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Distributed fractional-order PID control of multi-area interconnected power systems by population based extremal optimization

机译:基于种群的极值优化的多区域互联电力系统分布式分数阶PID控制

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How to design an effective and efficient load-frequency controller (LFC) for a multi-area interconnected power system to improve its power quality is a challenging issue. In order to deal with this issue, this paper presents a novel distributed fractional-order PID (DFOPID) control method for the load-frequency control of each area by using an adaptive population-based extremal optimization (APEO) algorithm. The basic idea behind the proposed method is formulating the optimal design issue of DFOPID controller as a typical constrained optimization problem by minimizing the weighted sum of area control errors and the control signal subjecting to some pre-specified tolerances firstly, and then solving this problem by using APEO algorithm. The simulation results on a two-area interconnected power system with matched and mismatched system parameters have shown that the proposed method is superior to reported genetic algorithm, particle swarm optimization and chaotic NSGA-II algorithm based fractional-order LFC methods in terms of dynamic response and robustness.
机译:如何为多区域互连电力系统设计有效且高效的负载频率控制器(LFC)以提高其电力质量是一个具有挑战性的问题。为了解决这个问题,本文提出了一种新的分布式分数阶PID(DFOPID)控制方法,该方法通过使用基于种群的自适应极值优化(APEO)算法来控制每个区域的负载频率。提出的方法的基本思想是,通过最小化区域控制误差的加权和和控制信号的加权总和,首先将DFOPID控制器的最佳设计问题表述为典型的约束优化问题,然后通过以下方法解决该问题:使用APEO算法。在系统参数匹配和不匹配的两区域互联电力系统上的仿真结果表明,该方法在动态响应方面优于报道的遗传算法,粒子群算法和基于混沌NSGA-II算法的分数阶LFC方法。和鲁棒性。

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