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Data-Driven Controllability of Power Electronics Under Boundary Conditions – A Physics-Informed Neural Network Based Approach

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Abstract

This paper introduces physics-informed neural network (PINN) for control of grid connected converter by fusing its underlying equations into the training process, thereby reducing the requirement of qualitative training data. In comparison to the traditional data-driven methods, which either incur a significant computational burden, or use overly conservative surrogate models, it is explored that PINN can be easily optimized as per the performance requirements and is significantly superior in terms of computation time, data requirements (trained using only 3000 datapoints), and prediction accuracy (an average of 98.76%). As a result, PINN unravels new modeling orientation for power electronics, and is well-suited for commercial applications. Finally, its robustness under various grid disturbances has been validated under experimental conditions.

Artificial Intelligence Power Electronics Control Neural Networks Physics informed machine learning Physics informed neural network Grid connected converter Grid connected applications Active front end rectifier Machine learning