Parameters identification of photovoltaic models using niche-based particle swarm optimization in parallel computing architecture

Lin, Xin; Wu, Yuhang

doi:10.1016/j.energy.2020.117054

Cited by 78 publications

(26 citation statements)

References 27 publications

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“…These structures are influenced by natural events, such as swarming activities, mechanisms focused on nature, and physics. Genetic algorithm (GA) [18], [19], particle swarm optimization [20]- [22], enhanced leader particle swarm optimization algorithm (PSO) [23], niche particle swarm optimization in parallel computing algorithm [24], several versions of differential evolution (DE) [25]- [28], penalty-based DE algorithm [29], sunflower optimizer [30], grey wolf optimizer (GWO) [31], whale optimizer algorithm (WOA) [32], harris-hawk optimizer (HHO) [33], improved salp swarm algorithm (ISSA) [34], several version of JAYA algorithm [35], multiple learning backtracking search algorithm [36], coyote optimization algorithm [37], teaching-learning-based optimization and its various versions [38]- [44], political optimizer (PO) [4], evolutionary shuffled frog leaping algorithm [45], slime-mould optimizer (SMO) [46], [47], marine predator algorithm (MPA) [48], equilibrium optimizer (EO) [49], ions motion optimization (IMO) [50], improved PSO (IPSO) [51], Forensic-based investigation algorithm [52], and improved learning-search algorithm [53] are among good heuristic-based structures. Some studies have endeavored to hybridize a few of these strategies to boost their performance, such as hybrid grey wolf optimizer with cuckoo search algorithm [54], hybrid firefly with pattern search algorithms [55], hybrid grey wolf optimizer with particle swarm algorithm [56], hybrid WO with DE algorithm [26], hybrid GA with simulated annealing algorithm [18], etc.…”

Section: Introductionmentioning

confidence: 99%

Identification of Solar Photovoltaic Model Parameters Using an Improved Gradient-Based Optimization Algorithm With Chaotic Drifts

Premkumar¹,

Jangir²,

Ramakrishnan³

et al. 2021

IEEE Access

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When discussing the commercial applications of photovoltaic (PV) systems, one of the most critical problems is to estimate the efficiency of a PV system because current (I)voltage (V) and power (P) voltage (V) characteristics are highly non-linear. It should be noted that most of the manufacturer's datasheets do not have complete information on the electrical equivalent parameters of PV systems that are necessary for simulating an effective PV module. Compared to conventional approaches, computational optimization and global research strategies are more acceptable as an effective alternative to parameter estimation of solar PV modules. Recently, a Gradient-based optimizer (GBO) is reported to solve the engineering design optimization problems. However, the basic GBO algorithm is stuck in local optima when handling complex non-linear problems. In this sense, this paper presents a new optimization technique called the Chaotic-GBO (CGBO) algorithm to derive the parameters of PV modules while offering precise I-V and P-V curves. To this end, the CGBO algorithm is based on a chaotic generator to obtain the PV parameters combined with the GBO algorithm. There are five case studies considered to validate the performance of the proposed CGBO algorithm. A quantitative and qualitative performance evaluation reveals that the proposed CGBO algorithm has improved results than other state-of-the-art algorithms in terms of accuracy and robustness when obtaining PV parameters. The average RMSE values and runtime of five case studies are equal to 9.8427E-04, 2.3700E-04, 2.4251E-03, 4.3524E-03 and 1. 8349E-03, and 18.44, 17.78, 18.18, 18.28 and 17.97, respectively. The results proved the superiority of the proposed CGBO algorithm over the different selected algorithms. For future research, this study will be backed up with external support at https://premkumarmanoharan.wixsite.com/mysite.

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Section: Introductionmentioning

confidence: 99%

Identification of Solar Photovoltaic Model Parameters Using an Improved Gradient-Based Optimization Algorithm With Chaotic Drifts

Premkumar¹,

Jangir²,

Ramakrishnan³

et al. 2021

IEEE Access

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“…us they are widely used for solving optimization problems, such as mission planning [4][5][6][7], image segmentation [8][9][10], feature selection [11][12][13], and parameter optimization [14][15][16][17][18]. Metaheuristic algorithms find optimal solutions by modeling physical phenomena or biological activities in nature.…”

Section: Introductionmentioning

confidence: 99%

A Modified Slime Mould Algorithm for Global Optimization

Tang

Han

et al. 2021

Computational Intelligence and Neuroscience

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Slime mould algorithm (SMA) is a population-based metaheuristic algorithm inspired by the phenomenon of slime mould oscillation. The SMA is competitive compared to other algorithms but still suffers from the disadvantages of unbalanced exploitation and exploration and is easy to fall into local optima. To address these shortcomings, an improved variant of SMA named MSMA is proposed in this paper. Firstly, a chaotic opposition-based learning strategy is used to enhance population diversity. Secondly, two adaptive parameter control strategies are proposed to balance exploitation and exploration. Finally, a spiral search strategy is used to help SMA get rid of local optimum. The superiority of MSMA is verified in 13 multidimensional test functions and 10 fixed-dimensional test functions. In addition, two engineering optimization problems are used to verify the potential of MSMA to solve real-world optimization problems. The simulation results show that the proposed MSMA outperforms other comparative algorithms in terms of convergence accuracy, convergence speed, and stability.

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“…Literature [6,7] proposed a GPU-based parallel optimization design and implementation of particle filtering to improve the computational speed of the tracking algo-rithm. Literature [8][9][10] designed and implemented a parallel particle swarm optimization algorithm based on CUDA, which uses a large number of GPU threads to accelerate the convergence speed of the whole particle swarm. Parallel statute algorithms are used in the abovementioned literature for parallel particle filtering algorithms to simplify thread operations.…”

Section: Introductionmentioning

confidence: 99%

Parallel Differential Evolutionary Particle Filtering Algorithm Based on the CUDA Unfolding Cycle

Huang

Cao

2021

Wireless Communications and Mobile Computing

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Aiming at the problem of low statute efficiency of prefix sum execution during the execution of the parallel differential evolutionary particle filtering algorithm, a filtering algorithm based on the CUDA unfolding cyclic prefix sum is proposed to remove the thread differentiation and thread idleness existing in the parallel prefix sum by unfolding the cyclic method and unfolding the thread bundle method, optimize the cycle, and improve the prefix sum execution efficiency. By introducing the parallel strategy, the differential evolutionary particle filtering algorithm is implemented in parallel and executed on the GPU side using the improved prefix sum computation during the algorithm update. Through big data analysis, the results show that this parallel differential evolutionary particle filtering algorithm with the improved prefix sum statute can effectively improve differential evolutionary particle filtering for nonlinear system states and real-time performance in heterogeneous parallel processing systems.

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Parameters identification of photovoltaic models using niche-based particle swarm optimization in parallel computing architecture

Cited by 78 publications

References 27 publications

Identification of Solar Photovoltaic Model Parameters Using an Improved Gradient-Based Optimization Algorithm With Chaotic Drifts

Identification of Solar Photovoltaic Model Parameters Using an Improved Gradient-Based Optimization Algorithm With Chaotic Drifts

A Modified Slime Mould Algorithm for Global Optimization

Parallel Differential Evolutionary Particle Filtering Algorithm Based on the CUDA Unfolding Cycle

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