Self Adaptive Firefly Algorithm Based Transmission Loss ... - IEEE Xplore

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Proceedings of the "International conference on Advanced Nanomaterials & Emerging Engineering Technologies" (ICANMEET-20 /3) h1, July, 2013. _26 . .

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Self Adaptive Firefly Algorithm Based Transmission Loss Minimization using TCSC * R.Selvarasu#l, C.Christober Asir Rajan 2

*

" Department ofEEE, JNTUH, Hydera bad, India. Department of EEE, Pondicherry Engineering College, Puducherry, India.

[email protected] 2asir [email protected]

annealing (SA),Ant Colony Optimization (ACO), Bees algorithms (BA), Differential Evolution (DE), and Particle Swarm Optimization (PSO) etc [7].Optimal location of multi transmission loss in power system network. Self-Adaptive Firefly Algorithm is proposed to identify the optimal type FACTs devices in a power system to improve the location and parameter of TCSC. To validate the loadability by means of Genetic Algorithm has been proposed algorithm, simulations are performed on IEEE successfully implemented [9].PSO has been applied to determine the optimal location of FACTS devices, reactive 14-bus and IEEE 30-bus system using MATLAB software power and voltage control [10-11]. PSO has been proposed to package. Analyzing the simulation results the identified improve the power system stability by determining the optimal location and parameter of TCSC is able to minimize the location and controller design of STATCOM [12]. PSO has transmission loss in the power system network. Keywords-Firefly Algorithm, Loss Minimization, been proposed to select the optimal location and setting parameter of SVC and TCSC to mitigate small signal Optimal Location, TCSC oscillations in multi machine power system [13]. Bacterial I INTRODUCTION Foraging algorithm has been used to find the optimal location In recent years, the demand for electrical energy is of UPFC devices with objectives of minimizing the losses and exponentially increasing. The construction of new generation improving the voltage profile [14]. Firefly Algorithm has been developed by Xin-She Yang [7system, power transmission networks can solve these demands. However, there are some limitations to construct new system. 8] which is found superior over other algorithms in the sense They involve installation cost, environment impact, political, that it could handle multi modal problems of combinational large displacement of population and land acquisition. One of and numerical optimization more naturally and efficiently [15]. the alternative solutions to respond the increasing demand is by It has been then applied by various researchers for solving minimization of transmission loss using Flexible Alternating various problems, to name a few: economic dispatch [16-17], Current Transmission Systems (FACTS) devices. The FACTS fault identification [18], crypto analysis of knapsack problems is a concept proposed by N.G.Hingorani [1] as a well known [19], scheduling [20], Unit commitment [21] and image term for higher controllability in power system by means of compression [22] etc. In this paper Self Adaptive Firefly Algorithm is proposed to power electronic devices. Better utilization of an existing power system capacity by installing FACTS devices has identify the optimal location and parameter of TCSC, which become essential in the area of ongoing research. FACTS minimizes the transmission loss in the power system network. devices have the capability to control the various electrical Simulations are performed on IEEE 14-bus and IEEE 30-bus parameters in transmission network in order to achieve better system using MATLAB software package. Simulations results are presented to demonstrate the effectiveness of the proposed system performance. FACTS devices can be divided in to three categories. approach. Shunt Connected, Series connected and combination of both II TCSC MODEL [2]. The Static Var Compensator (SVC) and Static Synchronous Compensator (STATCOM) are belongs the The Thyristor Controlled Series Compensator (TCSC) is shunt connected devices and are in use for a long time in capacitive reactance compensator, which consists of a series various places. Consequently, they are variable shunt reactors capacitor bank shunted by a thyristor controlled reactor in which inject or absorb reactive power in order to control the order to provide a smoothly variable series capacitive voltage at a given bus [3].Both Thyristor Controlled Series reactance [2]. The TCSC can be connected in series with the Compensator (TCSC) and Static Synchronous Series transmission line to compensate the inductive reactance of the Compensator (SSSC) are series connected devices. The TCSC transmission line. The reactance of the TCSC depends on its and SSSC mainly control the active power in a line by varying compensation ratio and the reactance of the transmission line the line reactance. They are in operation at a few places but where it is located. The TCSC model is shown in Fig.l. The are still in the stage of development [4-5].Unified Power Flow TCSC modeled by the reactance, Xn: c: as follows, s Controller (UPFC) is belongs to Combination of Shunt and Xij = Xline + Xrcsc Series devices. UPFC is able to control active power, reactive (1) power and voltage magnitude simultaneously or separately [6]. Optimal location of various types of FACTS devices in the (2) power system has been experienced using different Meta­ heuristic algorithm such as Genetic Algorithm (GA),Simulated Abstract--This

paper presents the use of Thyristor

Controlled Series Compensator (TCSC) to minimize the

978-1-4799-1379-4/13/$31.00©2013 IEEE

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Proceedings of the "International Conference on Advanced Nanomaterials & Emerging Engineering ech logies" (ICANMEET-20J3) t organized by Sathyabama Umverslty, Chennal, IndIa III associatIOn wIth DRDO, New Deihl, IndIa, 24 -26, July, 2013.

where Xline is the reactance of the transmission line and rrcsc is the compensation factor of the TCSC.

specific optimization problem. However, typically a population size of 20 to 50 is used for PSO and Firefly Algorithm for most applications [10, 25]. Each

ni

h

firefly is denoted by a vector

(3) where nd -the number of decision variables. The search space is limited by the following inequality constraints

Fig.I.TCSC Model

III. FIREFLY ALGORITHM A.

Classical Firefly Algorithm

Firefly Algorithm is a recent nature inspired meta- heuristic algorithms which has been developed by Xin She Yang at Cambridge university in 2007[7]. The algorithm mimics the flashing behavior of fireflies. It is similar to other optimization algorithms employing swarm intelligence such as PSO. But Firefly Algorithm is found to have superior performance in many cases [8].1t employs three ideal rules. First, all fireflies are unisex which means that one firefly will be attracted to other fireflies regardless of their sex. Secondly, the degree of the attractiveness of a firefly is proportional to its brightness, thus for any two flashing fireflies, the less bright one will move towards the brighter one. More brightness means less distance between two fireflies. However if any two flashing fireflies are having same brightness, then they move randomly. Finally the brightness of a firefly is determined by the value of the objective function. For a maximization problem, the brightness of each firefly is proportional to the value of the objective function. In case of minimization problem, the brightness of each firefly is inversely proportional to the value of objective function. Firefly Algorithm initially produces a swarm of fireflies located randomly in the search space. The initial distribution is usually produced from a uniform random distribution. The position of each firefly in the search space represents a potential solution of the optimization problem. The dimension of the search space is equal to the number of optimizing parameters in the given problem. The fitness function takes the position of a firefly as input and produces a single numerical output value denoting how good the potential solution is. A fitness value is assigned to each firefly. The brightness of each firefly depends on the fitness value of that firefly. Each firefly is attracted by the brightness of other fireflies and tries to move towards them. The velocity or the pull a firefly towards another firefly depends on the attractiveness. The attractiveness depends on the relative distance between the fireflies. It can be a function of the brightness of the fireflies as well. A brighter firefly far away may not be as attractive as a less bright firefly that is closer. In each iterative step, Firefly Algorithm computes the brightness and the relative attractiveness of each firefly. Depending on these values, the positions of the fireflies are updated. After a sufficient amount of iterations, all fireflies converge to the best possible position on the search space. The number of fireflies in the swarm is known as the population size, N. The selection of population size depends on the

(4)

xV(min)�xV �xV(max) v = 1,2,.··,nd

Initially, the positions of the fireflies are generated from a uniform distribution using the following equation

l�l = XV (min) + ( xv (max) -xv (min) ) x rand

(5)

X

Here, rand is a random number between 0 and I, taken from a uniform distribution. The initial distribution does not significantly affect the performance of the algorithm. Each time the algorithm is executed, the optimization process starts with a different set of initial points. However, in each case, the algorithm searches for the optimum solution. In case of multiple possible sets of solutions, the algorithm may converge on different solutions each time. But each of those solutions will be valid as they all will satisfy the requirements. The light intensity of the

ni firefly, �n is given by

The attractiveness between by

h

ni and nth h

firefly,

f3

is given

m,n

Pm,n = (Prm;x,m,n - Pmin,m,n ) exp(-rmrm,n 2) + Prnil,"m,n (7) where

rlTI,n

is Cartesian distance between

ni and nth firefly h

(8) The value of {J,nin is taken as 0.2 and the value of Pmax is taken as 1. r is another constant whose value is related to the dynamic range of the solution space. The position of firefly is updated in each iterative step. If the light intensity of n

th

firefly is larger than the light intensity of the

then the motion at

__ Jh m firefly

J(

h

ni firefly, h

moves towards the nth firefly and its

iteration is denoted by the following equation:

xm(k) = xm(k-l)+It,n (xm(k-l)-xm(k-l)) +a( ram-O.5)

684

(9)

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The random movement factor a is a constant whose value depends on the dynamic range of the solution space. At each iterative step, the intensity and the attractiveness of each firefly is calculated. The intensity of each firefly is compared with all other fireflies and the positions of the fireflies are updated using (9). After a sufficient number of iterations, all the fireflies converge to the same position in the search space and the global optimum is achieved. B.

To achieve the better utilization of an existing power system, the optimal location and parameter of TCSC to be identified in the power transmission network to minimize the total real power loss. The objective of this paper is to identify the optimal location and parameter of the TCSC which minimize the real power loss. Rrrltll?POWY S)8lemIUa

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SelfAdaptive Firefly Algorithm

In the above narrated Firefly Algorithm, each firefly of the swarm travel around the problem space taking into account the results obtained by others, still applying its own randomized moves as well. The random movement factor (a) is very effective on the performance of Firefly Algorithm. A large value of a makes the movement to explore the solution through the distance search space and smaller value of a tends to facilitate local search. In this paper the random movement factor (a) is dynamically tuned in each iteration. The influence of other solutions is controlled by the value of attractiveness (7), which can be adjusted by modifying three parameters a , Pmin and r.ln general the value of Pmax should be used from 0 to I and two limiting cases can be defined: The algorithm performs cooperative local search with the brightest firefly strongly determining other fireflies positions, especially in its neighborhood, when /l,nax. = I and

only non-cooperative distributed random search with Pmax = o. On the other hand, the value of r determines the variation of attractiveness with increasing distance from communicated firefly. Setting r as 0 corresponds to no variation or attractiveness is constant and conversely putting r as results in attractiveness being close to zero which again is equivalent to the complete random search. In general r in the range of 0 to I 0 can be chosen for better performance. Indeed, the choice of these parameters affects the final solution and the convergence of the algorithm. Each firefly with nd decision variables in the Firefly Algorithm will be defined to encompass nd +3.Firefly Algorithm variables in a self-adaptive method, where the last three variables represent am ' Pmin.m and r m . A firefly can be represented as

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