A General Dynamic State Estimation Framework for Monitoring and Control of Permanent Magnetic Synchronous Generators-Based Wind Turbines

Abstract

Existing Dynamic State Estimation (DSE) techniques for Permanent Magnetic Synchronous Generators-based Wind Turbines (PMSG-WTs) are impractical as they blend the physical dynamics of PMSG-WTs (i.e. plants) with the digital dynamics of the controllers. In this paper, a general DSE framework for PMSG-WT monitoring and control is proposed, which decouples the plant model from the controller model. Based on the decoupled models, the state transition equations and measurement equations of the plant are derived respectively. Then, based on the equivalence between the correction stage of iterated extended Kalman filtering (IEKF) and the weighted least squares (WLS) regression, a DSE algorithm that can effectively filter out noise and bad data is presented. Simulation results in the IEEE 39-bus system show that the DSE improves the accuracy of state trajectory monitoring than the raw measurements by 64.9%-78.4% and the accuracy of control setpoint tracking by 25.5%-33.9%.

Keywords

• Permanent magnetic synchronous generators
• dynamic state estimation
• Kalman filtering
• situational awareness
• power system control
• wind generation