A deep residual neural network identification method for uneven dust accumulation on photovoltaic (PV) panels

Fan, Siyuan; Wang, Yu; Cao, Shengxian; Zhao, Bo; Sun, Tianyi; Liu, Peng

doi:10.1016/j.energy.2021.122302

Cited by 81 publications

(27 citation statements)

References 35 publications

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“…Table 3 records the combined results of IHGS at 10D, 30D, 50D, and 100D based on the Friedman test and Wilcoxon singed-rank test with a significance level of 0.05. From the perspective of symbols of "+/À/=", among the 30 examples in lower dimensions (10D), the IHGS algorithm has 20,8,17,20,7,29,22,1,21,9,18,4,17,29 examples outperforming OBSCA, EB_LSHADE, SCADE, CBA, EAGDE, CESCA, AMFOA, EBOW, RCBA, JSO, HGWO, LSHADESPACMA, BMWOA, MSFOA, and there are 7, 21, 7, 5, 21, 0, 8, 28, 5, 18, 10, 19, 9, 1 examples weaker than them, while there are 3, 1, 6, 5, 2, 1, 0, 1, 4, 3, 2, 7, 4 examples with the same performance as them. As the problem's dimensionality increases, IHGS is more advantageous in handling high-dimensional problems.…”

Section: Detailed Results For the Ieee Cec 2017 Function Setmentioning

confidence: 99%

Quantum Nelder‐Mead Hunger Games Search for optimizing photovoltaic solar cells

Heidari

Kuang

et al. 2022

Intl J of Energy Research

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Summary The inherent multimodal and nonlinear characteristics of solar photovoltaic (PV) systems make it challenging to accurately extract PV parameters. Therefore, this study proposes an efficient and accurate optimization technique, improved Hunger Games Search, to identify unknown parameters in PV systems named IHGS. IHGS makes full use of the population information generated by the differential vectors while incorporating the quantum rotation gate strategy and the Nelder‐Mead simplex method (NMs) are fused to further increase the population diversity while rotating the corresponding positions of the search agents, thus efficiently exploring the neighborhood of the optimal solution in the decision space and improving the stability of the algorithm in developing the global optimal solution. The first part of the experiments is to simply verify the effectiveness and the stable merit‐seeking ability of the IHGS to handle complex functions by putting the proposed IHGS to test on the IEEE CEC 2017 benchmark test suite. The efficiency of IHGS is verified based on Wilcoxon singed‐rank test and Friedman tests. Next, we apply IHGS to three different PV module models (single‐diode, double‐diode, and PV module) for PV parameter extraction experiments to verify the efficiency and accuracy of IHGS in extracting PV parameters. The root mean square error produced by the IHGS proposed in this study is 9.8602E‐04, 9.8248E‐04, and 2.4251E‐03 for SDM, DDM, and PV modules, respectively. Examples show that IHGS outperforms HGS and other advanced algorithms in terms of convergence speed, data fitting, consistency, and stability. Finally, we perform the PV module parameter extraction problem on three commercial PV models (thin‐film ST40, monocrystalline SM55, and multicrystalline KC200GT). We observe that IHGS can still maintain high accuracy when dealing with commercial PV models under complex environments such as different irradiation and temperature. The results show that IHGS is a valuable optimization technique for solar PV parameter estimation based on multi‐model prediction.

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Section: Detailed Results For the Ieee Cec 2017 Function Setmentioning

confidence: 99%

Quantum Nelder‐Mead Hunger Games Search for optimizing photovoltaic solar cells

Heidari

Kuang

et al. 2022

Intl J of Energy Research

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“…References [21,22] may be utilized to acquire more information. The following notations should be considered: When a series is converging, the sign {η k } ∞ k=1 indicates whether it is converging strongly or weakly; when a sequence is converging, the symbol {η k } ∞ k=1 indicates whether it is converging strongly or weakly; and when a series is converging, the symbol {η k } ∞ k=1 indicates whether it is converging strongly or weakly.…”

Section: Important Concepts and Preliminariesmentioning

confidence: 99%

“…When utilizing closed convex simple sets, SEM restricted the performance of the metric projection to a subset of the set's members, which was not the case when the closed convex simple set was not itself a simple set, as was the case in the absence of such an assumption. A less challenging approach is to predict the intersection of a smaller number of non-empty, convex closed sets first, followed by the intersection of a larger number of such closed sets [20][21][22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Computational Analysis of Variational Inequalities Using Mean Extra-Gradient Approach

Cai

Liu

et al. 2022

Mathematics

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An improved variational inequality strategy for dealing with variational inequality in a Hilbert space is proposed in this article as an alternative; if Hilbert space is used as the domain of interest, the original extra-gradient method is proposed for resolving variational inequality. This improved variational inequality strategy can be used as a substitute for the original extra-gradient method in some situations. Mann’s mean value method, coupled with the widely used sub-gradient extra-gradient strategy, makes it possible to update all of the previous iterations in a single step, thus saving time and effort. All of this is made feasible via the use of Mann’s mean value technique in conjunction with the convex hull of all prior iterations of the algorithm. It is guaranteed that the mean value iteration will result in an acceptable resolution of a variational inequality issue as long as one or more of the criteria for the averaging matrix are fulfilled. Numerous experiments were performed in order to demonstrate the correctness of the theoretical conclusion obtained.

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“…Deep residual neural network [27] Mini-grid/large grid Online Acoustic wave method [28] Mini-grid/large grid Online Imaging technique [29] Mini-grid/large grid Offline Weather Inverse distance weighting [30] Home/mini-grid/large grid Online Data analytics [31] Home/mini-grid/large grid Online Theta-krill herd algorithm [32] Home/mini-grid/large grid Online Delamination Electric discharge channel [33] Mini-grid/large grid Online Thermal imaging [34] Mini/large grid Offline Aging test [35] Mini/large grid Offline Discoloration I-V characteristic analysis [36] Mini/large grid Offline Accelerated testing (AT) [37] Mini-grid/large grid Online Spectroscopic investigation [38] Mini-grid/large grid Offline…”

Section: Dust Accumulationmentioning

confidence: 99%

An Effective Evaluation on Fault Detection in Solar Panels

et al. 2021

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The world’s energy consumption is outpacing supply due to population growth and technological advancements. For future energy demands, it is critical to progress toward a dependable, cost-effective, and sustainable renewable energy source. Solar energy, along with all other alternative energy sources, is a potential renewable resource to manage these enduring challenges in the energy crisis. Solar power generation is expanding globally as a result of growing energy demands and depleting fossil fuel reserves, which are presently the primary sources of power generation. In the realm of solar power generation, photovoltaic (PV) panels are used to convert solar radiation into energy. They are subjected to the constantly changing state of the environment, resulting in a wide range of defects. These defects should be discovered and remedied as soon as possible so that PV panels efficiency, endurance, and durability are not compromised. This paper focuses on five aspects, namely, (i) the various possible faults that occur in PV panels, (ii) the online/remote supervision of PV panels, (iii) the role of machine learning techniques in the fault diagnosis of PV panels, (iv) the various sensors used for different fault detections in PV panels, and (v) the benefits of fault identification in PV panels. Based on the investigated studies, recommendations for future research directions are suggested.

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A deep residual neural network identification method for uneven dust accumulation on photovoltaic (PV) panels

Cited by 81 publications

References 35 publications

Quantum Nelder‐Mead Hunger Games Search for optimizing photovoltaic solar cells

Quantum Nelder‐Mead Hunger Games Search for optimizing photovoltaic solar cells

Computational Analysis of Variational Inequalities Using Mean Extra-Gradient Approach

An Effective Evaluation on Fault Detection in Solar Panels

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