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Hybrid active power filter (HAPF) is a novel technique of harmonic filter which combines superiorities of both active and passive filters. However, extracting appropriate parameters of the HAPF, including active filter gain, passive inductive, and capacitive reactance within a constraint space is still a challenging task. To obtain more accurate parameters of HAPF, this paper proposed a new population-based algorithm named ASC-MFO. In ASC-MFO, the swarm is divided into two sub-swarms, i.e., exploitation group and the exploration group. The exploitation group adopts the SFM in the MFO algorithm to enhance the exploitation ability, while the exploration group utilizes the SCM in the SCA algorithm to emphasize exploration. Besides, a personal best flame generation (PFG) strategy and a hybrid exemplar generation (HEG) strategy are developed for the exploitation group and the exploration group to further enhance the exploitation ability and the exploration ability of the two subgroups, respectively. Moreover, an adaptive strategy is proposed to automatically resize the population number of two sub-swarms during the iterative process, which can precisely balance the exploration and exploitation ability between groups in every single generation. The proposed ASC-MFO is applied to design the two most commonly used topologies of the HAPF, where each topology contains four actual cases. Comprehensive experimental results demonstrate that ASC-MFO obtains an excellent performance among those well-established algorithms, especially in the aspect of accuracy and reliability.

An Adaptive Sine-Cosine Moth-Flame Optimization Algorithm for Parameter Identification of Hybrid Active Power Filters in Power Systems