粒子群优化
电压
数学优化
交流电源
多目标优化
多群优化
渡线
计算机科学
遗传算法
趋同(经济学)
元启发式
控制理论(社会学)
分类
算法
数学
工程类
人工智能
控制(管理)
经济增长
电气工程
经济
作者
Ahadu Hilawie,Fekadu Shewarega
出处
期刊:Engineering research express
[IOP Publishing]
日期:2023-11-20
卷期号:5 (4): 045062-045062
标识
DOI:10.1088/2631-8695/ad0afc
摘要
Abstract In this study an improved multi objective particle swarm optimization (IMOPSO) algorithm is proposed for power system reactive power optimization with the objective of ensuring voltage security. The multi objective particle swarm optimization (MOPSO) is improved by introducing an adapted binary crossover (ABX) to the new positions obtained by the basic particle swarm optimization (PSO) algorithm. Additionally, diversity maintenance strategy is added to the algorithm by employing crowding distance (CD) calculation. The developed algorithm is tested and compared with standard MOPSO and non dominated sorting genetic algorithm (NASGA II). The comparison is made based on the degree of closeness to the true pareto front, as measured by the inverted generational distance (IGD), and based on diversity, as measured by the CDs . The test is made using ZDT1, ZDT2, and ZDT3 test functions. The IMOPSO showed improved performance over MOPSO and NASGA II algorithms in terms of convergence to the true pareto front (PF) and in terms of the speed of convergence as well as in maintaining diversity. The algorithm is then implemented to reactive power optimization of IEEE 14 bus test system. For the implementation purpose, the voltage stability and voltage deviation components of voltage security are formulated as a multi objective functions. The implementation has resulted diverse options of optimal settings of reactive power controlling parameters. The optimal settings proved to produce an improved voltage security as measured in terms of voltage deviation and voltage stability.
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