Condition Monitoring of DC-link Capacitors in Grid-tied Solar Inverters Using Data-Driven Techniques

DC-link capacitors are at the heart of every power electronic inverter. A major drawback of DC-link capacitors is their aging factor which is due to various thermal and electrical stresses. This results in reduced system efficiency and complete system failure if left untreated. This paper proposes a data-driven condition monitoring system for the DC-link capacitor in grid-tied solar inverters. Compared to the literature, the condition of a DC-link capacitor is estimated without directly predicting the value of its capacitance or ESR. By treating the solar inverter system as a black box and by using the existing system sensors, the proposed condition monitoring method outputs the condition of the capacitor and classifies it as healthy, half-degraded or fully degraded. The proposed method is non-invasive and low-cost, where no additional sensors are required. By relying on classification techniques, this work addresses the difficulty of collecting real-world datasets with small incremental capacitance steps used in training regression models that predict capacitance values. Features not previously used in the literature are explored and result in very high classification accuracy. The proposed method is tested on a single-stage, single-phase grid tied PV inverter simulation model. The proposed method achieves 99% accuracy, a value similar to that found in the state of the art. The feasibility of the proposed method is tested on a low-cost microcontroller.