A model-based hybrid approach for circuit breaker prognostics encompassing dynamic reliability and uncertainty

Prognostics predictions estimate the remaining useful life of assets. This information enables the implementation of condition-based maintenance strategies by scheduling intervention when failure is imminent. Circuit breakers are key assets for the correct operation of the power network, fulfilling both a protection and a network reconfiguration role. Certain breakers will perform switching on a deterministic schedule, while operating stochastically in response to network faults. Both types of operation increase wear on the main contact, with high fault currents leading to more rapid ageing. This paper presents a hybrid approach for prognostics of circuit breakers, which integrates deterministic and stochastic operation through Piecewise Deterministic Markov Processes. The main contributions of this paper are (i) the integration of hybrid prognostics models with dynamic reliability concepts for a more accurate remaining useful life forecasting and (ii) the uncertain failure threshold modelling to integrate and propagate uncertain failure evaluation levels in the prognostics estimation process. Results show the effect of dynamic operation conditions on prognostics predictions and confirm the potential for its use within a condition-based maintenance strategy

Towards a hybrid power cable health index for medium voltage power cable condition monitoring

Power cables are critical assets for the safe and cost effective operation of the power grid. However, the health assessment of cables is intricate due to its various degradation mechanisms. Motivated by the generation of a consistent cable health index metric, this paper presents a novel hybrid cable health index approach which combines data-driven and physics-of-failure degradation models. Data-driven models are focused on partial discharge tests and physics-of-failure models are focused on cable lifetime estimation based on thermal and electrical stresses. The combination of these models offers an enhanced cable health indicator which supports engineers in the power cable maintenance related decision-making processes

Towards a data analytics framework for medium voltage power cable lifetime mangement

Power cables are critical assets for the reliable and cost-effective operation of nuclear power plants. The unexpected failure of a power cable can lead to lack of export capability or even to catastrophic failures depending on the plant response to the cable failure and associated circuit. Prognostics and health management (PHM) strategies examine the health of the cable periodically to identify early indicators of anomalies, diagnose faults, and predict the remaining useful life. Traditionally, PHM-related strategies for power cables are considered separately with the associated penalties involved with this decision. Namely, there is a lack of consideration of the interactions and correlations between failure modes and PHM tests, which results in scalability issues of ad-hoc experiments, and accordingly incapability to exploit the full potential for PHM strategies in an effective manner. An effective and flexible PHM strategy should be able to consider not only different PHM strategies independently, but also it should be able to fuse these tests into a cable health state indicator. The main contribution of this paper is the proposal of a PHM-oriented data analytics framework for medium voltage power cable lifetime management which incorporates anomaly detection, diagnostics, prognostics, and health index modules. This framework includes the characterization of existing data sources and PHM-oriented data analytics for cable condition monitoring. This process enables the creation of a database of existing datasets, identification of complementary PHM techniques for an improved condition monitoring, and implementation of an end-to-end PHM framework