It is a usual practice to consider first bus as slack bus. Each bus has four state variablesĮach bus has either two of the four above variables described or given. PROBLEM STATEMENT Determination of voltage magnitude and phase angle obtained at each bus Determine the active and reactive power flow in each power line. This technique was first introduced by Kennedy and Eberhart motivated by social behavior of swarms such as fish schools and bird flocking. PSO has been fortuitously applied to solve optimization problems in the area of electric power systems such as: economic dispatch, Reactive Power Control and Power Losses Reduction function, Optimal Power Flow (OPF), Power System Controller Design, artificial neural network training, generation expansion planning, load forecasting, feeder-switch relocation problem and fuzzy system control. Implementation of PSO is quite convenient as only few parameters requires adjustment. This is the best possible value that has been obtained by any particle in the neighborhood.

There is another best value which is known as global best and is tracked by the PSO. Particles have the capability toĬhange their position by forming communication with neighboring particles by utilizing the best position encountered by itself and its neighbors. Every particle in the swarm tries to look for best possible position which is linked as the best possible solution that has been so far attained by that particle. In this system, particles fly around in multidimensional search space. PSO is an optimization technique in which particles change their position with time. Moreover particle swarm optimization technique will be adopted to search for appropriate bus voltages and phase angles. The output of power flow analysis is the real and reactive power, slack bus power and line losses. The important information which we acquire from this analysis is To solve these parameters it is required to have fast, accurate and efficient numerical techniques. Power flow study solves the system for a set non-linear algebraic equations for the two unknown variables. The study identifies the operational state of a system for given loading. Power flow analysis also known as load flow analysis. KeywordsParticle Swarm Optimization, Load Flow Analysis, Newton Raphson, Gauss Siedel For reference comparison of PSO is made between Gauss- Siedel and Newton- Raphson and the results are also verified through Matlab Codes. Main aim of this research is to build algorithm to obtain optimized results.

Classical iterative methods such as Newton Raphson method, and Gauss- Siedel Method are also applied with artificial intelligence based algorithm Particle Swarm Optimization (PSO). Particle Swarm Optimization (PSO) is a computational method that optimizes a given problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. Additionally, Particle Swarm Optimization Technique (PSO) is also utilized. It is a necessity that the bus voltages should remain within a specified limit. Through the load flow studies obtained parameters are the voltage magnitudes and angles at each bus in the stationary state. Load always remains stationary and it is the power that flows through transmission lines, due to which load flow analysis is preferred to be called as Power Flow Analysis. Main aim is to apply Power Flow Analysis provide information about system variables and these variables are complex voltage V, complex power P, and consequently currents, voltages in constant state. MS Electrical Engineering, University of Engineering and Technology Lahore, Pakistan AbstractPower flow analysis are one of the main aspects for planning and operation of power system and its analysis. Lab Engineer, Institute of Electrical, Electronics & Computer Engineering University of the Punjab Comparative Analysis of Particle Swarm Optimization with Classical Methods for Load Flow Analysis

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