Fault detection and diagnosis in grid-connected PV systems under irradiance variations

Hajji, Mansour; Yahyaoui, Zahra; Mansouri, Majdi; Nounou, Hazem; Nounou, Mohamed

doi:10.1016/j.egyr.2023.03.033

Cited by 20 publications

(2 citation statements)

References 29 publications

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“…Fault detection accuracies ranging from 83 % up to 100 % [ 3 , 26 , 83 , [101] , [102] , [103] ] were reported in the literature when using electrical data analysis methods for fault detection. The classification accuracy was reported to be between 81.70 % and 100 % [ 3 , [19] , [20] , [21] , 43 , 85 , 99 , 100 , 104 ], when using electrical data characterisation methods for the diagnosis of open- and short-circuit failures, module mismatches, partial shading conditions, bypass diode failures, soiling and degradation. With regards, to imaging techniques, the detection and classification accuracies ranged from 83 % up to 99.91 % [ 25 , 26 , [105] , [106] , [107] ].…”

Section: Failure Modes In Pv Systems and Existing Approachesmentioning

confidence: 99%

A comprehensive review of unmanned aerial vehicle-based approaches to support photovoltaic plant diagnosis

Michail,

Livera,

Tziolis

et al. 2024

Heliyon

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Section: Failure Modes In Pv Systems and Existing Approachesmentioning

confidence: 99%

A comprehensive review of unmanned aerial vehicle-based approaches to support photovoltaic plant diagnosis

Michail,

Livera,

Tziolis

et al. 2024

Heliyon

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“…The MLP's design generally comprises three levels: input, hidden, and output layers, as depicted in Figure 3. Therefore, gradient descent or conjugate gradient techniques are frequently used to back-propagate errors between targets, or desired values, and network outputs when training MLP [42,43]. Each layer is built of a set of nodes and weights that connect them.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Enhanced Neural Network Method-Based Multiscale PCA for Fault Diagnosis: Application to Grid-Connected PV Systems

Attouri

Mansouri

Hajji

et al. 2023

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In this work, an effective Fault Detection and Diagnosis (FDD) strategy designed to increase the performance and accuracy of fault diagnosis in grid-connected photovoltaic (GCPV) systems is developed. The evolved approach is threefold: first, a pre-processing of the training dataset is applied using a multiscale scheme that decomposes the data at multiple scales using high-pass/low-pass filters to separate the noise from the informative attributes and prevent the stochastic samples. Second, a principal component analysis (PCA) technique is applied to the newly obtained data to select, extract, and preserve only the more relevant, informative, and uncorrelated attributes; and finally, to distinguish between the diverse conditions, the extracted attributes are utilized to train the NNs classifiers. In this study, an effort is made to take into consideration all potential and frequent faults that might occur in PV systems. Thus, twenty-one faulty scenarios (line-to-line, line-to-ground, connectivity faults, and faults that can affect the normal operation of the bay-pass diodes) have been introduced and treated at different levels and locations; each scenario comprises various and diverse conditions, including the occurrence of simple faults in the PV1 array, simple faults in the PV2 array, multiple faults in PV1, multiple faults in PV2, and mixed faults in both PV arrays, in order to ensure a complete and global analysis, thereby reducing the loss of generated energy and maintaining the reliability and efficiency of such systems. The obtained outcomes demonstrate that the proposed approach not only achieves good accuracies but also reduces runtimes during the diagnosis process by avoiding noisy and stochastic data, thereby removing irrelevant and correlated samples from the original dataset.

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Optimal detection and classification of grid connected system using MSVM-FSO technique

Stallon,

Anand,

Kannan

et al. 2024

Environ Sci Pollut Res

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Fault detection and diagnosis in grid-connected PV systems under irradiance variations

Cited by 20 publications

References 29 publications

A comprehensive review of unmanned aerial vehicle-based approaches to support photovoltaic plant diagnosis

A comprehensive review of unmanned aerial vehicle-based approaches to support photovoltaic plant diagnosis

Enhanced Neural Network Method-Based Multiscale PCA for Fault Diagnosis: Application to Grid-Connected PV Systems

Optimal detection and classification of grid connected system using MSVM-FSO technique

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