Optimal Fuzzy Controller ForPower Quality Improvement Of Dynamic Voltage Restorer Using Bacterial Foraging Algorithm

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ISSN: 2005-4238 IJAST 10

Optimal Fuzzy Controller ForPower Quality Improvement Of Dynamic Voltage Restorer Using Bacterial Foraging Algorithm

1S.Deepa , ²Lavanya Dhanesh ,³P.Elangovan

1

Professor, EEE, Panimalar Institute of Technology, India.

2

Associate Professor, EEE, Panimalar Institute of Technology, India.

3

Associate Professor, EEE, Sreenivasa Institute of Technology and Management Studies, India

.

Abstract: This paper deals with various issues of power quality problems such as Voltage sag &

swell,, surges, harmonic etcusing Dynamic voltage Restorer (DVR).The conventional approach of PI tuning is inefficient due to the non-linearity presentin the system. In the proposed method most popularly used optimization technique namely Bacterial Foraging Optimization Algorithm is used to tune the PI controller betteroutput performance. Thevalidation of the proposed technique forpower systems to minimize the major power quality indices such as voltage sag and total harmonic Distortion(THD). These indices in sensitive loads at fault conditions has been simulated and tested.

Therefore, the multi-objective optimization algorithm is considered in order to achieve a better performance in solving the related problems.

Keywords: Bacterial Foraging Optimization, Dynamic Voltage Restorer, Fuzzy Logic Controller, Total harmonic Distortion, voltage Sag.

I Introduction

The major problem in most of the industries are the power quality issues. The general power quality problems are voltage sags and swells, harmonic distortion, interruptionsetc.Due to increased number of automation in almost all the industries, reducing such issues is very important. Among the several FACTS devices, DVR is one of the most technically advancedeconomical mitigationdevice to solve the power quality problems..

Fuzzy logic(FL)provides an inexpensive solution for controlling ill-known complex systems (Visioli et al, 2001). Fuzzy controllers have satisfactory attention in motion control systems as they have non-linear characteristics and a precise model is most often unknown. Fuzzy controller is already applied to DVR (Zhang, 2005).

The tuning of conventional fuzzy controller is a heuristic work. In order to eliminate such a problem the evolutionarytechniques is applied for tuning the FLC parameters. The Genetic Algorithm, Ant Colony Optimization , , Bacterial Foraging Optimization(BFO)etc have been effectively used in optimizing the Fuzzy logic controller. The GA method is used to optimize the complex non-linear problems (Wu et al, 2008),but in recent research studies they found that some problems arise in GA performance (Kristinsson et al, 1992).

Bacterial foraging is comparatively a very recent technique that is being used for solving multidimensional global optimization problems. In foraging theory, it is assumed that the objective of the animals is to search for and obtain nutrients in such a fashion that the energy intake per unit time is maximized [23]. Jen Su et al (2010) proposed a self adaptive bacterial foraging optimization approach to improve the proportional(P), integral(I) and derivative PID parameters which are optimized for estimating the fuzzy PID controller performance [24].

In many sensitive loads, the level of THD index may be very important. In few research studies , voltage THD and control criteria is taken as objective function (Newman et al, 2005).

However, in the aforesaid studies, practical problem have been raised if implement the algorithms with high level of complexity (Ljung, 1987). In order to have better performance during

voltage sag/swell and decrease voltage THD, two objective optimization will be considered in this paper. In this method voltage sag/swell will be considered as first objective and voltage THD will be considered as second THD in DVR system. In this paper fuzzy based BFO technique is used for optimizing the control parameter of the DVR. In this paper optimal fuzzy controller for power quality improvement of DVRusing BFA have been discussed in the following section.

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II. Problem Formulation

The proposed method involves in tuning the controller parameters of the DVR. The mainobjective is to determine the controller parameters in order to minimize the performance criteria(voltage THD and voltage sag/swell). Thus performance indexes are characterized by the performance of the Fuzzy PI control system. The proposed work aims at minimizing the objective function.

The main objective of controller is as follows

(a)Minimization of Average Voltage Deviation:It is defined as the deviation of the voltage magnitude of bus i from the unity as where Vi-ref and Viare the reference and actual voltages at

2 i ref i i

dev (V V)

V _  _  (1)

busi, respectively. Therefore, the average voltage deviation in the system per unit (p.u.) can be expressed using the summation of normalized Vdev-ii for all buses given by

M V V

f

m

1 i

norm i dev avr

dev i







 



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Where M is the total number of system buses.

(b)Minimization of Average Voltage Total Harmonic Distortion (THDV): The average of the normalized THDin the system buses to control the THDV level of the whole system is obtained using

M THD THD

f

m

1 i

norm i v avr

v 2







 



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Where

norm i

THDv_

I is the normalized THDvin bus i.

Bus Voltage Limits: Each bus voltage Vimust be maintained around a permissible voltage band owing to the effect of D-STATCOM installation on system bus voltages. This is achieved using

max i i min

i V V

V_   _ (4)

WhereViis the voltage at bus i.

The overall optimal DVR problem can be configured as a constrained multi-objective optimization problem. Therefore, the weighted sum method will be considered in the proposed method to combine the individual objective functions. The final objective function to be minimized is expressed as

) 0 , V V

max(

) 0 , V V [max(

f w f w

F _i _max _i

m i

max i i 2

2 1

1     

 _

 



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Wherewiand λ are the relative fixed weight factors assigned to the individual objectives and the penalty multipliers for violated constraints, respectively, and are large, fixed scalar numbers. In addition, P and M are the total DVR number and the total bus number, respectively.

III.BACTERIALFORAGINGOPTIMIZATION

BFO was proposed by K.M. Passino, is inspired by the social foraging behavior of Escherichia Coli (E. Coli) bacterium. Natural selection tends to eliminate animals with poor foraging strategies and favor the propagation of genes of those animals that have successful foraging strategies, since they are most likely to enjoy reproductive success. After many generations, poor foraging strategies are either eliminated or shaped into good ones. BFO is employed for optimization problem to minimize certain performance index.

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IV(a) Methodology

Algorithm begins with generation of initial population that contains 100 chromosomes. Each chromosome of this population is in the form of an array which consists of 22 columns. Each column indicates possible optimal value of corresponding FLC parameter. In this study, voltage sag and THD should be minimized while selecting best individuals. For this purpose, in the M-file, generated parameters are loaded to the fuzzy logic controller by the command “set_param()” and converter fed drive circuit is run in a Simulink Mdl-file by the command “sim()”. During simulation, angular velocity error is calculated and total error is sent to an array. This procedure is performed for each chromosome of initial population and finally an array with 100 rows is formed.

After the production of initial population and calculating total angular velocity error for each chromosome in the population, BFO iterations begin. Finally, converter fed drive is made to run again using new parameters to obtain new fitness values. This cycle terminates when predetermined number of iteration is reached. The fittest chromosome of each population is stored in an array and at the end of the iterations the fittest chromosome of all populations is obtained.

IV(b) Selection of Parameters

The Particle Swarm Optimization parameters such as Swarm Size(Ss), number of iterations(N), Wmax, Wmin, C1 and C2 are selected through experiment. Table.1. represents the parameter chosen for implementation of FPSO.. Table.2. represents the parameter chosen for implementation of FBFO.

Table 1.Parameters chosen for FPSO implementation

Parameter Ss N Wmax Wmin C1 C2

Value 20 100 0.9 0.1 2 2

Table 2.Parameters chosen for FBFO implementation

Parameter S Nc Ns Nre Ned dattract Xattract hrepellent xrepellent Ped

Value 20 10 10 4 2 0.1 0.2 0.1 10 0.02

V Results and Discussion

The proposed algorithm is assessed for DVR system to enhance the power quality issue. In the case study, power distribution system consists of two load buses that one of themis included as sensitive load. This simple electrical network is shown in Figure 1.

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Figure 1.Power distribution system schematic

To simulate morecritical conditions, two faults have been simulated. The first fault is just after series injection transformer with fault and earth resistances equal to 4.6Ω and 0.1Ω respectively, and the second one is near to a non sensitive load with the same resistance values. Simulation results revealedan improvement in the voltage sag and voltage THD of sensitive load. Figure 2 and figure 3 shows voltage signals at PCC, the sensitive load voltage, injected voltage from DVR and the sensitive load voltage deviation signal from the base voltage in PSO-FLC and BFO-FLC case under fault conditions respectively.

Figure 2 Voltage signals at PCC, the sensitive load voltage, injected voltage from DVR and the sensitive load voltage deviation signal from the base voltage in GA-FLC case under fault

conditions

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Figure 3 Voltage signals at PCC, the sensitive load voltage, injected voltage from DVR and the sensitive load voltage deviation signal from the base voltage in PSO-FLC case under fault

conditions

The DVR is able to generate the required voltage andit helps to maintain a balanced constant load voltage at 1.00pu. In order to reduce the losses, DVR system is in null state under grid voltage is in normal state. When voltage sag is detected, DVR inject the required ac voltage as fast as possible

Figure 4 FFT Analysis for PSO-FC.

Figure 4 shows theft analysis for PSO-FC is 0.84%. Improvement in THD signal and also deviation signal in line voltage caused by network faults in the algorithm, have been studied. As it can be seen, during simulation the requirements of IEEE-519 standard have been considered. The result has been shown in Table 3.

Table 3. Comparison of results

Voltage sag average THD (%) Controller Fault

location

Value Improvement(%) Value Improvement(%) Standard

PSO

1 0.0198 - 3.54 -

2 0.0209 - 2.98 -

PSO-FLC 1 0.0177 10.6 1.46 58.75

2 0.0203 2.8 1.59 46.64

BFO-FLC 1 0.0131 33.8 0.84 76.27

2 0.0178 14.8 0.91 69.46

As it is clear, performance of proposed controller based on BFO-FLC is improved in comparison with PSO -FLC type . In proposed controller both voltage sag and voltage THD indices

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have been reduced in comparison to other controllers which have already been introduced. In other words, by considering control signal of voltage THD as second objective, we can guide BFO algorithm toward better answer for controlling and compensating DVR. By considering these results, using BFO -FLC algorithm is more reasonable.

VI CONCLUSION

In this proposed method ,the FBFO based controller has been used for power quality improvement of Dynamic voltage improvement. In order to validate the effectiveness of the proposed BFO -FLC approach, the results have been compared with the conventional controllers. The performance of the BFO -FLC outperforms the other controllers by reducing average voltage sags and THD. Thus the incorporation ofBFO -FLC method helps to obtain a good power quality of the DVR system.

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