Research on Multi-objective Active Power Optimization Simulation of Novel Improved Whale Optimization Algorithm

The Whale optimization algorithm (WOA) is adopted to solve the non-convex optimal power flow (OPF) problem in this paper. To balance the exploration and exploitation of standard WOA algorithm in solving the OPF problem, the multi-objective novel improved whale optimization algorithm (MONIWOA) is pro...

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Veröffentlicht in:IAENG international journal of applied mathematics Jg. 51; H. 3; S. 1 - 20
Hauptverfasser: Chen, Gonggui, Zhao, Xilai, Xiang, Yi, Zeng, Xianjun, Long, Hongyu
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Hong Kong International Association of Engineers 01.09.2021
Schlagworte:
Evolutionary algorithms
Evolutionary computation
Exploration
Multiple objective analysis
Optimization algorithms
Pareto optimum
Particle swarm optimization
Power flow
ISSN:1992-9978, 1992-9986
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Zusammenfassung:The Whale optimization algorithm (WOA) is adopted to solve the non-convex optimal power flow (OPF) problem in this paper. To balance the exploration and exploitation of standard WOA algorithm in solving the OPF problem, the multi-objective novel improved whale optimization algorithm (MONIWOA) is proposed. In the MONIWOA approach, the piecewise non-linear strategy and dual dynamic weights mode are applied to balance the global exploration and local development capabilities, the Lévy flight mechanism can increase the solutions' diversity with its random step pattern, which plays a vital role in the global exploration. Moreover, a constrains-prior Pareto-dominant rule (CPDR) strategy is proposed to ensure the non-violation of state variables' inequality constraints concurrently. To obtain better-distributed Pareto optimal solution sets (POS), a sorting method with crowding distance and rank strategy (CDRS) is adopted. What's more, an effective fuzzy sorting method is presented to obtain the best compromise solution (BCs) in solving multi-objective optimal power flow (MOOPF) problems. Eight test cases are carried on the standard IEEE 30-bus, IEEE 57-bus and IEEE 118-bus systems. The Pareto front sets (PFs) and BCs obtained by the MONIWOA are more superior to the ones of multi-objective Particle Swarm Optimization (MOPSO) and multi-objective Differential Evolution algorithm (MODE). Furthermore, the analyses of the obtained solutions using GD and SP show that the MONIWOA has significant advantages to gain more uniform distribution and better convergence.
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ISSN:1992-9978
1992-9986