Title: Spacetime graph convolution driven sensing edge node collaboration for photovoltaic fault diagnosis

Authors: Dingyou Wang

Addresses: School of Electrical Engineering, Jiangxi Vocational College of Mechanical and Electrical Technology, Nanchang 330013, China

Abstract: Focusing on issues of insufficient feature characterisation and low accuracy of fault diagnosis in current photovoltaic fault diagnosis methods, this paper firstly designs an improved sensing edge node arrangement strategy to collect photovoltaic fault data comprehensively. Then, the similarity graph modelling method is used to construct the sensing edge node graph, and the one-dimensional convolutional network and residual structure are used to build the characteristic pre-extraction function to form the spatio-temporal graph data. The graph convolutional network and the characteristic pre-extraction function are connected to form a feature extractor to capture the time-spatial correlation features at different scales in the spatio-temporal map data. Finally, a multi-class classifier is designed to classify fault features to realise the diagnosis of photovoltaic faults. Experimental outcome indicates that the diagnosis accuracy of the designed model is improved by at least 4%, which attests to the model's superior performance.

Keywords: photovoltaic fault diagnosis; sensing edge node; residual structure; convolutional neural network; spatiotemporal graph convolutional neural network.

DOI: 10.1504/IJSNET.2025.149128

International Journal of Sensor Networks, 2025 Vol.49 No.2, pp.97 - 110

Received: 13 Jul 2025
Accepted: 03 Aug 2025
Published online: 14 Oct 2025 *

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