A cluster chaotic optimization for solving power loss and voltage profiles problems on electrical distribution networks

Abstract

The growing demand for electricity poses significant challenges in maintaining a reliable and efficient power supply. Optimal Capacitor Placement (OCP) in electrical engineering addresses this issue by strategically positioning capacitor banks within constrained Radial Distribution Networks (RDNs). Traditional optimization methods often struggle with this problem; alternative approaches, such as metaheuristic algorithms, present promising solutions. Despite advances in optimization techniques, challenges in achieving optimal solutions continue. To address these challenges, recent hybrid computational methods, such as the cluster chaotic optimization (CCO) algorithm, have emerged to enhance stability and robustness in finding optimal solutions. The effectiveness of the CCO algorithm lies in its combination of Evolutionary Computation (EC) and Machine Learning (ML) approaches. These approaches improve the search strategy by leveraging information extracted from the solution landscape, resulting in high performance in discovering optimal solutions. In this context, this work aims to utilize the strengths of the CCO algorithm to solve real-world challenges and evaluate its potential in addressing the OCP. The CCO algorithm was tested on three benchmark RDNs to assess its efficacy. Results were compared with those obtained from classical and recently developed methods and analyzed using non-parametric tests. The findings indicate that the CCO algorithm is competitive and robust in solving the OCP, outperforming similar strategies, and demonstrates its effectiveness in optimizing complex real-world problems in electrical engineering.