Design of optimized hybrid active power filter for electric arc furnace load

Jhapte, Rajkumar; Patel, R. N.; Neema, D. D.

doi:10.1109/rdcape.2017.8358249

Cited by 4 publications

(1 citation statement)

References 17 publications

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“…Tiwari et al presents an efficient technique for harmonic compensation using Ant Colony Optimization (ACO) algorithm based on hybrid active power filter [34]. Jhapte et al studied an optimal controller optimized by the harmony search algorithm (HSA) to minimize the total harmonics of the shunt hybrid active power filter [35]. And the literature [36], [37] and [38] proposed the design technique of HAPF, but did not increase the possibility of source nonlinearity.…”

Section: Introductionmentioning

confidence: 99%

An Improved Dragonfly Algorithm With Higher Exploitation Capability to Optimize the Design of Hybrid Power Active Filter

Dai

Cui

et al. 2020

IEEE Access

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Hybrid power active filter (HAPF) is an important device to suppress the harmonics of the power system. In HAPF, the parameters estimation has a great impact on ensuring the power quality in the power system. Aiming at the problem of minimizing the harmonic pollution (HP) in the power system, this paper proposes a new technology namely IEDA for parameter optimization of hybrid power active filters, which is an improved dragonfly algorithm (DA) with higher exploitation capability. DA is a global search algorithm with sufficient ability to avoid falling into local optimization, however, DA performs poorly for local search. In the IEDA, we adopt a strategy of division of labor to divide particles into exploitation population and exploration population. In the exploitation population, we introduce the information exchange mechanism of the differential evolution (DE) and set up an exemplar pool to enhance its exploitation capability. In the exploration population, we use the global search ability of the DA to prevent particles from falling into a local optimum. Through the division of labor between exploitation population and exploration population, the problems of low accuracy and slow convergence of DA are effectively solved. Experimental results show that the algorithm has greatly improved accuracy and reliability compared with seven well-established algorithms.

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