Adaptable Parameters Estimation for Microgrid Distributed Energy Resources Using Modified Physics-Informed Neural Network

Parameters estimation in microgrids remains challenging due to ambiguous system dynamics brought by distributed energy resources (DERs) and scarcity of data. This study presents a modified physics-informed neural network (PINN) paradigm designed for parameters estimation under such constraints. This paper introduces two main innovations: First, by combining small-signal analysis with the PINN framework for ordinary differential equations, we introduce an adaptable parameter estimation paradigm applicable to different types of DERs in microgrid. Second, we introduce a modified data transformation that reduces training time by up to 82.87% compared to traditional PINN approaches at best. To validate our approach, we conducted simulation on two typical system setups based on open-source real-world microgrid using real-time digital simulation to generate data. We evaluate the proposed method by using an error margin below 5% as a key metric to confirm its robustness and accuracy for different types of DERs. The experimental results demonstrate the effectiveness and adaptability of the proposed method across varying ordinary differential equations to diverse mathematical models. Additionally, suboptimal and failed cases are analyzed and discussed to provide a comprehensive evaluation of the method's limitations.