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

Abstract

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. INDEX TERMS Sine cosine algorithm (SCA), Moth flame optimization (MFO), Swarm Global optimization, Hybrid active power filter (HAPF).

Moth Flame Optimization: Theory, Modifications, Hybridizations, and Applications

“…A new hybrid MFO algorithm has been developed by Wu et al [ 64 ] to obtain the HAPF (Hybrid active power filter). The proposed method is known as ASC-MFO.…”

A comprehensive survey of sine cosine algorithm: variants and applications

“…( 2018a ) SCA + MFO ASC-MFO – Parameter identification of hybrid active power filters Better performance of ASC-MFO compared to some well-established algorithms Wu et al. ( 2020 ) SCA + GWO GWO-SCA – Real life optimization problems GWO-SCA yields highly competitive solutions compared to classical SCA, GWO, PSO, ALO, WOA, HAGWO, and MGWO Singh and Singh ( 2017 ) MHGWO-SCA Fault diagnosis of rotating machinery Superiority and availability of MHGWO-SCA compared to 7 relevant methods existing in the literature Fu et al. ( 2019a ) IHGWO-SCA Multi-step short-term wind speed prediction Superiority and effectiveness of IHGWO-SCA compared to some relevant single and hybrid techniques Fu et al.…”

“…( 2020 ), Wu et al. ( 2020 ) Devarapalli and Bhattacharyya ( 2020 ), Fu et al. ( 2020 ) Devarapalli and Bhattacharyya ( 2021 ), Sahu et al.…”

Improved Complete Ensemble Robust Local Mean Decomposition With Adaptive Noise for Slewing Bearings Performance Degradation Assessment

“…However, the randomness of hyper parameter (noise amplitude and ensemble trials) is a major drawback of noise-assisted approach [24]. Moth-flame optimization (MFO), which is a relatively new intelligent optimization algorithm [25], has been widely applied in many field [26], [27]. Therefore, the proposed method named improved MFO-CERLMDAN exploits the merits of CERLMDAN approach to handle multiscale signals and the benefits of MFO method to handle parameter optimization.…”