Fault prediction and diagnosis method for energy storage system based on artificial intelligence

Abstract

The invention discloses an energy storage system fault prediction and diagnosis method based on artificial intelligence. The method comprises the following steps: acquiring operation data of an energy storage system; preprocessing the operation data to generate a preprocessed multi-dimensional feature sequence; performing feature representation transformation on the multi-dimensional feature sequence, and extracting low-dimensional potential feature representation; constructing a symbol regression network; executing symbol expression search, applying dynamic weight penalty in the search process, and outputting a target expression; the future operation state of the energy storage system is predicted, and fault early warning information is generated; and analyzing the contribution degree of each variable and an operator in the target expression, determining a key parameter causing a fault, and judging a corresponding fault category. According to the method, the energy storage mechanism constraint operator and the residual error distribution driven game adaptive penalty mechanism are introduced, so that high-precision prediction and interpretable identification of the fault of the energy storage system are realized.

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