficients ‘α’ and static error ‘ξ’ in the closed loop system with RPSS and CPSS in different operating conditions of

the power system

Reactive

Power

αPSS ξPSS αPSSH∞ ξPSSH∞ αWPSSEABC ξWPSSEABC αTHPSSEABC ξTHPSSEABC

-0.2033 0.6574 0.00119 0.6846 0 0.7121 0 0.7322 0

-0.2449 0.6564 0.0012 0.6853 0 0.7211 0 0.7441 0

-0.1238 0.6695 0.00112 0.6960 0 0.7321 0 0.7510 0

-0.3402 0.6671 0.00089 0.7038 0 0.7382 0 0.7624 0

-0.6840 0.6574 0.00071 0.6877 0 0.7401 0 0.7743 0

Table II: Settling time ‘Ts’ and peak time ‘Tp’ in the closed loop system with RPSS and CPSS in different operating conditions of the power

system

Reactive

Power

TS PSS TP PSS TS PSSH∞ TP PSSH∞ TS WPSSEABC TP WPSSEABC TS THPSSEABC TP THPSSEABC

-0.2033 0.93 0.51 0.6 0.464 0.38 0.34 0.298 0.28

-0.2449 0.92 0.51 0.594 0.461 0.372 0.334 0.291 0.261

-0.1238 0.65 0.5 0.59 0.46 0.367 0.3217 0.269 0.25

-0.3402 0.81 0.46 0.549 0.435 0.3211 0.312 0.258 0.233

-0.6840 0.84 0.47 0.56 0.44 0.303 0.304 0.251 0.219

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Australian Journal of Basic and Applied Sciences, 10(15) October 2016, Pages: 261-271

Conclusion:

This paper proposed a systematic model for automated power system stabilizer using enhanced ABC

algorithm to improve damping and reducing tie line power oscillations in deregulated environment. In this work,

the thermal power is given as input and the output is compared with the CPSS and Wind PSS. The Simulink

model was developed and tested using MATLAB software. The response of speed deviation, stator terminal

voltage, Interior angle and active power were plotted. Results of simulation show that enhanced ABC optimized

controller provides good damping performance. Comparison with the existing methods shows that the proposed

controller can achieve good robustness over the other controllers. In future, fuzzy controller or ANN controller

can replace the PID controller in order achieve more robust power system stabilizer.

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