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arXiv 2203.04815eess.SYcs.LGcs.SYmath.DS

Machine Learning based Optimal Feedback Control for Microgrid Stabilization

University of Tennessee, Knoxville(田纳西大学诺克斯维尔分校);Naval Postgraduate School(海军研究生院)

Tianwei Xia, Kai Sun, Wei Kang

英文摘要:

Microgrids have more operational flexibilities as well as uncertainties than conventional power grids, especially when renewable energy resources are utilized. An energy storage based feedback controller can compensate undesired dynamics of a microgrid to improve its stability. However, the optimal feedback control of a microgrid subject to a large disturbance needs to solve a Hamilton-Jacobi-Bellman problem. This paper proposes a machine learning-based optimal feedback control scheme. Its training dataset is generated from a linear-quadratic regulator and a brute-force method respectively addressing small and large disturbances. Then, a three-layer neural network is constructed from the data for the purpose of optimal feedback control. A case study is carried out for a microgrid model based on a modified Kundur two-area system to test the real-time performance of the proposed control scheme.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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