A fault state adaptive early warning method for battery energy storage system

Abstract

The invention discloses a fault state self-adaptive early warning method of a battery energy storage system. The NJW clustering algorithm is used to reduce the dimension of high-dimensional monitoring data. The only alternative to the original data for cluster analysis, solves the singularity problem that often occurs in traditional methods for non-convex data clustering; uses DTW to dynamically adjust the time axis of asynchronous monitoring data, and maps the two sets of monitoring data to the synchronous time axis to overcome the problem. The observation error caused by asynchronous sampling of monitoring data is solved, and the problem that the time axis of heterologous data of sampling points cannot be one-to-one corresponding to the traditional method is solved. Finally, a sliding window model is constructed to suppress the influence of a small number of outliers in the monitoring data, and clustering is carried out based on the DTM distance. Class analysis, through sparse coefficient LSR and fault threshold FT, objectively select fault clustering points, avoid over-estimation or under-estimation of fault state by traditional fault clustering method, and realize BESS fault state adaptive early warning.

Description

technical field [0001] The invention relates to the technical field of battery energy storage system detection, in particular to an adaptive early warning method for a fault state of a battery energy storage system. Background technique [0002] Most of the safety problems encountered by the battery energy storage system (BESS) come from the cell level, mainly including overcharge, overdischarge, internal short circuit and external short circuit. The parameter estimation method realized by establishing an accurate electrothermal simulation model or the threshold limit method realized based on the empirical estimation method only identifies abnormal data during the operation of BESS, and relies heavily on normal data for reference correction, while ignoring normal batteries Observational error and process noise during the cycle. However, large-capacity battery energy storage devices are generally composed of hundreds or thousands of battery cells in series and parallel, and ...

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Problems solved by technology

[0012] In view of the above problems, the purpose of the present invention is to provide a battery energy storage system self-adaptive early warning method for fault state, based on the NJW spectral clustering algorithm to process heterogeneous data, and to group battery boxes with similar working conditions and fault state comparisons. Class, use dynamic regularization algorithm to solve the problem of inconsistent time axis of asynchronous data, build a sliding window model to suppress the influence of a small number of outliers in monitoring data, perform cluster analysis based on DTW distance, and objectively select fault clusters through sparse coefficient and fault threshold points to realize self-adaptive early warning of BESS fault status

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