Generator Early Warning System Based on Partial Discharge & Operation Parameters to Prevent Catastrophic Failure

Abstract

Generators are essential components in the power generation industry, responsible for maintaining a continuous and reliable electricity supply. Ensuring their health is critical to avoid costly downtime and catastrophic failures. Traditional offline health assessments delay the detection of potential issues and may not provide accurate diagnostics. Partial Discharge (PD) analysis has become a valuable tool for identifying insulation faults in generator stators by measuring discharge magnitudes. However, despite the implementation of PD technology, catastrophic failures still occur, often due to a lack of understanding of PD analysis and the absence of an effective early warning system. To address these issues, an innovative online early warning system has been developed, utilizing Digital Signal Input Modules (DSIM) connected to Program Vision for real-time data collection and PD analysis. This system significantly enhances diagnostic capabilities by not only monitoring PD magnitude trends but also incorporating operational parameter comparisons to swiftly identify the source of any anomalies. The creation of a comprehensive online monitoring dashboard, which integrates all generator operational parameters, enables real-time health assessments and provides operators with actionable insights, thereby improving maintenance strategies and drastically reducing the risk of unexpected failures. This enhanced system empowers operators to proactively address potential issues, ensuring greater generator reliability and minimizing operational disruptions.