Microgrid Optimum Identification Location Based on Accelerated Particle Swarm Optimization Techniques Using SCADA System

The microgrid has become significant and commonly used;it has localized electricity sources and loads connected to a centralized electrical power network system when the need arises and disconnects to island mode. A microgrid can effectively be integrated with various distribution generators, which can improve the voltage level on the transmission line by reducing the real power losses. In this work, new technologies will permit power grids to be better prepared for future requirements. The numbers and diversity of such decentralized power plants require a new type of management in the operation of power grids and intelligent networks or “smart grid.” A SCADA system will improve coordination between power demand and generation and use of modern information technology such as the internet, sensors, controllers, and wireless transmission equipment and use smart metering. The Accelerated Particle Swarm Optimization technique will be used to select the optimum location of a wind turbine to install in the power grid considering minimum power losses with optimal operation consideration of the number of iterations, the execution time of the program, and the memory capacity. The analysis and the study are carried out in MATLAB and the SCADA system.

Acceleration Factor Harmonious Particle Swarm Optimizer

A Particle Swarm Optimizer （PSO） exhibits good performance for optimization problems, although it cannot guarantee convergence to a global, or even local minimum. However, there are some adjustable parameters, and restrictive conditions, which can affect the performance of the algorithm. In this paper, the sufficient conditions for the asymptotic stability of an acceleration factor and inertia weight are deduced, the value of the inertia weight w is enhanced to （ 1, 1）. Furthermore a new adaptive PSO algorithm - Acceleration Factor Harmonious PSO （AFHPSO） is proposed, and is proved to be a global search algorithm. AFHPSO is used for the parameter design of a fuzzy controller for a linear motor driving servo system. The performance of the nonlinear model for the servo system demonstrates the effectiveness of the optimized fuzzy controller and AFHPSO.