Physics-informed neural networks improve power converter parameter estimation
Sequential Learning-Based Physics-Informed Neural Network Modeling for Parameter Estimation on a Power Converter
Machine Learning
Summary
Measuring exact electrical properties in devices like power converters can be hard, especially when small resistances affect performance. This paper presents a step-by-step machine learning method that uses physics knowledge to accurately estimate these properties from voltage and current data. The authors show that their method is more precise and faster than training all parameters at once, even when multiple properties change at the same time. This approach helps better understand and model power converters without complicated equipment.
What this means in practice
- •For power electronics engineers: Estimate key component values in DC buck converters from operating data to improve design and fault detection.
- •For renewable energy system integrators: Use accurate parameter models for power converters to enhance control and reliability in solar and wind systems.
Authors
Samuela Rokocakau, Panagiotis Kakosimos, Giulia Tresca, Pericle Zanchetta
Abstract
A staged physics-informed neural network (PINN) is proposed for identifying the inductance, capacitance and parasitic resistances of a DC buck converter from time-domain data. Gradients are released in the order of L to C to rL to rC, a sequence derived from sensitivity analysis. The five-stage schedule recovers L and C to within 5% and the series ESR to 10%, while a three-stage variant attains the same accuracy 11% faster. An 84 factorial sweep confirms that the trained PINN remains accurate for simultaneous 8% perturbations of all four components, achieving a waveform RMSE of 2.6 x 10-2 over 5 ms-an order-of-magnitude better than a simultaneous-training baseline.