Diminution of Active Power Loss by Communal Expressive Optimization Algorithm

Human society is a multifarious collection which is more effectual than other animal groups. Consequently, if one algorithm imitates the human society, then the efficiency may be healthier than other swarm intelligent algorithms which are stimulated by other animal groups. In this paper Communal Expressive (CE) Optimization Algorithm has been utilized to solve reactive power problem. The key feature of solving Optimal Reactive Power Problem is to reduce the real power loss and to maintain the voltage profile within the specified limits. The proposed Communal Expressive (CE) Optimization Algorithm has been authenticated, by applying it in standard IEEE 118 & practical 191 bus test systems. The results have been compared to other standard methods and the projected algorithm converges to finest solution

Progressive Particle Swarm Optimization Algorithm for Solving Reactive Power Problem

In this paper a Progressive particle swarm optimization algorithm (PPS) is used to solve optimal reactive power problem. A Particle Swarm Optimization algorithm maintains a swarm of particles, where each particle has position vector and velocity vector which represents the potential solutions of the particles. These vectors are modernized from the information of global best (Gbest) and personal best (Pbest) of the swarm. All particles move in the search space to obtain optimal solution. In this paper a new concept is introduced of calculating the velocity of the particles with the help of Euclidian Distance conception. This new-fangled perception helps in finding whether the particle is closer to Pbest or Gbest and updates the velocity equation consequently. By this we plan to perk up the performance in terms of the optimal solution within a rational number of generations. The projected PPS has been tested on standard IEEE 30 bus test system and simulation results show clearly the better performance of the proposed algorithm in reducing the real power loss with control variables are within the limits

Social Emotional Optimization Algorithm for Solving Optimal Reactive Power Dispatch Problem

The main feature of solving Optimal Reactive Power Dispatch Problem (ORPD) is to minimize the real power loss and also to keep the voltage profile within the specified limits. Human society is a complex group which is more effective than other animal groups. Therefore, if one algorithm mimics the human society, the effectiveness maybe more robust than other swarm intelligent algorithms which are inspired by other animal groups. So in this paper Social Emotional Optimization Algorithm (SEOA) has been utilized to solve ORPD problem. The proposed algorithm (SEOA) has been validated, by applying it on standard IEEE 30 bus test system. The results have been compared to other heuristics methods and the proposed algorithm converges to best solution

Multi-Objective Solution Based on Various Particle Swarm Optimization Techniques in Power Systems

A proposed optimization technique based on fuzzy logic and particle swarm is presented in this paper. This technique is referred to as Fuzzy Adaptive Particle Swarm Optimization (FAPSO). In this technique, the fuzzy logic is employed to adjust the parameters of the particle swarm. The proposed technique is applied to the IEEE-30-bus-system model along with previous optimization methods to obtain a multiobjective solution to the voltage control, the voltage deviation, and the real power loss problems in power systems. The multi-objective problem is subjected to the same constraints for all methods and a comparison between the results obtained by various methods is presented. It has been demonstrated that the results of the proposed technique superseded that of all previous optimization technique methods