Differential Evolutionary Particle Swarm Optimization with Orthogonal Learning for Wind Integrated Optimal Power Flow

Wenlei Bai, Fanlin Meng, Ming Sun, Haoxiang Qin, Richard Allmendinger, Kwang Y. Lee

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Abstract

This study develops a novel variant of particle swarm optimization (PSO), which improves its balance of exploration and exploitation by modifying neighborhood topology, self-adaptive parameter strategies and deep search, namely differential evolutionary evolution PSO with orthogonal learning (OL), i.e., DEEPSO-OL in short. Evolutionary computing can explore the solution space efficiently because of its self-evolving attribute as iteration continues. The OL enhances its exploitation by focusing on deeper search for promising solutions. It utilizes the concept of orthogonal experimental design (OED) which predicts the best combination of control variables without exhaustive evaluation of all possible combinations. In addition, to avoid premature convergence in a local optimum, a stochastic star topology for particles is proposed. Such topology ensures just enough communication among the best performing particles, while encouraging them to explore other spaces. The efficacy of the algorithm is evaluated through real-world scenarios such as optimal power flow (OPF) and wind integrated OPF, which are hard to solve with classical mathematical methods. The proposed algorithm is run on a modified IEEE 30-bus test system and compared to the state-of-the-art evolutionary computing algorithms for a variety of cost objective functions with high levels of non-linearity and non-convexity. The DEEPSO-OL demonstrates its performance to generate more accurate feasible solutions and construct promising and efficient search method for real-world complex optimization problems.

Original languageEnglish
JournalApplied Soft Computing
DOIs
Publication statusAccepted/In press - 19 Apr 2024

Keywords

  • Particle swarm optimization (PSO)
  • differential evolution (DE)
  • orthogonal learning (OL)
  • wind power
  • optimal power flow (OPF)

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