Fault Diagnosis of Hydropower Units Based on Gramian Angular Summation Field and Parallel CNN

Xiang Li, Jianbo Zhang, Boyi Xiao, Yun Zeng (), Shunli Lv (), Jing Qian and Zhaorui Du
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Xiang Li: School of Metallurgy and Energy Engineering, Kunming University of Science and Technology, Kunming 650093, China
Jianbo Zhang: School of Metallurgy and Energy Engineering, Kunming University of Science and Technology, Kunming 650093, China
Boyi Xiao: School of Metallurgy and Energy Engineering, Kunming University of Science and Technology, Kunming 650093, China
Yun Zeng: School of Metallurgy and Energy Engineering, Kunming University of Science and Technology, Kunming 650093, China
Shunli Lv: School of Metallurgy and Energy Engineering, Kunming University of Science and Technology, Kunming 650093, China
Jing Qian: School of Metallurgy and Energy Engineering, Kunming University of Science and Technology, Kunming 650093, China
Zhaorui Du: Changchun Thermal Power Plant of Huaneng Jilin Power Generation Co., Changchun 130022, China

Energies, 2024, vol. 17, issue 13, 1-19

Abstract: To enhance the operational efficiency and fault detection accuracy of hydroelectric units, this paper proposes a parallel convolutional neural network model that integrates the Gramian angular summation field (GASF) with an Improved coati optimization algorithm–parallel convolutional neural network (ICOA-PCNN). Additionally, to further improve the model’s accuracy in fault identification, a multi-head self-attention mechanism (MSA) and support vector machine (SVM) are introduced for a secondary optimization of the model. Initially, the GASF technique converts one-dimensional time series signals into two-dimensional images, and a COA-CNN dual-branch model is established for feature extraction. To address the issues of uneven population distribution and susceptibility to local optima in the COA algorithm, various optimization strategies are implemented to improve its global search capability. Experimental results indicate that the accuracy of this model reaches 100%, significantly surpassing other nonoptimized models. This research provides a valuable addition to fault diagnosis technology for modern hydroelectric units.

Keywords: Gramian angular summation field (GASF); parallel convolutional neural network (parallel CNN); hydropower units; fault diagnosis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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