Optimal mileage-based PV array reconfiguration using swarm reinforcement learning

Zhang, Xiaoshun; Li, Chuanzhi; Li, Fulin; Yin, Xueqiu; Yang, Bo; Gan, Lingxiao; Yu, Tao

doi:10.1016/j.enconman.2021.113892

Cited by 57 publications

(20 citation statements)

References 53 publications

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“…The fitness function is used to evaluate directly the resolution quality of each agent, which is generally designed to simultaneously take account of objective function and limitations. The fitness function can be configured to only take into account the objective function as expressed in Equation 36 as 29,50,51

F = ρ \sum_{tϵT} P ((), t) . normalΔ T - C - M_{P} .

…”

Section: Mh Methods For Pv Array Reconfigurationsmentioning

confidence: 99%

“…The GMPP location is observed at 13 kW under nonuniform irradiance levels of 200–900 W/m 2 . Zhang et al, 29 have studied numerous MH approaches such as PSO, GA, BOA, GOA, HHO, Q‐learning, and swarm reinforcement learning (SRL) algorithms to reconfigure PV arrays and investigated how to achieve higher GMPP under progressive shading scenarios with nonuniform levels of 400–1000 W/m 2 .…”

Section: Introductionmentioning

confidence: 99%

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Imperative role of optimization techniques to reconfigure solar photovoltaic array systems to diminish shading effects

Pachauri

2021

Circuit Theory & Apps

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Solar photovoltaic (PV) efficiency improvement is critical to the use of largescale power generation. The effects of partial shading are a major source of concern for reduced power generation and increased power loss (PL).Researchers explored an extensive way to resolve the shading issue using the metaheuristic approach for the reconfiguration of PV arrays. In this perspective, a detailed review of metaheuristic approaches involved in PV array reconfiguration solutions to provide improved performance. Furthermore, all metaheuristic approaches are classified as (i) evolutionary, (ii) trajectory, (iii) art inspired, (iv) ancient inspired, and (v) nature inspired. The evolutionary and nature-inspired metaheuristic approaches are used in the application of PV array reconfiguration and are critically studied in terms of ease of implementation of global power maxima under partial shading conditions (PSCs). In this present paper, an experimental study validates the MATLAB/Simulink results investigated under shading scenario for conventional total-cross-tied (TCT) and maximum-minimum tier equalization swapping (MMTES) metaheuristic technique-based PV array configurations. The obtained results in terms of percentage of power gain are 11.88% and 11.52% with respect to TCT in MATLAB/Simulink and experimental studies. Further, this study assesses the potential of each algorithm by comparing it with well-established procedures involved in PV array reconfiguration. This paper examines an important review on the types and nature of objective functions, figure of merits, pros and cons, challenges, and future research opportunities.

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F = ρ \sum_{tϵT} P ((), t) . normalΔ T - C - M_{P} .

…”

Section: Mh Methods For Pv Array Reconfigurationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Imperative role of optimization techniques to reconfigure solar photovoltaic array systems to diminish shading effects

Pachauri

2021

Circuit Theory & Apps

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“…Firstly, OAR should satisfy the constraints of electrical switching states since each PV array can only exchange its row with another array from the same column, which can be described as follows (Zhang et al, 2021):…”

Section: Constraintsmentioning

confidence: 99%

“…In this work, a 30-MW PV power plant (Zhang et al, 2021) with 30 identical sub-systems is introduced to evaluate the performance of the proposed method, in which each subsystem is formed by the 10 × 10 TCT PV arrays. The specific parameters of the testing system can be found in Zhang et al (2021). The operating temperature for all the PV arrays is set to be 25°C, while the irradiation distribution for each sub-system at different minutes are given in Figure 4.…”

Section: Case Studiesmentioning

confidence: 99%

Optimal Array Reconfiguration of a PV Power Plant for Frequency Regulation of Power Systems

He¹,

Li²,

Wu³

et al. 2021

Front. Energy Res.

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This paper establishes a novel optimal array reconfiguration (OAR) of a PV power plant for secondary frequency control of automatic generation control (AGC). Compared with the existing studies, the proposed OAR can further take the AGC signal responding into account except the maximum power output, in which the battery energy storage system is used to balance the power deviation between the AGC signals and the PV power outputs. Based on these two conflicted objects, the OAR is formulated as a bi-objective optimization. To address this problem, the efficient non-dominated sorting genetic algorithm II (NSGA-II) is designed to rapidly obtain an optimal Pareto front due to its high optimization efficiency. The decision-making method called VIKOR is employed to determine the best compromise solution from the obtained Pareto front. To verify the effectiveness of the proposed bi-objective optimization of OAR, three case studies with fixed, step-increasing, and step-decreasing AGC signals are carried out on a 10 × 10 total-cross-tied PV arrays under partial shading conditions.

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“…Further, nowadays some artificial intelligence algorithm based schemes are proposed, for example, fuzzy algorithm in [28,29], neural networks algorithm in [30,31]. Lately, a kind of reinforcement learning algorithm is also proposed to be used in PV array reconfiguration process in [32]. However, in most of the artificial intelligence algorithm based array reconfiguration schemes, to ensure the accuracy, experts' knowledge or large amounts of kinds of data are required, which is difficult to obtain in reality.…”

Section: Introductionmentioning

confidence: 99%

Followed The Regularized Leader (FTRL) prediction model based photovoltaic array reconfiguration for mitigation of mismatch losses in partial shading condition

Gao

Deng

Zheng

et al. 2021

IET Renewable Power Gen

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Non-uniform irradiance, for example, partial shading, would affect the performance of the whole Photovoltaic (PV) array and reduce the output power. Since the layout of PV array is also an important factor influencing the performance of PV array, reconfiguration scheme has been researched popularly. Since plenty of array reconfiguration techniques are not realistic to be implemented in reality, in this paper considering the simplicity, robustness and economical aspects, a Followed The Regularized Leader algorithm (FTRL) regression prediction model based switches controlled PV array reconfiguration method is proposed to enhance the power output from solar PV array and alleviate the effects of partial shadings. The proposed scheme has no additional labor and wire requirement, since the proposed connection architecture of PV array involves no complex switch matrix design. Subsequently, to testify the performance of the proposed scheme, simulations with various shades in different PV arrays are made and evaluation parameters, for example, Fill Factor (FF) and mismatch loss are calculated. Furthermore, outdoor experiments are conducted to validate the simulation and the analyses. Besides, extensive analyses of cost analysis and energy savings are given. At last, a comprehensive evaluation is performed to check the quality of the proposed scheme by comparing with various techniques in literature.

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Optimal mileage-based PV array reconfiguration using swarm reinforcement learning

Cited by 57 publications

References 53 publications

Imperative role of optimization techniques to reconfigure solar photovoltaic array systems to diminish shading effects

Imperative role of optimization techniques to reconfigure solar photovoltaic array systems to diminish shading effects

Optimal Array Reconfiguration of a PV Power Plant for Frequency Regulation of Power Systems

Followed The Regularized Leader (FTRL) prediction model based photovoltaic array reconfiguration for mitigation of mismatch losses in partial shading condition

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