Joint Operation between a PSO-Based Global MPP Tracker and a PV Module Array Configuration Strategy under Shaded or Malfunctioning Conditions

Chen, Pi-Yun; Chao, Kuei‐Hsiang; Liao, Bo-Jyun

doi:10.3390/en11082005

Cited by 7 publications

(3 citation statements)

References 20 publications

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“…The more common forms are series-parallel, bridge-link, TCT, Suduku, etc [17]. In addition, some meta-inspiration algorithms have also been applied to the PV array reconfiguration, such as genetic algorithms (GA) [18], particle swarm optimization (PSO) [19], and butterfly optimization algorithms (BOA) [20], which can capture the maximum output power of PV arrays under dynamic and variable PSCs for TCT topology. However, these meta-inspiration algorithms easily fall into local optima because of the inherent defects of strong randomness [21].…”

Section: Introductionmentioning

confidence: 99%

Optimal PV array reconfiguration under partial shading condition through dynamic leader based collective intelligence

Wang,

Yang

2023

Prot Control Mod Power Syst

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This paper applies the innovative idea of DLCI to PV array reconfiguration under various PSCs to capture the maximum output power of a PV generation system. DLCI is a hybrid algorithm that integrates multiple meta-heuristic algorithms. Through the competition and cooperation of the search mechanisms of different metaheuristic algorithms, the local exploration and global development of the algorithm can be effectively improved to avoid power mismatch of the PV system caused by the algorithm falling into a local optimum. A series of discrete operations are performed on DLCI to solve the discrete optimization problem of PV array reconfiguration. Two structures (DLCI-I and DLCI-II) are designed to verify the effect of increasing the number of sub-optimizers on the optimized performance of DLCI by simulation based on 10 cases of PSCs. The simulation shows that the increase of the number of sub-optimizers only gives a relatively small improvement on the DLCI optimization performance. DLCI has a significant effect on the reduction in the number of power peaks caused by PSC. The PV array-based reconstruction system of DLCI-II is reduced by 4.05%, 1.88%, 1.68%, 0.99% and 3.39%, when compared to the secondary optimization algorithms.

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Section: Introductionmentioning

confidence: 99%

Optimal PV array reconfiguration under partial shading condition through dynamic leader based collective intelligence

Wang,

Yang

2023

Prot Control Mod Power Syst

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“…In Reference [36], a GA based optimal reconfiguration technique is applied to a PV array to maximize the output power at partial shading condition. There are some other reconfiguration techniques are developed in References [25,37], to reduce MML due to partial shading.…”

Section: Introductionmentioning

confidence: 99%

Performance Comparison of Mismatch Power Loss Minimization Techniques in Series-Parallel PV Array Configurations

2019

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The mismatch in current-voltage (I-V) characteristics of photovoltaic (PV) modules causes significant power loss in a large PV array, which is known as mismatch power loss (MML). The PV array output power generation can be improved by minimizing MML using different techniques. This paper investigates the performance of different module arrangement techniques to minimize MML both for long series string (LSS) and long parallel branch (LPB) in series-parallel (SP) array configurations at uniform irradiance condition. To investigate the significance of MML LSS-SP configuration with dimensions: 1 × 40, 2 × 20, 4 × 10, 5 × 8 and LPB-SP configuration with dimensions: 40 × 1, 20 × 2, 10 × 4, 8 × 5 were used. A comparative analysis is made to find the effectiveness of MML reduction techniques on PV arrays with three different power ratings. Simulation results show that the PV modules arrangement obtained by the genetic algorithm (GA) and current based arrangement (Im) performed better than the arrangements obtained by all other techniques in terms of PV array output power and MML minimization. The performance of the proposed technique was analyzed for both LSS-SP and LPB-SP array configurations in 400 W, 3400 W, and 9880 W arrays. To substantiate the simulation results experiment was performed using a 400 W PV array in outdoor weather condition and obtained similar results. It was also observed that the percentage of recoverable energy (%RE) obtained by arranging the modules using the GA method was higher than Im based method for both LSS-SP and LPB-SP array configurations. A maximum %RE of 4.159 % was recorded for a 5 × 8 LSS-SP array configuration by applying the GA based MML reduction method.

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“…As one of the widely used SI algorithms, PSO is easy to implement and converges fast. Since its inception, PSO has attracted great interest from the evolutionary computation (EC) community, and theoretical 2 of 27 researchers [10][11][12][13], and real-life applications [14][15][16][17][18] of PSO have been reported over the past two decades. Zhang et al [19] have summarized the recent advancements in PSO.…”

Section: Introductionmentioning

confidence: 99%

Multi-Leader Comprehensive Learning Particle Swarm Optimization with Adaptive Mutation for Economic Load Dispatch Problems

Lin

Sun

2018

Energies

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Particle swarm optimization (PSO) is one of the most popular, nature inspired optimization algorithms. The canonical PSO is easy to implement and converges fast, however, it suffers from premature convergence. The comprehensive learning particle swarm optimization (CLPSO) can achieve high exploration while it converges relatively slowly on unimodal problems. To enhance the exploitation of CLPSO without significantly impairing its exploration, a multi-leader (ML) strategy is combined with CLPSO. In ML strategy, a group of top ranked particles act as the leaders to guide the motion of the whole swarm. Each particle is randomly assigned with an individual leader and the leader is refreshed dynamically during the optimization process. To activate the stagnated particles, an adaptive mutation (AM) strategy is introduced. Combining the ML and the AM strategies with CLPSO simultaneously, the resultant algorithm is referred to as multi-leader comprehensive learning particle swarm optimization with adaptive mutation (ML-CLPSO-AM). To evaluate the performance of ML-CLPSO-AM, the CEC2017 test suite was employed. The test results indicate that ML-CLPSO-AM performs better than ten popular PSO variants and six other types of representative evolutionary algorithms and meta-heuristics. To validate the effectiveness of ML-CLPSO-AM in real-life applications, ML-CLPSO-AM was applied to economic load dispatch (ELD) problems.

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Joint Operation between a PSO-Based Global MPP Tracker and a PV Module Array Configuration Strategy under Shaded or Malfunctioning Conditions

Cited by 7 publications

References 20 publications

Optimal PV array reconfiguration under partial shading condition through dynamic leader based collective intelligence

Optimal PV array reconfiguration under partial shading condition through dynamic leader based collective intelligence

Performance Comparison of Mismatch Power Loss Minimization Techniques in Series-Parallel PV Array Configurations

Multi-Leader Comprehensive Learning Particle Swarm Optimization with Adaptive Mutation for Economic Load Dispatch Problems

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