Application of Artificial Neural Networks to photovoltaic fault detection and diagnosis: A review

Li, Baojie; Delpha, Claude; Diallo, Demba; Migan‐Dubois, Anne

doi:10.1016/j.rser.2020.110512

Cited by 145 publications

(47 citation statements)

References 112 publications

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“…As the core part of the drive system, the power MOSFET is more prone to failure due to its frequent on-off movements and the influence of thermal and electrical overstress. If the failure of the power MOS-FET cannot be detected effectively, it will get an adverse effect on the motor drive system and result in immeasurable economic losses [4,16,20].…”

Section: Introductionmentioning

confidence: 99%

Adaptive feature extraction and fault diagnosis for three-phase inverter based on hybrid-CNN models under variable operating conditions

Sun

et al. 2021

Complex Intell. Syst.

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The increasing reliability and availability requirements of power electronic systems have drawn great concern in many industrial applications. Aiming at the difficulty in fault characteristics extraction and fault modes classification of the three-phase full-bridge inverter (TFI) that used as the drive module of brushless DC motor (BLDCM). A hybrid convolutional neural network (HCNN) model consists of one-dimensional CNN (1D-CNN) and two-dimensional CNN (2D-CNN) is proposed in this paper, which can tap more effective spatial feature for TFI fault diagnosis. The frequency spectrum from the three-phase current signal preprocess are applied as the input for 1D-CNN and 2D-CNN to conduct feature extraction, respectively. Then, the feature layers information are combined in the fully connected layer of HCNN. Finally, the performance status of TFI could be identified by the softmax classifier with Adam optimizer. Several groups of experiments have been studied when the BLDCM under different operating conditions. The results show that the fusion features can get a higher degree of discrimination so as to the presented network model also obtains better classification accuracy, which verify the feasibility and superiority to the other networks.

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Section: Introductionmentioning

confidence: 99%

Adaptive feature extraction and fault diagnosis for three-phase inverter based on hybrid-CNN models under variable operating conditions

Sun

et al. 2021

Complex Intell. Syst.

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“…IRT is the most commonly accepted technique for categorising light poles [23], centred on image processing techniques to distinguish between healthy and defective panels of all image processing-based approaches. Various patterns, challenges, and opportunities for the implementation of ANN light poles are highlighted [24]. Random forests were the most reliable among the various forecasting techniques used by the site and regional forecasters [25].…”

Section: Introductionmentioning

confidence: 99%

Short-Term Power Prediction of Building Integrated Photovoltaic (BIPV) System Based on Machine Learning Algorithms

Kabilan¹,

Venkatesan

Yogapriya³

et al. 2021

International Journal of Photoenergy

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One of the biggest challenges is towards ensuring large-scale integration of photovoltaic systems into buildings. This work is aimed at presenting a building integrated photovoltaic system power prediction concerning the building’s various orientations based on the machine learning data science tools. The proposed prediction methodology comprises a data quality stage, machine learning algorithm, weather clustering assessment, and an accuracy assessment. The results showed that the application of linear regression coefficients to the forecast outputs of the developed photovoltaic power generation neural network improved the PV power generation’s forecast output. The final model resulted from accurate forecasts, exhibiting a root mean square error of 4.42% in NN, 16.86% in QSVM, and 8.76% in TREE. The results are presented with the building facade and roof application such as flat roof, south façade, east façade, and west façade.

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“…Solar irradiation is a vital variable that facilitates the solar energy process. The prediction of faults in solar panels necessitates some suspicions depending on environmental parameters such as temperature, cloud amount, dust, irradiance level, and relative humidity [1]. Solar panel faults are not only the reason for the less efficient and frequent services of the plant but also could culminate into abnormal contexts.…”

Section: Introductionmentioning

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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Application of Artificial Neural Networks to photovoltaic fault detection and diagnosis: A review

Cited by 145 publications

References 112 publications

Adaptive feature extraction and fault diagnosis for three-phase inverter based on hybrid-CNN models under variable operating conditions

Adaptive feature extraction and fault diagnosis for three-phase inverter based on hybrid-CNN models under variable operating conditions

Short-Term Power Prediction of Building Integrated Photovoltaic (BIPV) System Based on Machine Learning Algorithms

An Effective Evaluation on Fault Detection in Solar Panels

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