A New Optimization Framework for Harmonic Compensation Considering Plug‐in Electric Vehicle Penetration Using Adaptive Particularly Tunable Fuzzy Chaotic Particle Swarm Optimization

Abstract

Plug‐in electric vehicles (PEVs) can contribute to eliminating undesirable harmonics generated by nonlinear loads. In this study, a novel stochastic optimization approach for harmonic compensation is proposed which is capable of optimizing contrary objectives, including total harmonic distortion and harmonic inject current, simultaneously, while meeting the relevant constraints. This problem can be influenced by the uncertainty of PEVs which is reflected in the force outage rate concept. The Monte–Carlo simulation technique is implemented to consider the uncertainty associated with PEVs by generating plausible scenarios with the aim of converting the mentioned framework to the respective deterministic equivalents. Afterward, adaptive particularly tunable fuzzy chaotic particle swarm optimization (APTFCPSO) is utilized, based on the weighted sum method, and the acquired results are compared with those obtained by other implemented swarm intelligence‐based algorithms. Accordingly, at first, several benchmark optimization functions are considered to verify the performance of the APTFCPSO. Afterward, active power line conditioners (APLCs) and PEVs are separately employed for harmonics cancellation in the deterministic form. After adopting the scenario reduction technique, the optimization framework is solved for each remaining scenario by the mentioned procedure. The statistical analysis reveals that PEVs outperform APLCs to cancel harmonic orders defined in a 14‐node micro‐grid.

An Intelligent Tunicate Swarm Algorithm Based MPPT Control Strategy for Multiple Configurations of PV Systems Under Partial Shading Conditions

“…Particle swarm optimization (PSO) is an excellent example and finds many applications in optimization tasks. [45] However, the limitations of metaheuristic techniques come from their information-sharing mechanism, mathematical modeling complexity, probabilistic nature, and heavy computation resources required to implement those techniques. [44,46] To improve the search efficiency of PSO, introduce additional coefficients, which slightly increase the power efficiency at the expense of additional computational burden, but still yield slow GM tracking due to multiple explorations of PSO.…”