Abstract
In wind turbine systems, the state of the generator is always disturbed by various unknown perturbances, which leads to system instability and inaccurate state estimation. In this paper, an intermediate-variable-based distributed fusion estimation method is proposed for the state estimation problem in wind turbine systems. By constructing an augmented state error system and using the idea of bounded recursive optimization, the local estimators and distributed fusion criterion are designed, which can be used to estimate the disturbance signals and system states. Then, the local estimator gains and the distributed weighting fusion matrices are obtained by solving the established convex optimization problems. Furthermore, a compensation strategy is designed by using the estimated disturbance signals, which can potentially reduce the influence of the disturbance signals on the system state. Finally, a numerical simulation is provided to show that the proposed method can effectively improve the accuracy of the estimation of the wind turbine state and disturbance, and the superiority of the proposed method is illustrated as a comparison to the Kalman fusion method.
Highlights
As a new, eco-friendly, low-cost power generation technology, wind power generation fills up the energy demand gap, and reduces the use of fossil energy
For the unknown nonlinear input signal in the wind generator model, a dynamic state estimation method based on Kalman filtering was proposed in [3], which could accurately estimate the state of the generator under the condition of uncertain wind speed
A distributed fusion estimation method based on the intermediate variable was designed to estimate the state and unknown disturbance signals of the doubly fed induction generator system
Summary
Eco-friendly, low-cost power generation technology, wind power generation fills up the energy demand gap, and reduces the use of fossil energy. As a result of the remote location of the wind farm and the harsh working environment, the control centers are ordinarily away from the wind farms, and various sensors are installed inside the generator to detect its status In this sense, this paper will focus on the multi-sensor fusion estimation problem for the wind turbine state and disturbance signals. For the unknown nonlinear input signal in the wind generator model, a dynamic state estimation method based on Kalman filtering was proposed in [3], which could accurately estimate the state of the generator under the condition of uncertain wind speed. Based on the linear variable parameter model, an unknown input observer was designed to estimate the actuator and sensor fault signals of the wind power system by constructing an augmented system [12]. The effectiveness and the advantages of the proposed method are verified by a numerical simulation
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