Physics-Informed Neural Network for Parameter Identification: a Buck Converter Case Study

System-level condition monitoring methods estimate the electrical parameters of multiple components in a converter to assess their health status. The estimation accuracy and variation can differ significantly across parameters. For instance, inductance estimations are generally more accurate and stable than inductor resistance in a buck converter. However, these performance differences remain to be analyzed with a more systematic approach otherwise the condition monitoring results can be unreliable. Therefore, this paper analyzes the training loss landscape against multiple parameters of a buck converter to provide a systematic explanation of different performances. If the training loss is high and smooth, the estimated circuit parameter typically is accurate and has low variation. Furthermore, a novel physics-informed neural network (PINN) is proposed, offering faster convergence and lower computation requirements compared to an existing PINN method. The proposed method is validated through simulations, where the loss landscape identifies the unreliable parameter estimations, and the PINN can estimate the remaining parameters.

A Data-Driven Condition Monitoring Method for Capacitor in Modular Multilevel Converter

The modular multilevel converter (MMC) is a topology that consists of a high number of capacitors, and degradation of capacitors can lead to converter malfunction, limiting the overall system lifetime. Condition monitoring methods can be applied to assess the health status of capacitors and realize predictive maintenance to improve reliability. Current research works for condition monitoring of capacitors in an MMC mainly monitor either capacitance or equivalent series resistance (ESR), while these two health indicators can shift at different speeds and lead to different end-of-life times. Hence, monitoring only one of these parameters may lead to unreliable health status evaluation. This paper proposes a data-driven method to estimate capacitance and ESR at the same time, in which particle swarm optimization (PSO) is leveraged to update the obtained estimations. Then, the results of the estimations are used to predict the sub-module voltage, which is based on a capacitor voltage equation. Furthermore, minimizing the mean square error between the predicted and actual measured voltage makes the estimations closer to the actual values. The effectiveness and feasibility of the proposed method are validated through simulations and experiments.