中圖分類號(hào)： TP207；TM914 文獻(xiàn)標(biāo)識(shí)碼： A DOI：10.16157/j.issn.0258-7998.222585 中文引用格式： 李莎，陳澤華，劉海軍. 基于ST-TCN的太陽(yáng)能光伏組件故障診斷方法[J].電子技術(shù)應(yīng)用，2022，48(12)：79-83，88. 英文引用格式： Li Sha，Chen Zehua，Liu Haijun. Fault diagnosis method of solar panel module based on ST-TCN[J]. Application of Electronic Technique，2022，48(12)：79-83，88.
Fault diagnosis method of solar panel module based on ST-TCN
Li Sha1，Chen Zehua1，Liu Haijun2
1.College of Data Science，Taiyuan University of Technology，Jinzhong 030600，China； 2.Jinneng Clean Energy Co.，Ltd.，Taiyuan 030001，China
Abstract： This paper analyzes the current characteristic curves of photovoltaic modules under different working conditions and finds that the current data of photovoltaic modules superpose complex performance characteristics and high noise. In order to accurately diagnose the fault types of photovoltaic modules, a soft thresholding temporal convolutional network(ST-TCN) photovoltaic module fault diagnosis model is proposed. The ST-TCN network uses the dilated convolution layer, ReLU layer, and Dropout layer of multiple residual modules to extract current numerical and time series features, uses the soft thresholding of residual modules to de-noise the extracted features, and finally uses the full connection layer to diagnose and classify the extracted features of residual modules. The experimental results show that the ST-TCN network has a simple structure, fast convergence, and high accuracy in fault diagnosis, reaching 92.99%.
Key words : photovoltaic modules；temporal convolutional network；soft threshold；fault diagnosis
