PV Module Fault Detection Using Combined Artificial Neural Network and Sugeno Fuzzy Logic

Vieira, Romênia Gurgel; Dhimish, Mahmoud; Araújo, Fábio Meneghetti Ugulino de; Guerra, Maria Izabel da Silva

doi:10.3390/electronics9122150

Cited by 14 publications

(4 citation statements)

References 18 publications

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“…Using intelligent MPPT algorithms can be a double-edged sword of course, it increases the system efficiency, but it also masks different symptoms of faults. Works that combine ANN [ 94 , 95 ] and even FL [ 96 ] are already beginning to be explored.…”

Section: Discussionmentioning

confidence: 99%

Artificial Neural Networks in MPPT Algorithms for Optimization of Photovoltaic Power Systems: A Review

et al. 2021

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The use of photovoltaic systems for clean electrical energy has increased. However, due to their low efficiency, researchers have looked for ways to increase their effectiveness and improve their efficiency. The Maximum Power Point Tracking (MPPT) inverters allow us to maximize the extraction of as much energy as possible from PV panels, and they require algorithms to extract the Maximum Power Point (MPP). Several intelligent algorithms show acceptable performance; however, few consider using Artificial Neural Networks (ANN). These have the advantage of giving a fast and accurate tracking of the MPP. The controller effectiveness depends on the algorithm used in the hidden layer and how well the neural network has been trained. Articles over the last six years were studied. A review of different papers, reports, and other documents using ANN for MPPT control is presented. The algorithms are based on ANN or in a hybrid combination with FL or a metaheuristic algorithm. ANN MPPT algorithms deliver an average performance of 98% in uniform conditions, exhibit a faster convergence speed, and have fewer oscillations around the MPP, according to this research.

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Section: Discussionmentioning

confidence: 99%

Artificial Neural Networks in MPPT Algorithms for Optimization of Photovoltaic Power Systems: A Review

et al. 2021

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“…The green blocks are input variables, the yellow blocks are output variables, the orange blocks are PV module parameters, and the blue blocks are masks containing equations developed and discussed previously. Furthermore, a first-order filter was employed as a feedback transfer function to avoid a loop error [37]. T C is the filter time constant, and the T C value is equal to the sample time.…”

Section: Pv Module Modelingmentioning

confidence: 99%

Assessing Maximum Power Point Tracking Intelligent Techniques on a PV System with a Buck–Boost Converter

et al. 2021

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Classic and intelligent techniques aim to locate and track the maximum power point of photovoltaic (PV) systems, such as perturb and observe (P&O), fuzzy logic (FL), artificial neural networks (ANNs), and adaptive neuro-fuzzy inference systems (ANFISs). This paper proposes and compares three intelligent algorithms for maximum power point tracking (MPPT) control, specifically fuzzy, ANN, and ANFIS. The modeling of a single-diode equivalent circuit-based 3 kWp PV plant was developed and validated to achieve this purpose. Then, the MPPT techniques were designed and applied to control the buck–boost converter’s switching device of the PV plant. All three methods use the ambient conditions as input variables: solar irradiance and ambient temperature. The proposed methodology comprises the study of the dynamic response for tracking the maximum power point and the power generated of the PV systems, and it was compared to the classic P&O technique under varying ambient conditions. We observed that the intelligent techniques outperformed the classic P&O method in tracking speed, tracking accuracy, and reducing oscillation around the maximum power point (MPP). The ANN technique was the better control algorithm in energy gain, managing to recover up to 9.9% power.

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“…The detection of system underperformance occurs when the predefined difference values are reached. Other studies have proposed alternative methods based on hardware redundancy [8], as well as the combination of standard statistical models with artificial intelligence techniques [35,36], specifically machine learning algorithms [37] and neural network algorithms [38]. The developed fault detection algorithms depend on the variations of the voltage and the power of the PV systems; thus, they are capable of detecting faulty PV modules and different conditions.…”

Section: Related Workmentioning

confidence: 99%

Identification of Degrading Effects in the Operation of Neighboring Photovoltaic Systems in Urban Environments

Radovanović

Popović

2021

Electronics

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As photovoltaics technologies have emerged as one of the most promising renewable energy resources in urban environments, monitoring and maintaining of such systems have gained significance. In order to support reliable system operation during the projected in-field operation lifetime, effective strategies for identifying potential problems in photovoltaic systems operation are needed. In this paper, novel methods for the identification of degrading effects in the operation of neighboring photovoltaic systems are presented. The proposed methods are applicable for identifying panel aging properties, soiling effects, and the operation of photovoltaic systems under different shading scenarios. Since the proposed methods are based on the cross-correlation of the operation of neighboring systems, they are particularly suitable performance assessment in urban environments. The proposed identification methods are integrated according to the adopted fog computing model, providing a scalable solution capable of uniform integration into the distributed applications for monitoring and maintenance of photovoltaic systems in urban areas. The details regarding the implementation of the identification methods in the form of data processing services and service operation and dependencies are also provided in this paper. The identification methods, integration concept, and related service operation are verified through the presented case study.

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PV Module Fault Detection Using Combined Artificial Neural Network and Sugeno Fuzzy Logic

Cited by 14 publications

References 18 publications

Artificial Neural Networks in MPPT Algorithms for Optimization of Photovoltaic Power Systems: A Review

Artificial Neural Networks in MPPT Algorithms for Optimization of Photovoltaic Power Systems: A Review

Assessing Maximum Power Point Tracking Intelligent Techniques on a PV System with a Buck–Boost Converter

Identification of Degrading Effects in the Operation of Neighboring Photovoltaic Systems in Urban Environments

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