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ANN-BASED SYNCHRONOUS GENERATOREXCITATION FOR TRANSIENT STABILITYENHANCEMENT AND VOLTAGEREGULATIONAbdul Ghani Abro a & Junita Mohamad Saleh aa School of Electrical & Electronics Engineering, EngineeringCampus, Universiti Sains Malaysia , Penang , MalaysiaPublished online: 10 Jan 2013.

To cite this article: Abdul Ghani Abro & Junita Mohamad Saleh (2013) ANN-BASED SYNCHRONOUSGENERATOR EXCITATION FOR TRANSIENT STABILITY ENHANCEMENT AND VOLTAGE REGULATION, AppliedArtificial Intelligence: An International Journal, 27:1, 20-35, DOI: 10.1080/08839514.2013.747369

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ANN-BASED SYNCHRONOUS GENERATOR EXCITATIONFOR TRANSIENT STABILITY ENHANCEMENT ANDVOLTAGE REGULATION

Abdul Ghani Abro and Junita Mohamad SalehSchool of Electrical & Electronics Engineering, Engineering Campus, Universiti SainsMalaysia, Penang, Malaysia

& Control of the synchronous generator, also referred to as an alternator, has always remained verysignificant in power system operation and control. Alternator output is proportional to load angle,but as the parameter is moved up, the power system security approaches the extreme limit. Hence,generators are operated well below their steady state stability limit for the secure operation of a powersystem. This raises demand for efficient and fast controllers. Artificial intelligence, specifically arti-ficial neural network (ANN), is emerging very rapidly and has become an efficient tool for operationand control of power systems. ANN requires considerable time to tune weights, but it is fast and accu-rate once tuned properly. Previously, ANNs have been trained with high-dimensional input space orhave been trained online. Hence, either one requires considerable time to yield the control signal or isa bit risky technique to apply in interconnected power systems. In this study, a multilayer perceptron(MLP) ANN is proposed to control generator excitation trained with low-dimensional input space.Moreover, MLP has been trained offline to avert the risk potential of online training. The resultsillustrate preeminence of the proposed neurocontroller-based excitation system over the conventionalcontrollers-based excitation system.

INTRODUCTION

A power grid is a complex and variable network with many opera-tional levels made up of a wide range of energy sources with numerousinteraction points (Venayagamoorthy and Harley 2001). Following ever-growing industrialization and population, the demand for electric powersupply is continuously increasing. Because of the de-regularization ofpower systems, the current trend is toward interconnected networks of

The authors gratefully acknowledge the Institute of Postgraduate Studies, University SainsMalaysia, fellowship scheme for the financial support.

Address correspondence to Junita Mohamad Saleh, School of Electrical & Electronics Engineer-ing, Engineering Campus, Universiti Sains Malaysia, 14300 Nibong Tebal, Seberang Perai Selatan,Penang, Malaysia. E-mail: jms@eng.usm.my

Applied Artificial Intelligence, 27:20–35, 2013Copyright # 2013 Taylor & Francis Group, LLCISSN: 0883-9514 print=1087-6545 onlineDOI: 10.1080/08839514.2013.747369

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transmission lines linking generators with loads in different areas, leavingpower system security at high risk (Vittal 2000; Shahidehpour, Tinney,and Fu 2005).

Transient stability and small-signal stability analysis are essential compo-nents of power system security (Vittal 2000). Recently, power systems havegained more attention from researchers in employment of nonlinear con-trollers, particularly for transient stability enhancement. The results haveshown much improvement over considerable operating range (Johansson,Angquist, and Nee 2010). However, nonlinear controllers are complicated,difficult to implement, and require the exact system modeling. Thecomputational intelligence methods, in particular the artificial neural net-works, have shown to be able to provide opportunity to overcome the afore-mentioned problems of nonlinear control (Venayagamoorthy and Harley2001).

Artificial neural networks (ANNs) are parallel distributed processing sys-tems capable of synthesizing a complex, transparent, and highly nonlinearmapping from space of input features to output space (Fukuda and Shibata1992; Basheer and Hajmeer 2000; Rafienia et al. 2010). ANN, with its learn-ing ability, avoids the complexmathematical analysis in solving control prob-lems when plant dynamics are complex and highly nonlinear. This is adistinct advantage over the traditional nonlinear control methods (Fukudaand Shibata 1992). Learning a general solution is not only an importantcapability but also offers high processing speed at the same time (Bishop1994).

The use of ANN in power system control is not new (Vankayala and Rao1993). Both the static and dynamic neural networks have found many appli-cations, ranging from generator control (Amjady and Majedi 2007;Cabrera-Vazquez et al. 2007; Felix, Sanchez, and Loukianov 2009), high-voltage DC transmission (HVDC; Dash, Routray, and Mishra 1999; Mazonet al. 2001; Yilmaz et al. 2007), flexible AC transmission (FACTS; GwangWon and Lee 2005; Ma 2007; Modi, Singh, and Sharma 2007), motor con-trol (Kowalski and Orlowska-Kowalska 2003; Ren and Chen 2006; Nouri,Dhaouadi, and Braiek 2008), to power quality enhancement (Jayasree,Devaraj, and Sukanesh 2009). In fact, a work (Sharaf and Lie 1994) has pro-posed an ANN-based classification of stability detection, based on the lossof excitation or short circuit, and it also has considered the severity of fault.Patterns from simulated data have been extracted using fast Fourier trans-form (FFT) and used as input to ANN for the classification. The reportedresults are not satisfactory because of the used stopping criterion.

Djukanovic and colleagues (1995) designed an ANN-based controllerfor a third-order generator model with a static (ST-1) excitation system.In the article, the feedback-coordinated stabilizing control law is treatedas a mapping from the space of actual system measurement to the space

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of synthesized control actions. The chosen ANN considered 49 input fea-tures, had no hidden nodes, and had two output nodes trained up toleast-square-error reduced to 0.000007.

A radial basis function (RBF)-based controller with six input vectors hasbeen conceived to replace AVR (Swidenbank et al. 1999). In Venayaga-moorthy and Harley (2001) two 3-layer multilayer perceptron (MLP) withtime-delayed inputs neural networks, trained online for a static excitation(ST-1) system and turbine control of a turbo generator, have been reported.The intelligent controller consists of a neuro-identifier (NI) and a neuro-controller (NC). The NI and NC, having twelve and six inputs respectively,have been trained through unsupervised learning. The NC used 80weights. The work by Venayagamoorthy and Harley (2002) is an extensionof Venayagamoorthy and Harley (2001). Instead of using one NC for bothexcitation and turbine, separate NCs have been employed to reduce thecomputational burden on the controller. The results show slight improve-ment because of the lesser computational burden (Venayagamoorthyand Harley 2002). In this architecture, each NC has used 56 weights.

MLP and RBF are types of a static feed-forward neural network and bothare universal approximators. For the approximation of nonlinear input=output mapping, MLP normally requires a fewer number of parametersthan RBF, for the same degree of accuracy. It is well known that during adap-tation, the variance of Gaussians can become very broad, and hence, RBFmay lose its local nature. Complex implementation and huge memoryrequirement are demerits of dynamic neural networks. Unsupervised tech-nique is very risky and is prone to instability. Supervised learning is efficientbecause of the availability of more information sources than are available forunsupervised learning. Moreover, as ANN tends toward local optima traps, itis very risky to train ANN online for controlling power systems usingerror-backpropagation methods (Abro and Mohamad-Saleh 2012).

The simple architecture and low memory requirement has made MLPan attractive choice for mapping any complex function. MLP constitutesof input, hidden, and output layers. A hidden layer adds the nonlinearlearning capability to MLP. In most function-approximation problems,one hidden layer is sufficient to approximate any continuous function(Bishop 1994; Basheer and Hajmeer 2000). ANN architecture selection isa multifaceted problem. An ANN with minimum size is less likely to learnthe idiosyncrasies or noise in the training data, and hence, may generalizethe data more appropriately (Bishop 1994; Swidenbank et al. 1999).

A highly challenging characteristic for a trained ANN is how well it per-forms when presented with a new set of data; in other words, the data that ithas not seen before. This is referred to as the generalization characteristicand it is an essential feature of an ANN. Figure 1 illustrates this character-istic. Typically, the data is scattered as a result of noise. This requires an

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ANN with the appropriate degree of flexibility to learn the trend in the data(solid line) without tightly overfitting the data (dotted line).

Another important criterion of ANN training is its stopping condition.Most of the previous research works relied on the mean error as the stoppingcondition for the ANN training, which resulted in the poor generalization.This training-stopping condition is ambiguous because training errordecreases with every subsequent run, irrespective of ANN learning. More-over, high-dimensional and intercorrelated input data, for ANN training,increases processing elements and, consequently, the information-processingtime. Therefore, decreasing dimensions of input space may enhance the per-formance of an ANN-based controller.

This article is divided into three phases. The immediate phase describesthe impact of high-speed AVR on power system transient stability. Thesecond phase focuses on the model simulation, and the last phasediscusses results.

EFFECT OF FAST-ACTING AVR ON POWER SYSTEM STABILITY

A power system comprises generation, transmission, and distribution sys-tems. The synchronous generator, a prime element of a power system, com-prises the stator (also called the armature), and the rotor (also known as thefield). The field is responsible for spreading magnetic flux in an air gap.For proper operation of a synchronous generator, the prime condition issynchronism between armature and field. Strength of the synchronism lar-gely depends on the strength of air-gap magnetic flux. Furthermore, theexcitation system is responsible for keeping air-gap flux strength constant.The synchronism can be jolted by faults induced anywhere in a power sys-tem, but the extreme disturbance is fault introduced at the terminals of agenerator. Fault deteriorates the strength of magnetic flux, as explained

FIGURE 1 Showing generalization (solid line) versus overfitting phenomenon (dotted line).

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by an armature reaction phenomenon, and so affects the synchronism. Themechanical angle between rotor magnetic field and armature magnetic fluxof a generator is known as the load angle or power angle, symbolized as d.

Power system stability is the ability of an electric power system to regain astate of operating equilibrium after being subjected to a physical disturb-ance or fault. In addition, neither a unit at a generating station nor a por-tion of a power system should lose synchronism with respect to thegenerating station or the power system (Kundur et al. 2004). Power systemstability is divided into three major types; angle stability, voltage stability, andfrequency stability. These are further classified into transient andsmall-signal stability according to the magnitude of disturbance. In transientstability, disturbances are considered to be sufficiently large that lineariza-tion of system equations do not hold true, whereas, in small-signal stability,disturbances are conceived to be small, and thus linearization of systemequations holds true (Kundur et al. 2004). Power system stability may beenhanced by either reducing the first swing or by enhancing system damp-ing (Machowski, Bialek, and Bumby 2008). In the power system, a three-phase-to-ground fault is a large disturbance, hence, the fault type has beenconsidered in this simulation work. In Figure 2, the power transfer capabilityof a system with respect to active power, voltage, and power factor has beenillustrated. The figure also shows the relationship of active power, voltage,and power factor.

7As the figure shows, an increase in power factor from lagging (lag) toleading (lead) increases voltage level and consequently increases the activepower-transfer capability and vice versa. Fault current is highly inductive innature as is evident from Figure 2. Voltage drop is higher at more laggingpower factor. During fault, drop in voltage drops the electrical power transferfrom its pre-fault value to a very low level. However, input mechanical powerremains the same because of a slow-responding turbine. This difference ininput mechanical power (Pm) and output electrical power (Pem) generates

FIGURE 2 Relationship between voltage, active power, and power factor.

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positive accelerating power (Pa), as given in the following equation.

Pa ¼ Pm � Pem: ð1Þ

The accelerating power causes the rotor to swing forward and may lead tolosing the synchronism following unstable oscillations. Basically, power sys-tem stability is the equilibrium between the applied mechanical torqueand the induced electromagnetic torque. Power system stability enhance-ment can well be understood by equal area criterion as shown in Figure 3.

Area A1 is the area associated with acceleration of the generator duringfault, and A2 is the deceleration area during the post-fault. Higher A2assures power system stability. Power system stability can be enhanced bydecreasing area A1, which will ultimately lead to increasing A2. A1 may bereduced either by removing the fault early or by increasing the magnitudeof electrical power output during fault. There are many methods to dam-pen oscillations and restore the pre-fault equilibrium point, but the meth-ods employed in this study are damper winding and excitation control(AVRþExciter).

The induction of electromagnetic torque (Tem) in damper windingagainst any speed deviation from synchronous speed is based on Lenz’slaw. The magnitude of Tem is higher for even small speed change, andnonlinear behavior can be observed at higher slip values. The strongerthe excitation system, the more effective will be the role of damper wind-ings in damping out the oscillations (Machowski, Bialek, and Bumby 2008).

The fault causes a decrease in air-gap flux density. However, decrease inair-gap flux density depends on the direct and quadrature (d-q) axis’ sub-transient and transient time constant and the duration of fault. Moreover,it also depends on the total decrease in terminal voltage. This leads toincrease in DV, so the output of the excitation system will shoot up to com-pensate the error. Transient stability may be enhanced by rapidly increasingexcitation current (Wang et al. 1993). The fast-acting excitation systems can

FIGURE 3 Variation of active power in relation to load angle.

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increase the excitation up to its ceiling value before clearance of the fault.This has two positive effects (Machowski, Bialek, and Bumby 2008):

1. It causes increase in E0 (transient voltage), so the accelerating powerdecreases.

2. When the fault is cleared, the system will follow higher-power angle char-acteristics owing to new E0. This will increase the decelerating area.

The effect of fast excitation on oscillations can be well explained byEquation (2).

Et ¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiE 02 þ 2þ 2X 0

dE0VL cos dX þ X 0

dVL

X

� �2r

X 0d

X þ 1

0BB@

1CCA; ð2Þ

where Et is the terminal voltage, X is reactance, (0) indicates the transientparameter, and d indicates direct axis.

The Et (terminal voltage) reaches its minimum when d0 equals p=2.However, the minimum value of Et is dependent on X 0

d=X value. The caseconsidered in this simulation is X 0

d=X > 1. Hence, after removal of fault,Et recovers well in spite of large d0 values. Consequently, this high terminalvoltage will force the AVR to reduce the field current, and thus, a decreasein back swing is achieved. This will reduce the subsequent forward andbackward swings, hence, effective damping occurs.

This article considers an existing multimachine power plant. The multi-machine plant has been converted to single-machine infinite bus system(SMIB), which simulates a single generator connected with the rest of thepower system. The excitation model considered here is as per IEEE 2005 rec-ommendation (2006). The high initial response excitation system AC4A hasbeenmodeled as shown in Figure 4. It utilizes a full wave-controlled bridge rec-tifier, and only AC-type excitation systems allow negative-field voltage forcing.

In Figure 4, VC is the compensating voltage, used to keep voltage con-stant at any preset point. It is given by

VC ¼ jEt þ ðRC þ jXCÞIt j; ð3Þ

FIGURE 4 The exciter AC4A model.

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where Et is the terminal voltage, It is the terminal current, RC is the compen-sating resistance, and XC is the compensating reactance.

The VUEL is under excitation limiter (UEL) voltage. UEL senses a com-bination of voltage and current of the synchronous generator. The UELoutput is applied in the voltage regulator at the HV gate to override thenormal action of the voltage regulator (Figure 4). Its function is to preventoverheating of the stator-end region of the synchronous machine.

SIMULATION AND IMPLEMENTATION

The power system model considered in this work is shown in Figure 5.The parameters of the generator are given in Table 1, where X0d is the sub-transient direct axis reactance, X0q is the subtransient quadrature axis reac-tance, T is the time constant, (00) indicates transient and K is a constant.The excitation system is proportional-and-integrator (PI) controlled. ThePI controller is simple to implement, yet very effective in damping anoscillatory mode (Guo, Crow, and Sarangapani 2009). Figure 6 showsPI-compensated AVR and exciter. Their parameters are given in Table 2.

The turbine model has been the same during testing of both conven-tional controller and NC. The compensating point considered for VC isthe terminals of the generator. Initially, the VUEL was not considered in thissimulation. Later, it was observed that after the fault clearance, as exciteroutput falls below zero value, the induction motor, connected at infinitebus, experiences a problem in restoring pre-fault electromagnetic torque.This ultimately leads to load angle oscillations at the generator end, whichurges the use of VUEL. Interested readers can find a modeling explanationof VUEL (see 2006).

MLP has been used in this work because of its advantages discussed inthe previous section. In this work, generated data have been divided intothree parts: training, validation, and testing in 4:2:4 ratios. To avoid theoverfitting problem, a method called validation has been employed to stop

FIGURE 5 Single machine-infinite bus model.

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the training process instead of using training error. The best MLP has beenselected based on the one with the smallest test error.

The basic work stages are shown in the flow chart in Figure 7. MLPshave been trained on the randomized data based on a 60msec three-phase-to-ground fault at load (0.051þ j0.24) X. Simulation of the model has beencarried out on MATLAB=Simulink. 13.8 KV, 300MW, 50Hz generator simu-lates the combinedmultimachine system. Training of ANN has been triedwith different sampling rates. The minimum error has been obtained atfour samples per cycle.

The network growing technique has been used to obtain an optimalMLP size. Network growing basically adds one hidden node at a time intothe ANN. Each hidden node employs a sigmoidal activation function. Out-put of the simulated network has been obtained on the sequential data.Every time MLP size has been grown by adding an extra hidden node,ANN weight tuning has been carried out thirty times with random initialweights. At the end of every tuning, the mean square error (MSE) andmean absolute error (MAE) of the datasets have been calculated. The bestMLP has been selected on the basis of minimum MSE and MAE of the testdataset.

FIGURE 6 Block diagram of AVR and exciter combination.

TABLE 1 The Simulated Synchronous Generator Parameters

Xd 1.86 pu Xq 1.72pu RStator 0.003 puX0

d 0.25 pu X0q 0.43pu Inertia 3.6

X00d 0.21 pu X00

d 0.28pu Hz 50Hz

T0d 0.3 s T00

d 0.04 s T00q 0.031 s

TABLE 2 Parameters of Excitation System (see Figure 6)

T1 8 s T4 1 sT2 16.4 s T5 0.3 sT3 0.2 s K 8K2 1

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For every MLP, the same training method has been carried out for fourdifferent well-known learning algorithms: the Levenberg–Marquardt errorbackpropagation (LM), Bayesian regularization (BR), resilient backpropa-gation (RP), and gradient descent with momentum (GDM) to investigatethe differences in their performance.

RESULTS AND DISCUSSION

The results of MLP prediction with different learning algorithms andwith different numbers of hidden nodes are shown in Figure 8. Thesupremacy of LM error backpropagation is very evident from Figure 8.Initially, very rapid decline of error in all the learning algorithms can beobserved, but soon they attend saturation level. The MAE of RP is initiallylesser than BR, but afterward, BR supersedes RP. The MLP with GDMconverges very slowly, and its convergence is the slowest among all the

FIGURE 7 ANN training flow chart.

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compared algorithms. MAE and MSE error magnitudes of LM are the leastfor MLP, which has nine hidden neurons. Hence, the selected MLP forrepresenting the problem is a three-layer MLP with two input nodes, ninesigmoidal hidden nodes, and one linear output node. With sigmoidal hid-den nodes, the universal approximation properties of the MLP hold even ifthe output units have linear activation function.

Figures 9 and 10 show two plots of change in terminal voltage withrespect to �10% change in reference voltage at load (0.051þ j0.024) Xand load (0.007þ j0.004) X. The figures clearly depict the superiority ofthe NC over the conventional controller (Conv). As mentioned earlier,the conventional controller is a PI controller. At higher load, with the

FIGURE 8 MSE and MAE with change in hidden nodes of different learning algorithms.

FIGURE 9 Performance at �10 change to Vref at (0.051þ j0.24) X.

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conventional controller at the end of 3 s, the generator was not able to attenda steady state position as it did at lower load. However, with the NC, the sys-tem neither oscillates at the lower load nor at the higher load conditions.The NC has been trained by the conventional controller data, but the NCcontroller has appropriately extracted the trend in the data and, hence,resulted in better performance than the conventional controller.

FIGURE 10 Performance at �10 Change to Vref at (0.007þ j0.004) X.

FIGURE 11 Terminal voltage; 120ms fault at (0.051þ j0.24) X load.

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Figures 11 and 12 show the terminal voltage and load angle responseafter removal of 120ms three-phase-to-ground fault, at lower load. Beforeand after the fault, the terminal voltage produced by the conventional con-troller and the NC is overlapping. Nevertheless, a few oscillations can beobserved immediately after the clearance of the fault using the conven-tional controller. Overshoot and drop in voltage are more evident with

FIGURE 12 Load angle; 120ms fault at (0.051þ j0.24) X.

FIGURE 13 Load angle; 120ms fault at (0.007þ j0.004) X.

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the conventional controller than with the NC, whereas oscillations in loadangle have been damped out almost equally by the conventional controllerand the NC.

In Figures 13 and 14, nonlinear capability of the NC has been depicted.At higher load, 120ms fault has been simulated at generator terminals. Bothfigures show dominance of NC over conventional controller more vividly.The perfomance of the conventional controller deteriorates more as the sys-temmoves away from its linearized point. Again, during and after removal ofa fault, the voltage drop and the voltage rise for the conventional controllerhas been more than that for the NC. To clearly show the voltage fluctuationsafter removal of the fault, that portion has been clipped. In addition, therise and settling of steady-state terminal voltage with the NC is far aheadof that of the conventional controller, whereas load angle comparison showsthe asymptotically unstable behavior of the conventional controller and thestable behavior of the NC. In addition, the first swing of the NC is also lesserthan the conventional controller in both figures.

CONCLUSION

In this article, the effect of fast-acting excitation systems on transientstability and voltage regulation has been demonstrated. The adaptive andnonlinear nature of an MLP-based neurocontroller has also been explainedand verified. The proposed neurocontroller has been trained on low-dimensional input space of actual signals and has been trained offline. Data

FIGURE 14 Terminal voltage; 120ms fault at (0.007þ j0.004) X.

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for the proposed neurocontroller training has been generated using theconventional PI-controller. However, the proposed neurocontroller trainedon the data has outperformed the conventional controller for generator-excitation control. The proposed neurocontroller is easier to design in com-parison with conventional adaptive and gain-scheduling nonlinear control-lers. Moreover, offline training of neurocontrollers is simpler than onlinetraining. Furthermore, it is more secure to install offline-trained neurocon-trollers than online-trained neurocontrollers. Also, the proposed neurocon-troller allows generators to be operated closer to their steady-state stabilitylimits and hence, release in their capacity. Therefore, the proposedneurocontroller-based generator can carry a higher load in comparison tothe conventional controller-based generator.

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