An energy storage converter fault prediction method

Abstract

An energy storage converter fault prediction method includes the following steps: taking the historical monitoring signals of the energy storage converter cluster of the same battery energy storage system as the original feature library; using a sparse self-coding algorithm to extract the main feature matrix of the energy storage converter cluster at each sampling time from the original feature library; using a fast clustering algorithm to search for the central energy storage converter at each sampling time; calculating the cumulative eccentric distance matrix of the energy storage convertercluster, normalizing the cumulative eccentric distance matrix and setting the warning threshold, and finally realizing the fault prediction of the energy storage converter. The invention realizes thefault prediction of the energy storage converter, can be operated on-line, is convenient to calculate, has no special requirement restriction, is suitable for the energy storage converter clusters ofdifferent scales, has good portability, is favorable for the maintenance personnel to establish a reasonable and effective maintenance plan, and ensures the safe and stable operation of the power network.

Description

technical field [0001] The invention relates to a fault prediction method for an energy storage converter, which belongs to the technical field of power grids. Background technique [0002] In the smart grid, energy storage has become an important supporting technology for large-scale centralized and distributed new energy generation access and consumption. As a key component of the battery energy storage system, the energy storage converter controls the energy flow between the battery and the grid, and its health status directly affects the safety and stability of the entire battery energy storage system. With the expansion of the application scale of energy storage systems, the power grid has also put forward higher requirements for the health status assessment technology of energy storage converters. Therefore, real-time monitoring of the operating status of the energy storage converter and timely and accurate prediction of the occurrence of energy storage converter fail...

AI Technical Summary

Problems solved by technology

[0003] At present, the maintenance of energy storage converters usually adopts after-the-fact maintenance, and it is difficult for maintenance personnel to grasp the health status of energy storage converters in real time

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

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