A battery fault prediction method for large-scale lithium battery energy storage power station

Abstract

The invention discloses a battery fault prediction method for a large-scale lithium battery energy storage power station, which comprises the following steps: taking the historical monitoring signals of the battery box clusters of the large lithium battery energy storage power station as an original feature library, and using a sparse self-encoding algorithm to obtain the original feature library Extract the main feature matrix of the battery box cluster at each sampling moment, search for the cluster center battery box at each sampling moment based on the fast clustering algorithm, calculate the cumulative eccentricity distance matrix of the battery box cluster, and normalize the cumulative eccentricity distance matrix and set the warning threshold, and finally realize the prediction of the battery failure of the energy storage power station. The invention realizes the prediction of battery faults in large-scale lithium battery energy storage power stations, can be operated online, is convenient to calculate, and has no special requirements and restrictions. An effective maintenance plan ensures the safe and stable operation of the power grid.

Description

technical field [0001] The invention relates to a battery fault prediction method for a large-scale lithium battery energy storage power station, which belongs to the technical field of power grids. Background technique [0002] Energy storage is an important supporting technology for large-scale centralized and distributed new energy generation access and utilization. Under the background of a new round of energy transformation characterized by large-scale development and utilization of new energy, the role and status of energy storage are becoming more and more important. . As a common energy storage solution, large-scale lithium battery energy storage power stations have been widely used on the power generation side, grid side and user side. The battery is a key component of a large-scale lithium battery energy storage power station, and its health status directly affects the safety and stability of the entire energy storage power station. At present, with the continuou...

AI Technical Summary

Problems solved by technology

At present, the maintenance of batteries in energy storage power stations usually adopts the method of regularly replacing the battery box or replacing the battery box afterwards, and it is difficult for maintenance personnel to grasp the health status of the battery in real time

Fault prediction technology can help maintenance personnel predict possible battery failures in advance. However, most of the existing fault prediction methods rely on the operating data of the entire life cycle of the equipment, and the established fault prediction model is only applicable to a single device. The portability is poor, and there is still a lack of an effective and scalable battery failure prediction method

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