Automatic Control

ISSN 1974-6059 Vol. 6 N. 6 November 2013

International Review of

Automatic Control (IREACO) Contents:

IN

Chattering-Free Included Sliding Mode Control for an IM by A. Ltifi, M. Ghariani, M. Ayadi, R. Neji

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Theory and Applications

673 681

A Neural Network Controller for a Temperature Control Electrical Furnace by A. El Kebir, A. Chaker, K. Negadi

689

A High Precision Angle Compensation Controller for Dish Solar Tracker Installed on a Moving Large Ship by Budhy Setiawan, Mauridhi Hery Purnomo, Mochamad Ashari

695

Optimal Placement and Sizing of Multiple Capacitors in Radial Distribution Systems Using Modified TLBO Algorithm by Manas Ranjan Nayak, Kumari Kasturi, Pravat Kumar Rout

701

A Backstepping Approach for Airship Autonomous Robust Control by Y. Meddahi, K. Zemalache Meguenni, M. Tahar, M. A. Larbi

714

Advanced Interactive Tools for Analysis and Design of Nonlinear Robust Control Systems by Kamen M. Yanev

720

Analysis of an Improved Single Input Fuzzy Logic Controller Designed for Depth Control Using Microbox 2000/2000c Interfacing by Aras M. S. M., S. S. Abdullah, Aziz M. A. A., A. F. N. A. Rahman

728

Design of Hybrid PWM Algorithm for the Reduction of Common Mode Voltage in Direct Torque Controlled Induction Motor Drives by V. Anantha Lakshmi, V. C. Veera Reddy, M. Surya Kalavathi

734

LMI Design of a Direct Yaw Moment Robust Controller Based on Adaptive Body Slip Angle Observer for Electric Vehicles by L. Mostefai, M. Denai, Khatir Tabti, K. Zemalache Meguenni, M. Tahar

745

Intelligent Control of a Small Climbing Robot by A. Jebelli, M. C. E. Yagoub, N. Lotfi, B. S. Dhillon

751

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Integrated CAD/CAE/CAM and RP for Scorbot-ER Vu Plus Industrial Robot Manipulator by N. Prabhu, M. Dev Anand, P. Classic Alex

(continued on outside back cover)

Copyright © 2013 Praise Worthy Prize S.r.l. - All rights reserved

International Review of Automatic Control (IREACO)

Editorial Board:

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Indian Institute of Technology, Roorkee Russian Academy of Sciences - Institute for Problems of Mechanical Eng. University of Namur (FUNDP) – Department of Mathematics Ohio State University - Department of Electrical and Computer Engineering Okayama University - Department of Systems Engineering University of Manchester - Control Systems Centre Institute for Dynamic Systems and Control (IDSC) University of Agder- Faculty of Technology and Science Jožef Stefan Institute KAIST - Division of Electrical Engineering University of Glamorgan - Faculty of Advanced Technology Technical University Of Crete - Dynamic Systems & Simulation Laboratory Helsinki University of Technology - Automation Technology Laboratory University of Saskatchewan - Department of Mechanical Engineering Supelec – Département Automatique Idaho State University - Measurement and Control Eng. Research Center AGH Univ. of Science and Technology - Faculty of Mining and Metallurgy Peking University –Department of Industrial Engineering and Management University of Manchester - Control Systems Centre Tsinghua University - Department of Automation (CFINS)

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(India) (Russia) (Belgium) (U.S.A.) (Japan) (U.K.) (Switzerland) (Norway) (Slovenia) (Korea) (U.K.) (Greece) (Finland) (Canada) (France) (U.S.A.) (Poland) (China) (U.K.) (China)

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Srinivasan Alavandar Boris Andrievsky Frank Callier Jose B. Cruz Mingcong Deng Zhengtao Ding Hans Peter Geering Hamid Reza Karimi Juš Kocijan Ju-Jang Lee Guoping Liu Markos Papageorgiou Sirkka-Liisa Jämsä-Jounela Yang Shi Houria Siguerdidjane Desineni Subbaram Naidu Ryszard Tadeusiewicz Jiandong Wang Hong Wang Qian-Chuan Zhao

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International Review of Automatic Control (I.RE.A.CO.), Vol. 6, N. 6 ISSN 1974-6059 November 2013

Optimal Placement and Sizing of Multiple Capacitors in Radial Distribution Systems Using Modified TLBO Algorithm Manas Ranjan Nayak, Kumari Kasturi, Pravat Kumar Rout

IN

T

Abstract – Nowadays due to development of distribution systems and increase in electricity demand, the use of capacitors in parallel are increased. Determining the installation location and size are two significant factors affecting network loss reduction and improving network performance. The optimal capacitor placement and sizing problem is formulated as a mixed integer nonlinear optimization problem subject to highly nonlinear equality and inequality constraints. This paper, proposes an efficient method based on Modified Teaching Learning Based Optimization Algorithm (MTLBO) for optimal placement and sizing of multiple capacitors in a radial distribution system(RDS) is proposed, which can greatly envisaged with problems. The objective function is to minimize the network active power losses, improving system voltage profile, increasing voltage stability index and load balancing within the frame work of system operation and security constraints. The proposed method is implemented on 33 and 69 bus radial distribution systems and the results are compared with results of other popular optimization methods available in published articles. Test results show that the proposed method is more effective and has higher capability in finding optimum solutions. Copyright © 2013 Praise Worthy Prize S.r.l. - All rights reserved. Keywords: Radial Distribution System (RDS), Capacitor Placement, Modified Teaching Learning

Nomenclature

Pj Qj

Reactive power flowing out of bus j

P Li Q Li

Active power load connected at bus i Reactive power load connected at bus Active power load connected at bus j

i

avg

I ijmax

V i,nom Vi Vj V imin

V imax

P Loss  i, j 

Active power loss of the line section between buses i and j

P T ,Loss n P SUB Q SUB nCap M Nf

Total active power loss Total number of buses Active power injection of substation Reactive power injection of substation Number of Capacitors Total number of branches Total number of sources Reactive power injection of i th Capacitor Lower limit of reactive power injection of the ith Capacitor Upper limit of reactive power injection of the ith Capacitor Capacitor installation cost (1000 $ / Capacitor) Capacitor marginal cost (3$/kVAr) Load duration (8760 hrs) Capacitor energy cost of losses (0.06 $ / kWh)

Q Cap,i

Reactive power load connected at bus j

R

P Lj Q Lj

I ij

EP

Active power flowing out of bus i Reactive power flowing out of bus i Active power flowing out of bus j

Pi Qi

I ij

R

Based Optimization (MTLBO), Active Power Loss, Voltage Deviation, Voltage Stability Index

Current in line section between buses i and j Average current in line section between buses i and j Maximum current in line section between buses i and j

min Q Cap,i max Q Cap,i

K cf Kc Ti Ke

Nominal voltage of bus i Voltage of buse i Voltage of buse j Minimum value of bus voltage magnitude Maximum value of bus voltage magnitude

I.

Introduction

The analysis of the customer failure statistics of most

Manuscript received and revised October 2013, accepted November 2013

701


Manas Ranjan Nayak, Kumari Kasturi, Pravat Kumar Rout

IN

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From the literature survey, it is observed that most of these population based optimization techniques have successfully used to determine size, placement, and loss minimization problem of capacitor in radial distribution system. However, many of them suffer from local optimality and require large computational time for simulation. These motivate the present authors to introduce new, simple, efficient and fast population based optimization techniques to solve optimal capacitor placement and sizing problem of radial distribution system. In this study modified teaching learning based optimization (MTLBO) is proposed to determine optimal size and location of multiple capacitors to minimize different objective functions of 33 and 69 bus RDS. To show the effectiveness and superiority, the performance of the proposed method was tested on 33 and 69 bus RDS .The results were compared with the results of other popular optimization techniques. The rest of the paper is organized as follows. In Section 2, modeling of power flow in RDS is discussed. The problem formulation with system constraints and economic evaluation are addresses in Section 3.Overview of the proposed MTLBO algorithm is briefly described in Section 4. Application of MTLBO to solve the optimal capacitor placement and sizing problem of radial distribution system are explained in Section 5. In Section 6, Capacitor placement & sizing evaluation indices are described. Test system description, simulation results and analysis are reported in Section 7. Finally, conclusions are drawn in Section 8.

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utilities indicates that the distribution system makes the greatest individual contribution to the unavailability of supply to a customer [1]. Therefore, the analysis of a distribution system is an important area of activity [1]-[20]. Distribution system has a major share of losses in power system. High R/X ratio and also a significant voltage drop in the system causing significant losses in these systems. Distribution systems typically have radial feeders. The increase of electrical demand and consequently the development of distribution systems cause further voltage drop and power losses increment and load imbalance, which reduce the voltage stability. The reactive power support is one of the well-recognized methods for the reduction of power losses together with other technical benefits; such as improved voltage profile, improved voltage stability index, improve the power factor, increases available capacity of feeders and improvement in system reliability and security. On the other hand, economic advantages of installing capacitors include various investments for improving facilities, operational costs decrement, optimized production, decreasing the costs of energy losses, and an increment in protection of critical loads. Reactive power addition can be beneficial only when correctly applied. Correct application means choosing the correct location and size of the reactive power support. The problem of proper locating and sizing of shunt capacitor banks in distribution systems become a challenge for power system researchers and planners. Various methods have been investigated for load flow analysis and capacitor placement problems by many researchers. Shirmohammadi and Semlyen [2] presented a new compensation-based power flow method for the solution of weakly meshed distribution and transmission networks. Ghosh and Das [3] proposed a simple and efficient method for the load flow of radial distribution network using the evaluation based on algebraic expression of receiving end. Goswami and Basu [4] have presented a direct method for solving radial and meshed distribution networks. Das [5] has presented an optimal capacitor placement method using a fuzzy-GA method. Sundhararajan and Pahwa [6] have solved the general capacitor placement problem in a distribution system using a genetic algorithm. Kasaei and Gandomkar [7] have presented an optimal capacitor placement method using ant colony algorithm. Sarma and Rafi [8] have solved optimal capacitor location and size to reduce the power losses using plant growth simulation algorithm. Sydulu and Prakash [9] presented a novel Particle Swarm Optimization based approach for capacitor placement on RDS. Taher and Bagherpour [10] used a hybrid honey bee colony optimization method for minimization of the power losses and unbalances and also voltage profile improving. Tabatabaei and Vahidi [11] presented the bacterial foraging with a PSO algorithm used to determine the optimal placement of capacitors.

II.

Modeling of Load Flow

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In this paper, network topology based backward and forward sweep method [12] is used to find out the load flow solution for balanced radial distribution system. Conventional NR and Gauss Seidel (GS) methods may become inefficient in the analysis of distribution systems, due to the special features of distribution networks, i.e. radial structure, high R/X ratio and unbalanced loads, etc. These features make the distribution systems power flow computation different and somewhat difficult to analyze as compared to the transmission systems. Various methods are available to carry out the analysis of balanced and unbalanced radial distribution systems and can be divided into two categories. The first type of methods is utilized by proper modification of existing methods such as NR and GS methods. On the other hand, the second group of methods is based on backward and forward sweep processes using Kirchhoff’s laws. Due to its low memory requirements, computational efficiency and robust convergence characteristic, backward and forward sweep based algorithms have gained the most popularity for distribution systems load flow analysis. The voltage magnitude and phase angle of the source should to be specified.


International Review of Automatic Control, Vol. 6, N. 6

702


Also the complex values of load demands at each node along the feeder should be given. Starting from the end of the feeder, the backward sweep calculates the line section currents and node voltages (by KCL and KVL) back to the source. The calculated voltage at the source is compared with its original specified value. If the error is beyond the limit the forward sweep is performed to update the node voltages along the feeder. In such a case, the specified source voltage and the line section currents already calculated in the previous backward sweep are used. The process keeps going back and forth until the voltage error at the source becomes within the limit. The shunt admittance at any bus to ground is not considered. The single line diagram of distribution system is shown in Fig. 1.

( V j ) are expressed by the following set of recursive equations: 2

P j  P i  P Lj  R ij 

P i2  Q i Vi

(4)

2

2

Q j  Q i  Q Lj  X ij 

P i2  Q i Vi

V

2 j

2

(5)

2





 V i  2 Ri j  Pi  X i j  Qi  2

  R i2 j  X i2 j  

(6)

P 2j  Q j j

T

V

2

Backward Sweep

R

II.1.

IN

Fig. 1. Distribution system with capacitor installation at any location .

Hence, if the Vo , Po , Qo at the first bus of the network are known, then the same quantities at the other nodes can be calculated by applying the above branch equations. By applying the backward and forward update methods, we can get a power flow solution. The active power loss of the line section connecting between buses i and j is calculated as:

By starting from the ending buses and moving backward to the slack bus (substation bus), the power flow through each branch is expressed by the following set of recursive equations:

EP P' j2  Q j V

Q i  Q j  Q Lj  X ij 

R

V

2

2

Vi  V

j

j

'2 P' j2  Q j

(8)

III. Problem Formulation



'2 P' j2  Q j

The optimum placement and sizing of the capacitors are optimization problems with nonlinear objective function that has equality and inequality constraints. The proposed objective function includes: reducing power losses, reducing voltage deviation and improving voltage stability index of the system and help in balancing the current of system in a given radial distribution network.

(3)

2 j

where P' j  P j  P Lj and Q' j  Q  Q Lj . j II.2.

 P loss  i, j 

j



V

(7)

i 1

 2 R ij  P' j  X ij  Q' j 

  R ij2  X ij2  

2

n 1

P T ,Loss 

(2)

2

Vi

losses of all line sections of the feeder, which is described as:

(1)

2

P i2  Q i

The total active power loss of the all lines sections in n bus system ( P T ,Loss ) is calculated by adding up the

'2

P i  P j  P Lj  R ij 

2

P Loss  i, j   R ij 

Forward Sweep III.1. Objective Function

By starting from the slack bus (substation bus) and moving forward to ending bus, the active and reactive power flows at the receiving end of branch ( P j and

In this paper, three objective functions are considered separately as single objective for the capacitors placement and sizing problem in the distribution network.

Q j ) and the voltage magnitude at the receiving end



703


Buses with the minimum of the voltage stability index may be unstable, and it is very important to identify weak buses. SI j must be maximized for improving voltage stability as its result the proposed objective function will be minimized.

A. Minimization of the active power losses ( f 1 ) The active power loss of the line section connecting buses i and j can be computed as: 2

PLoss  i, j   R ij

P i2  Q i

(9)

V i2

D. Load balancing ( f 4 ) The term f 4 in the objective function is considered

The total active power loss of the all lines sections is described as:

for load balancing of the lines. Eq. (16) represents the load balancing:

n 1

f 1  P T ,Loss 

 P Loss  i, j 

n 1 

I ij  f 4    avg    i 1  I ij 

(10)

i 1

B. Minimization of Voltage deviation ( f 2 )

T avg I ij 

f 2  max V i  V i,nom

1 n 1 I i, j n  i i 1



(17)

IN

(11)

i 1

(16)

where:

The objective function for minimization of voltage deviation is defined as: n

2

III.2. System Constraints

C. Improvement of the voltage stability index ( f 3 )

I ij 

The objective function is subjected to the following constraints: a) Power balance constraints:

R

Fig. 1 shows a branch of radial system. In radial distribution system each receiving bus is fed by only one sending bus. From Fig. 1: V i V j R ij  j X ij

(12)

P  j  j Q  j  V *j I ij

EP

(13)

R

2

n

 Q Cap,i   i Q Li  QT ,Loss

(19)

b) Bus Voltage limit:

V imin  V i  V imax

(20)

I ij  I ijmax

(21)

c) Thermal Limits:

2

4  P j R ij  Q j X ij  V i

nCap

Q SUB 

(18)

i 1

when the capacitor is connected to distribution network, the index of voltage stability for distribution network will be changed. This index, which can be evaluated at all buses in radial distribution systems, was presented by Chakravorty and Das [13]. Eq. (14) represents the voltage stability index. Using Eq. (12) and (13): 4 SI j  V i  4  P j X ij  Q j R ij  

n P SUB   i PLi  P T , Loss

(14)

d) Radial structure of the network:

M  nN f

Objective function for improving voltage stability index is given by (15):

(22)

e) Power limits of Capacitor:

 1  , j  2,3,...,n f3  SI j   

(15)

min max  Q Cap,i  Q Cap,i Q Cap,i

For stable operation of the radial distribution systems, SI j  0 and the maximum value of SI j for j  2 ,3,......n , causing minimum value of f 3 , so that;

(23)

III.3. Economic Evaluation of Installing Capacitors For economical evaluation of the proposed method in each objective function, the mathematical formulation [14] for different terms of costs presented as follows:

there is a feasible solution.



704


A. Installation cost of capacitors Capacitor installation cost is presented as follows:

to the students. A student’s result is analogous to fitness value and the value of objective function represents the knowledge of a particular students. The best solution is considered as the teacher. The teaching phase and learning phase concept of MTLBO algorithm are described below. (a) Teacher phase This is the first phase of the MTLBO algorithm where students improve their learning using teaching aids of the teacher and the teacher tries to improve the average result of the class room through teaching from an initial value to his own level, though it is practically impossible and the teacher can only improve the average grade of the class to some extent. If the new average grade of the k th subject at d th iteration is  dnew.k the difference between

nCap

C Inst. 

 K cf

$

(24)

i 1

where capacitor installation cost is taken as same value for all capacitor. B. Cost of capacitors Cost of capacitor is presented as follows: nCap

C Cap. 



K c Q Cap,i

$

(25)

i 1

the existing mean and new mean of the k th subject at the d th iteration may be formulated as [15]:

CEL  NOCap  K e Ti PT ,Loss,NOCap

$

(26)

(30)

where rand is a random number between [0, 1]; t f is the teaching factor (either 1 or 2) which is evaluated randomly by the following equation:

t f  round 1  rand  0 ,1 

(31)

The grade of k th subject of the p th student is updated by: d 1  X dp,k   diff (32) X dp,k .k

R

where P T ,Loss,No Cap is the active power loss before

d  t f  kd   ddiff .k  rand    new.k

IN

C. Economical Saving Energy cost of losses before installing capacitors in a radial distribution system for one year period (8760hrs) is defined as:

T

where capacitor marginal cost is taken as same value for all capacitor.

capacitor installation (Base case). Energy cost of losses after installing capacitors in a radial distribution system for one year period (8760hrs) is defined as:

where

1 are the grade of them k th subject of where X dp,k , X dp,k

EP

CEL  Cap  K e Ti PT ,Loss,Cap P T ,Loss, Cap

$

(27)

the p th student at d th and  d  1 th

at d th and  d  1 iteration. (b) Learner phase It is the final phase of the proposed algorithm where students enhance their knowledge through mutual interaction with other students. Each student randomly chooses another student for interaction and learns new things from him if the selected student has better knowledge than him. Mathematically, this learning phenomenon may be expressed as:

capacitor installation:

R

Benefit (Saving) of reduction in energy cost ($) of losses for one year = C EL  NOCap  C EL  Cap  $  (28)

IV.

iteration;  ddiff .k is

the difference between the mean grade of the k th subject

is the active power loss after

Net annual savings ($)= =  C EL  NOCap  C EL  Cap    C Inst.  C Cap.

th

(29)

Overview of MTLBO Technique



1  X dp,k  rand  X dp,k  X ds,k X dp,k

The MTLBO algorithm is a new efficient population based algorithm developed by Rao et al. [15]. The algorithm mimics the teaching-learning ability of the teacher and learners in a classroom. Teachers are the most learned person in society and they try to increase the knowledge of the students. The algorithm describes two basic phases of learning; they are teaching phase and learning phase. In this method, a group of students in a class is considered as a population and design variables are the subjects offered

if

f X

p

  f  X s



1  X dp,k  rand  X ds,k  X dp,k X dp,k

if

f X

p

   X s





(33)

(34)

where X dp,k1 , X dp,k are the value of k th control variable th

of the p th student at d th and  d  1 iterations; X ds,k is



705


which coincides with the number of units to place in the distribution system ( k ) and limits of design variables (upper, U L and lower, L L of each case). Define the optimization problem as: Minimize f  X  ,

the value of k th control variable of the s th student (randomly selected) at d th iteration; f  X s  is the grade point of the s th student. The proposed MTLBO algorithm that is introduced here is shown in the flow chart of Fig. 2.

where f  X  is the objective function, X is a vector for design variables such that L L  X  U L . Step 2: Generate a random population according to the number of students in the class ( p ) and number of

BEGIN Define optimization problem

subjects offered ( k ). This population is mathematically expressed as:

Initialize the optimization parameters:

p,G,k ,U L and L L Generate randomly the initial population

p th student. Step 3: Evaluate the average grade of each subject offered in the class. The average grade of the k th subject is given by:

 k  mean  X 1,k , X 2 ,k ,..., X

R

Accept each new individual if it gives a better function value than the original

p,k



(36)

Step 4: Based on the grade point (objective value) sort the students (population) from best to worst. The best solution is considered as teacher and is given by:

EP

Learner phase: Modify the population based on Eq.39 & 40 and discretize it

X tearcher  X

Calculate the objective function value of all individuals

(37)

f  X   min

Step 5: Modify the grade point of each subject (control variables) of each of the individual student. Modified grade point of the k th subject of the p th student is given by:

Accept each new individual if it gives a better function value than the original

R

(35)

where X p,k is the initial grade of the k th subject of the

IN


. X 1,k  . X 2 ,k  . .   . .  . X p,k 

T


Teacher phase: Modify the population based on Eq.38 and discretize it

. . . . .

 X 11, X 1,2   X 2 ,1 X 2 ,2 . V  .  .  .  X p,1 X p,2 

old X new p,k  X p,k  r 1    X tearcherk  round 1  r 2    k  (38)

NO

Is the termination Criterion satisfied?

where r 1, r 2 are random numbers between [0, 1]. Step 6: Every student improves grade point of each subject through the mutual interaction with the other students. The grade point of the k th subject of the p th student is modified by:

YES

Final value of solutions END Fig. 2. Flow diagram of the MTLBO algorithm

old X new p,k  X p,k  rand   X

The following steps give explanations to the MTLBO algorithm. Step 1: Initialize the population size or number of students in the class ( p ), number of generations ( G ), number of design variables or subjects (courses) offered

if

f X

p

p,k

 X s,k 

  f X s 

old X new p,k  X p,k  rand   X s,k  X

if


f X

p

  f  X s

p,k



(39)

(40)


706


V.

grid, the vector will have a dimension of 1 2nCap  .

Application of MTLBO to Solve the Optimal Capacitor Allocation Problem

Depending upon the population size, initial solution M is created which is given by:

The procedure for implementing the MTLBO algorithm in solving optimal capacitor placement problem can be summarized by the following steps: Step 1: Read the system data, constraints, the population size ( P ), the maximum number of iterations ( G ), the number of capacitors to be installed in the distribution network, limits of placement of capacitor Buses and limits of size of the capacitors. Step 2: The capacitor placement Buses are positive integers, while the variables that represent the capacitor unit size variables are continuous. The placement of capacitor Buses and size of the capacitors are randomly generated and normalized between the maximum and the minimum operating limits. The placement of capacitor Buses and size of the capacitors of j th capacitor is normalized to b jCap and

T

Step 3: Compute the objective functions using eq. (10), (11), (15) and (16) independently. Step 4: Identify the best solution and assign that solution as the teacher of the class. Step 5: Modify the grade of each subject (independent variables of radial distribution system) of each student based on the teacher knowledge using eq. (38). Step 6: Update grade of each subject of each student based on the learners’ knowledge by utilizing the knowledge of some other learner of the same group using Eq. (39) and (40). Step 7: Check whether the independent variables violate the operating limits or not. If any independent variable is less than the minimum level it is made equal to minimum value and if it is greater than the maximum level, it is made equal to maximum level. Step 8: Go to step 2 until the current iteration number reaches the pre specified maximum iteration number.

as given below to satisfy the placement of

capacitor Buses and size of the capacitors constraints:

  random    j j b Cap  round  b Cap,min      b jCap, max  b jCap,min   j  b Cap  N , b Cap  b Cap,min b Cap,max 

j where b Cap represents bus location.



VI.

Capacitor Placement &Sizing Indices

There are various technical issues that need to be addressed when considering the presence of distributed generators in distribution systems. To study the effect of capacitor units on the performance of power systems, some indices are used as shown in Table I.

R



    (41) 

(44)

IN

j Q cap

M   M 1, M 2..., M j ..., M P 

EP

j Main distribution substation is designated as b Cap =1:

PL : The penetration level of capacitor units is defined by PL , where Q Cap and Q load are the reactive power of

j  Cap, max  

 Q j  Q jCap,min   random     Q Cap   Q j   Cap,min   (42)   j  R, Q Cap  Q Cap,min Q Cap,max  Q Cap

capacitor/capacitors and the total reactive load of the network, respectively.

APLR and RPLR : APLR and RPLR show active and reactive loss reduction after installing Capacitor/ Capacitors, where NO Cap represents the base case and Cap , the case after capacitor installation. Higher values of APLR and RPLR indicate better performance of capacitors in loss reduction.

R

Select capacitor placement Buses randomly from all the buses and the capacitors are installed in these selected buses. The rating of all the installed capacitors, comprise a vector which represents the grade of different subjects of a particular student and it also represents a candidate solution for the optimal capacitor allocation problem. Each set of the feasible solution of matrix M i represents a potential solution which is given by:

Q Capi,1,Q Capi,2......,Q Capi,nCap Mi   b Capi,1,b Capi,2......,b Capi,nCap

  

VDR : It shows voltage deviation reduction after installing capacitor/capacitors. Higher value of VDR indicate better bus voltage. VSIR : It shows voltage stability index reduction after installing capacitor/capacitors. Higher value of VSIR indicate better improvement of voltage stability of the system.

(43)

VDI : The determination where V i  nom V i  nom = 1 p.

The dimension of the vector is two variables per capacitor installed (the positive integer bus number and the capacitor reactive power output). Moreover for multiple capacitor units ( nCap ) to be installed in the


VDI index is a good indicator for the of deviation from bus voltage nominals, is the desired voltage at bus i (usually, u.) and V i Cap is the bus voltage when


707


capacitor is presented in the network, both in per unit. n is the number of buses. Closer the index to zero will give the better performance of the network so this index must be minimized to improve the voltage profile.

3802.19 kW and 2694.6 kVAr, respectively. It is demonstrated in Fig. 4 [16].

VPI : In order to have better voltage profile, VPI index must be maximized. TABLE I EVALUATION INDICES DUE TO INSTALLATION OF CAPACITORS IN RDS Impact index Formula

APLR 

Q Cap Q load

 P T , Loss , NO Cap       P T , Loss , Cap  P T , Loss ,

Reactive power loss reduction

RPLR 

Fig. 4. Single-line diagram of the 69-bus RDS

 100%

VD NO Cap

VSI

Voltage stability index reduction

VSI 

Voltage deviation index

VDI  max  i 1





 100%

NO Cap

VPI 

n

V i  nom  V i  Cap



V i  nom

V i  Cap  V i  NO

i 1

  

Cap

 100

n

V

i  NO Cap

i 1

VII.

Test System Description, Simulation Results and Analysis Test Systems

R

VII.1.

Assumptions and Constraints

(1) Power flow calculation is performed using S base = 100 MVA and V base =12.66 kV. (2) Single and Three capacitors that inject reactive power are installed in to the systems. (3) The limits of capacitor unit sizes for installation at different systems bus locations are assumed to be 200 kVAr to 1200 kVAr with step of 2 kVAr. (4) Voltage at the primary bus of a substation is 1.0p.u. (5) The upper and lower limits of voltage for each bus are 1.05p.u. and 0.9 p.u., respectively. (6) The maximum allowable number of the parallel capacitor is one, in each bus. (7) The load model which is used in the simulations in uniform with constant power (active and reactive) throughout year.

EP

1

VII.2.

VSI Cap

R

VSI n



NO Cap

n

Voltage profile index

 100%

NO Cap

VD NO Cap  VD Cap

VDR 

 100%

NO Cap

Q T , Loss , NO Cap      Q   T , Loss , Cap  Q T , Loss ,

Voltage deviation reduction

Fig. 3. Single-line diagram of the 33-bus radial distribution network  100%

T

Active power loss reduction

PL 

IN

Capacitor penetration level

The proposed method has been programmed using MATLAB and run on a personal computer having dual core processor, 1.86GHz speed and 2GB RAM. The proposed MTLBO algorithm is run for 50 population size and 100 iterations for each case. The effectiveness of the proposed method for loss reduction by capacitor placement is tested on 33 and 69 bus RDS. The first system is a 12.66 kV, 33 bus RDS consisting of 33 buses configured with one substation, one main feeder, 3 laterals and 32 branches. The total active and reactive loads on this system are 3715 kW and 2300 kVAr, respectively. It is demonstrated in Fig. 3 [16]. The second system is a 12.66 kV, 69 bus large scale RDS with consisting of 69 buses configured with one substation, one main feeder, 7 laterals and 68 branches. The total active and reactive loads on this system are

VII.3.

Simulation Results

Simulation results are divided to two parts: A. Simulation results related to system performance and technical advantages In this section, the technical results of proposed method for 33 and 69 bus RDS are presented. Table II shows the pre installation objective function values of capacitors. Table III shows objective functions values after installation of single capacitor and three capacitors. The detailed results using proposed (MTLBO) algorithm for both systems during pre installation and post installation of the capacitors (single and three) are described in Tables IV and V for minimization of the active power losses.



708


TABLE II OBJECTIVE FUNCTION VALUE OF THE RDS BEFORE CAPACITORS INSTALLATION Objective function value System 33-Bus 69-Bus

f 1 (kW)

f 2 (p.u.)

f 3 (p. u.)

f 4 (p.u.)

210.07 224.54

0.095 0.089

1.49 1.45

65.56 200.85

TABLE VI EVALUATION INDICES FOR 33 AND 69 BUS RDS 33 Bus 69 Bus Impact index Single 3No.of Single 3No.of Cap. Cap. Cap. Cap. Capacitor penetration 52.17 85.92 44.54 68.07 level (%) Active power loss 28.55 35.02 32.56 35.68 reduction (%) Reactive power loss 27.74 34.66 31.06 33.94 reduction (%) Voltage deviation 14.3 37.57 22.38 25.16 reduction (%) Voltage stability index 5.82 14.43 8.37 9.33 Reduction (%) Voltage deviation 4.24 3.46 2.19 1.96 index Voltage profile index 0.03 0.06 0.006 0.01

TABLE III OBJECTIVE FUNCTION VALUE OF THE RDS AFTER CAPACITORS INSTALLATION Objective function value f 1 (kW)

33-Bus 69-Bus

150.09 151.42

33-Bus 69-Bus

136.5 144.41

f

2

(p. u.)

Singe Capacitor 0.069 0.065 3No. of Capacitor 0.0216 0.0287

f

3

(p. u.)

f 4 (p.u.)

1.33 1.31

47.61 139.63

1.09 1.12

39.26 98.15

Tables VII and VIII give the results which are compared with other existing techniques for 33 and 69 bus RDS respectively. It may be observed from the simulation results that distribution losses achieved due to installation of capacitors in optimal position obtained by different algorithms are reduced significantly.

T

System

TABLE VII COMPARISON OF THE PROPOSED METHOD RESULTS WITH PREVIOUS PUBLICATIONS FOR 33 BUS RDS WITH THREE CAPACITORS Items

PGSA [8]a

135.4/ Total Active power 202.67 loss(kW)(Compensated / Uncompensated) Active power loss 33.19 reduction (%) Optimal location of 6,28,29 Capacitor Optimal size of Capacitor 1200, (kVAr) 760,200 Total kVAr placed 2160 N/A Min. voltage (p. u.) (Compensated / Uncompensated) a Slight difference in load and line data.

EP

R

TABLE IV PERFORMANCE ANALYSIS OF THE 33 BUS RDS AFTER CAPACITORS INSTALLATION After capacitor Before installation System capacitor Single 3No.of installation capacitor capacitors Optimal location of 30 24,14,30 capacitors Optimal size of 1200 508,416, capacitors (kVAr) 1052 Total kVAr placed 1200 1976 Total Active power 210.07 150.09 136.5 loss (kW) Total Reactive 142.53 102.99 93.12 power loss (kVAr) Voltage deviation 0.0958 0.0821 0.0598 (p.u.) Voltage Stability 1.4960 1.4089 1.2800 Index (p.u.) Overall power factor 0.8487 0.9640 0.9983 Vmin (p.u.) / Bus No. 0.9042/18 0.9179/ 18 0.9402/18

IN

To clarify the effect of capacitor units on the performance of power systems, some indices are calculated as shown in Table VI.

Two stage method[17]

Proposed (MTLBO)

144.04/ 210.80

136.5/ 210.07

31.66

35.02

7,29,30

24,14,30

850, 25,900

508,416, 1052 1976 0.9402/ 0.9042

1755 0.9251/ 0.9038

TABLE VIII COMPARISON OF THE PROPOSED METHOD RESULTS WITH PREVIOUS PUBLICATIONS FOR 69 BUS RDS WITH THREE CAPACITORS Two stage PGSA Proposed Items PSO [9] method [8] (MTLBO) [17] 152.48/ 148.91/ 147.40/ 144.41/ Total Active 224.98 224.79 224.98 224.54 power loss(kW) (Compensated / Uncompensated) Active power loss 32.22 33.75 34.48 35.68 reduction (%) Optimal location 46,47,50 19,62,63 57,58,61 21,61,11 of Capacitor Optimal size of 781, 225, 1200, 238,1200, Capacitor (kVAr) 803,479 900, 225 274, 200 396 Total kVAr 2063 1350 1674 1834 placed N/A 0.9289/ N/A 0.9328/ Min.voltage (p.u.) (Compensated / 0.9092 0.9102 Uncompensated)

R

TABLE V PERFORMANCE ANALYSIS OF THE 69 BUS RDS AFTER CAPACITORS INSTALLATION After capacitor Before installation System capacitor Single 3No.of installation capacitor capacitors Optimal location of 61 21,61,11 capacitors Optimal size of 1200 238,1200, capacitors (kVAr) 396 Total kVAr placed 1200 1834 Total Active power 224.54 151.42 144.41 loss (kW) Total Reactive power 101.96 70.28 67.35 loss (kVAr) Voltage deviation 0.0898 0.0697 0.0672 (p.u.) Voltage Stability 1.457 1.335 1.321 Index (p.u.) Overall power factor 0.8084 0.9373 0.9800 Vmin (p.u.) / Bus No. 0.9101/65 0.9302/ 65 0.9325/65



709


Obj. function (Active power loss) in KW

162 160 158 156 154 152 150

0

10

20

30

40

50

60

70

80

90

100

No of Iterations

Fig. 7. Objective function ( f 1 ) Variation for 33 bus RDS with single capacitor Obj.Function(Active power loss) in KW

151 150 149 148

146 145 144 10

20

30

40

50 60 No of Iterations

70

IN

0

T

147

80

90

100

Fig. 8. Objective function ( f 1 ) Variation for 69 bus RDS with 3 No. of capacitors

60

Without capacitor

Active Power Loss (KW)

50

With single capacitor With 3 No.of capacitor

40

R

In addition, it should be pointed out that for 33 bus RDS accomplished with the installation of three numbers of capacitors, the proposed MTLBO algorithm attains active power loss reduction of 35.02 kW which is better than previously reported methods. Similarly, for 69 bus system, active power loss reduction of 35.68 kW is accomplished with the installation of three numbers of capacitors using the proposed MTLBO algorithm which is far better than the active power loss reduction of previously reported methods. Therefore, it can be concluded that MTLBO technique is more efficient than other techniques in reducing the power loss of 33 and 69 bus radial distribution systems. The convergence characteristics of objective function after the installation of capacitors (single and three numbers) obtained by the proposed algorithm for 33 and 69 bus RDS are illustrated in Figs. 5-6 and 7-8 respectively. Figs. 9 and 10 depict active power loss of each bus in 33 and 69 bus RDS respectively. It is observed that the three numbers of capacitors injecting reactive power results in higher real power loss reduction in the systems as compared to the single capacitor and without capacitor. Figs. 11 and 12 gives reactive power loss of each bus in 33 and 69 bus RDS respectively. It is seen that the three numbers of capacitors injecting reactive power results in higher reactive power loss reduction in the systems as compared to the single capacitor and without capacitor. Figs. 13 and 14 depicts voltage profile of each bus in 33 and 69 bus RDS respectively. The results show the different voltage levels during pre installation and post installation of the capacitors for proposed method.

30 20 10

0

151.5

151

150.5

0

10

20

30

40


70

80

90

10

15

20

25

30

35

50

Without capacitor

45

With single capacitor 40

100

Fig. 5. Objective function ( f 1 ) Variation for 33 bus RDS with single capacitor

With 3 No. of capacitors

35 30 25 20 15 10 5 0 0

141 Obj. function (Active power loss) in KW

5

Fig. 9. Active power loss (kW) before & after capacitor installation for 33 bus RDS

R

150

0

Branch No.

Active power loss (KW)

152

EP

Obj. function (Active power loss)in KW

152.5

10

20

30

40

50

60

70

Branch No.

140

Fig. 10. Active power loss (kW) before & after Capacitor installation for 69 bus RDS

139

Fig. 6. Objective function ( f 1 ) Variation for 69 bus RDS with 3 No. of capacitors

Pre installation of capacitors, voltage level in a 33 and 69 bus RDS are low. After installation of the single and three capacitors, the voltage levels are improved in the proposed method. Figs. 15 and 16 give bus voltage deviation of each bus in 33 and 69 bus RDS respectively. It is observed that the three numbers of capacitors injecting reactive power results in higher voltage deviation reduction in the systems as compared to the single capacitor and without capacitor.



138

137

136 0

10

20

30

40


70

80

90

100

710


0.09

35 Without capacitor With single capacitor With 3 No.of capacitors

25

Voltage deviation (p.u.)

Reactive power loss (KVAR)

30

20 15 10 5

0.08

Without capacitor

0.07

With single capacitor With 3 No. of capacitors

0.06 0.05 0.04 0.03 0.02 0.01

0

0

5

10

15

20

25

30

35

0

Branch No.

0

10

20

30

40

50

60

70

Bus No.

Fig. 11. Reactive power loss (kVAr) before & after Capacitor installation for 33 bus RDS

Fig. 16. Bus voltage deviation (p. u.) before and after capacitor installation for a 69 bus RDS

20 Without capacitor With 3 No. of capacitors

Figs. 17 and 18 give voltage stability index of each bus in 33 and 69 bus RDS respectively. The results show that the three numbers of capacitors injecting reactive power results in higher voltage stability index reduction in the systems as compared to the single capacitor and without capacitor.

10

5

0 0

10

20

30

40

50

60

T

Reactive power loss (KVAR)

With single capacitor 15

70

Branch No.

Without capacitor

1.6 Voltage stability index (p.u.)

1.02 Without capacitor With single capacitor With 3 No. of capacitors 0.98

1.4 1.3 1.2 1.1

0.96

1

0

0.94 0.92 0.9 0

5

10

15

20

25

30

EP

Voltage stability index (p.u.)

Wit hout capacit or

1.02

Wit h single capacit or

Wit h 3 No. of capacit ors

1 0.98 0.96 0.94

R

0.92

0

10

20

30

10

15

20

25

30

35

Bus No.

Fig. 17. Voltage stability index (p. u.) before and after capacitor installation for a 33 bus RDS

35

Fig. 13. Bus voltage level (p. u.) before and after Capacitor Installation for a 33 bus RDS 1.04

5

1.5

Bus No.

Voltage magnitude ( p.u.)

With 3 No. of capacitors

1.5

R

Voltage magnitude (p.u.)

1

With single capacitor

IN

Fig. 12. Reactive power loss (kVAr) before & after Capacitor installation for 69 bus RDS

40

50

60

Without capacitor With single capacitor

1.4

With 3 No.of capacitors 1.3

1.2

1.1

1 0

10

20

30

Bus No.

40

50

60

70

Fig. 18. Voltage stability index (p. u.) before and after capacitor installation for a 69 bus RDS

70

Bus No.

B. Simulation results relating to economical saving In this section, the economical saving of proposed method for 33 and 69 bus RDS are presented and discussed. Following detailed case studies have been carried out that energy saving by using optimal placement and sizing of capacitors. In this way total cost of capacitors (Section III.3) is obtained and calculated. Tables IX and X show comparison the installation costs of capacitor, costs of capacitor and costs of energy losses for pre installation and after installation of capacitors in both systems. Saving or benefit of reduction in energy cost of losses and net savings or benefits including the total costs in a one year time period can be seen in Tables IX and X.

Fig. 14. Bus voltage level (p. u.) before and after capacitor installation for a 69 bus RDS

0.12 Without capacitor With single capacitor

Voltage deviation (p.u.)

0.1

With 3 No.of capacitors 0.08 0.06 0.04 0.02 0 0

5

10

15

20

25

30

35

Bus No.

Fig. 15. Bus voltage deviation (p. u.) before and after capacitor installation for a 33 bus RDS



711


[3]

[4]

TABLE IX COMPARISON OF FINAL SOLUTION AND COMMERCIAL INFORMATION OF CAPACITORS FOR 33 RDS 33 Bus Information Without Single Cap. 3No.of Cap Cap. Capacitor installation cost ($ ) 1000 3000 Cost of capacitor ( $) 3600 5928 Energy cost of losses for one 110412 78887 71744 year ($ ) Benefit of reduction in energy 31525 38668 cost ($) Net annual savings ($) 26925 29740 Net annual savings (%) 24.38 26.93

[6]

[7]

[8]

IN

[9]

[10]

[11]

R

TABLE X COMPARISON OF FINAL SOLUTION AND COMMERCIAL INFORMATION OF CAPACITORS FOR 69 BUS RDS 69 Bus Information Without Single Cap. 3No.of Cap. Cap. Capacitor installation cost ($ ) 1000 3000 Cost of capacitor ( $) 3600 5502 Energy cost of losses for one 118018 79586 75901 year ($ ) Benefit (Saving) of reduction 38432 42117 in energy cost of losses for one year ($) Net annual savings ($) 33832 33615 Net annual savings (%) 28.66 28.48

[5]

[12]

[13]

Conclusion

[14]

EP

VIII.

In the paper MTLBO method was proposed to solve placement and sizing problems for capacitors simultaneously in 33 and 69 bus radial distribution systems. The proposed method stated less objective function values in state of existence capacitors. Also this method showed less real power losses in comparing with the results of other popular optimization techniques. After capacitors installation, the both methods have shown major improvement in voltage profile, increase the voltage Stability index and balance the loads for the proposed method. By using the proposed method in addition to its technical advantages, an economic saving or benefit is obtained after one year. Considering active power losses, reactive power losses, voltage stability index, voltage deviation index, voltage profile index, load balancing and the value for objective function along with economic issues, it can be concluded that the proposed method exhibited a higher capability in finding optimum solutions compared to results of other popular optimization methods.

[15]

R

[16]

[17]

[18]

[19]

[20]

References [1]

Shirmohammadi D., Hong H. W., Semlyen A. and Luo G. X., A Compensation - Based Power Flow Method for Weakly Meshed Distribution and Transmission Networks, IEEE Transactions on Power Systems, vol. 3, no. 2, 1988, pp. 753-762. Ghosh S. and Das D., Method for load-flow solution of radial distribution networks, IEEE Proceedings on Generation, Transmission & Distribution, vol. 146, no. 6, 1999, pp. 641-648. Goswami, S.K., and Basu, S.K., Direct solution of distribution systems, IEEE Proc. C., 188, (I), 1991, pp.78-88. Das D., Optimal Placement of Capacitors in Radial Distribution System using a Fuzzy-GA method, Electrical Power and Energy System, vol. 30, 2008, pp.361-367. Sundhararajan S. and Pahwa A., Optimal Selection of Capacitors for Radial Distribution Systems using Genetic Algorithm, IEEE Transactions on Power Systems, vol. 9, no. 3, 1994, pp. 4991507. Kasaei M. J., Gandomkar M., Loss Reduction in Distribution Network Using Simultaneous Capacitor Placement and Reconfiguration with Ant Colony Algorithm, IEEE Transaction on Power and Energy Engineering Conference (APPEEC), 15 April 2010,pp.1-4. Sarma A. Kartikeya and Rafi K. Mohammand, Optimal Selection of Capacitors for Radial Distribution Systems Using Plant Growth Simulation Algorithm, International journal and science and technology, vol.30, May, 2011, pp.43-54. Sydulu M. and Prakash K., Particle swarm optimization based capacitor placement on radial distribution systems in: IEEE Power Engineering Society general meeting 2007. pp. 1-5. Taher S, Bagherpour R. A new approach for optimal capacitor placement and sizing in unbalanced distorted distribution systems using hybrid honey bee colony algorithm. Int J Electr Power Energy Syst.vol. 49, 2013, pp.430–48. Tabatabaei SM , Vahidi B. Bacterial foraging solution based fuzzy logic decision for optimal capacitor allocation in radial distribution system. Electr Power Syst Res, vol.81(4), 2011,pp.1045-50. Haque, M.H., Efficient load flow method for distribution systems with radial or mesh configuration.IEE Proc. On Generation, Transmission and Distribution. Vol. 143(1), 1996, pp. 33-38. Charkravorty M., Das D., Voltage stability analysis of radial distribution networks, International journal of Electrical Power and Energy Systems.vol. 23(2), 2001, pp.129-135. Srinivas R. and Narasimham S.V.L., Optimal capacitor placement in a radial distribution using plant growth simulation algorithm, Proceedings of world academy of science, Engineering and Technology,vol.35, 2008, pp.716-723. Rao, R.V., Savsani, J.V., Balic, J., ‘Teaching-learning based optimization algorithm for unconstrained and constrained realparameter optimization problems’, Engg. Opt., vol. 44 (12), 2012, pp.1447-62. Moradi MH, Abedini M. A combination of genetic algorithm and particle swarm optimization for optimal DG location and sizing in distribution systems. Int J Electr Power Energy Syst , vol.34(1), 2012,pp. 66–74. Ahmed R., Abul wafa . Optimal capacitor allocation in radial distribution systems for loss reduction: A two stage method. Electric Power Systems Research, vol.95, 2013, pp. 168–174. Aman, M.M., Jasmon, G.B., Bakar, A.H.A., Mokhlis, H., Optimum capacitor placement and sizing for distribution system based on an improved voltage stability index, (2012) International Review of Electrical Engineering (IREE), 7 (3), pp. 4622-4630. Shashank, T.R., Rajesh, N.B., Analysis of Fast Voltage Stability Index on long transmission line using Power World Simulator, (2013) International Review on Modelling and Simulations (IREMOS), 6 (3), pp. 888-892. Sattianadan, D., Sudhakaran, M., Dash, S.S., Vijayakumar, K., Cost / loss minimization by the placement of DG in distribution system using ga and PSO - A comparative analysis, (2013) International Review of Electrical Engineering (IREE), 8 (2), pp. 769-775.

T

[2]

According to the Table IX, net annual savings of 33 RDS for single and three capacitors are 26925 $ (24.38%) and 29740 $ (26.93%), respectively. From the Table X, net annual savings of 69 RDS for single and three capacitors are 33832 $ (28.66%) and 33615 $ (28.48%), respectively.

Billinton R., Allan R.N., Reliability evaluation of Power Systems (Plenum, New York, 1996, 2nd edn.).



712


Authors’ information

IN

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Kumari Kasturi was born in 1986, India. She received her B.Tech degree in Electrical Engineering from Biju Pattanaik University (BPUT), Odisha, India and M. Tech. degree in Electrical and Electronic Engineering from I.T.E.R,SOA University, Odisha, India. Since 2008, she has been with Electrical Engineering Deptt. , I.T.E.R, Siksha ‘O’ Anusandhan University, Bhubaneswar, Odisha, India-751030 and continuing as an Assistant Professor. Her research interests include power system operation and planning, Distribution Network, Distributed Generation and Application of Soft computing techniques to power system optimization. Postal address: 126, Ratnakarbag, Tankapani Road, Bhubaneswar, Odisha, India, Pin-754018.

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Manas Ranjan Nayak was born in 1972, india. He received his B.E. degree in Electrical Engineering from I.G.I.T.Sarang (Utkal University, India) and M.E. degree in Electrical Engineering from U.C.E., Burla (Sambalpur University, India) in 1994 and 1995 respectively. For 1998 – 2008, he was with Orissa Hydro Power Corporation Ltd. (A Govt. Odisha PSU) as Asst. Manager (Electrical) and since 2008 he has been with Electrical Engineering Deptt. , ITER, Siksha ‘O’ Anusandhan University, Bhubaneswar, Odisha, India-751030 and continuing as an Associate Professor. His research interests include power system operation and planning, Distribution Network, Distributed Generation, FACTS and Application of Soft computing techniques to power system optimization. Prof. Nayak has membership in professional societies i.e. IET (70472641) and ISTE (LM-71207) Postal address: House No.-51, Road No.-12, Jagannath Nagar, P.O.G.G.P.,Bhubaneswar,Odisha,India,Pin-751025. Tel.: +919437332558 E-mail: [email protected]

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EP

Dr. Pravat Kumar Rout was born in 1969, India. He is a Professor with the department of Electronic & Electrical Engineering, under the Faculty of Engineering & Technology, Siksha ‘O’ Anusandhan University, Bhubaneswar. He received his M.E. degree in Electrical Engineering from Thigarajar College of Engineering, Madurai, and Ph.D degree in Electrical Engineering from Biju Patnaik University of Technology, India, in 1995 and 2010, respectively. His research interests include power system control and power system optimization through evolutionary computation, Distribution Network, Distributed Generation, Custom Power (DFACTS) and FACTS. Postal address: Plot.No.-614/2081, Lane No. 8, Shree Vihar, Chandrasekharpur, Bhubaneswar, Odisha, India, Pin-751031.



713

International Review of Automatic Control (IREACO) Aims and scope The International Review of Automatic Control (IREACO) is a peer-reviewed journal that publishes original theoretical and applied papers on all aspects of Automatic Control. The topics to be covered include, but are not limited to:

T

Control of linear/nonlinear systems, Stability, Controllability and Observations, Modeling Estimation and Prediction, RealTime Systems control, Real-Time and Fault-Tolerant Systems, Multidimensional Systems control, Large Scale Control Systems, Robust Control, Intelligent Control Systems; Stochastic Control, Fuzzy Control Systems, Neuro-Controllers, Neuro-Fuzzy Controllers, Genetic Algorithms, Adaptive Control Techniques; Control methods, modelling and identification of processes in the fields of industry, ecology, natural resources, including physical, biological and organizational systems; the coverage includes but are not limited to: Power System Control, Automatic Control of Chemical Processes, Automotive Control Systems, Thermal System Control, Robot and Manipulator Control, Process Control, Aerospace Control Systems, Motion and Navigation Control, Traffic and Transport Control, Defense and Military Systems Control, Studies on nuclear systems control, Control analysis of Social and Human Systems, Biomedical control systems.

Instructions for submitting a paper

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Autorizzazione del Tribunale di Napoli n. 54 del 08/05/2008

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Introduction to the Discrete LSDP Controller and the Performance by Exploiting the Gap Metric Theory by Ali Ameur Haj Salah, Tarek Garna, Hassani Messaoud

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Design and Comparison of a PI and PID Controller for Effective Active and Reactive Power Control in a Grid Connected Two Level VSC by Rajiv Singh, Asheesh Kumar Singh

1974-6059(201311)6:6;1-N Copyright © 2013 Praise Worthy Prize S.r.l. - All rights reserved

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