Photovoltaic Fuzzy Logical Control MPPT Based on Adaptive Genetic Simulated Annealing Algorithm-Optimized BP Neural Network

Yan, Zhang; Wang, Yajun; Zhang, Yong; Tao, Yu

doi:10.3390/pr10071411

Cited by 9 publications

(4 citation statements)

References 28 publications

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“…In the NARX-BP-based prediction model, 70% of the data in the two working conditions were selected for neural network training, and the remaining 30% of the data were tested. In most studies without special requirements, for BP neural networks, 70% of the samples are selected as the training set, and 30% of the samples are used as the testing set [33,34]. This choice ensures both a sufficient training sample and the correctness of the test results.…”

Section: Experiments Using the 06 M Ctwtmentioning

confidence: 99%

Mach Number Prediction for 0.6 m and 2.4 m Continuous Transonic Wind Tunnels

2023

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With the development of the design technology, more and more advanced and diverse wind tunnels have been constructed to match complex requirements. However, it is hard to design a precise physical model of a wind tunnel that can be controlled. In addition, if a new wind tunnel is designed, the experimental data may be insufficient to build a controlling model. This article reports research on the following two models: (1) for a 0.6 m continuous transonic wind tunnel supported by a large amount of historical data, the false nearest neighbor (FNN) algorithm was adopted to calculate the order of the input variables, and the nonlinear auto-regressive model with the exogenous inputs–backpropagation network (NARX-BP) was proposed to build its Mach number prediction model; (2) for a new 2.4 m continuous transonic wind tunnel with only a small amount of experimental data, the method of model migration, the input and output slope/bias correction–particle swarm optimization (IOSBC-PSO) algorithm, was developed to convert the old model of the 0.6 m wind tunnel into the new model of the 2.4 m wind tunnel, so that the new Mach number prediction could be conducted. Through simulation experiments, it was found that by introducing the NARX-BP algorithm to build the Mach number prediction model, the root-mean-square error (RMSE) of the model decreased by 44.93–77.90%, and the maximum deviation (MD) decreased by 64.05–85.32% compared to the BP model. The performance of the IOSBC-PSO migration model was also better than that of the non-migration model, as evidenced by the 82.06% decrease of the RMSE value and the 78.25% decrease of the MD value. The experiments showed the effectiveness of the proposed strategy.

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Section: Experiments Using the 06 M Ctwtmentioning

confidence: 99%

Mach Number Prediction for 0.6 m and 2.4 m Continuous Transonic Wind Tunnels

2023

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“…However, since current ANN and FL methods have the subjective definition of their initializations, leading to the lack of systematicity and interpretability of the controlling process, e.g., the widely used BP network [40] or classic FL controller [34] use fixed transfer or membership functions. These methods, in general, have good accuracy but loss flexibility, and they may not meet the actual tracking requirements.…”

Section: Model Constructionmentioning

confidence: 99%

“…Specifically, based on the heuristic algorithms, Zhang Yan et al [40] provided a new network structure of BP networks to optimize MPP oscillation. Zhanghong et al [34] designed fuzzy rules to achieve good MPP control.…”

Section: Discussmentioning

confidence: 99%

W-IFL: An Improved Maximum Power Point Control Model to Promote Renewable-Powered Vehicles

2022

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Driven by economic development and environmental protection, vehicles are gradually renovating their power to renewable energy. As an essential part of renewable energy, photovoltaic (PV) energy is highly valued and studied worldwide. Future social development is inseparable from it when facing the current situation of exhausting fossil energy and highly polluting. To solve the problem of the low utilization of converting solar power to electrical energy, this paper proposes a wavelet-improved fuzzy logic (W-IFL) maximum power point (MPP) control model. The W-IFL designs a wavelet network for predicting the MPP and fuzzy rules for tracking the MPP, which achieves full online control on the basis of a neural-fuzzy structure. Comparative analysis indicates that W-IFL outperforms other widely used MPP tracking (MPPT) methods, which reduces oscillation at MPP, prediction error, and tracking time, and improves training efficiency and controlling ability, thus making it more rational to promote the development of the vehicle industry.

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“…Millah et al [23] proposed grey wolf optimization algorithm, which used weighted average value, pop-up behavior, and convergence factor to accelerate tracking speed. At the same time, researches on intelligent algorithms such as ant colony optimization algorithm [24], firefly optimization algorithm [25][26][27][28], and genetic algorithm [29][30][31] also further improve the efficiency of MPPT.…”

Section: Introductionmentioning

confidence: 99%

Research on the MPPT of Photovoltaic Power Generation Based on Improved WOA and P&O under Partial Shading Conditions

Zhong,

Zhang,

Qin

2024

ENERGY

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Partial shading conditions (PSCs) caused by uneven illumination become one of the most common problems in photovoltaic (PV) systems, which can make the PV power-voltage (P-V) characteristics curve show multipeaks. Traditional maximum power point tracking (MPPT) methods have shortcomings in tracking to the global maximum power point (GMPP), resulting in a dramatic decrease in output power. In order to solve the above problems, intelligent algorithms are used in MPPT. However, the existing intelligent algorithms have some disadvantages, such as slow convergence speed and large search oscillation. Therefore, an improved whale algorithm (IWOA) combined with the P&O (IWOA-P&O) is proposed for the MPPT of PV power generation in this paper. Firstly, IWOA is used to track the range interval of the GMPP, and then P&O is used to accurately find the MPP in that interval. Compared with other algorithms, simulation results show that this method has an average tracking efficiency of 99.79% and an average tracking time of 0.16 s when tracking GMPP. Finally, experimental verification is conducted, and the results show that the proposed algorithm has better MPPT performance compared to popular particle swarm optimization (PSO) and PSO-P&O algorithms.

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Photovoltaic Fuzzy Logical Control MPPT Based on Adaptive Genetic Simulated Annealing Algorithm-Optimized BP Neural Network

Cited by 9 publications

References 28 publications

Mach Number Prediction for 0.6 m and 2.4 m Continuous Transonic Wind Tunnels

Mach Number Prediction for 0.6 m and 2.4 m Continuous Transonic Wind Tunnels

W-IFL: An Improved Maximum Power Point Control Model to Promote Renewable-Powered Vehicles

Research on the MPPT of Photovoltaic Power Generation Based on Improved WOA and P&O under Partial Shading Conditions

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