Partial discharge (PD) can lead to the degradation of insulation systems over time, significantly reducing the lifespan and reliability of HV equipment. The early identification of PD is crucial because it serves as an initial indicator of potential insulation breakdowns. By detecting these potential breakdowns before they occur, asset managers can implement proactive measures to prevent sudden failures, ensuring personnel safety and avoiding costly, unplanned outages.

Continuous monitoring and timely maintenance help slow insulation degradation, ultimately extending the operational life of expensive HV assets and improving overall return on investment. This condition-based, predictive approach to maintenance also reduces expenses compared to traditional time-based strategies. By focusing resources only where needed, companies avoid unnecessary inspections and repairs, making asset management both cost-effective and efficient.

Monitra’s Kronos Monitors attached to PD sensors and data analytics to offer real-time insights into the health of HV equipment. These systems can detect increases in discharge activity, providing asset managers with an understanding of insulation condition. Machine learning algorithms further enhance monitoring by interpreting data patterns, distinguishing normal behaviour from concerning activity, and delivering actionable insights.

While periodic testing identifies issues at a single moment, real-time monitoring continuously streams information, allowing for immediate fault detection as problems arise. This approach reduces response times to threats, enables data-driven insights for improved asset management, and lowers the overall risk profile of HV infrastructure.

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