Model prediction control method of nonlinear unstable wind turbine cabin suspension system

Abstract

According to the invention, nonlinear unstable wind turbine cabin suspension system control is divided into a stabilization part and a performance improvement part; the system comprises a cabin suspension stabilization controller, a cabin suspension prediction controller and a model mismatch compensator of an RBF neural network. Cabin suspension current reference setting is cooperatively completed; an unstable cabin suspension system is stabilized by adopting state feedback; the invention discloses a stabilization-based cabin suspension system model. Constructing a discretized model predictionmodel; and comprehensively considering air gap tracking errors, air gap speed and multi-objective optimization state variables input by control variables, implementing prediction of reference current, adopting a model mismatch compensator of an RBF neural network to approach a model mismatch value online, and combining a model mismatch dynamic adjustment coefficient to jointly complete model mismatch compensation. According to the method, the influence of model mismatch on model prediction control performance is effectively solved, Meanwhile, an intelligent compensation way is provided for online optimization and adjustment of model prediction.

Description

technical field [0001] The invention discloses a suspension control method for a fan nacelle, which is a model predictive control method applied to a nonlinear unstable wind-force magnetic levitation yaw system, and belongs to the field of electrical engineering control. Background technique [0002] The wind maglev yaw system adopts the yaw after the nacelle is suspended, which improves the yaw stability of the nacelle and greatly reduces the yaw power consumption. However, the research on suspension control strategies mostly focuses on the fields of suspension balls, suspension trains and suspension bearings, and mostly focuses on PID control, sliding mode control, adaptive control, etc., but the above-mentioned controls are essentially differential adjustments. It adjusts the system based on the deviation e of the controller target, and there is hysteresis and uncertainty. The model prediction is advanced and has been widely used in the field of process control and indus...

AI Technical Summary

Problems solved by technology

However, the wind turbine nacelle suspension system is essentially a nonlinear and unstable system, and the working conditions are extremely complex and changeable. It is difficult to obtain satisfactory results based on the unsteady model to predict the control input of the future time, especially the model mismatch problem also seriously affects the nacelle. suspension pe

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