Gray-Box Modeling for Distribution Systems with Inverter-Based Resources
Junhui Zhang, Yuxi Men, Lizhi Ding, Xiaonan Lu, Wei Du
Abstract
Junhui Zhang, Yuxi Men, Lizhi Ding, Xiaonan Lu, Wei Du
Abstract
In this paper, we develop a novel gray-box modeling approach for distribution systems with inverter-based resources (IBRs). The proposed gray-box modeling method aims to improve estimation accuracy by taking advantages of both physics-based (white-box) and data-driven (black-box) modeling approaches. To this end, the gray-box modeling framework is constructed by encoding prior physical knowledge of the system into a white-box model and then embedding the output variables of the white-box model into the input vector of a black-box model. Especially, the white-box modeling component is constructed alongside the equivalent network model simplified with Kron reduction. Furthermore, case studies demonstrate that our gray-box modeling approach effectively improves estimation accuracy compared to purely physics-based or data-driven methods.
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In this paper, we develop a novel gray-box modeling approach for distribution systems with inverter-based resources (IBRs). The proposed gray-box modeling method aims to improve estimation accuracy by taking advantages of both physics-based (white-box) and data-driven (black-box) modeling approaches. To this end, the gray-box modeling framework is constructed by encoding prior physical knowledge of the system into a white-box model and then embedding the output variables of the white-box model into the input vector of a black-box model. Especially, the white-box modeling component is constructed alongside the equivalent network model simplified with Kron reduction. Furthermore, case studies demonstrate that our gray-box modeling approach effectively improves estimation accuracy compared to purely physics-based or data-driven methods.
Key concepts: White box, Black box, Box model, Gray (unit), Computer science, Embedding, Data modeling, Inverter