Development of a Real-Time Online Condition Monitoring System using Tan delta leakage current for Current Transformer Health Assessment

One of the critical limitations in existing diagnostic frameworks is the absence of continuous real-time monitoring of insulation health. Addressing this gap, this study proposes a practical and innovative method to monitor CT insulation condition continuously, by analyzing leakage current at the solidly grounded Tan Delta terminal.

The core of the proposed system involves the installation of a precision Current Transducer in the grounding circuit of the Tan Delta test point. This setup does not interfere with the system’s grounding integrity and is engineered to transmit real-time current data to the substation’s

SCADA interface using existing communication protocols. The method enables dynamic tracking of leakage current profiles and facilitates the development of intelligent thresholds and alarm logic for predictive maintenance.

The initial deployment will focus on CTs already showing potential insulation anomalies identified through Dissolved Gas Analysis (DGA) or abnormal tan delta values from past tests.

By correlating real-time leakage current data with these established indicators, the system aims to validate its effectiveness in early fault detection. Continuous monitoring will enable proactive interventions, thereby reduce the likelihood of in-service failure and improve equipment lifespan.

Key advantages of this approach include enhanced asset reliability, early fault identification, and protection of surrounding infrastructure from secondary damage. The monitoring also contributes to the shift toward Reliability-Centered Maintenance (RCM) strategies, allowing utilities to move away from time-based inspections and adopt condition-based practices. This not only optimizes maintenance schedules but also reduces human resource demands and equipment downtime.

Although this methodology is relatively new in field applications, its implementation is straightforward and leverages existing infrastructure. Ongoing data collection and field validation will be essential in refining diagnostic thresholds and adapting the system to diverse

CT configurations and service conditions.

In conclusion, this research introduces a forward-looking, real-time condition monitoring solution for CTs that is scalable, cost-effective, and aligned with modern asset management practices. It promises a transformative shift from reactive to predictive maintenance in power systems.