Artificial Intelligence for Optimal Sizing and Location of DG in Power Systems

Mohammed Jawad Abed; Omar Abdullatif  Jassim; Anis Mhalla

doi:10.30572/2018/KJE/170103

Authors

Mohammed Jawad Abed Laboratory of Automation Electrical Systems Environment (LAESE), National Engineering School of Monastir (ENIM). Monastir, Tunisia
Omar Abdullatif Jassim Medical Instrumentation Techniques Engineering Department, Al-Hikma University College (HIUC) Baghdad, Iraq
Anis Mhalla Laboratory of Automation Electrical Systems Environment (LAESE), National Engineering School of Monastir (ENIM). Monastir, Tunisia https://orcid.org/0000-0002-7703-8205

DOI:

https://doi.org/10.30572/2018/KJE/170103

Keywords:

Distributed generation, Particle Swarm Optimization, artificial intelligence, Power Factory

Abstract

Distributed generation (DG) is regarded as one of the energy resources concentrated in many and diverse areas and depends on numerous resources, which are distributed and relatively small energy resources, and the generated capacity ranges from the kilowatt to megawatts, where reliable local capacity is supplied, enhancing the efficiency of the energy sources use, and reducing radiation to the lowest possible value. The AI based systems have been employed to simulate smart grids to solve many pressing problems like obtaining the best location and size of distributed generation systems. An example of these algorithms: Genetic Algorithms (GA), Monte Carlo method, Artificial Immune System AIS, Particle Swarm Optimization (PSO), Evolutionary Algorithm (EA) and Ant Colony Optimization (ACO). The majority of these approaches use population-based optimization, where the search direction and exploration in objective function search space are crucial to efficiently identify a solution. In this article, we will study the Particle Swarm Optimization (PSO) and its simulation in MATLAB. The particle-based optimization algorithms used have improved the quality condition indicators from the maximum voltage deviation and average voltage deviation. The used method provides the best and optimal way to reduce energy losses by choosing the optimal size and location of generator distribution systems and achieving the lowest cost

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