储能科学与技术 ›› 2025, Vol. 14 ›› Issue (4): 1698-1700.doi: 10.19799/j.cnki.2095-4239.2025.0356

• 储能测试与评价 • 上一篇    下一篇

人工智能在储能系统故障检测中的应用

李石明()   

  1. 四川工商学院,四川 成都 610000
  • 收稿日期:2025-02-05 出版日期:2025-04-28 发布日期:2025-05-20
  • 作者简介:李石明(1980—)男,博士,讲师,研究方向为管理学、经济学,E-mail:dashujufenxi@126.com

Application of artificial Intelligence in the fault detection of energy storage system

Shiming LI()   

  1. Sichuan Technology and Business University, Chengdu 610000, Sichuan, China
  • Received:2025-02-05 Online:2025-04-28 Published:2025-05-20

摘要:

随着新能源产业与储能式电力系统的发展,市场对储能系统的需求迅速增加。但储能电芯大型化与储能系统工作环境日益复杂,导致储能系统故障发生率大幅增加。将人工智能技术应用于储能系统故障检测,有效提升了储能系统故障检测效率,减少了人工干预,降低了储能系统故障带来的损失。具体实践过程中,基于人工智能的储能系统故障检测机制需要经过原始数据输入、特征提取、模型建立及故障检测四部分,从海量数据中发现储能系统发生故障的规律,实现储能系统故障预警,精准识别故障发生位置与类型,预测故障发展趋势,大幅提升储能系统工作效率,推动储能系统向智慧化、智能化方向发展。

关键词: 人工智能, 储能系统, 故障检测

Abstract:

With the development of new energy industry and energy storage power system, the market demand for energy storage system is rapidly increasing. However, the enlargement of energy storage cells and the increasingly complex working environment of energy storage system lead to a sharp increase in the failure rate of energy storage system. The application of artificial intelligence to the fault detection of energy storage system can effectively improve the fault detection efficiency of energy storage system, reduce the manual intervention, and minimize the loss caused by the failure of energy storage system. In practice, through raw data input, feature extraction, model building and fault detection, the fault detection mechanism of the energy storage system based on artificial intelligence can find the rule of the energy storage system failure from the massive data, provide early warning for the energy storage system failure, accurately identify the fault location and type, and predict the development trend of the fault, so as to greatly improve the efficiency of the energy storage system, and promote the intelligentization of the energy storage system.

Key words: artificial intelligence, energy storage system, fault detection

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