储能科学与技术2025，Vol.14Issue(5)：2114-2116,3.DOI:10.19799/j.cnki.2095-4239.2025.0412
基于大数据和人工智能的储能系统故障预测与诊断方法研究
Research on fault prediction and diagnosis methods for energy storage systems based on big data and artificial intelligence
摘要
Abstract
As the core of power resource application and development,energy storage systems are constantly becoming more complex and precise.How to improve the accuracy of energy storage system fault detection and diagnosis has become the key to the development of modern power technology.The article provides a detailed overview of new energy storage system fault prediction methods based on big data and artificial intelligence technology,based on common faults in modern energy storage systems.Through analysis and research,it can be clarified that the current fault prediction and diagnosis methods for energy storage systems mainly include data model diagnosis and data-driven diagnosis.The former constructs data models through big data technology,determines problem data,and obtains diagnostic results,while the latter relies more on artificial intelligence technologies such as machine learning to obtain diagnostic results through knowledge driven and data-driven approaches.Future research tends to focus more on mining and summarizing physical quantity data,establishing more accurate comparative models,and achieving rapid diagnosis of energy storage system faults.关键词
大数据/人工智能/故障预测Key words
big data/artificial intelligence/fault prediction分类
能源科技引用本文复制引用
韩松..基于大数据和人工智能的储能系统故障预测与诊断方法研究[J].储能科学与技术,2025,14(5):2114-2116,3.基金项目
国家自然科学基金青年科学基金项目(52304262),中国安全生产科学研究院基本科研业务费专项资金项目(2023JBKY06). （52304262）
