TEMPERATURE PREDICTION OF WIND FARM ELECTRICAL CABINETS CONSIDERING DISTORTION OF THERMAL IMAGING ELEMENTS

Wang Xiaodong; Du Gaoying; Wang Yachao; Liu Yingming

doi:10.19912/j.0254-0096.tynxb.2022-0910

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Acta Energiae Solaris Sinica ›› 2023, Vol. 44 ›› Issue (10) : 384-390. DOI: 10.19912/j.0254-0096.tynxb.2022-0910

TEMPERATURE PREDICTION OF WIND FARM ELECTRICAL CABINETS CONSIDERING DISTORTION OF THERMAL IMAGING ELEMENTS

Wang Xiaodong¹, Du Gaoying¹, Wang Yachao², Liu Yingming¹

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Abstract

A hybrid deep neural network-based temperature correction and prediction method for wind farm electrical cabinets is proposed to address the problems of mapping bias and low prediction accuracy of temperature values caused by lens distortion in thermal imaging temperature monitoring of electrical cabinets in wind farms. The method uses Gaussian convolutional kernel structure to filter noisy image elements so as to complete image element denoising. A multilayer Convolutional Neural Networks (CNNs) model is developed. The denoised image elements are used as input to eliminate the distortion points in the image elements and improve the accuracy of the mapping between the temperature values and the target measurement points. The XGBoost-LSTM weighted fusion prediction model is built based on the corrected and assigned temperature data, and the output temperature prediction values of key monitoring points can be used in wind farm equipment fault warning, and the weights are trained to improve the prediction accuracy of the fusion model based on the structural variability of the model. The results show that the proposed method can effectively correct the distortion of the graphical elements and significantly improve the prediction accuracy.

Key words

wind farm / temperature prediction / distortion correction / convolutional neural network / XGBoost / LSTM

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Wang Xiaodong, Du Gaoying, Wang Yachao, Liu Yingming. TEMPERATURE PREDICTION OF WIND FARM ELECTRICAL CABINETS CONSIDERING DISTORTION OF THERMAL IMAGING ELEMENTS[J]. Acta Energiae Solaris Sinica. 2023, 44(10): 384-390 https://doi.org/10.19912/j.0254-0096.tynxb.2022-0910

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