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Cables in power generation and delivery are under high thermal stress cycles. Such high temperature can lead to cable insulation degradation, which will reduce the projected lifetime. Existing methods mainly focus on cable fault detection or insulation degradation mechanism. There is no existing tools for diagnosing the insulation degradation level and predicting the remaining useful life of the cable. The goal of my Ph.D. research is to develop reflectometry and data based approaches to monitor the health status of cables. The research will be conducted in three steps: (1) development of reflectometry based method to monitor the cable insulation degradation; (2) feature extraction and cable insulation degradation dynamic modeling based on the accelerated aging test data; (3) development of risksenstive particle filtering based fault diagnosis and prognosis algorithms for cable degradation; and (4) verification and validation the proposed solution with new experiment data and comparison with existing approaches.

Novel Waveforms, Models, Algorithms for Cable Health Monitoring