Photovoltaic Failure Diagnosis Using Sequential Probabilistic Neural Network Model

Zhu, Hongguang; Zaki, Sayed A.; Alfakih, Mohammed Ahmed; Abdelbaky, Mohamed Abdelkarim; Sayed, Ahmed Rabee; Saif, Mubaarak Abdulrahman Abdu

doi:10.1109/access.2020.3043129

Cited by 8 publications

(8 citation statements)

References 26 publications

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“…This is an interesting solution in the fault detection domain, where the authors introduced a recursive linear model to detect faults in the system, primarily through the use of irradiance on the PV panel as the input signals and power as the output. Similarly, the work published in [ 33 ], utilized a probabilistic framework to classify various faults and, thereby, yielded a good accuracy of 94.69%. This model is also a novel contribution to the fault detection domain and achieved encouraging results by employing several sequential steps.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Similarly, the work published in [ 33 ], utilized a probabilistic framework to classify various faults and, thereby, yielded a good accuracy of 94.69%. This model is also a novel contribution to the fault detection domain and achieved encouraging results by employing several sequential steps.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…In [ 32 ], the authors also used the probabilistic method to detect the OC, the SC, the GF, and the hotspots. In [ 33 ], the developed model was designed for two different cases of faults, which were (i) single fault occurrence, which included the OC, the LL, and the shading, and (ii) multi-fault occurrence at the same time. The model was tested on a 1.22 kW PV array at the university of NCEPU, China.…”

Section: Related Workmentioning

confidence: 99%

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A Machine-Learning-Based Robust Classification Method for PV Panel Faults

Memon

Javed

Kim

et al. 2022

Sensors

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Renewable energy resources have gained considerable attention in recent years due to their efficiency and economic benefits. Their proportion of total energy use continues to grow over time. Photovoltaic (PV) cell and wind energy generation are the least-expensive new energy sources in most countries. Renewable energy technologies significantly contribute to climate mitigation and provide economic benefits. Apart from these advantages, renewable energy sources, particularly solar energy, have drawbacks, for instance restricted energy supply, reliance on weather conditions, and being affected by several kinds of faults, which cause a high power loss. Usually, the local PV plants are small in size, and it is easy to trace any fault and defect; however, there are many PV cells in the grid-connected PV system where it is difficult to find a fault. Keeping in view the aforedescribed facts, this paper presents an intelligent model to detect faults in the PV panels. The proposed model utilizes the Convolutional Neural Network (CNN), which is trained on historic data. The dataset was preprocessed before being fed to the CNN. The dataset contained different parameters, such as current, voltage, temperature, and irradiance, for five different classes. The simulation results showed that the proposed CNN model achieved a training accuracy of 97.64% and a testing accuracy of 95.20%, which are much better than the previous research performed on this dataset.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Simulation Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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A Machine-Learning-Based Robust Classification Method for PV Panel Faults

Memon

Javed

Kim

et al. 2022

Sensors

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“…Recently, different artificial intelligence (AI) techniques were incorporated as the core methodologies of PV fault detection and classification due to their excellent capabilities in addressing feature extraction and classification problems. Some notable AI approaches such as the convolutional neural network [6] and sequential probabilistic neural network [7] were applied to improve the accuracy of fault classification in a PV system. Apart from fault classification, some supervised machine learning techniques were also applied to focus on the feature extraction process in [8], whereas an adaptive neurofuzzy inference system (ANFIS) was used in [9] to address the tracking and detection of faulty issues in PV.…”

Section: Introductionmentioning

confidence: 99%

“…Apart from fault classification, some supervised machine learning techniques were also applied to focus on the feature extraction process in [8], whereas an adaptive neurofuzzy inference system (ANFIS) was used in [9] to address the tracking and detection of faulty issues in PV. For this reason, many computational fault detection models based on machine learning algorithms were proposed [7,8,[10][11][12]. Substantial amounts of literature studies revealed that the availability of labeled fault data is one of the key factors that enable these intelligence-based algorithms to perform effective fault diagnosis for PV systems [12].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Deep Learning Model for Fault Detection and Classification of Grid-Connected Photovoltaic System

et al. 2022

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Effective fault detection and classification play essential roles in reducing the hazards such as electric shocks and fire in photovoltaic (PV) systems. However, the issues of interest in fault detection and classification for PV systems remain an open-ended challenge due to manual and time-consuming processes that require the relevant domain knowledge and experience of fault diagnoses. This paper proposes a hybrid deep-learning (DL) model-based combined architectures as the novel DL approaches to achieve the real-time automatic fault detection and classification of a PV system. This research employed the wavelet packet transform (WPT) as a data preprocessing technique to handle the PV voltage signal collected and feeding them as the inputs for combined DL architectures that consist of the equilibrium optimizer algorithm (EOA) and long short-term memory (LSTM-SAE) approaches. The combined DL architectures are able to extract the fault features automatically from the preprocessed data without requiring any previous knowledge, therefore can override the traditional shortages of manual feature extraction and manual selection of optimal features from the extracted fault features. These desirable features are anticipated to speed up the fault detection and classification capability of the proposed DL model with higher accuracy. In order to determine the performance of the proposed fault model, we carried out a comprehensive evaluation study on a 250-kW grid-connected PV system. In this paper, symmetrical and asymmetrical faults have been studied involving all the phases and ground faults such as single phase to ground, phases to phase, phase to phase to ground, and three-phase to ground. The simulation results validate the efficacy of the proposed model in terms of computation time, accuracy of fault detection, and noise robustness. Comprehensive comparisons between the simulation results and previous studies demonstrate the multidisciplinary applications of the present study.INDEX TERMS Deep distributed energy, equilibrium optimizer algorithm (EOA), fault detection and classification, grid-connected photovoltaic systems, optimal feature selection, wavelet packet transform (WPT).

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