improvement of power transfer capability of hvdc ... · tion of faults, reliable fault identi...

IMPROVEMENT OF POWERTRANSFER CAPABILITY OF HVDCTRANSMISSION SYSTEM USINGARTIFICIAL NEURAL NETWORK

(ANN) CONTROLLER.

M. Ramesh1, Dr. K B V S R Subrahmanyam2,1Professor & HOD, Assoc. Prof2

Department of EEE1Vaageswari College of Engineering, Karimnagar,

2S R Engg.College,Warangal,Telangana, India

marpuramesh223@ gmail.com, [email protected]

October 11, 2018

Abstract

Strides in Industrial Power System demands increasedelectrical energy consumption. With the increase in size andintricacies of power system, appropriate control strategyneed be developed to ensure power delivery with minimumloss, which also is economical, reliable and with in technicallimits. Transport of major capacity of power through longdistances require higher levels of generation with efficientpower transmission and should address the problems asso-ciated with long AC large capacity power transmission. Toensure safety of AC-DC system, strict monitoring of systemsignals, rapid classification of perturbations, supplement themaking of a protective control decision. HVDC systems ne-cessitate decisive actions in micro to millisecs range. Opti-mal operations of the systems depend on knowledge and fast

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International Journal of Pure and Applied MathematicsVolume 120 No. 6 2018, 299-327ISSN: 1314-3395 (on-line version)url: http://www.acadpubl.eu/hub/Special Issue http://www.acadpubl.eu/hub/

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clearance of faults such as common commutation failures,requiring millisecs to detect . In view of large HVDC sys-tems being integrated with main transmission lines, detec-tion, classification and fault clearance in the least possibletime is essential. The mitigating action depends on locat-ing source/cause of disturbance. A feasible secure approachresults, when signals are monitored for accurate classifica-tion of faults, reliable fault identification, for a safe controldecision. Time and frequency domain methods used forHVDC faults analysis suffer from (inherent inability of purefrequency domain method) being unstable and for analysistransients, time domain methods are influenced by noise.Developments in HVDC technology was made possible be-cause of the tremendous advancements in power electron-ics component and artificial intelligence techniques such as,ANN. The ANN has the capability to learn and extractinformation in systems where the non-linearity and timedependency do not permit one to use methods such as fre-quency or modal analysis. Although it is always possibleto linearize such a system around an operating point andconduct such studies, such derived models always remainvalid only within the limited region. The CIGR model asone of the conventional methods has been studied and newcomplementary characteristics have been added to improveits stability and damping rate of voltage and current os-cillations during the disturbance in the ac systems and toincrease the efficiency of the proposed model.

Key Words: HVDC transmission, CIGR Benchmarkmodel, Faults in HVDC System,Proprtional Integartor(PI)Controller,ANN Controller.

1 INTRODUCTION

With the development of HVDC transmission technology, today,HVDC system not only makes long-distance transmission moreeconomy, but also provides a rapid and effective control method forthe power system .Because the high-voltage DC transmission sys-tem is a typical nonlinear system and the control parameter is verycomplex and changeable, the traditional PI controller cannot sat-isfy the requirement.HVDC systems traditionally use PI controllers

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with fixed gains. Although such controllers have certain disadvan-tages [1], they are never-the-less rugged and operate satisfactorilyfor perturbations within a small operating range. On the otherhand, ANN controllers have some specific advantages [2-4], wherebythe use of ANN controllers has been shown to introduce flexibilityand fault tolerance into the performance of the controllers. ANNshave been extensively used for the fault diagnosis, load demandforecasting, system identification, state estimation etc., in powersystems [5], but only a few publications exist in the application ofcontrollers for power systems. The increasing use of AI paradigmsin recent years in the area of HVDC control systems is indicativeof the promising features associated with these new techniques [l,6, 7].CIGR model has been introduced in 1991 [8]. This model, asa known model is suitable for the simulation of power systems [9].In this model, the control strategy is based on the voltage and cur-rent simultaneous control and the frequencies of two ac systems areconsidered constant. Therefore, the frequencies deviations do notplay any role as the disturbance occurs. In this paper, an investiga-tion into the architectural and functional aspects of an online ANNbased current controller for an HVDC (plant) system is performedto consider: (a) the simplest architecture sufficient to control thesystem, (b) the impact of learning/momentum constants on thecontroller performance, (c) the contribution of each neuron to con-troller performance, (d) the performance assessment under noiseconditions, and (e) the comparative assessment with a traditionalPI controller.

2 HVDC SYSTEM MODEL

HVDC model, fig.1 represents the MATLAB/SIMULINK Simu-lated system connected to an AC system having a SCR of 2.5.The advantage of simplicity and ease of computation of a 6 pulseconverter over a 12 pulse converter was demonstrated in this modelof two 6 pulse converters in series. The HVDC transmission testsystem is composed of three sub-systems, as given by:

• Sub-system 1:The six pulse bipolar HVDC system configuration has a fixedimpedance (R = 2,160 and L = 150.8 mH) with AC filters

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Source voltage of 345 kV for source at 50 Hz is used for theAC side of the system.

• Sub-system 2:On the DC side 500 kV (nominal) is supplied by the inverterwhich is constant. The DC current of 2,000 A, nominal power(Pd) of 1,000 MW smoothing reactor (Ld = 597 mH) are usedat rectifier side.

• Sub-system 3:With the simplification to emphasis rectifier and controllerto RC snubber circuit (R = 2,000 and C = 0.1 F) with thevalues as shown are used.

Fig.1 Six-pulse Bipolar HVDC System.

PI controllers are traditionally used in the HVDC power con-vertersystems. In a two terminal HVDC system, the currentmargincontrol method is normally utilized whereby the rectifieris kept incurrent control (CC) and the inverter is in constantextinction angle(CEA) control. An error signal, I,, which is the difference betweenthe reference current, I, and the measured current, Id, from thesystem is fed to the PI-controller.The error output of the controlleris acted upon by thePI gains to provide the required alpha order for

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the HVDC converter. Due to uncertainties in system parameters,the optimalchoice of gains is quite difficult.Proportional integral(PI) controllers are commonly used in HVDC system in addition toAI controllers. A mathematical model of the real plant is requiredfor the controller design with conventional methods. The difficultyof identifying the accurate parameters for a complex nonlinear andtime-varying nature of real plants may render, in many cases, thefine tuning of parameters which is time consuming. PI controllersare vary much sensitive to parameter variations inherent in realplant operations. The PI control is given byT = Kpe +Ki

∫edt

The output of the PI controller is updated by updating the PIcontroller gains (Kpand Ki) based on the control law in the pres-ence of parameter variation and drive nonlinearity.Fig.2 Shows thestructure of PI controller.

Fig.2 Structure of PI Controller.

3 ARTIFICAL NEURAL NETWORK

HVDC systems traditionally use PI controllers with fixed gains.Although such controllers have certain disadvantages [I], they arenever-the-less rugged and operate satisfactorily for perturbations

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within a small operating range. On the other hand, ANN con-trollers have some specific advantages [11-13], whereby the use ofANN controllers has been shown to introduceflexibility and faulttolerance into the performance of thecontrollers. ANNs have beenextensively used for the, stability analysis in power systems [14].Oneof the most important features of Artificial Neural Networks (ANN)is their ability to learn and improve their operation using a neu-ral network training data[15-16]. The basic element of an ANN isthe neuron which has a summer and an activation function. Themathematical model of a neuron is given by:

y = φ

N∑

i=1

Wi ∗Xi + b (1)

where (x1, x2 xN) are the input signals of the neuron, (w1,w2, wN) are their corresponding weights and b as bias parameter.φ is a tangent sigmoid function and y is the output signal of theneuron. The ANN can be trained by a learning algorithm whichperforms the adaptation of weights of the network iteratively untilthe error between target vectors and the output of the ANN is lessthan a predefined threshold. The most popular supervised learningalgorithm is back- propagation, which consists of a forward andbackward action. In the forward step, the free parameters of thenetwork are fixed, and the input signals are propagated throughoutthe network from the first layer to the last layer. In the forwardphase, we compute a mean square error.

E(k) =1

N

N∑

i=1

(di(k)− yi(k))2 (2)

where di is the desired response, yi is the actual output producedby the network in response to the input xi, k is the iteration num-ber and N is the number of input-output training data. The secondstep of the backward phase, the error signal E(k) is propagatedthroughout the network in the backward direction in order to per-form adjustments upon the free parameters of the network in orderto decrease the error E(k) in a statistical sense. The weights asso-ciated with the output layer of the network are therefore updatedusing the following formula:

Wji(k + 1) = wji(k)− η δE(k)

δwji(k)(3)

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where wji is the weight connecting the jth neuron of the outputlayer to the ith neuron of the previous layer, η is the constant learn-ing rate.The objective of this Neural Network Controller(NNC)is to develop a back propagation algorithm such that the outputof the neural network speed observer can track the target one.Fig. 3 depicts the network structure of the NNC, which indi-cates that the neural network has three layered network struc-ture. The first is formed with five neuron inputs ∆(ωANN(k +1)),∆(ωANN(k)), ωANN , ωs(k − 1),∆(ωs(k − 2)). The second layerconsists of five neurons. The last one contains one neuron to givethe command variation ∆(ωs(k)). The aim of the proposed NNCis to compute the command variation based on the future out-put variation (ANN(K+1)).Hence, with this structure, a predictivecontrol with integrator has been realised. At time k, the neural net-work computes the command variation based on the output at time(k+1), while the later isn’t defined at this time. In this case, it isassumed that ωANN(k + 1) ≡ ωANN(k).The control law is deducedusing the recurrent equation given by,

ωs(k) = ωs(k − 1) +G∆ωs(k) (4)

The proposed Neural network controller is shown in Fig. 3[17-18].The proposed HVDC system with Neural Network Controller isshown in Fig. 4.

Fig. 3 Neural network controller.

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Fig. 4HVDC system with Neural Network Controller.

(A) The Feed forward Architecture

Figure 5 represents a feed forward NN with two inputs and oneoutput. In a feed forward network, the output of the neurons inone layer acts as the input to the neurons of the following layer;with no feedback connection present in the network. This topologyis chosen as (1) it is similar to FL architecture with two inputs andone output and hence can be combined to form a NF system; (2)learning algorithms such as steepest descent, can be convenientlyused with such network architecture.

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Figure 5 Feed forward Artificial Neural Network Architecture withtwo inputs one output.

The proposed ANN has two inputs, either one or two hiddenlayers or one output neuron; the simplest architecture version con-sists of a single hidden layer. The input layer simply acts as afan-out input to the hidden layer where two neurons are used. Theoutputs are transformed through a sigmoidal AF and fed to theoutput layer through their weights. The output layer has only oneneuron with a sigmoidal AF and three inputs (two from the hiddenlayer and one constant bias). The output is multiplied by a con-stant scaling factor, , to get the required alpha order. One of threepossible inputs are used to study the performance of this controller:

• Inputl: The reference current, Iref, and bias,

• Input2: The measured dc current, Id, and bias, or

• Input3: The current error, Ie=Iref- Id, and bias.

(B) Online reinforcement learning method

In this approach, an error reinforcement method is used to de-termine the target controller output (alpha order) from the HVDCplant response (Id). In the Back Propagation (BP) method, thecontroller output (alpha-order) is compared with the desired known

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alpha-order and the error is back propagated to control the output.Therefore the use of conventional BP requires a priori knowledge ofthe alpha order [l]. In the reinforcement learning method used here,the current error, I,, is used to adjust the weights of all the ANNlayers. The HVDC plant current output, Id, which is the perfor-mance measure, is controlled by an alpha order resulting from theneuron in the output layer. Since the HVDC plant performance isdirectly related to the output (alpha order) of the ANN, this criti-cal block has been investigated in detail in this work. (C) Weightadjustment /learning

The error function, E, to be minimized for a given input pattern isgiven by:

E =1

2(Iref − Id)2 (5)

The change in weights of the input and the output layers areadjusted according to the Generalized Delta rule [19] in the negativedirection of (1) as:dw ∝ δE

δwδE

δw=δE

δviX

δviδneti

Xδnetiδw

(6)

Where for the kth layer of the ith neuronδvi = 1

(1+e−neti )(For asymmetrical sigmoid), neti =

∑wjiyji(weighted

sum at The ith neuron), wji = weights connected between the jth

to the ith Neuron and yj =output from the jth to the ith neuron.Note that although the second and third partial derivatives (eq.

2) can be explicitly evaluated, the first term cannot be evaluatedexplicitly. As derived in [1], the adjustment of weights using thestandard BP algorithm requires a priori knowledge of the system,i.e., the ANN requires the error to be back propagated through thedc system. Therefore, the term δE

δviis expressed as being propor-

tional to the current error, Ie. Hence the change in weights for boththe output and hidden layers is:

∆wt+1ij = −ηXIeXf ′(neti)Xyi + ∆wij(t)Xµ

equationWhere, η is the learning parameter, Ic is the error,f ′(neti) is

the derivative of the sigmoidal function yi, is the input to the ith

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neuron, µ is the momentum constant and ∆w(t+1)ij and ∆w

(t)ij are

the change in weights at the instants (t+l) and (t) respectively.

4 SIMULATION RESULTS

The test system used in all the case studies in the current researchwork is shown in Fig. 3.13 which is CIGRE Bench mark model 2.The specifications are as follows:

Fig.6 Simulink diagram of the HVDC Circuit

Configuration of the 6 pulse bridges with approx.600 phase shiftresulting in cancellation of harmonic oscillations. Fig. 3.13 givesthe schematic of the transmission line of 850 km line and 0.597Hsmoothing reactors. The converter transformers (Wye grounded/Wye/Delta)are modeled using a Three-Phase Transformer (Three-Winding)blocks. The rectifier and the inverter are 12-pulse converters withtwo Universal Bridge blocks connected in series. The 12 pulsethyristor converter is implementation using 2 six pulse converterswithout the transformers being simulated. Two Graetz bridge 6pulse converters in series simulated for 12 pulse convertor risingstar - star and star delta connection on MATLAB/SUMULINKhave incorporated the filters for harmonic reduction. The Voltagecontrol for minimum firing angle is carried out. The DC link withconstant voltage and adjustable values of current result in the min-imizing power loss by variation of the firing angle and extinctionangles the simulation is carried out.

(a) Without Fault

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Figure 7 Voltage and Current on the DC side at Rectifier.

Fig 7 shows the system no fault voltage and Current waveformsat Rectifier on the DC side used the Artificial Neural Networks(ANN) control. From the simulation results it is observed that DCvoltage and current reaches the reference value of 1.0pu and1.0 purespectively at 0.5 second, i.e. about 0.2 seconds later after startingHVDC. The whole system reaches stable state after 0.5 sec.

Figure 8 Voltage and Current on the DC side at Inverter.

Fig 8 shows the system no fault voltage and Current waveformsat Inverter on the DC side used the Artificial Neural Networks

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(ANN) control. From the simulation results it is observed that DCvoltage and current reaches the reference value of 1.0pu and1.0 purespectively at 0.5 second, i.e. about 0.2 seconds later after startingHVDC. The whole system reaches stable state after 0.5 sec.

Figure 9 Simulation results of the active power.

Fig.9 shows the change process of the active power of HVDCSystem without fault with PI Controller and the Artificial NeuralNetworks (ANN) control. it is clear that for no fault , both thecontrollers perform well but the Artificial Neural Networks (ANN)controller gives a better transient performance and quite a low over-shoot as compared to the conventional PI controller. (a) WithFaults

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Figure 10 Simulation results when DC fault occurs on Rectifier.

In Fig.10, it is observed that DC fault occursRectifier side ofHVDC system. The Artificial Neural Networks (ANN) Controlleractivates and clears the fault. Fig.10 shows the waveforms after0.6sec DC fault at the inverter. A large number of oscillations havebeen observed in DC link current and voltage magnitudes in caseof a conventional controller. From the Rectifier DC voltage plotsit is clear that in case of conventional control the Rectifier valvesundergo commutation failure several times as compared with Artifi-cial Neural Networks (ANN) Controller. Artificial Neural Networks(ANN) controller reduces the recovery time by 1.7 sec after the dis-turbance. Once the fault is cleared, at t=1.7 sec the system comesback to its normal operation.

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Figure 11 Simulation results when DC fault occurs on Inverter.

In Fig.11, it is observed that DC fault occursInverter side ofHVDC system. The Artificial Neural Networks (ANN) Controlleractivates and clears the fault. Fig.11 shows the waveforms after0.6sec DC fault at the inverter. A large number of oscillations havebeen observed in DC link current and voltage magnitudes in case ofa conventional controller. From the inverter DC voltage plots it isclear that in case of conventional control the inverter valves undergocommutation failure several times as compared with Artificial Neu-ral Networks (ANN) Controller. Artificial Neural Networks (ANN)controller reduces the recovery time by 1.7 sec after the disturbance.Once the fault is cleared, at t=1.7 sec the system comes back to itsnormal operation.

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Figure 12 Simulation results of the active power when DC faultoccurs.

Fig.12 shows the change process of the active power of HVDCSystem after a disturbance of a DC fault with PI Controller andthe Artificial Neural Networks (ANN) control. it is clear that forDC fault , both the controllers perform well but the Artificial Neu-ral Networks (ANN) controller gives a better transient performanceand quite a low overshoot as compared to the conventional PI con-troller.

Figure 13 Voltage and Current waveforms when 20% change inload occurs on rectifier.

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A step change of 20% is applied to the reference current andVoltage at Rectifier side. Fromthe simulated results (Figure 13), itis clear that for a 20% step change, both the controllers perform wellbut the Artificial Neural Networks (ANN) controller gives a bettertransient performance and quite a low overshoot as compared tothe conventional PI controller.

Figure 14 Voltage and Current waveforms when 20% change inload occurs on Inverter.

A step change of 20% is applied to the reference current andVoltage at Inverter side. Fromthe simulated results (Figure 14), itis clear that for a 20% step change, both the controllers perform wellbut the Artificial Neural Networks (ANN) controller gives a bettertransient performance and quite a low overshoot as compared tothe conventional PI controller

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Figure 15 Simulation results of the active power when 20% changein load occurs.

Fig15 shows the change process of the active power of HVDCSystem when 20% change in load occurswith PI Controller and theArtificial Neural Networks (ANN) control. it is clear that for a 20%step change, both the controllers perform well but the ArtificialNeural Networks (ANN) controller gives a better transient perfor-mance and quite a low overshoot as compared to the conventionalPI controller.

Figure 16 Simulation results when a line-to-ground fault occurs onrectifier side.

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In Fig.16, it is observed that a line-to-ground fault occurs onphase A of the rectifier side of HVDC system. The Artificial Neu-ral Networks (ANN) Controller activates and clears the fault. TheArtificial Neural Networks (ANN) controller better than the fixed-gain PI controller. The rectifier side DC Time in seconds currentsuffers from prolongs oscillations and consecutively more commuta-tion failures in the case of fixed-gain PI controller. The fixed-gainPI controller takes longer time to recover after fault is cleared dueto the narrow range of optimum controller gain parameters. Onthe other hand, the Artificial Neural Networks (ANN) controllerhas the ability to extend its optimum range of gain parameters de-pending on the system contingencies. Artificial Neural Networks(ANN) controller reduces the recovery time by 1.5 sec after the dis-turbance. Once the fault is cleared, at t=1.5 sec the system comesback to its normal operation.

Figure 17 Simulation results when a line-to-line fault occurs onRectifier side.

In Fig.17, it is observed that a line-to-line fault occurs on phaseA of the rectifier side of HVDC system. The Artificial Neural Net-works (ANN) Controller activates and clears the fault. The Arti-ficial Neural Networks (ANN) controller better than the fixed-gainPI controller. The rectifier side DC Time in seconds current suf-fers from prolongs oscillations and consecutively more commutationfailures in the case of fixed-gain PI controller. The fixed-gain PI

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controller takes longer time to recover after fault is cleared dueto the narrow range of optimum controller gain parameters. Onthe other hand, the Artificial Neural Networks (ANN) controllerhas the ability to extend its optimum range of gain parameters de-pending on the system contingencies. Artificial Neural Networks(ANN) controller reduces the recovery time by 1.5 sec after the dis-turbance. Once the fault is cleared, at t=1.5 sec the system comesback to its normal operation.

Figure 18Simulation results when a Three-phase fault occurs onrectifier side.

In Fig.18, it is observed that a Three-phase fault occurs onphase A of the rectifier side of HVDC system. The Artificial Neu-ral Networks (ANN) Controller activates and clears the fault. TheArtificial Neural Networks (ANN) controller better than the fixed-gain PI controller. The rectifier side DC Time in seconds currentsuffers from prolongs oscillations and consecutively more commuta-tion failures in the case of fixed-gain PI controller. The fixed-gainPI controller takes longer time to recover after fault is cleared dueto the narrow range of optimum controller gain parameters. Onthe other hand, the Artificial Neural Networks (ANN) controllerhas the ability to extend its optimum range of gain parametersdepending on the system contingencies. Fuzzy logic controller re-duces the recovery time by 1.5 sec after the disturbance. Once thefault is cleared, at t=1.5 sec the system comes back to its normaloperation.

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Figure 19 Simulation results of the active power when aline-to-ground fault occurs.

Fig.19 shows the change process of the active power of HVDCSystem after a disturbance of a line-to-ground fault with PI Con-troller and Artificial Neural Networks (ANN) control. It is clearthat for a line-to-ground fault, both the controllers perform wellbut the Artificial Neural Networks (ANN) controller gives a bettertransient performance and quite a low overshoot as compared tothe conventional PI controller.

Figure 20Simulation results of the active power when a line-to-line fault occurs.

Fig.20 shows the change process of the active power of HVDCSystem after a disturbance of a line-to- line fault with PI Controller

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and theArtificial Neural Networks (ANN). It is clear that for a line-to- line fault, both the controllers perform well but the ArtificialNeural Networks (ANN)controller gives a better transient perfor-mance and quite a low overshoot as compared to the conventionalPI controller.

Figure 21 Simulation results of the active power when a ThreePhase fault occurs.

Figure 21 Simulation results of the active power when a Three-phase fault occurs.Fig.21 shows the change process of the activepower of HVDC System after a disturbance of three phase faultwith PI Controller and theArtificial Neural Networks (ANN). It isclear that for a Three-phase fault, both the controllers perform wellbut the Artificial Neural Networks (ANN)controller gives a bettertransient performance and quite a low overshoot as compared tothe conventional PI controller.

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Figure 22 Simulation results when a line-to-ground fault occurs onphase A of the Inverter side.

In Fig.22, it is observed that a line-to-ground fault occurs onphase A of the Inverter side of HVDC system. The Artificial Neu-ral Networks (ANN) Controller activates and clears the fault. TheArtificial Neural Networks (ANN) Controller better than the fixed-gain PI controller. The rectifier side DC Time in seconds currentsuffers from prolongs oscillations and consecutively more commuta-tion failures in the case of fixed-gain PI controller. The fixed-gainPI controller takes longer time to recover after fault is cleared dueto the narrow range of optimum controller gain parameters. Onthe other hand, the Artificial Neural Networks (ANN) controllerhas the ability to extend its optimum range of gain parameters de-pending on the system contingencies. Artificial Neural Networks(ANN) controller reduces the recovery time by 1.5 sec after the dis-turbance. Once the fault is cleared, at t=1.5 sec the system comesback to its normal operation.

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Figure 23 Simulation results when a line-to-line fault occurs onInverter side.

In Fig.23, it is observed that line-to-line fault occurs on phaseA of the Inverter side of HVDC system. The Artificial Neural Net-works (ANN) Controller activates and clears the fault. The Arti-ficial Neural Networks (ANN) controller better than the fixed-gainPI controller. The rectifier side DC Time in seconds current suf-fers from prolongs oscillations and consecutively more commutationfailures in the case of fixed-gain PI controller. The fixed-gain PIcontroller takes longer time to recover after fault is cleared dueto the narrow range of optimum controller gain parameters. Onthe other hand, the Artificial Neural Networks (ANN) controllerhas the ability to extend its optimum range of gain parameters de-pending on the system contingencies. Artificial Neural Networks(ANN) controller reduces the recovery time by 1.5 sec after the dis-turbance. Once the fault is cleared, at t=1.5 sec the system comesback to its normal operation.

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Figure 24 Simulation results when a Three-phase fault occurs onInverter side.

In Fig.24, it is observed that a Three-phase fault occurs onphase A of the Inverter side of HVDC system. Artificial NeuralNetworks (ANN) Controller activates and clears the fault. TheArtificial Neural Networks (ANN) controller better than the fixed-gain PI controller. The rectifier side DC Time in seconds currentsuffers from prolongs oscillations and consecutively more commuta-tion failures in the case of fixed-gain PI controller. The fixed-gainPI controller takes longer time to recover after fault is cleared dueto the narrow range of optimum controller gain parameters. Onthe other hand, the Artificial Neural Networks (ANN) controllerhas the ability to extend its optimum range of gain parameters de-pending on the system contingencies. Artificial Neural Networks(ANN) controller reduces the recovery time by 1.5 sec after the dis-turbance. Once the fault is cleared, at t=1.5 sec the system comesback to its normal operation.

5 CONCLUSIONS

For HVDC transmission links, where very large transient conditionsare involved in the plant operation, it is important to design controlstrategies, which are robust under all possible normal and abnor-mal situations. The above objective can be achieved only whenthere is a total knowledge of the system parameters. But for such

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systems where the AC subsystems are subjected to change theirtopology from time to time it is very difficult to have an accuratemathematical model. Also the non-linear nature of the link makesit difficult to design a time invariant robust controller for this sys-tem. In this paper the possibility of replacing a PI controller witha NN based controller for the rectifier terminal of an HVDC linkwas explored. This NN controller has a part which is trained on-line. Compared with the traditional PI control, the ANN controlhas higher response accuracy and faster response speed. Besides,the system controlled by ANN control also has smaller overshootand oscillation when the system is in the processfrom system faulttime to steady time. ANN controller is somewhat slower for veryfast transients perhaps due to the inadequate training. Appendix’A’ Following are the parameters of the HVDC System chosen forthe simulation studies:

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[18] M. NasirUddin and Muhammad Hafeez., FLC-Based DTCScheme to Improve the Dynamic Performance of an IM Drive,IEEE Trans.on Industry Applications , Vol -48 , No 2,Mar/Apr 2012; 823-831.

[19] L. Fausett, Fundamentals of Neural Networks, Prentice Hall,1994.

M Ramesh is working as a Associate Professor and HODEEE Dept, MedakCollege of Engineering and Technology, Konda-pakMeadkDist, and pursuing Ph.D. at JNT University, Anantapuris B.Tech. Electronics & Electronics Engineering and M.Tech inAdvanced Power Systems, JNTU,Kakinada.He has many researchpublications in various international and national journals and con-ferences. His current research interests are in the areas of HVDCand Power System.

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improvement of power transfer capability of hvdc ... · tion of faults, reliable fault identi...

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