A Novel Combination of Genetic Algorithm, Particle Swarm Optimization, and Teaching-Learning-Based Optimization for Distribution Network Reconfiguration in Case of Faults

Abstract

Reconfiguring distribution networks involves modifying their topological structure by managing switch states. This process is crucial in smart grids, as it can isolate faults, minimize power loss, and enhance system stability. However, in existing research, the reconfiguration task is often treated as a problem of either single- or multi-objective optimization and frequently overlooks the issue's multimodality. As a result, the solutions derived may be inadequate or unfeasible when facing environmental changes. In this study, the objective function of minimizing power loss considers the case of faults in the distribution grid. Coordinating the initial population division of the Genetic Algorithm (GA) with the Particle Swarm Optimization (PSO) and the Teaching and Learning-Based Optimization (TLBO) algorithms accelerates the process of finding the optimal solution, resulting in faster and more reliable results. The proposed method was tested on the IEEE-33 bus test system and was compared with other methods, demonstrating reliable results and superior efficiency.

Keywords

• genetic algorithm
• particle swarm optimization
• teaching-learning-based optimization
• reconfiguration distribution network
• power loss reduction