1,366 research outputs found

    A New Method of PV Array Faults Diagnosis in Smart Grid

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    A new fault diagnosis method is proposed for PV arrays with SP connection in this study, the advantages of which are that it would minimize the number of sensors needed and that the accuracy and anti-interference ability are improved with the introduction of fuzzy group decision-making theory. We considered five “decision makers” contributing to the diagnosis of PV array faults, including voltage, current, environmental temperature, panel temperature, and solar illumination. The accuracy and reliability of the proposed method were verified experimentally, and the possible factors contributing to diagnosis deviation were analyzed, based on which solutions were suggested to reduce or eliminate errors in aspects of hardware and software

    Safety issues in PV systems: design choices for a secure fault detection and for preventing fire risk

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    Photovoltaic systems have played a key role over the last decade in the evolution of the electricity sector. In terms of safety design, it's important to consider that a PV plant constitutes a special system of generation, where the Direct Current (DC) presence results in changes to the technical rules. Moreover, if certain electrical faults occur, the plant is a possible source of fire. Choices regarding the grounding of the generator and its protection devices are fundamental for a design that evaluates fire risk. The subject of the article is the analysis of the relation between electrical phenomena in PV systems and the fire risk related to ensuring appropriate fault detection by the electrical protection system. A description of a grid-connected PV system is followed firstly by a comparison of the design solutions provided by international Standards, and secondly by an analysis of electrical phenomena which may trigger a fire. A study of two existing PV systems, where electrical faults have resulted in fires, is then presented. The study highlights the importance of checking all possible failure modes in a PV system design phase, to assess fire risk in advance. Some guidelines for the mitigation of electrical faults that may result in a fire are finally provided

    A novel fault diagnosis technique for photovoltaic systems based on artificial neural networks

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    This work proposes a novel fault diagnostic technique for photovoltaic systems based on Artificial Neural Networks (ANN). For a given set of working conditions - solar irradiance and photovoltaic (PV) module's temperature - a number of attributes such as current, voltage, and number of peaks in the current-voltage (I-V) characteristics of the PV strings are calculated using a simulation model. The simulated attributes are then compared with the ones obtained from the field measurements, leading to the identification of possible faulty operating conditions. Two different algorithms are then developed in order to isolate and identify eight different types of faults. The method has been validated using an experimental database of climatic and electrical parameters from a PV string installed at the Renewable Energy Laboratory (REL) of the University of Jijel (Algeria). The obtained results show that the proposed technique can accurately detect and classify the different faults occurring in a PV array. This work also shows the implementation of the developed method into a Field Programmable Gate Array (FPGA) using a Xilinx System Generator (XSG) and an Integrated Software Environment (ISE)

    Industrial Applications: New Solutions for the New Era

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    This book reprints articles from the Special Issue "Industrial Applications: New Solutions for the New Age" published online in the open-access journal Machines (ISSN 2075-1702). This book consists of twelve published articles. This special edition belongs to the "Mechatronic and Intelligent Machines" section

    A novel intelligent detection schema of series arc fault in photovoltaic (PV) system based convolutional neural network

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    Series Arc Fault (SAF) can be defined as the failure that occurs between any electrical contact and any electrical circuitry. However, it considered one of the common failures that affect the operation of the PV system and causes serious problems such as fires and electrical shock hazards. Several reasons increase the possibility of this type of failures such as incorrect installation, irregular maintenance, and some environmental effects. This paper presents a new intelligent and accurate detection method of SAF in the PV system. In this method, Convolutional neural networks (CNN) which is a discriminative (supervised) deep learning algorithm used for the process of fault detection. In normal cases, the signal consists of DC component, inverter component and noise of Network. In the case of SAF, a new component will add to the signal; therefore, CNN used to discriminate against the new component to accurately detect the SAF. PSCAD is used to generate the Arc fault model; Performance evaluation and the results of the proposed method implemented using Python. The achieved accuracy of the proposed detection method is 98.9%.

    A Smart Algorithm for the Diagnosis of Short-Circuit Faults in a Photovoltaic Generator

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    International audienceThis paper deals with a smart algorithm allowing short-circuit faults detection and diagnosis of PV generators. The proposed algorithm is based on the hybridization of a support vector machines (SVM) technique optimized by a k-NN tool for the classification of observations on the classifier itself or located in its margin. To test the proposed algorithm performance, a PV generator database containing observations distributed over classes is used for simulation purposes

    A Review on Artificial Intelligence Applications for Grid-Connected Solar Photovoltaic Systems

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    The use of artificial intelligence (AI) is increasing in various sectors of photovoltaic (PV) systems, due to the increasing computational power, tools and data generation. The currently employed methods for various functions of the solar PV industry related to design, forecasting, control, and maintenance have been found to deliver relatively inaccurate results. Further, the use of AI to perform these tasks achieved a higher degree of accuracy and precision and is now a highly interesting topic. In this context, this paper aims to investigate how AI techniques impact the PV value chain. The investigation consists of mapping the currently available AI technologies, identifying possible future uses of AI, and also quantifying their advantages and disadvantages in regard to the conventional mechanisms
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