New Intelligent Fault Diagnosis (IFD) approach for grid-connected photovoltaic systems

Khelil, Chérifa Kara Mostefa; Amrouche, Badia; Benyoucef, Abou Soufiane; Kara, Kamel; Chouder, Aissa

doi:10.1016/j.energy.2020.118591

Cited by 25 publications

(11 citation statements)

References 31 publications

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“…• In presence of shading case the accuracy decreases to 96.2% [18] • Two ANNs are used in conjunction with a combinational logic block. • Very careless misclassification voltage.…”

Section: • Four Different Methodologies Ofmentioning

confidence: 99%

“…In the precedent works cited in [18,20], an intelligent algorithm based on ANN was developed and used with success in the diagnosis of a PV installation, of which the components and characteristics of the small grid connected PV plan are summarized in Table 2 To make sure the quality, efficiency and reliability of grid connected PV system, fault detection and identification take a significant place which has been developed and adopted through the proposed approach. This approach is based on a model submitted under the same installation conditions and it is divided in four important steps describes in Figure 1: • The first step takes in consideration the outputs of both real installation and simulated bloc, the difference will be exploit in order to compare the cost criteria and detect the fault.…”

Section: Methodsmentioning

confidence: 99%

“…Thus, the competencies of frequent maintenance, detection as well as diagnosis of faults get to be increasingly significant in the goal to warrant an excellent power production, efficiency, reliability, quality and safety in global PV systems. Where real time fault identification and localization have been a crucial interest by a large number of international scientists proved and discussed in different articles [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. The Table 1 below illustrates all these references talking about Artificial Intelligence in fault detection and diagnosis of PV systems with their advantages and limitations.…”

Section: Introductionmentioning

confidence: 99%

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Fault detection and diagnosis of GCPV systems using bayesian neural network

Khelil

Amrouche

Kara

2022

J. Phys.: Conf. Ser.

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At present, artificial neural networks are massively and successfully used for faults diagnosis of grid connected PV (GCPV) systems thanks to their capabilities of learning and generalization. In fact, the use of ANN permits the development of intelligent algorithms that can learn all the PV systems working status and then be able to detect and identify possible malfunctions status and causes independently of the ANNs type. However each type of ANN owns its proper characteristics from point of view of its accuracy, efficiency, reliability as well as its response of time in term of faults detection and diagnosis compared to others. In this article, fault detection and diagnosis of small GCPV array based on experimental setup using Bayesian Neural Network is presented. This type of Artificial Neural Network can secure the identification and localization of the greatest recurring cases in PV generator in particular short-circuit, open-circuit and healthy cases. This study requires four input data: irradiation, cell temperature, current and voltage of maximum power point. Results show that, this study proved an excellent level of accuracy displaying (99.88%) in term of detection and localization of treated faults with global response time equals to 16 min22.

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“…• In presence of shading case the accuracy decreases to 96.2% [18] • Two ANNs are used in conjunction with a combinational logic block. • Very careless misclassification voltage.…”

Section: • Four Different Methodologies Ofmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fault detection and diagnosis of GCPV systems using bayesian neural network

Khelil

Amrouche

Kara

2022

J. Phys.: Conf. Ser.

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“…Then, different methods coming from cross-domain applications have been implemented for PV system fault diagnosis. Classification tools have already widely been tested to detect PV faults, such as Decision Tree [ 22 ], the Probabilistic Neural Network classifier [ 23 ], Random Forest [ 24 ], the Artificial Neural Network classifier [ 25 ], a One-class Support Vector Machine [ 26 ] and Machine Learning Based on Gaussian Process Regression [ 27 ]. Machine learning has been widely experienced these last years.…”

Section: Introductionmentioning

confidence: 99%

PV System Failures Diagnosis Based on Multiscale Dispersion Entropy

Lebreton

Kbidi

Graillet

et al. 2022

Entropy

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Photovoltaic (PV) system diagnosis is a growing research domain likewise solar energy’s ongoing significant expansion. Indeed, efficient Fault Detection and Diagnosis (FDD) tools are crucial to guarantee reliability, avoid premature aging and improve the profitability of PV plants. In this paper, an on-line diagnosis method using the PV plant electrical output is presented. This entirely signal-based method combines variational mode decomposition (VMD) and multiscale dispersion entropy (MDE) for the purpose of detecting and isolating faults in a real grid-connected PV plant. The present method seeks a low-cost design, an ease of implementation and a low computation cost. Taking into account the innovation of applying these techniques to PV FDD, the VMD and MDE procedures as well as parameters identification are carefully detailed. The proposed FFD approach performance is assessed on a real rooftop PV plant with experimentally induced faults, and the first results reveal the MDE approach has good suitability for PV plants diagnosis.

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“…Faults must be identified to avoid energy losses and fire hazards, which would prolong the lifetime of the PV system and hence can reduce the maintenance cost [4]. Various studies were conducted on fault detection which include thermal infrared method [17], [18], artificial intelligent technique [19], [20], time-domain reflectometry method [21], [22], and mathematical model approach [23], [24]. There was also research on the analysis of PV string failure and the PV systems' health monitoring [13], [25].…”

Section: Introductionmentioning

confidence: 99%

Acceptance Ratio Analysis: An Early Fault Indicator for Grid-Connected Photovoltaic System

Shukor

Zainuddin

Muhammad

et al. 2021

Int. J. Adv. Sci. Eng. Inf. Technol.

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This paper presents the Acceptance Ratio (AR) analysis for three different grid-connected photovoltaic (GCPV) systems working under the Malaysia tropical climate. AR is a ratio between actual AC power, PAC_actual, and predicted AC power, PAC_predicted. According to Malaysian Standard MS2692:2020, the AR value must ≥ 0.9 to classify as accepted in testing and commissioning test. In contrast, a rate < 0.9 indicates a non-accepted GCPV system. Historical data of the AC power output, solar irradiance, and module temperature from January 1 to December 31, 2019, were used for the analysis. The analysis procedure was carried out using Matlab and Microsoft Excel software. The analysis covers the AC power analysis and the AR analysis based on the threshold of 0.9. The plotted monthly AC power graph shows that all systems have lower than 15 % differences between actual and predicted AC power. On the AR analysis, System 1 was found to show early fault indicator with a monthly cumulative percentage of AR < 0.9 ranges from 34 % to 71 %, meanwhile System 2 and System 3 have a lower cumulative percentage of AR < 0.9 ranges from 5 % to 19 %. This result suggested that only System 2 and 3 are fault-free GCPV systems and working in good condition. The outcome of this study has succeeded in providing preliminary AR analysis results for three GCPV systems located in Malaysia. This study would help to evaluate AR threshold reliability to indicate an early fault of a GCPV system.

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New Intelligent Fault Diagnosis (IFD) approach for grid-connected photovoltaic systems

Cited by 25 publications

References 31 publications

Fault detection and diagnosis of GCPV systems using bayesian neural network

Fault detection and diagnosis of GCPV systems using bayesian neural network

PV System Failures Diagnosis Based on Multiscale Dispersion Entropy

Acceptance Ratio Analysis: An Early Fault Indicator for Grid-Connected Photovoltaic System

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