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A Photovoltaic Array Fault Diagnosis Method Considering the Photovoltaic Output Deviation Characteristics
摘要： There are a large number of photovoltaic (PV) arrays in large-scale PV power plants or regional distributed PV power plants, and the output of different arrays fluctuates with the external conditions. The deviation and evolution information of the array output are easily covered by the random fluctuations of the PV output, which makes the fault diagnosis of PV arrays difficult. In this paper, a fault diagnosis method based on the deviation characteristics of the PV array output is proposed. Based on the current of the PV array on the DC (direct current) side, the deviation characteristics of the PV array output under different arrays and time series are analyzed. Then, the deviation function is constructed to evaluate the output deviation of the PV array. Finally, the fault diagnosis of a PV array is realized by using the probabilistic neural network (PNN), and the effectiveness of the proposed method is verified. The main contributions of this paper are to propose the deviation function that can extract the fault characteristics of PV array and the fault diagnosis method just using the array current which can be easily applied in the PV plant.
关键词： photovoltaic array,deviation characteristics,fault diagnosis,probabilistic neural network
更新于2025-09-23 15:21:01
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EMD- PNN based welding defects detection using laser-induced plasma electrical signals
摘要： The plasma electrical signal has gained extensive attention for characterizing the behavior of the laser-induced plasma due to the advantages of easy acquisition and feedback control. In this paper, the electrical signals were measured by a passive probe based on the principle of plasma sheath effect. To explore the mutation characteristics of plasma electrical signals during defect generation in laser deep penetration welding, wavelet packet transform (WPT) and empirical mode decomposition (EMD) were used to compress data and extract features, respectively. Based on the analysis of the time-frequency spectrum of a typical plasma electrical signal, the approximate coefficients of 0?390 Hz frequency range were reconstructed. The residual term which characterizes the change trend of electrical signal was obtained by the further adaptive decomposition. For better identifying weld defects, another two statistical features, mean value and standard deviation, were extracted by carrying out statistical analysis in the time domain. The feature database is built with above features and used as inputs of the predictive model based on the probabilistic neural network (PNN). The result showed the average prediction accuracy was as high as 90.16% when recognizing five statuses of weld seam, including sound weld and four kinds of weld defects.
关键词： Wavelet packet transformation,Empirical mode decomposition,Laser welding,Plasma electrical signal,Probabilistic neural network
更新于2025-09-12 10:27:22
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[IEEE 2019 International Conference on Advances in Big Data, Computing and Data Communication Systems (icABCD) - Winterton, South Africa (2019.8.5-2019.8.6)] 2019 International Conference on Advances in Big Data, Computing and Data Communication Systems (icABCD) - Failure Analysis of Photovoltaic Equipment Based on ISOMPNN
摘要： ISOMPNN, a new incremental learning method based on the self-organizing map (SOM) and probabilistic neural network (PNN) is proposed to tackle the problem of model self-adaptation when new categories of equipment failures occur. It uses a modular SOM to learn each category of photovoltaic device data and then constructs a PNN using the prototype vector of each category of data after training as a model neuron of that category. Incremental SOMPNN can incrementally learn new classes of different data to complement existing models. In the incremental learning process, only the new data is used to adjust the model, instead of reusing the original data, reducing training time and reducing storage space. Its effectiveness can be well verified in known photovoltaic device data.
关键词： self-organization map,photovoltaic equipment,probabilistic neural network,incremental learning
更新于2025-09-11 14:15:04