Multiclass adaptive neuro-fuzzy classifier and feature selection techniques for photovoltaic array fault detection and classification

Belaout, Abdesslam; Krim, Fateh; Mellit, A.; Talbi, Billel; Arabi, Abderrazak

doi:10.1016/j.renene.2018.05.008

Cited by 94 publications

(47 citation statements)

References 18 publications

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“…In this section Wavelet-Based Fault Detection technique [96][97][98][99] is proposed which aims at finding the optimum combination of mother wavelets and the number of wavelet decomposition levels that help extracting the most important attributes from the signal, which are needed for fault diagnosis in a PV module. Figure 5 shows the hierarchical structure of the selection of wavelet transform parameters in which three main parameters need to be determined.…”

Section: I-v Characteristics-based Fault Detectionsmentioning

confidence: 99%

Fault diagnosis of Photovoltaic Modules

Haque

Bharath

Khan

et al. 2019

Energy Science & Engineering

120

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Fault diagnosis and condition monitoring are important to increase the efficiency and reliability of photovoltaic modules. This paper reviews the challenges and limitations associated with fault diagnosis of solar modules. A thorough analysis of various faults responsible for failure of solar modules has been discussed. After reviewing relevant work, a monitoring tool is designed using thermography and artificial intelligent systems that allows the detection of various types of faults in PV modules and at the same time the designed tool aims to filter the nonsignificant anomalies. A neural network (NN) classifier is applied to the transfer characteristics (I‐V data) of the faulty PV module for the diagnosis which adapts multilayer perceptron (MLP) networks to identify the type and location of occurring faults. The Discrete wavelet transform (DWT) based signal processing technique is utilized in the feature extraction process to reduce the NN input size. The developed detection algorithm is adapted for 24/7 automated surveillance. For a given fault condition, the average fault detection time is observed to be <9 seconds, which is lower than the previous work done. The developed algorithm achieved 100% accuracy when tested on a predetermined fault data set.

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Section: I-v Characteristics-based Fault Detectionsmentioning

confidence: 99%

Fault diagnosis of Photovoltaic Modules

Haque

Bharath

Khan

et al. 2019

Energy Science & Engineering

120

View full text Add to dashboard Cite

show abstract

“…As the number of the membership functions increases, it results in a higher prediction cost. When the prediction accuracy plays a crucial role, the ANFIS model with a pi-5-membership function or a gauss-5-membership function could be recommended to predict the current, power and thermal efficiency of the thermoelectric generator system for waste heat recovery [40]. The prediction accuracy of the ANFIS model for the current, power and thermal efficiency is shown in Table 4a for pi membership function and Table 4b for gauss membership function, respectively.…”

Section: Prediction Results From Anfis Modelsmentioning

confidence: 99%

Artificial Neural Network and Adaptive Neuro-Fuzzy Interface System Modelling to Predict Thermal Performances of Thermoelectric Generator for Waste Heat Recovery

et al. 2020

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The present study elaborates the suitability of the artificial neural network (ANN) and adaptive neuro-fuzzy interface system (ANFIS) to predict the thermal performances of the thermoelectric generator system for waste heat recovery. Six ANN models and seven ANFIS models are formulated by considering hot gas temperatures and voltage load conditions as the inputs to predict current, power, and thermal efficiency of the thermoelectric generator system for waste heat recovery. The ANN model with the back-propagation algorithm, the Levenberg–Marquardt variant, Tan-Sigmoidal transfer function and 25 number of hidden neurons is found to be an optimum model to accurately predict current, power and thermal efficiency. For current, power and thermal efficiency, the ANFIS model with pi-5 or gauss-5-membership function is recommended as the optimum model when the prediction accuracy is important while the ANFIS model with gbell-3-membership function is suggested as the optimum model when the prediction cost plays a crucial role along with the prediction accuracy. The proposed optimal ANN and ANFIS models present higher prediction accuracy than the coupled numerical approach.

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“…Therefore, it has the advantage of a learning ability of neural networks and a logical ability of fuzzy logic. In [100], a multiclass adaptive neurofuzzy classifier (MC-NFC) has been developed for PV array fault detection and classification. An experiment data was collected for the training and validation of the model.…”

Section: Other Techniques (Ots)mentioning

confidence: 99%

Review and Performance Evaluation of Photovoltaic Array Fault Detection and Diagnosis Techniques

Appiah

Zhang

Ayawli

et al. 2019

International Journal of Photoenergy

101

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The environmentally clean nature of solar photovoltaic (PV) technology causes PV power generation to be embraced by all countries across the globe. Consequently, installation and utilization of PV power systems have seen much growth in recent years. Although PV arrays of such systems are robust, they are not immune to faults. To guarantee reliable power supply, economic returns, and safety of both humans and equipment, highly accurate fault detection, diagnosis, and interruption devices are required. In this paper, an overview of four major PV array faults and their causes are presented. Specifically, ground fault, line-line fault, arc fault, and hot spot fault have been covered. Next, conventional and advanced fault detection and diagnosis (FDD) techniques for managing these faults are reviewed. Moreover, a single evaluation metric has been proposed and utilized to evaluate the performances of the advanced FDD techniques. Finally, based on the papers reviewed, PV array fault management future trends and possible recommendations have been outlined.

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Multiclass adaptive neuro-fuzzy classifier and feature selection techniques for photovoltaic array fault detection and classification

Cited by 94 publications

References 18 publications

Fault diagnosis of Photovoltaic Modules

Fault diagnosis of Photovoltaic Modules

Artificial Neural Network and Adaptive Neuro-Fuzzy Interface System Modelling to Predict Thermal Performances of Thermoelectric Generator for Waste Heat Recovery

Review and Performance Evaluation of Photovoltaic Array Fault Detection and Diagnosis Techniques

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