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Optimal Power Flow Analysis by Using Hybrid Cuckoo Search Algorithm International Journal of Engineering Research & Technology (IJERT) ISSN: 2278-0181 Vol. 3 Issue 5, May - 2014 Optimal Power Flow Analysis by using Hybrid Cuckoo Search Algorithm M. Balasubba Reddy Dr. Y. P. Obulesh Dr. S. Sivanaga Raju Ch Venkata Suresh Department of EEE Deprtment of EEE Deprtment of EEE Deprtment of EEE Prakasam Engineering College LBR College of Engineering UCE Kakinada, UCE Kakinada Kandukur, India Mylavaram, India Kakinada Kakinada Abstract— This paper proposes a novel algorithm for point based algorithms for the solution of optimal power flow continuous non linear optimal power flow problem. The objective problems for the minimization of overall generation cost, of the proposed method is to find the steady state operating point minimization of active power losses, maximization of power which minimizes the fuel cost with proper system performance in system loadability and minimization of the amount of load terms of limits on generator power voltage and line flow. The curtailment [4]. An approach for the multi objective OPF proposed approach employs hybrid cuckoo search algorithm for problem using „differential evolution‟ is presented by optimal setting of OPF control variables. This optimization M.Varada Rajan, K.S.Swarup[5]. Xiaoqing Bai etl,. He algorithm is inspired by the life style cuckoo bird. Similar to the described new solution using the semi definite programming other evolutionary algorithms it starts with an initial population to (SDP) technique to solve the optimal power flow problems solve the optimization problem. The proposed technique is tested on (OPF). The proposed method involves reformulating the OPF the standard IEEE 30 bus system various objectives and is compared with a conventional method. The simulation results problems into a SDP model and developing an algorithm of verify the effectiveness of the proposed method. interior point method (IPM) for SDP [6]. Xin-She Yang etl., he intend to formulate a new meta-heuristic algorithm, called Keywords—Optimal power flow, HCSA, Fuel cost, Cuckoo Search (CS), for solving optimization problems [7]. Transmission power loss, L-index, T.Niknam, M.R.Narimani etl [8] has proposed „improved particle swarm optimization for multi objective OPF considering cost, loss, emission voltage stability index. Ramin I. INTRODUCTION Rajabioun proposed a novel evolutionary algorithm Cuckoo Power flow studies are of great importance for reliable, Optimization Algorithm, suitable for continuous nonlinear stable and secure operation of a power system and for properIJERTIJERT optimization problems [9]. planning as well as designed for future extension. In the past Xin-She yang,Suash Deb uses cuckoo search algorithm for few decades, optimal power flow (OPF) problem has received Multi objective design optimisation [10].Multi objective greater attention, because it is one of the most powerful tools to harmonic search algorithm for OPF has been formulated by analyze static systems of electrical energy. The main aim of S.Sivasubramani, K.S.Swarup [11] to give well distributed OPF problem solution is to optimize a selected objective pareto optimal solution. A technique was developed from the function such as fuel cost, power loss etc. In solving OPF inspiration of swarm behaviors in nature namely „gravitational problem, objective function is optimized by adjusting system search algorithm‟ by A Bhaltacharya for solving multi- control variable while satisfying the various constraints. objective OPF problem [12]. Modified ABC algorithm used Constraints are of two types, equality constraints normally by A Khorsandi etl [13] based on fuzzy multi-objective power flow equations and inequality constraints which are technique for optimal power flow problem to minimize total limits on control variables and limits of power system fuel cost of thermal units, total emission, and total power loss dependant variables. In the past conventional methods were and voltage deviation. employed for solving OPF problem. Recently several classical optimization techniques have been employed for the solution of Careful study of the former literature reveals that there is a OPF problem. multiple objective optimal power flow in which number of objectives can be optimized by a various evolutionary Santos Jr., G.R.M. da Costa, describes a new approach to algorithms. But in this chapter we proposed a comprehensive the optimal-power-flow problem based on Newton‟s method optimization technique known as hybrid cuckoo search which it operates with an augmented original problem [1]. algorithm to solve OPF problem in power system. In this Momoh, et,l., proposed an improved quadratic interior point algorithm cross over technique is used with levy flights to (IQIP) method is used to solve comprehensive OPF problem modify the existing nests. Hence there are more chances to get with a variety of objective functions, including economic best nest leads to optimal solution. dispatch, VAR planning and loss minimization [2]. M. R. AlRashidi etl., he investigated the applicability of Hybrid particle swarm optimization (HPSO) in solving the OPF problem under different formulations and considering different objectives [3]. Florin Capitanescu etl., he proposed Interior- IJERTV3IS051815 www.ijert.org 1514 International Journal of Engineering Research & Technology (IJERT) ISSN: 2278-0181 Vol. 3 Issue 5, May - 2014 II. OPF PROBLEM FORMULATION 2 Ci PGi ai PGi bi PGi di $/ hr (7) Optimal power flow solution aim is to optimize a selective objective function through optimal adjustment of control th Where ai , bi and di are i generating unit cost variables by satisfying equality and inequality constraints. The th OPF problem can be mathematically formulated as follows: coefficients, PGi is real power generation of i generating unit, NG is total number of generating units Minimize Cx,u (1) 2) Active power loss Subjected to constrain gx,u 0 (2) Second objective function is to minimize the real power transmission line loss in the system which can be expressed hmin hx,u hmax (3) as, Where, nl (8) C Loss i Cx,uis the objective function, x is the vector of i1 dependent variables, u is the vector of independent or control Power loss through a line is a function of power flow variables, gx,u represents equality constraints, hx,u through it, which can be obtained from power flow solution. represents inequality constraints. Optimal power flow solution 3) L-index (or) Voltage stability index gives a set of optimal variables to achieve the main objective The significance of L-index of load buses in a power function as minimum generation cost, power loss etc. subjected system is to monitor the voltage stability. It uses information to all the equality and inequality constraints. Here x is the from the normal load flow. It is in the range of 0 to 1. Voltage vector of dependent variables consists of Active power output collapse can be controlled by minimizing the sum of squares of L-indices for a given operating condition. of generator at slack bus PG1 , Load bus voltage VL , NB Reactive power output of generator QG , Line flow limits 2 C Lj (9) Sl jNG1 Thus x can be written as, Where, T NB is the total number of buses in the system. x PG1, VL1, ... VLNL, QG1, ... QGNG, Sl1 ... Slnl (4) Where NL =Number of load buses, NG =Number of NG V L 1 C i (10) generator buses, nl =Number of lines j ji i1 V j u is the vector of independent variables such as continuousIJERTIJERT and discreet variables consists of Generator active power Where, output PG at all generators except at slake bus, Generator j NG 1,...., NB voltages VG , Transformer tap settings T , Shunt VAr C ji is obtained from Ybusmatrices compensation(or) reactive power injections Qc . B. Constraints Here P , V are continuous variables and T and Q are G G c Constraints made on OPF problem are usually two types. the discrete variables. Hence u can be expressed as They are equality constraints and inequality constraints uT P ... P , V ... V , Q ... Q , T ... T (5) G2 GNG G1 GNG c1 cNC 1 NT 1) Equality constraints: These constraints mentioned in NT & NC are number of regulating transformers and equation (2) are usually load flow equations described as NB VAr compensators PGi PDi Vi Vj Yij cosij i j 0 (11) A. Objective functions j1 The main objective of OPF problem is to minimize the total NB fuel cost, real power loss of a transmission line in a system and QGi QDi Vi Vj Yij cosij i j 0 (12) L-Index. j1 1) Fuel cost (or) Generation cost Where, The fuel cost curves of thermal generators are modeled as a th th quadratic cost curve which can be represented as, i , j are phase angles of voltages at i and j bus NG Yij , ij are the bus admittance magnitude and angle C Ci PGi (6) i1 between ith and j th bus IJERTV3IS051815 www.ijert.org 1515 International Journal of Engineering Research & Technology (IJERT) ISSN: 2278-0181 Vol. 3 Issue 5, May - 2014 2) Inequality Constraints eggs each cuckoo has and cuckoo‟s distance to the best These are the constraints represents the system operational habituate egg laying radii is calculated. Now cuckoo starts to and security limits which are continuous and discrete lay egg within the egg laying radius. Thus best habitat with constraints. maximum profit value is obtained where maximum cuckoo population is gathered. In an optimization problem, the value Generator Constraints: of problem variables must be formed as an array. In cuckoo These are the generator real and reactive power constraints optimization algorithm such an array is called habitat. Habitat x , x , ....... x (20) PGi min PGi PGi max ; i 1,2,..... , NG (13) 1 2 n Where, habitat is an array of n-variables representing ; (14) QGi min QGi QGi max i 1,2,..... , NG current living position of cuckoos. The profit of habitat is estimated by evaluating profit function as, Voltage Constraints: Generation bus voltages are restricted by their upper and profit Fhabitat= Fx1, x2 ,.....
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