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Multi-objective based on parallel vector evaluated particle swarm optimization for optimal steady-state performance of power systems

机译:基于并行矢量评估的粒子群算法的多目标优化电力系统稳态性能

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In this paper the state-of-the-art extended particle swarm optimization (PSO) methods for solving multi-objective optimization problems are represented. We emphasize in those, the co-evolution technique of the parallel vector evaluated PSO (VEPSO), analysed and applied in a multi-objective problem of steady-state of power systems. Specifically, reactive power control is formulated as a multi-objective optimization problem and solved using the parallel VEPSO algorithm. The results on the IEEE 30-bus test system are compared with those given by another multi-objective evolutionary technique demonstrating the advantage of parallel VEPSO. The parallel VEPSO is also tested on a larger power system this with 136 busses.
机译:本文介绍了解决多目标优化问题的最新技术扩展粒子群优化(PSO)方法。在这些中,我们强调了并行向量评估PSO(VEPSO)的协同进化技术,在电力系统稳态多目标问题中进行了分析和应用。具体来说,无功功率控制被公式化为一个多目标优化问题,并使用并行VEPSO算法求解。将IEEE 30总线测试系统上的结果与另一种证明并行VEPSO优势的多目标进化技术给出的结果进行比较。并行VEPSO也已在具有136条总线的更大电源系统上进行了测试。

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