Fault prediction and preventive maintenance strategy of new energy power station based on deep learning

Résumé fourni par la source

As renewable energy occupies an increasingly important position in the global energy structure, how to effectively manage and maintain these power stations has become an urgent problem to be solved. Firstly, through an in-depth analysis of the common fault types and their causes in new energy power stations, combined with the review of current fault detection and prevention technologies, this paper proposes a new method for fault prediction using deep learning. Subsequently, the application of deep learning in fault prediction is elaborated, and a complete fault prediction model based on deep learning is proposed, including data collection and pre-processing, feature engineering, model selection and training, and evaluation and optimization. Finally, based on the prediction results, a preventive maintenance strategy was formulated, covering the selection of maintenance timing, the formulation and optimization of plans, and the allocation and management of maintenance resources. The research results in this paper are expected to provide technical support for the efficient operation of new energy power stations, reduce the failure rate, and improve equipment availability and economic benefits.

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