Line-to-Line Faults Detection for Photovoltaic Arrays Based on I-V Curve Using Pattern Recognition

Eskandari, Aref; Milimonfared, Jafar; Aghaei, Mohammadreza; Oliveira, Aline Kirsten Vidal de; Rüther, Ricardo

doi:10.1109/pvsc40753.2019.8981385

Cited by 9 publications

(8 citation statements)

References 9 publications

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“…A hot spot can occur in a PV module due to various causes, such as cells mismatching, partial shading or interconnection failures. 9,[43][44][45][46][47] Indeed, when the cell is affected by partial shading, it results in a short circuit condition or a reverse bias. 45,48 An entire cell can form a hot spot in a module; alternatively, the cell temperature can also be non-uniform.…”

Section: Hot Spotsmentioning

confidence: 99%

Review of technology specific degradation in crystalline silicon, cadmium telluride, copper indium gallium selenide, dye sensitised, organic and perovskite solar cells in photovoltaic modules: Understanding how reliability improvements in mature technologies can enhance emerging technologies

Kettle

Aghaei

Ahmad

et al. 2022

Progress in Photovoltaics

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Section: Hot Spotsmentioning

confidence: 99%

Review of technology specific degradation in crystalline silicon, cadmium telluride, copper indium gallium selenide, dye sensitised, organic and perovskite solar cells in photovoltaic modules: Understanding how reliability improvements in mature technologies can enhance emerging technologies

Kettle

Aghaei

Ahmad

et al. 2022

Progress in Photovoltaics

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“…Unintentional connections between two different points in a PV array are known as line-to-line (L-L) faults [14]. DC connectors damage, animal chewing, and cable age may cause the L-L faults [15].…”

Section: Line To Line Faultsmentioning

confidence: 99%

Review on maintenance of photovoltaic systems based on deep learning and internet of things

Hammoudi¹,

Idrissi²,

Boukabous³

et al. 2022

IJEECS

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Many solar plants have been installed globally, and they must be continuously protected and supervised to ensure their safety and reliability. Photovoltaic plants are susceptible to many defects and failures, and fault detection technology is used to protect and isolate them. Despite numerous inter-national standards, invisible photovoltaic defects continue to cause major is-sues. As a result, smart technologies like AI (Artificial Intelligence) and IoT are being developed for remote sensing, problem detection, and diagnosis of photovoltaic systems. Solar plants generate not only green electricity but also a lot of data, such as power output. With AI, a clear picture of electricity yields should be possible. The output of entire solar parks could be monitored and analyzed. The AI could also detect malfunctions within a solar park, according to the research. This would speed up and simplify maintenance work. Deep learning (DL) and IoT applications for photovoltaic plants are discussed. The most advanced techniques, such as DL, are discussed in terms of precision and accuracy. Incorporating DL and IoT approaches for fault detection and diagnosis into simple hardware, such as low-cost chips, maybe cost-effective and technically feasible for photovoltaic facilities located in remote locations.

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“…This dataset was recorded under different conditions and scenarios from the original dataset. These conditions are the combinations of irradiance levels (350, 550, and 850 W/m 2 ), operation temperatures (4,7,12,17,22, and 27 • C), fault impedances (3, 7, 12, and 17 Ω), and the mismatch level varies from 10% to 50% with an increment of 10%. Therefore, 60 normal conditions and 120 cases of LL faults are obtained by simulation setup in order to make an accurate and reliable assessment of the trained model.…”

Section: Data Acquisition Systemmentioning

confidence: 99%

“…Moreover, these diodes may fail and cause unnecessary losses. Numerous methods have been presented for the diagnosis of LL faults in the literature [7][8][9]. According to the approach that each method has adopted to identify faults, these methods can be divided into three categories.…”

Section: Introductionmentioning

confidence: 99%

“…Dhimish et al [11] presented a method based on the T-test statistical analysis, which used Voltage Ratio (VR) and Performance Ratio (PR) to specify the fault condition and the distinct faults, whereas the authors in [12] have used fuzzy-based decision making instead of the T-test. Moreover, there are Numerous methods have been presented for the diagnosis of LL faults in the literature [7][8][9]. According to the approach that each method has adopted to identify faults, these methods can be divided into three categories.…”

Section: Introductionmentioning

confidence: 99%

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Autonomous Monitoring of Line-to-Line Faults in Photovoltaic Systems by Feature Selection and Parameter Optimization of Support Vector Machine Using Genetic Algorithms

et al. 2020

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Photovoltaic (PV) monitoring and fault detection are very crucial to enhance the service life and reliability of PV systems. It is difficult to detect and classify the faults at the Direct Current (DC) side of PV arrays by common protection devices, especially Line-to-Line (LL) faults, because such faults are not detectable under high impedance fault and low mismatch conditions. If these faults are not diagnosed, they may significantly reduce the output power of PV systems and even cause fire catastrophe. Recently, many efforts have been devoted to detecting and classifying LL faults. However, these methods could not efficiently detect and classify the LL faults under high impedance and low mismatch. This paper proposes a novel fault diagnostic scheme in accordance with the two main stages. First, the key features are extracted via analyzing Current–Voltage (I–V) characteristics under various LL fault events and normal operation. Second, a genetic algorithm (GA) is used for parameter optimization of the kernel functions used in the Support Vector Machine (SVM) classifier and feature selection in order to obtain higher performance in diagnosing the faults in PV systems. In contrast to previous studies, this method requires only a small dataset for the learning process and it has a higher accuracy in detecting and classifying the LL fault events under high impedance and low mismatch levels. The simulation results verify the validity and effectiveness of the proposed method in detecting and classifying of LL faults in PV arrays even under complex conditions. The proposed method detects and classifies the LL faults under any condition with an average accuracy of 96% and 97.5%, respectively.

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Line-to-Line Faults Detection for Photovoltaic Arrays Based on I-V Curve Using Pattern Recognition

Cited by 9 publications

References 9 publications

Review of technology specific degradation in crystalline silicon, cadmium telluride, copper indium gallium selenide, dye sensitised, organic and perovskite solar cells in photovoltaic modules: Understanding how reliability improvements in mature technologies can enhance emerging technologies

Review of technology specific degradation in crystalline silicon, cadmium telluride, copper indium gallium selenide, dye sensitised, organic and perovskite solar cells in photovoltaic modules: Understanding how reliability improvements in mature technologies can enhance emerging technologies

Review on maintenance of photovoltaic systems based on deep learning and internet of things

Autonomous Monitoring of Line-to-Line Faults in Photovoltaic Systems by Feature Selection and Parameter Optimization of Support Vector Machine Using Genetic Algorithms

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