An Improved Brain Storming Optimization Algorithm for Estimating Parameters of Photovoltaic Models

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Obtaining suitable parameters of photovoltaic models based on measured current-voltage data of the PV system is vital for assessing, controlling, and optimizing photovoltaic systems. To acquire specific parameters of photovoltaic models, we proposed a meta-heuristic algorithm named hybrid symbiotic differential evolution moth-flame optimization (HSDE-MFO) algorithm. The proposed algorithm implements our new proposed symbiotic algorithm structure (SAS). This structure is inspired by soybean-rhizobium nodule symbiosis in nature. The proposed SAS divides the population into two parallel working subgroups , i.e., soybean group and rhizobium group. Soybean group that focuses on exploration is updated by the strategies in the DE algorithm; the rhizobium group that emphasizes on exploitation is renewed by the strategies in the MFO algorithm. Artificial particle selection strategy and artificial flames generation strategy are developed to generate high-quality mutant materials and high-quality flames, respectively. The above-proposed methods balance the exploration ability and exploitation ability and ensure a bionic structure of the proposed algorithm. Moreover, a new elite strategy is developed to offer a chaotic particle to further refine the quality of the current population. The proposed HSDE-MFO is employed to solve the parameters identification problem of photovoltaic models, i.e., single diode, double diode, and photovoltaic module and compared with recently wellestablished algorithms. Experimental results indicate that HSDE-MFO can acquire precise parameters of the three photovoltaic models and stable performance in 30 independent runs. INDEX TERMS Photovoltaic (PV)，moth flame optimization algorithm (MFO), differential evolution algorithm (DE), Parameter identification, Soybean-rhizobium nodule symbiosis.I. INTRODUCTION

Section: ⅴ Analysis Of the Experimental Resultsmentioning

confidence: 99%

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confidence: 99%

Hybrid Symbiotic Differential Evolution Moth-Flame Optimization Algorithm for Estimating Parameters of Photovoltaic Models

Chen

et al. 2020

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“…These meta-heuristic algorithms are widely used to solve various practical engineering problems. For example, the parameter evaluation problem of photovoltaic model [24], the demand estimation of water resources [25], the flow shop scheduling [26] and big data optimization problems [27] [28]. Accordingly, these meta-heuristic techniques are also suitable for designing filters in power systems.…”

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

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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.

“…In addition, the number of adjustable parameters is few, as a result, these algorithms are effortless to implement. Therefore, these algorithms have been extensively used for scientific and industrial processes [29]- [33], which leads to the emergence of more efficient SI algorithms.…”

Section: Introductionmentioning

confidence: 99%

An Improved Moth Flame Optimization Algorithm for Minimizing Specific Fuel Consumption of Variable Cycle Engine

Cui

Huang

et al. 2020

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The effective selection of Variable Cycle Engine (VCE) parameters plays a key role in achieving low specific fuel consumption (SFC) of fighters. However, the selection of VCE parameters is a continuous multimodal issue involving substantial local optima, so that most swarm intelligence (SI) algorithms are easily trapped into local optimal solutions, and cannot obtain satisfactory performance. To address this problem, an improved moth flame optimization algorithm with adaptive Lévy-Flight perturbations (ALFMFO) is proposed. In ALFMFO, the current population aggregation status can be accurately judged based on the difference in fitness variance between two successive moth generations. According to the population aggregation status, the Lévy-Flight disturbance strategy can adaptively adjust the perturbation probability to enhance the ability of ALFMFO to escape from local optimal solutions and realize the minimum SFC optimization of VCE. Experimental results suggest that ALFMFO is effective and superior to other compared SI algorithms in terms of accuracy and robustness.