Neuro-fuzzy classifier for fault detection and classification in photovoltaic module

Belaout, Abdesslam; Krim, Fateh; Mellit, A.

doi:10.1109/icmic.2016.7804289

Cited by 20 publications

(6 citation statements)

References 29 publications

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“…However, the proposed technique is not able to localize faults. In [22], the authors discussed the application of the KNN algorithm for detection and classification of partial shading, short-circuit, open-circuit, and line-to-line faults for a PV system of size 3 × 3. A comparative analysis of ANN and FL implemented on a 1.1 kW PV installation was presented in [23].…”

Section: Machine-learning-based Fault Detectionmentioning

confidence: 99%

Unified Fuzzy Logic Based Approach for Detection and Classification of PV Faults Using I-V Trend Line

et al. 2022

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Solar photovoltaic PV plants worldwide are continuously monitored and carefully protected to ensure safe and reliable operation through detecting and isolating faults. Faults are very common in modern solar PV systems which interrupt normal system operation adversely affecting the performance of the PV systems. When undetected, faults not only cause significant reduction in the efficiency and life span of the PV system, but also result in damage and fire hazards compromising their reliability. Therefore, early fault detection and diagnosis of photovoltaic plants is a necessity for safe and reliable operation required for growing solar PV systems. Unfortunately, several recent fire incidents have been reported recently caused by undetected faults in solar PV systems. Motivated by this challenge, this paper, utilizing a proposed fuzzy logic algorithm, presents a novel technique for detecting and classifying faults in solar PV systems. Furthermore, the proposed method introduces fault indexing as a performance indicator that measures the degree of deviation from the normal operating conditions of the photovoltaic system. Various signatures of each fault scenario are identified in the shape of corresponding current-voltage trajectories and their extracted parameters. The effectiveness of the proposed technique is evaluated both in simulation and experimentally using a 5 kW grid connected solar array. It is demonstrated that the proposed technique is capable of diagnosing the occurrence of different faults with more than 98% accuracy.

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Section: Machine-learning-based Fault Detectionmentioning

confidence: 99%

Unified Fuzzy Logic Based Approach for Detection and Classification of PV Faults Using I-V Trend Line

et al. 2022

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“…Further examples of the industrial applications of fuzzy systems can be found in a study by Bagua et al [22], where type-1 and type-2 fuzzy systems were used to monitor a gas turbine process or in a study by Angiulli et al [23], who evaluated the resonant frequency of microstrip antennas. Likewise, the module faults in photovoltaic modules were characterized by Belaout et al [24] and two ANFIS were used to detect photovoltaic system faults by Bendary et al [25]. Finally, Chang et al [26] studied laser module temperature control; many other research studies can be found in the literature in this field.…”

Section: Literature Reviewmentioning

confidence: 99%

Application of a Fuzzy Inference System for Optimization of an Amplifier Design

Dieste-Velasco

2021

Mathematics

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Simulation programs are widely used in the design of analog electronic circuits to analyze their behavior and to predict the response of a circuit to variations in the circuit components. A fuzzy inference system (FIS) in combination with these simulation tools can be applied to identify both the main and interaction effects of circuit parameters on the response variables, which can help to optimize them. This paper describes an application of fuzzy inference systems to modeling the behavior of analog electronic circuits for further optimization. First, a Monte Carlo analysis, generated from the tolerances of the circuit components, is performed. Once the Monte Carlo results are obtained for each of the response variables, the fuzzy inference systems are generated and then optimized using a particle swarm optimization (PSO) algorithm. These fuzzy inference systems are used to determine the influence of the circuit components on the response variables and to select them to optimize the amplifier design. The methodology proposed in this study can be used as the basis for optimizing the design of similar analog electronic circuits.

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“…Bypass diodes are connected in reverse bias between a solar cell (or panel) positive and negative output terminals and has no effect on its output. Ideally there would be one bypass diode for each solar cell, but this can be rather expensive so generally one diode is used per small group of series cells [34]- [77].…”

Section: H Bypass Diode Fault Detection (Bfd)mentioning

confidence: 99%

Comparative Analysis of Photovoltaic Faults and Performance Evaluation of its Detection Techniques

et al. 2020

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Faults detection and analysis in PV system are considered critical for ensuring safety and increasing output power of PV arrays. PV faults do not only reduce output power and efficiency but also lessen the working life time of a system. Most common and chronic PV faults are line to line, line to ground, shadowing fault, and arc fault while less common and acute faults are hotspot, degradation, bypass diode, and connection faults. Event of PV fault detection failures, such as most recent in Mount Holly, USA in 2011 evinced the improvement in current fault detection and mitigation techniques to shrink such failures. There are various limitations in the existing fault detection techniques, as identified in this paper, which may cause misdetection of the faults. This paper is focused on mathematical formulation of various PV faults and lead to the latter's critical analysis in terms of efficiency, accuracy, complexity, and reliability. The presented work also helps to identify nature and causes of occurrence of a PV fault. This research work serves as a special set of references and recommendations for researchers and PV manufacturing industry to improve fault detection prospects in solar PV systems.

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Neuro-fuzzy classifier for fault detection and classification in photovoltaic module

Cited by 20 publications

References 29 publications

Unified Fuzzy Logic Based Approach for Detection and Classification of PV Faults Using I-V Trend Line

Unified Fuzzy Logic Based Approach for Detection and Classification of PV Faults Using I-V Trend Line

Application of a Fuzzy Inference System for Optimization of an Amplifier Design

Comparative Analysis of Photovoltaic Faults and Performance Evaluation of its Detection Techniques

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