an effective pi controller self tuning technique for hvdc...

International Journal of Artificial Intelligence and Computational Research, 3(1), 2011, pp. 51-62

*Corresponding author: [email protected]

An Effective PI Controller Self Tuning Technique forHVDC Links: A Hybridization by Blending of GA and ANN

1A. Srujana and 2S. V. Jayaram Kumar1Research Scholar, JNT University, Hyderabad

2Professor, Jawaharlal Nehru Technological University, Hyderabad

Today, HVDC transmission system has emerged as the only promising alternative available to handle large bulk of power.However, controlling and operating the HVDC system remains a challenging task, especially due to the complexities involvedin maintaining the system stability when a fault occurs. This can be accomplished by incorporating controllers and utilizingproper techniques to tune them. Hence, controlling effectiveness of the HVDC system relies on the effectiveness of the selftuning mechanism used by the controllers. In this paper, a hybrid technique is proposed to tune the PI controller-incorporatedHVDC system. The technique automatically tunes the PI controller parameters to effectively stabilize the HVDC operation.Basically, the technique is a blend of the two renowned Artificial Intelligence techniques, Artificial Neural Network andGenetic Algorithm. The Genetic Algorithm is used to generate the training dataset for the neural network. The neuralnetwork continually provides suitable controller parameters from the time of fault until the fault is corrected. The techniqueis simulated and compared with conventional and fuzzy-based self tuning techniques. The implementation results show thatthe performance of the proposed hybrid technique is superior to that of both the self tuning techniques.

Keywords: HVDC, fault clearance, Genetic Algorithm (GA), neural network, PI controller

1. INTRODUCTION

Today, increasing the capacity of power systems isoften a taxing choice because erection of large powerplants and high voltage lines are hindered by economic,environmental, and political constraints. Therefore,possible new solutions to deal with the abovementioned issue are explored. One such highlypromising technique recommends the change over ofexisting and planned conventional HVAC (HighVoltage Direct Current) transmission technologies withHVDC (High Voltage Direct Current) ones[1]. In recenttimes, HVDC systems that interconnect large powersystems providing several technical and economicadvantages have increased significantly.

In comparison with AC transmission, the featuresof the proven HVDC technology are universally moreappealing for certain applications like long submarinecable links and interconnection of asynchronoussystems [1]. Bipolar, mono-polar metallic return andmono-polar ground return modes are the three typesin which HVDC systems are designed for operation[5]. Since charging the capacitance of a transmissionline with the alternating voltage is not necessary,HVDC has the important advantage of more efficient

long distance transmission [21]. Due attention has notbeen paid to the system interconnection use of HVDCtransmission link [4]. The good features unique toHVDC converters in power transmission systems arehuge capacity and rapid controllability [18].

In recent years, due to the improvements made inthe power electronics sector, electricity transmissionand distribution has been significantly improved byHVDC based Voltage source converters (VSC-HVDC)transmission link that employs self-commutated valves(IGBTs, IGCTs and GTOs) [9]. VSC-HVDC system isone of the latest HVDC technologies and it employstwo VSCs one each for the rectifier and the inverter[8]. However, the fault occurrence remains an openchallenge in the system. The faults that commonlyoccur in power distribution and transmission systemsare line to ground fault , line to line fault, double lineto ground fault, and three-phase to ground fault [11].Hence, controllers are incorporated in the system toclear the fault. Conventionally, fixed gains PIcontrollers are used by HVDC systems [3]. But this isreplaced by self tuning controllers.

Several techniques are proposed in the literaturefor fault detection. One such method is based on thesequence components present in the fundamental

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52 International Journal of Artificial Intelligence and Computational Research

frequency of the post-fault current and voltage [14].Generally a Fault Detection and Diagnostic systemcarries out the task in two stages; they are symptomgeneration and diagnosis [1]. This is achieved bymaintaining the power system at the preferred operatinglevel through the employment of latest controltechniques [7]. Protection against line faults can beachieved using Artificial Neural Network, Fuzzysystem and Genetic algorithm based latest controlswhich are fast and reliable [13].

Generally they employ adaptive tuning of thecontroller for effective control. However, because asingle technique is deployed for this purpose, theeffectiveness remains a challenge as the necessity andcomplexity of HVDC system peaks. To overcome thisissue, we propose a hybrid technique in this paper toself tune the PI controller which controls the HVDCsystem whenever a fault occurs. The rest of the paperis organized as follows. Section 2 reviews the relatedworks briefly and section 3 details the proposedtechnique with sufficient mathematical models andillustrations. Section 4 discusses implementationresults and Section 5 concludes the paper.

2. RECENT RESEARCH WORKS:A BRIEF REVIEW

Chi-Hshiung Lin [22] has compared the misfire faultin the rectifier valve and the misfire fault in the invertervalve of an HVDC link. A dynamic simulation analysishas revealed that the resultant phenomena are notidentical. A misfire fault in the rectifier valve inducesa power disturbance on the rectifier side of systemfrequency which if any of the natural torsional modesare disrupted induces a considerable torsional torquein a turbine generator adjacent to the inverter station.But a misfire fault in an inverter valve is likely tobreakdown the HVDC link by creating commutationfailure in converters. The rectifier and the inverter sidesof the generator have been affected quite severely if acollapse occurred in the HVDC link.

Vinod Kumar et al. [23] have modeled a high speedhigh precision HVDC transmission system that workswith weak ac system and analyzed the fuzzy controlledcontrol strategy & performance of the system. In spiteof unsteadiness and big discrepancies of the inputpower, the system has been capable of feeding weakor even dead networks. Optimization of the linkefficiency under diverse disturbances has beenachieved by fuzzy logic-based control of the system.Models can be built for individual users own models

using the basic building blocks found in a typicalHVDC systems that has been provided by the proposedmodel. A DQ-type of phase-locked-loop that has beenpresented for synchronizing the firing pulses to theHVDC converter are specific contributions of theproposed method. In spite of polluted and harmonicdistorted commutation voltage, this gate-firing unit hasbeen capable of supplying a pure sinusoidalsynchronizing voltage. The capability of the proposedfuzzy logic based HVDC system to operate steadily,restore steadily in the event of a short circuit fault andits obvious advantages have been confirmed byPSCAD/EMTDC based simulations.

Mohamed Khatir et al. [24] have stated that therelative strength of the AC system considerably affectsthe performance of an HVDC link that is connected toit. However, the strength of the AC system relative tothe capacity of the DC link has significant influenceon the interaction between AC and DC systems andthe related problems. In a HVDC inverter, they haveinvestigated the effect of the DC control on recoveryfrom AC system fault produced commutation failures,in line commutated thyristor inverter feeding a weakAC system. The study system has focused on the ACsystem fault, single phase ground fault. For simulationstudies, MATLAB Simulink has been used.

Mohamed Khatir et al. [25] have discussed thatHVDC converter of capacitor commutated converter(CCC) type of topology has the potential foremployment in long distance transmission via cables.Therefore for HVDC transmission across large bodiesof water, this proposed method can be employed. Theyhave presented the Capacitor Commutated Converters(CCC) technology and demonstrated its advantages forhigh power transmission. For presenting the transientperformance evaluations PSCAD/EMTDC has beenused. The system has been obtained from the earliestCIGRE HVDC Benchmark model. The superiorperformance of a very weak AC system connected CCClink with regard to improved transmission capacity andbetter stability of the AC network has been confirmedby results.

Bandarabadi et al. [9] have discussed the use ofVSC-HVDC link based transmission network forpossible improvement of fault-ride through capabilityin 160 MW wind farm connection. The 80 numbers of2 MW permanent magnet synchronous generators thatconstituted the 160 MW wind farm have been separatedinto 4 groups with 40 MW nominal powers. In the

An Effective PI Controller Self Tuning Technique for HVDC Links: A Hybridization by Blending of GA and ANN 53

course of wind speed variations and after the removalof grid side faults the power losses for re-establishingthe voltage at the transmission network terminal hasto be minimized. It is important for the VSC-HVDCto support the voltage of the transmission network sidein the course of short circuit faults in main grid whichhas been termed as fault ride-through capabilityimprovement. Variable speed operation and fault ride-through capability improvement has beenrecommended by the proposed method for wind farmnetwork and transmission network respectively. Thebehavior of wind farm, transmission voltage and dcvoltage for diverse changes in wind speed and three-phase short circuit fault has been studied by performingsimulation in PSCAD/EMTDC software. Throughsimulation results the improvement achieved by theconnection method in performance and fault ride-through capability has been verified.

Khatir Mohamed et al. [26] have applied stepchanges of the active and reactive powers and balancedand unbalanced faults to VSC based HVDCtransmission system and investigated its steady-stateand dynamic performances. For all cases, the obtainedresults have revealed the capability of the proposedcontrol strategy to provide quick and satisfactorydynamic responses to the proposed system. Thecapability of VSC-HVDC to perform quick and bi-directional power transfer has been made evident bythe simulation results. It has been evident that exceptfor a small oscillation constant transmitted power canbe maintained during single line fault. But, during athree-phase fault, power flow by the DC link has beenreduced considerably because of the reduced voltageat the converter terminals. Rapid recovery of normaloperation has been possible after the fault is removed.

Lidong Zhang et al. [27] have proposed a controlmethod of grid-connected voltage-source converters(VSCs). The method has been highly significant inhigh-voltage dc (HVDC) applications, though it canbe commonly employed for all grid-connected VSCs.The principle of the proposed method resembles theoperation of a synchronous machine and utilizes theinternal synchronization mechanism in ac systemsunlike earlier control methods. In a weak ac-system,utilization of this type of power-synchronizationcontrol in the VSC has enabled prevention of instabilitycaused by the standard phase-locked loop. In addition,similar to a normal synchronous machine, the VSCterminal has been capable of providing strong voltage

support to the weak ac system. Analytical models andtime simulations have validated the proposed controlmethod.

3. THE HYBRID TECHNIQUE FOR SELF TUNINGPI CONTROLLER IN HVDC

The proposed technique for self tuning the PI controllerof HVDC links is a hybridization of the two thrivingtechniques, genetic algorithm and artificial neuralnetwork. Self tuning of the PI controller mainlyinvolves the automatic determination of theproportional and integral gains of the controller K

P and

KI, respectively. Self tuning has to be performed in the

controller enabled HVDC links, whenever a faultoccurs in it. As mentioned earlier, only single line-to-ground fault and line-to-line fault are considered.Because of these faults, the current lose its stabilityand the fault current dominates. According to the faultcurrent, the controller has to be tuned to give a stableoutput in spite of the fault current. The technique iscomprised of three stages, namely, GA-based trainingdataset generation, network training and faultclearance. The first two stages can be collectivelycalled as the training phase of the technique, becauseit is performed before the fault occurs. Once thetraining process is completed, the controller willbecome capable of self tuning in the event of theoccurrence of any of the two types of faults for whichit is trained. This is performed with the aid of theoptimal K

P and K

I controller gains obtained by the

trained network at periodic time intervals.

3.1. GA-Based Training Dataset Generation

The process of GA-based training dataset generationis depicted in Figure 1. The training dataset consistsof different possible error values and the correspondingoptimal values of K

P and K

I can be obtained from GA.

To perform the process, an error dataset E is generatedwithin the error limit [e

min, e

max]. The elements of error

dataset are given by E = {emin

, emin

+ eT, e

min + 2e

T, ...,

emax

}, where, eT is a threshold to generate elements in

a periodic interval. For every element of E, optimal KP

and KI are determined using GA, as described below.

(i) Chromosome Generation: Generate apopulation pool of size N

p with N

p number of arbitrary

chromosomes, ( ) ( )0 1 =

p ppX x x ; p = 0,1,..., N

p–1,

where, ( )0

px and ( )1

px are the two genes of the pth

chromosome that are generated arbitrarily in the


interval min max, P PK K and min max, I IK K respectively..

i.e. ( ) min max0 , ∈

pP Px K K and ( ) min max

1 , ∈ p

I Ix K K . Here,

Np is the size of the population pool, min

PK and maxPK are

the minimum and maximum range of KP and, min

Ik andmaxIk are the minimum and maximum range of K

I

respectively.

(ii) Fitness Evaluation: Determine the fitness forevery chromosome present in the population pool,using the following fitness function

[0, 1] 0

arg min | |∈ −

= ∆∫p

T

pp N

F I dt (1)

where,

( )( ) ( )0 1 0

∆ = − ⋅ + ⋅∫Tp p

refI I x e x e dt (2)

In Eq. (1), Fp is the fitness of the pth chromosome,

∆I is the change in current due to the pth chromosomeand T is the time maxima. In Eq. (2), I

ref is the reference

current and e is the error element of E.

(iii) Selection: Select the best Np/2 chromosomes

based on fitness value and place it in the matingpool.

(iv) Crossover: Crossover the chromosomes in themating pool at a crossover rate of C

r to obtain a child

Xchild for every parent chromosomes.

(v) Mutation: Mutate the chromosomes byrandomly selecting the genes at a mutation rate of M

r.

Replace the gene values by arbitrarily selecting thecorresponding range of values to obtain N

p/2 new

children for the Np/2 parent chromosomes.

(vi) Termination criteria: Refill the populationchromosomes by the N

p/2 mating pool chromosomes

and new Np/2 children chromosomes. Go to step 2 and

iteratively repeat the process until it reaches amaximum number of iterations I

max. Once the iteration

reaches Imax

, terminate the process and select thechromosome, which has best fitness in the mating pool,as the best chromosome.

The obtained best chromosome has an optimal KP

and KI for the particular element of E. Similarly,

optimal KP and K

I are obtained for all the elements of

E and the dataset is generated as follows

(1) (1)min

(2) (2)min

(3) (3)min

( ) ( )max

2

+ = +

p I

T p I

T p p

IEI IEIp I

e K K

e e K K

D e e K K

K Ke

(3)

where, D is the training dataset generated from theGA. The obtained training dataset is used to train theneural network in the upcoming phase of networktraining.

3.2. Training of Neural Network

Multilayer feed forward neural network is selected forour technique, and it is trained using the dataset givenin the Eq. (3). The network structure with parametersis depicted in Figure 2. In order to train the network,Back Propagation (BP) algorithm is used. The networktraining process is described below.

Figure 1: The Proposed GA-ANN Hybrid PI ControllerSelf Tuning Technique for HVDC Systems


Step 1: Generate arbitrary weights within theinterval [0, 1] and assign it to the hidden layer neuronsas well as the output layer neurons. Maintain a unityvalue weight for all neurons of the input layer.

Step 2: Input the training dataset D to the classifierand determine the BP error as follows

BPerr

= Dtar

– Dout

(4)

In Eq. (4), Dtar

is the target output and Dout

is thenetwork output, which can be determined as

(1) (2)2 2[ ]=outD y y , where (1)

2y and (2)2y are the

network outputs which directly represent KP and K

I

respectively. The network outputs can be determinedas

(1)2 2 1 1

1

( )=

= ∑HN

rr

y w y r (5)

(2)2 2 2 2

1

( )=

= ∑HN

rr

y w y r (6)

where,

11

1( )

1 exp( )=

+ − ⋅r in

y rw D (7)

Eq. (5) and Eq. (6) represents the activationfunction performed in the output layer and hidden layerrespectively.

Step 3: Adjust the weights of all neurons as w = w+ ∆w, where, ∆w is the change in weight which can bedetermined as

err.y. P∆ = γw B (8)

In Eq. (8), γ is the learning rate, usually it rangesfrom 0.2 to 0.5.

Step 4: Repeat the process from step 2, until BPerror gets minimized to a least value. Practically, thecriterion to be satisfied is BP

err < 0.1.

Once the process gets completed, the network iswell-trained and it would be suitable for providingoptimal K

P and K

I values for any error.

3.3. Fault Clearance

The fault clearance process is ultimately performed bythe PI controller which is auto-tuned by the proposedtechnique. When either of the aforesaid faults occur inthe link, the technique gets activated and determinesthe error e

test from the line as follows

etest

= Iref

– Itest

(9)

where, Iref

is the reference current that needs to bemaintained in the link and I

test is the measured current

from the link. The measured etest

is given as input tothe well-trained network. The network provides a best

KP and K

I value, termed as best

pK and bestIK respectively,,

to the PI controller for the corresponding etest

. For theobtained K

Pand K

I value, the PI controller controls the

HVDC fault voltage and current using the traditionalPI calculation,

0= + ∫

Tbest bestntest p test I testI K e K e dt (10)

where, Intest

is the output of the PI controller. For thecontrolled voltage/current, again e

test is measured and

the process is repeated. Iterative repetition of theprocess is performed until the HVDC voltage/currentreaches a stable value. Once the voltage/current reachesthe stable state, the technique is disabled and voltage/current monitoring is continued. Hence, if any faultoccurs, the technique is activated and the fault iscleared in a very short time because of the hybridizationand adaptiveness of the proposed technique.

4. RESULTS AND DISCUSSION

The proposed technique was implemented in theworking platform of MATLAB 7.10 and its operationwas simulated. For this, the reference HVDC model,which was taken from [28], is given in Figure 3.

Figure 2: The Structure of Multi-layer Feed Forward NeuralNetwork Utilized in the Proposed Technique

Figure 3: The Model of an HVDC System


The two aforesaid faults were considered in themodel and the different parameters obtained wereplotted. The methodology parameters are tabulated inTable I and the results are depicted in the followingfigures.

Table IThe Parametric Values used in the Proposed Technique

S.No Technique Parameters Values

1 emin

/ emax

0.1/5

2 eT

0.1

3 min maxp pK K 0/10

4 min maxI IK K 0/10

5 Cr

0.5

6 Mr

0.5

7 Np

10

8 Imax

50

9 NH

2

1.a

1.b

1.c

2.a

2.b


2.c

3.a

3.b

3.c

Figure 4: Performance Comparison between (1)Conventional, (2) the Fuzzy-based and (3) the Hybrid PI

Controller Self Tuning Technique in Clearing Single Line toGround Fault at Inverter

1.a

1.b


1.c

2.a

2.b

2.c

3.a

3.b


3.c


Controller Self Tuning Technique in Clearing Line-to-LineFault at Inverter

1.a

1.b

2.a

2.b

3.a


3.b


Controller Self Tuning Technique in Clearing Single Line-to-Ground Fault at Rectifier

1.a

1.b

2.a

2.b

3.a


Only the technique was implemented by MATLABcoding and the model and its operation were consideredfrom [28]. The performance of the proposed techniquewas compared with the conventional self tuningtechnique and fuzzy-based self tuning technique. Fromthe results, it is evident that the proposed techniquetakes considerably less time to stabilize the system thanthe other existing techniques with which it wascompared.

5. CONCLUSION

In this paper, a hybrid technique to self tune the PIcontrollers used in HVDC systems was proposed. Thetechnique was proposed with the intention ofsupporting the PI controller during the fault clearanceprocess. This has been accomplished by offeringoptimum PI controller parameters at every instant oftime during the fault clearance process which stabilizesthe system in a shorter time. The performance of thesystem has been evaluated from the implementationresults. Also, the system was validated by comparingthe hybrid technique with the conventional and fuzzy-based self tuning techniques. The comparison resultsproved that the hybrid technique consumesconsiderably less time to clear the fault voltage andcurrent and hence to stabilize the system. Therefore, itwas evident that the proposed technique makes thecontrolling of HVDC systems significantly moreeffective than other conventional self tuningtechniques.

3.bFigure 7: Performance Comparison between (1)

Conventional, (2) the Fuzzy-based and (3) the Hybrid PIController Self Tuning Technique in Clearing DC Line-to-

Line Fault at Rectifier

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