Data-Driven Reliability Optimization of Renewable Energy Power Converters Through Predictive Fault Detection and Adaptive Control

Author

Ravanbakhsh Leikh

Abstract

 Increasing penetration of renewable energy is shifting power systems toward converter-dominated architectures in which operational reliability depends strongly on the condition and control of power electronic interfaces. Variable generation, switching stress, thermal cycling, environmental exposure, and fluctuating loading progressively degrade semiconductor devices, DC-link capacitors, gate drivers, and cooling assemblies, creating failure mechanisms that conventional threshold-based protection may identify too late. This study develops a data-driven reliability optimization framework integrating multimodal condition monitoring, predictive fault detection, degradation estimation, and adaptive converter control. Electrical, thermal, switching, environmental, and operational measurements are transformed into condition indicators capturing resistance growth, voltage ripple, switching-loss variation, thermal stress, efficiency deterioration, and temporal degradation trajectories. Machine-learning models estimate anomaly states, fault probability, and remaining useful life, while uncertainty and feature attribution establish prediction confidence and physical interpretability. These outputs subsequently inform adaptive control actions including switching adjustment, thermal-stress reduction, power derating, load redistribution, and operating-point reconfiguration. By closing the loop between degradation prediction and control response, the framework shifts converter reliability management from reactive fault handling toward condition-aware operation that suppresses accumulated stress, extends component lifetime, reduces unexpected outages, and improves renewable-energy availability.

Keywords

Renewable Energy Power Converters; Predictive Fault Detection; Adaptive Control; Reliability Optimization; Remaining Useful Life; Condition Monitoring

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References

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