Photovoltaic array reconfiguration under partial shading conditions based on ant colony optimization

Cao, Rui; Ding, Yifan; Fang, Xiaolun; Liang, Siming; Qi, Fengyang; Yang, Qiang; Yan, Wenjun

doi:10.1109/ccdc49329.2020.9164084

Cited by 10 publications

(4 citation statements)

References 13 publications

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“…It is worth noting that different meta-heuristic algorithms might be suitable for different optimization tasks because of their inherent property of high randomness. Hence this work tested 10 meta-heuristic algorithms, include GA, GWO, WOA, MFO, ABC, PSO, BOA, jellyfish search algorithm (JSA) [38], ant colony algorithm (ACO) [39], and simulated annealing (SA) [40], as possible candidates to synthesize DLCI. After trial-and-error, the best five algorithms are chosen, i.e., GWO, WOA, MFO, ABC, and PSO.…”

Section: Discussionmentioning

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: Discussionmentioning

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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“…The power output of PV systems under PS was optimized via particle swarm optimization [22]. In addition, ant colony optimization has been used to optimize the placement and configuration of PV systems to lessen the impact of PSC [23]. Despite their effectiveness, these techniques have limitations in terms of computational efficiency, speed response, stability, and accuracy.…”

Section: Electrical Modeling Of a Pv Systems 21 Pv Systems Performanc...mentioning

confidence: 99%

Optimizing photovoltaic systems performance under partial shading using an advanced cuckoo search algorithm

Benlafkih,

El Moujahid,

Hadjoudja

et al. 2024

IJPEDS

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Partial shading negatively impacts power output in photovoltaic systems (PVs), causing multiple local maximum power points (LMPP) instead of a single global maximum power point (GMPP). The cuckoo search (CS) technique utilizes the maximum power point tracking (MPPT) technique to extract the global maximum power (GMP) from shaded PVs. CS is a metaheuristic technique that has gained widespread recognition. Moreover, the CS algorithm is associated with several challenges, including a failure rate, long response time, and noticeable oscillations during steady-state operation. To address these limitations, our proposed advanced cuckoo search (ACS) algorithm is designed to overcome the shortcomings of the standard CS algorithm. The algorithm iteratively evaluates individual solar panels and collectively explores the solution space using levy flight operations. Persistent variables are used to store and track the current state and previous iterations. Where the duty cycles of the solar panels are optimally set to enhance the overall power generation efficiency. We also evaluate and analyze the results obtained from the performance of our proposed technique and compare them to the performance of the four most recent CS optimization techniques. for all test cases, the tracking efficiency was improved to 99.98% with a fast-settling time of <44 ms.

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“…Likewise, a grasshopper optimizer has been utilized [9] for optimizing PV array under partial shading, with the objective of improving power output. Moreover, the Ant Colony Optimization (ACO) was proposed for reconfiguring photovoltaic (PV) arrays to maximize power extraction under partial shading conditions, compared with the Total Cross Tied (TCT) configuration [10]. Additionally, a ken-ken puzzle method [11] was adopted for reconnecting shaded panels within arrays to boost their power output.…”

Section: Introductionmentioning

confidence: 99%

Optimal PV Reconfiguration Under Partial Shading Based on White Shark Optimization

Kadhim,

Obed,

Abid

et al. 2024

IEEE Access

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The main issue is that photovoltaic (PV) systems have their power output reduced due to partial shading. Less energy is produced by photovoltaic modules when partial shading causes an imbalance in the levels of irradiation. Array reconfigurations, both static and dynamic, are suggested as a strategy to improve power capture and reduce the impact of partial shading. Furthermore, it is recognized that irradiance fluctuations, snow, ice, and dust are environmental elements that impact the efficiency of PV arrays. Using white shark optimization (WSO), this research introduces a new method for optimizing the power reconfiguration of PV arrays. Minimizing the disparity in row currents and maximizing power production are the main goals of this WSO-based technique. Four different types of shade patterns are considered in the research: short wide (SW), long wide (LW), short narrow (SN), and long narrow (LN). In order to confirm that this method works, we ran simulations in MATLAB-Simulink and compared the outcomes to those of other configurations, such as Total Cross Tied (TCT), Butterfly Optimization Algorithm (BOA), Harris Hawks Optimization (HHO), and Flower Pollination Algorithm (FPA). Simulation results show the effectiveness of the WSO method in enhancing power extraction from the PV array, especially under partial shading conditions. Notably, the WSO method significantly increases the Global Maximum Power (GMP) output across different scenarios: by 26.22% in SW, 18.51% in LW, 10.95% in SN, and 10% in LN. This confirms the ability of this method to improve the PV power generation in diverse operating environments.

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Photovoltaic array reconfiguration under partial shading conditions based on ant colony optimization

Cited by 10 publications

References 13 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

Optimizing photovoltaic systems performance under partial shading using an advanced cuckoo search algorithm

Optimal PV Reconfiguration Under Partial Shading Based on White Shark Optimization

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