“…The provision method for PV fault identification is an important tool in the effort to increase the PV system efficiency. In this case, the meta-heuristic optimization techniques, such as Genetic Algorithm (GA), Tabu Search (TS) algorithm and Grey Wolf Optimization (GWO) are utilized as the fault diagnostic tools to detect and classify the open circuit and short-circuit faults under nonuniform distribution of sunlight intensity and temperature [25]. These methods have achieved successful identification and localization of fault types under different testing scenarios.…”
Recently, detection and identification of faults in photovoltaic (PV) system applications have been attracting researchers worldwide. Some of them have investigated the causes of potential faults, have worked on how to monitor and identify the faults through fault diagnosis, and have elaborated on how to develop problem-solving methods for the early detection of faults in PV system operations. This paper attempts to provide information comprehensively from the previous research. It summarizes types of faults, methods used in early fault identification and monitoring, and PV fault prevention and protection systems. The paper also investigates future trends in PV system fault diagnosis which include real-time implementation. The paper is expected to bring new perspective about the literature associate with fault studies in PV systems. This survey will be beneficial for the future discussion on how to provide comprehensive solutions for PV system fault problems. It will help avoid research repetition on similar topics and focus on the improvement and performance development of PV fault diagnosis methods.
“…The provision method for PV fault identification is an important tool in the effort to increase the PV system efficiency. In this case, the meta-heuristic optimization techniques, such as Genetic Algorithm (GA), Tabu Search (TS) algorithm and Grey Wolf Optimization (GWO) are utilized as the fault diagnostic tools to detect and classify the open circuit and short-circuit faults under nonuniform distribution of sunlight intensity and temperature [25]. These methods have achieved successful identification and localization of fault types under different testing scenarios.…”
Recently, detection and identification of faults in photovoltaic (PV) system applications have been attracting researchers worldwide. Some of them have investigated the causes of potential faults, have worked on how to monitor and identify the faults through fault diagnosis, and have elaborated on how to develop problem-solving methods for the early detection of faults in PV system operations. This paper attempts to provide information comprehensively from the previous research. It summarizes types of faults, methods used in early fault identification and monitoring, and PV fault prevention and protection systems. The paper also investigates future trends in PV system fault diagnosis which include real-time implementation. The paper is expected to bring new perspective about the literature associate with fault studies in PV systems. This survey will be beneficial for the future discussion on how to provide comprehensive solutions for PV system fault problems. It will help avoid research repetition on similar topics and focus on the improvement and performance development of PV fault diagnosis methods.
“…Many models are used for the representing of the electrical PV cell characteristics. Two models such as single and dual diode model were mainly used and presented in Figure 1 and Figure 2 respectively [ 15 , 60 ]. Figure 1 Single diode electrical circuit from PV cell.…”
Parameters extraction is instrumental to standard PV cells design. Reports indicates that heuristic algorithms are the most effective methods for accurately determinining the values of parameters. However, local concentration is against recent heuristic methods, and they are inhibited producing optimal results. This paper seeks to show that combining the heuristics algorithms with the Newton Raphson method can considerably increased the accuracy of results. An inspired artifact technique from the drone squadron simulation from control center is proposed for the extraction of the best constitutive parameters. This study equally provides clarifications on the approaches recently reported and proposed to build objective function. Furthermore, comparative evaluation of the current ten best heuristics algorithms that are published in the PV estimation domain is also undertaken. Moreover, this study investigates the convergence of algorithms when points of the number of current-voltage characteristics are varied. The results from this study highlight the differences between the two formulation, and it shows the best formulation accuracy. The results obtained from seven study cases that are considered in this present study, with the combined Newton Raphson performance method and Drone Squadron optimisation, were employed to extract precise PV module parameters.The study of the numbers of points reveals that the algorithm converges and is more precise when the numbers of points of the I-V characteristic are reduced. However, if these points are minimal, the algorithm will be hindered from returning optimal results.
“…The algorithm differentiates between open and short circuit faults, ground, and shading effects, but is applicable only for small PV systems [90][91][92][93][94]. A new approach to defect diagnosis was employed for the efficient detecting, locating, and separating of OC from SC defects in the PV system [95]. Metaheuristic optimization techniques were used.…”
Section: Short Circuit Open Circuit and Asymmetrical Faultsmentioning
The worldwide electricity supply network has recently experienced a huge rate of solar photovoltaic penetration. Grid-connected photovoltaic (PV) systems range from smaller custom built-in arrays to larger utility power plants. When the size and share of PV systems in the energy mix increases, the operational complexity and reliability of grid stability also increase. The growing concern about PV plants compared to traditional power plants is the dispersed existence of PV plants with millions of generators (PV panels) spread over kilometers, which increases the possibility of faults occurring and associated risk. As a result, a robust fault diagnosis and mitigation framework remain a key component of PV plants. Various fault monitoring and diagnostic systems are currently being used, defined by calculation of electrical parameters, extracted electrical parameters, artificial intelligence, and thermography. This article explores existing PV fault diagnostic systems in a detailed way and addresses their possible merits and demerits.
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