Fault Diagnosis and Detection Based on Efficiency Loss Test of Photovoltaic Power Station

Xie, Xiaorong; Meng, Fanteng; Luo, Xin; Cai, Shixia; Ma, Xiaojun; Na, Zhixiong; Ma, Dayan; Yu, Sophia; Ge, Leyi; Liu, Yunchao

doi:10.1088/1755-1315/508/1/012060

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…The electrical method can be further divided into three categories, namely power loss diagnosis, measured voltage, and current diagnosis, and the current-voltage (I-V) curve diagnosis. For power loss diagnosis, a simulation power model is first established [15], and then the output power of it is compared with that of the actual PV system for fault diagnosis [16]. Dhimish et al [17] proposed a fuzzy inference system (FIS) using a Mamdanitype fuzzy controller.…”

Section: Introductionmentioning

confidence: 99%

A Photovoltaic System Fault Identification Method Based on Improved Deep Residual Shrinkage Networks

Cui

Gao

2022

Energies

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With the increasing installed capacity of photovoltaic (PV) power generation, it has become a significant challenge to detect abnormalities and faults of PV modules in a timely manner. Considering that all the fault information of the PV module is contained in the current-voltage (I-V) curve, this pioneering study takes the I-V curve as the input and proposes a PV-fault identification method based on improved deep residual shrinkage networks (DRSN). This method can not only identify single faults (e.g., short-circuit, partial-shading, and abnormal aging), but also effectively identify the simultaneous existence of hybrid faults. Moreover, it can achieve end-to-end fault diagnosis. The diagnostic accuracy of the proposed method on the measured data reaches 97.73%, is better than the convolutional neural network (CNN), the support vector machine (SVM), the deep residual network (ResNet), and the stage-wise additive modeling using multi-class exponential loss function based on the classification and regression tree (SAMME-CART). In addition, the possibility of the aforementioned method running on the Raspberry Pi has been verified in this study, which is of great significance for realizing the edge diagnosis of PV fault.

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Section: Introductionmentioning

confidence: 99%

A Photovoltaic System Fault Identification Method Based on Improved Deep Residual Shrinkage Networks

Cui

Gao

2022

Energies

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“…The host should be designed with a monitoring interface to display the working voltage and current, component backplane, temperature, PVA voltage and current, ambient illumination, and single component temperature in the PVA in real-time and locates mismatched components on the screen. Meanwhile, the host interface should be simple and easy to understand to reduce the workload of maintenance personnel, and the electrical and environmental parameters of the PVPGS shall be stored in the database to prevent data loss in case of system power or network failure[22][23][24]. (III) Positioning of mismatched components.…”

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confidence: 99%

Application of Wireless Sensor Network Technology Using Intelligent Algorithm in Mismatch Detection of Photovoltaic Power Generation

Xie

Chen

2022

Wireless Communications and Mobile Computing

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The paper was aimed at ensuring the stable operation of the photovoltaic power generation system (PVPGS) and improving the accuracy of automatic mismatch detection. Consequently, this paper presents a PVPGS-oriented mismatch detection system based on wireless sensing technology (WSN). Firstly, the photovoltaic array (PVA) is constructed using a microcontroller, power management chip, nRF24L01, temperature sensor, voltage, and current sensor. Then, a fault detection and localization (FDL) scheme based on the Hampel algorithm is optimized, and Matlab/Simulink implements the PVA simulation model. Finally, several typical mismatch faults are simulated to verify the feasibility of the proposed FDL scheme using the measured voltage and current data. The empirical findings corroborate that the proposed FDL scheme can automatically and regularly collect photovoltaic (PV) electrical characteristic data and quickly and accurately identify and position a mismatch. In the case of a PVA open-circuit fault, the output current loss of the PVA is equal to the sum of the current of the open-circuit fault string in the array during normal operation. When the PVA is short-circuited, the PVA output voltage loss equals the sum of the output voltages of the faulty components in the most serious fault string under normal operation. Overall, the classification accuracy of the proposed FDL scheme is 97.556%. Lastly, the experiment reveals that the classification accuracy of the proposed FDL scheme is 100% for array aging, shadow, and the open circuit. Therefore, the research proposal has a good application prospect.

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Fault Diagnosis and Detection Based on Efficiency Loss Test of Photovoltaic Power Station

Cited by 2 publications

References 7 publications

A Photovoltaic System Fault Identification Method Based on Improved Deep Residual Shrinkage Networks

A Photovoltaic System Fault Identification Method Based on Improved Deep Residual Shrinkage Networks

Application of Wireless Sensor Network Technology Using Intelligent Algorithm in Mismatch Detection of Photovoltaic Power Generation

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