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.

Key words: big data, artificial intelligence, fault prediction