Abstract:

With the rapid development and widespread application of new energy technologies, the role of energy storage systems in the power grid has become increasingly important. Traditional energy storage system operation and maintenance usually relies on manual experience, which has problems such as low efficiency, delayed response, and inaccurate fault diagnosis. This paper proposes an intelligent operation and maintenance model for energy storage systems based on big data. This model integrates multiple data sources for information collection and monitoring to achieve real-time collection and in-depth analysis of massive data. Combining machine learning and deep learning algorithms to conduct fusion analysis of real-time data and historical data, the system can identify fault patterns and patterns, and then predict equipment performance and health status. Through this intelligent operation and maintenance model, potential faults can be effectively predicted and intervention measures taken in advance, thereby improving equipment reliability and operating efficiency.

Key words: big data, energy storage systems, intelligent operation and maintenance, machine learning