hybrid technique for self tuning pi controller parameters in hvdc systems

8/8/2019 Hybrid Technique for Self Tuning PI Controller Parameters in HVDC Systems

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Hybrid Technique for Self Tuning PI Controller

Parameters in HVDC Systems

A.SrujanaResearch Scholar

JNT University, Hyderabad

[email protected]

Dr. S.V.Jayaram KumarProfessor

Jawaharlal Nehru Technological University

Hyderabad

Abstract— Nowadays, due to certain advantages, the HVDC

systems are commonly used in long distance transmissions. The

major drawback associated with HVDC system is that it takes a

longer duration to return to its steady state value after the

occurrence of a fault. In a HVDC system, when a fault occurs, the

current and voltage will deviate from their normal range and PI

controllers are used to maintain its current and voltage at the

normal steady state value. Controller parameter tuning plays a

significant role in maintaining the steady state current andvoltage of a HVDC system. Here, we propose a hybrid technique

to self tune the PI controller parameters. The proposed hybrid

technique utilizes fuzzy logic and neural network to self tune the

controller parameters. The fuzzy rules are generated using

different combinations of current error, rate and combined gain.

To train the neural network, different combinations of fuzzy

gain, proportional gain and integral gain are used. The neural

network is trained using a back propagation algorithm. By

experimentation it is shown that the system that uses this method

takes a very short time to return to its normal steady state. The

implementation results show that the performance of the

proposed hybrid technique is superior to that of both the self

tuning techniques.

Keywords- fuzzy logic; HVDC; neural network; fuzzy rules;

proportional and integral gain.

I. INTRODUCTION

Presently, due to economic, environmental, and politicallimitations which hinder the erection of large power plants andhigh voltage lines, increasing the power system capacity isoften difficult. Hence, to solve the above issues new solutionsare sought. One of the most promising solutions suggests thereplacement of conventional HVAC transmission technologiesby targeted deployment of HVDC (High Voltage DirectCurrent) ones [1]. Of late, there has been a significant increasein the HVDC systems that interconnect large power systemsoffering many technical and economic benefits [2].

HVDC is a proven technology and the features presentedby it have made it more alluring than AC transmission forcertain applications for example long submarine cable linksand interconnection of asynchronous systems [1]. Fixed gainsPI controllers are commonly used by HVDC systems [3]. Theoperating in which a HVDC system can be designed arebipolar mode; mono-polar metallic return and mono-polarground return modes [5]. Charging the capacitance of atransmission line with alternating voltage is not necessary for

HVDC, so it has the advantage of providing more efficientlong distance transmission [21]. System interconnection use of HVDC transmission link has not attracted much awareness[4]. In power transmission systems, HVDC converters havethe unique virtues of large capacity and fast controllability[18].

In recent years, because of the development of power

electronics, an active role is played by HVDC transmissionlink based Voltage source converters (VSC), using self-commutated valves (IGBTs, IGCTs and GTOs) in improvingthe electricity transmission and distribution system [9]. VSC-HVDC system is one of the most modern HVDC technologies,and it incorporates two VSCs, one function as a rectifier andthe other as an inverter [8].

In power distribution and transmission systems, line toground, line to line, double line to ground, and three-phase toground are the possible faults [11]. The literature presents lotof fault detection techniques. The method based on thesequence components of the fundamental frequency of thepost-fault current and voltage is an example for this [14]. A

general Fault Detection and Diagnostic scheme consists of twophases, namely symptom generation and diagnosis [1]. So asto accomplish this, by executing modern control strategies thepower system must be maintained at the preferred operatinglevel [7]. Contemporary controls which are based on ArtificialNeural Network, Fuzzy system and Genetic algorithm arefound to be quick, and reliable. Hence, they can be employedfor protection against the line faults [13].

Generally, the controller is tuned adaptively to perform thecontrolling effectively. However, because a single technique isdeployed for this purpose, the effectiveness remains achallenge as the necessity and complexity of HVDC systempeaks. To overcome this issue, in this paper, we propose ahybrid technique by means of which the PI controller that

controls the HVDC system is self tuned whenever a faultoccurs. The rest of the paper is organized as follows. SectionII reviews the related works briefly and section III details theproposed technique with sufficient mathematical models andillustrations. Section IV discusses implementation results andSection V concludes the paper.

II. RELATED WORKS

Chi-Hshiung Lin [22] has discussed the difference betweentwo faults in an HVDC link. A misfire fault in the rectifier

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value and when a fault occurs in the system, the current valueincreases and at that moment the PI controller parameters aretuned and this makes the current to remain at its referencevalue. Here we use a hybrid technique to tune the PI controllerparameters in HVDC. First the error and rate values arecalculated from the current value and they are given as inputto the fuzzy logic and the fuzzy logic produces a combinedgain as the output. The fuzzy gain is given as the input to theneural network which in turn gives the proportional and

integral gain as the output. By using this proportional andintegral gain, the controller parameters are adjusted and makescurrent to remain stable.

A. System Model

HVDC system model considered in our method is shownin Figure 1. HVDC systems are commonly used for longdistance transmission and its major problem is due to the faultthat occurs in the system.

Figure 1. HVDC system model

The faults considered in our system are

i. Single Line to Ground fault

ii. Line to Line fault

i. Single line to Ground faultThe single line to ground fault is a very common fault in

HVDC systems. During this fault the current value getsincreased and the corresponding voltage decreases.

ii. Line to Line faultThe line to line fault occurs between two transmission

lines. This fault is one of the common faults that occur inoverhead transmission lines.

When a fault occurs in the system the current valueincreases and due to this increased current more problemsoccur in the system. To control this current we used a hybridtechnique which is a combination of fuzzy logic and neuralnetwork. The fuzzy logic is trained by giving error and ratevalues are given as its input.

The error and rate values always depend on the current. If the current value is normal then the error is zero and if currentincreases the error also increases. The error and rate arecalculated by using the equations given below.

mref dc I I I −=∆ (1)

T I I pvdc

dc

∆

∆−∆=Ι∆•

(2)

)(1 dc I G E ∆⋅= (3)

)(2 dc I G R &∆⋅= (4)

where, ref I is the reference current, m I is the measured

current, T ∆ is the sampling rate, pv I ∆ is the previous value

of error, and 21,GG are the gains for normalization.

By using the formulas the error and rate are calculated andthese calculated values are given as input to the fuzzy logic.

B. Obtaining Fuzzy Gain

The fuzzy logic is used here, to obtain the combined gain.The current error and rate are the inputs given to the fuzzylogic for rectifier pole controller and inverter controller and itsoutput is the combined gain.

The error and rate are given as input to the fuzzy logic andthe combined gain is obtained as its output. For obtaining thisfuzzy logic, the generation of fuzzy rules and training areimportant processes and these processes are explained insection III.E and III.F respectively. By giving any value of error and rate as input to the fuzzy the related combined gaincan be got as its output. When the current value changes thecombined gain also changes accordingly. Then, the fuzzyoutput is given as an input to the neural network and theproportional and integral gain are obtained from the neuralnetwork as outputs. Based on the change in the combined gainthat is given as input to the neural network, the proportionaland integral gain values will change.

C. Obtaining PI Controller Parameters from Neural Network Artificial neural networks (ANNs) are excellent tools for

complex manufacturing processes that have many variablesand complex interactions. Basically, neural network consistsof three layers, namely input layer, hidden layer and outputlayer. In our model, input layer has one variable, hidden layerhas n variables and output layer has two variables.

The configuration of the network used is shown in Figure2.



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Figure 2. Single input and two output neural networks to obtain proportional

and integral gain

The input to the neural network is the fuzzy gain and itsoutputs are proportional and integral gain. For operation, theneural network must be trained using a training data set. Thetraining of neural network is explained in section III.F. Oncethe network is trained a general model is created for therelationship between its input and output. So, when thecombined gain value is given as an input to the network therelated proportional and integral gain will be got as the output.

By using this technique, the PI controller parameters aretuned automatically and the current is made to remain stable,even if any fault occurs in the system it returns to its stablevalue in a short time.

D. Fault Clearance

During normal condition, current will remain in its steadystate value and so when the function checks the value, itdecides that no change is necessary in the system. When afault occurs in the system the current will increase suddenly.This time when the function checks the current value, it

identifies the increase in current and calls our technique. Byusing our technique, the error and rate values are calculatedfrom the current value and they are given as input to the fuzzylogic. By giving error and rate values as an input to the fuzzylogic we get fuzzy gain as the output. This gain value is givenas input to the neural network and the neural network givesproportional and integral gain as output. By using theproportional and integral gain from the neural network thecurrent values are calculated using the equation given below.

∫ +=T

I pout edt K eK I 0

(5)

where, out I

is the output of the PI controller.

Aftercalculating the current values the function checks whether thesteady state value of current is reached or not. If the currenthas reached the steady state value then the function stops itsprocess. If the current has not reached the steady state valuethen the function will repeat the process by calculating theerror and rate values again and give them as input to the fuzzy.This process continues until the current reaches the steadystate values i.e., until error value reaches zero.

In our method the fault that occurs in both rectifier andinverter sides of the HVDC system are considered. In inverterside the maximum fault current for line to ground fault is 2KA and in line to line fault maximum fault current value is2.5KA. In rectifier side the maximum fault current value is 1.5KA for both single line to ground fault and line to line fault.When a fault occurs in the system the current reaches itsmaximum value and voltage becomes value. For maintainingthe current at its normal value the current value must bereduced and voltage value must be increased. By using our

technique pK and iK values are adjusted, to make the

current reach its normal value. By using this method, when afault occurs in the system the current can be made to return toits normal value within a fraction of a second.

E. Generation of Fuzzy Rules

For training the fuzzy logic, training data set and fuzzyrules are generated. The faults which are considered fortraining fuzzy logic are line to line fault and line to groundfault in both rectifier and inverter side. By considering thesefaults the input variables are selected and based on that thetraining data set for fuzzy logic is generated. Inputs are

fuzzified in to three sets i.e.; large, medium and small andoutputs are very large, large, medium, small and very small.The membership grades are taken as triangular andsymmetrical. Fuzzy rules are generated by considering bothnormal and abnormal conditions.

For different combination of input variables the generatedfuzzy rules are shown in table I. After generating fuzzy rulesthe next step is to train the fuzzy logic.

TABLE 1. FUZZY RULES

Sl.no Fuzzy rules

1

2

34

5

6

7

8

9

if , E=large and R=large, then G=very largeif , E=large and R=medium, then G=large

if , E=large and R=small, then G=smallif , E=medium and R=large, then G=large

if , E=medium and R=medium, then

G=medium

if , E=medium and R=small, then G=large

if , E=small and R=large, then G=small

if , E=small and R=medium, then G=large

if , E=small and R=small, then G=very small

For training fuzzy logic the first step is to generate thetraining data set. This training data set is generated bycalculating error and rate values for different current values.To perform the process, a current dataset I is generated within

the current limit ],[ minmax I I . The elements of currentdataset are given by

{ }maxminminmin ,,2,, I I I I I I I T T L++= where, T I is a

threshold to generate elements in a periodic interval. For everycurrent value the error and rate values are calculated. By usingthe calculated values fuzzy data set is generated. By using thegenerated data set the fuzzy logic training process isperformed.




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F. Neural Network Training

The first process for training the neural network is thegeneration of training data set. Training dataset is generatedfor training neural network with different combinations of fuzzy gain, proportional and integral gain. For generatingtraining dataset set of fuzzy gain, proportional gain andintegral gain are selected and this dataset is used for trainingthe neural network. After generating the training data set, the

network is trained using a back propagation algorithm.The neural network is trained using generated data set. For

training neural network back propagation algorithm is usedand steps for training neural network are explained below indetail.

The training steps are given as follows:

Step 1: Initialize the input weight of each neuron.

Step 2: Apply a training sample to the network.

Step 3: Determine the output at the output layer of thenetwork.

Step 4: Determine the value of and using the actual

output of the network.

Step 5: Repeat the iteration process till the output reaches

its least value.Once the training is completed, the network is ready to

tune the control parameters of PI controller and when fuzzygain changes the network output also changes to maintain thecurrent within the normal range.

IV. RESULTS AND DISCUSSION

The proposed technique was implemented in the workingplatform of MATLAB 7.10 and its operation was simulated.

1.a 1.b

1.c




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2.a 2.b

2.c

3.a 3.b




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3.c

Figure 3. Performance comparison between (1) conventional, (2) the fuzzy-based and (3) the hybrid PI controller self tuning technique in clearing single line to

ground fault at inverter.

1.a 1.b

1.c




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2.a 2.b

2.c

3.a 3.b




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3.c

Figure 4. Performance comparison between (1) conventional, (2) the fuzzy-based and (3) the hybrid PI controller self tuning technique in clearing line-to-line fault

at inverter.

1.a 1.b

2.a 2.b




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3.a 3.b

Figure 5. Performance comparison between (1) conventional, (2) the fuzzy-based and (3) the hybrid PI controller self tuning technique in clearing single line-to-

ground fault at rectifier.

1.a 1.b

2.a 2.b




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3.a 3.b

Figure 6. Performance comparison between (1) conventional, (2) the fuzzy-based and (3) the hybrid PI controller self tuning technique in clearing dc line-to-line

fault at rectifier.

Only the technique was implemented by MATLAB codingand the model and its operation were considered from [28].The performance of the proposed technique was compared

with the conventional self tuning technique and fuzzy-basedself tuning technique. From the results, it is evident that theproposed technique takes considerably less time to stabilizethe system than the other existing techniques with which itwas compared.

V. CONCLUSION

In this paper, a neuro-fuzzy hybrid technique to self tunethe parameters of the PI controller in a HVDC system, wasproposed. Faults which are considered in our system are lineto line fault and line to ground fault of both rectifier andinverter sides. When a fault occurs in the system the currentand voltage increases and by using this neuro-fuzzy hybridtechnique, the system voltage and current can be made to

return to their stable values within a fraction of a second. Theperformance of the system was evaluated from theimplementation results. The implementation results showedthat the fault clearance time of the hybrid technique is verylow compared to conventional methods and fuzzy based self tuning methods. Thus it was proved the proposed techniquemakes the controlling of HVDC systems significantly moreeffective than other conventional self tuning techniques.

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A.Srujana received the B.Tech Degree in Electrical Engineering fromKakatiya University ,Warangal,India in 1998.She received her M.Tech Degreein Electrical Engineering from Jawaharlal Nehru Technological UniversityHyderabad in 2002 .Currently she is persuing Ph.D from the same Universityunder the guidance of Dr S.V.Jayaram Kumar. Her research interests includePower Electronics and HVDC.

Dr S.V.Jayaram Kumar received the M.E degree in Electrical Engineeringfrom Andhra University ,Vishakapatnam ,India in 1979.He received the Ph.Ddegree in Electrical Engineering from Indian Institute of Technology ,Kanpur,in 2000Currently ,he is a Professor at Jawaharlal Nehru TechnologicalUniversity Hyderabad .His research interests include FACTS & Power SystemDynamics.




hybrid technique for self tuning pi controller parameters in hvdc systems

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