Research on Fault Identification and Diagnosis of Hydropower Station Main Transformer Online Monitoring System Based on Big Data
This study explores the innovative application of big data technology in the dynamic monitoring system for hydropower station main transformers,focusing on data-driven fault identification and diagnosis strategies.We upgraded the online monitoring system at Pengshui Hydropower Station with the Zhongfen 3000 Plus chromatographic real-time monitoring solution.This upgrade enables comprehensive monitoring of dissolved gases,trace moisture,and grounding current in transformer oil.Using an intelligent big data analysis platform,we process and analyze data in real-time,constructing efficient fault identification models.Advanced machine learning algorithms are used for fault prediction and diagnosis.Results demonstrate the system's excellent fault identification accuracy and warning performance,significantly enhancing transformer operational stability and safety,and supporting intelligent power equipment maintenance.
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