ACTIVE POWER LOSS MINIMIZATION WITH FACTS DEVICES USING SA AND PSO TECHNIQUES скачать в хорошем качестве

ACTIVE POWER LOSS MINIMIZATION WITH FACTS DEVICES USING SA AND PSO TECHNIQUES

Simulated annealing (MSA) and particle swarm optimization (PSO) techniques are proposed to minimize total losses in a network system with flexible AC transmission systems (FACTS) devices. The problem is decomposed in two sub-problems. The first sub-problem is optimal placement of FACTS devices using line loss sensitivity index and the second sub-problem is the load flow with FACTS parameters using modified SA/PSO techniques. The main objective of this project is to find out a more efficient approach for loss reduction in the system network. Simulations are performed on IEEE 14-bus system using Matlab Software. Simulated annealing is a method based on local search in which each movement is accepted if improves the system energy. Other possible solutions are also accepted according to a probabilistic criterion. Such probabilities are based on the annealing process and they are obtained as a function of the system temperature. The SA strategy starts with a high temperature giving a high probability to accept non-improving movements. The temperature and probability levels diminish as long as the algorithm advances to the optimal solution. In this way, a diversification procedure in the search algorithm is performed with care in the system energy. Therefore, SA has the ability to escape from local minima by accepting non-improving energy solutions during the first and medium stages of the algorithm. SA gives acceptable solutions when the initial temperature is high associated with a slow cooling procedure. The three most important parameters of the SA technique required to solve any optimization problem are as follows: 1) The annealing temperature (T): This parameter permits the SA technique not to be entrapped in local minima through the use of the Boltzmann’s function. 2) The number of iterations at constant temperature (Mo): A low number of Mo will result in being trapped in a local minimum. 3) Cooling strategy ( ρ0 ): If the annealing temperature is decreased too fast the algorithm will be trapped in local minimum regardless of the proper T and Mo tuning. Besides these three parameters, the selection of an initial solution plays an important role in the convergence process. In general, the SA technique is based on the initial solution taken from the randomly chosen variables. In this modified SA approach, the novelty is to take an initial solution through the N-R method which helps to achieve fast convergence and satisfactory results. PSO technique finds the optimal solution using a population of particles. Each particle represents a candidate solution to the problem. PSO is basically developed through simulation of bird flocking in two-dimensional space. The PSO definition is presented as follows: 1)Each individual particle i has the following properties: a current position in search space xi, a current velocity vi, and a personal best position in search space yi. 2) The personal best position yi corresponds to the position in search space, where particle i presents the smallest error as determined by the objective function f, assuming a minimization task. 3) The global best position denoted by y represents the position yielding the lowest error among all the yi ’s. Reference Paper: Active Power Loss Minimization with FACTS Devices Using SA/PSO Techniques Author’s Name: Subrata Majumdar, A K Chakraborty and P.K.Chattopadhyay Source: IEEE Year:2009 Request source code for academic purpose, fill REQUEST FORM below, http://www.verilogcourseteam.com/requ... If you need Matlab p-code(encrypted files) to check the results, contact us by email to info@verilogcourseteam.com You may also contact +91 7904568456 by WhatsApp Chat, for paid services. Visit Website: http://www.verilogcourseteam.com/ Visit Our Social Media Like our Facebook Page:   / verilogcourseteam   Subscribe:    / verilogcourseteamelectricalprojects   Subscribe:    / verilogcourseteammatlabproject   Subscribe:    / verilogcourseteam