Work

Switching Dynamic State Estimation and Event Detection for Inverter-Based Resources With Multiple Control Modes

Year: 2024

Type: article

Abstract: The Dynamic State Estimation (DSE) for Inverter-Based Resources (IBRs) is an emerging topic as IBRs gradually replace Synchronous Generators (SGs) in power systems. Unlike SGs, the dynamic models of IBRs heavily depend on their control algorithms, and conventional DSE methods for SGs, which assume a unchanged state space and dynamic model, cannot handle IBRs with control mode changes in real time, particularly when the power grid operators are unaware of the current control mode of the IBRs. In response to these challenges, an Expectation-Maximization Sliding-Window Iterated Extended Kalman Filter (EM-SW-IEKF) method is proposed in this paper. It theoretically achieves maximum likelihood estimation under different modes through the EM algorithm, providing the most probable control mode of the system as well as the corresponding state estimate. This method is validated in various IBR systems (battery energy storage systems and solar photovoltaic systems) and under different control mode transitions (switching between grid-following and grid-forming controls and between low voltage ride through and maximum power point tracking controls).

Source: IEEE Transactions on Power Systems

DOI: 10.1109/tpwrs.2024.3523490

Authors: Heqing Huang, Yuzhang Lin

Institution: New York University

Language: English

Topic: Power System Optimization and Stability

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $2,495