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首页> 外文期刊>Journal of Electrical and Computer Engineering >Application of Hybrid MOPSO Algorithm to Optimal Reactive Power Dispatch Problem Considering Voltage Stability
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Application of Hybrid MOPSO Algorithm to Optimal Reactive Power Dispatch Problem Considering Voltage Stability

机译:混合MOPSO算法在考虑电压稳定性的无功优化调度中的应用

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This study presents a novel hybrid multiobjective particle swarm optimization (HMOPSO) algorithm to solve the optimal reactive power dispatch (ORPD) problem. This problem is formulated as a challenging nonlinear constrained multiobjective optimization problem considering three objectives, that is, power losses minimization, voltage profile improvement, and voltage stability enhancement simultaneously. In order to attain better convergence and diversity, this work presents the use of combing the classical MOPSO with Gaussian probability distribution, chaotic sequences, dynamic crowding distance, and self-adaptive mutation operator. Moreover, multiple effective strategies, such as mixed-variable handling approach, constraint handling technique, and stopping criteria, are employed. The effectiveness of the proposed algorithm for solving the ORPD problem is validated on the standard IEEE 30-bus and IEEE 118-bus systems under nominal and contingency states. The obtained results are compared with classical MOPSO, nondominated sorting genetic algorithm (NSGA-II), multiobjective evolutionary algorithm based on decomposition (MOEA/D), and other methods recently reported in the literature from the point of view of Pareto fronts, extreme, solutions and multiobjective performance metrics. The numerical results demonstrate the superiority of the proposed HMOPSO in solving the ORPD problem while strictly satisfying all the constraints.
机译:这项研究提出了一种新颖的混合多目标粒子群优化算法(HMOPSO),以解决最优无功功率分配(ORPD)问题。考虑到三个目标,即最小化功耗,同时改善电压分布和提高电压稳定性,该问题被表述为具有挑战性的非线性约束多目标优化问题。为了获得更好的收敛性和多样性,这项工作提出了将经典MOPSO与高斯概率分布,混沌序列,动态拥挤距离和自适应变异算子结合使用的方法。而且,采用了多种有效策略,例如混合变量处理方法,约束处理技术和停止标准。在标称和偶发状态下,在标准的IEEE 30总线和IEEE 118总线系统上验证了所提出算法解决ORPD问题的有效性。将获得的结果与经典MOPSO,非主导排序遗传算法(NSGA-II),基于分解的多目标进化算法(MOEA / D)以及最近从帕累托前沿,极端解决方案和多目标绩效指标。数值结果表明,提出的HMOPSO在严格满足所有约束的同时,在解决ORPD问题方面具有优势。

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