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Numerical simulation of a novel fuzzy control and back propagation neural network (BPNN) control for divergent instability based on classical flutter of 5-DOF wind turbine blade section driven by pitch adjustment has been investigated. The work is dedicated to solving destructive flap/lag/twist divergent instability from classical flutter, which might occur during the gust wind action, and might cause fracture failure of the blade itself and tower body. In order to investigate the optimal control method, the parameters of blade section are specially designed so as to simulate the actual situation, which lead to absolutely divergent motions (ADM) under gust wind load. The control of ADM often leads to limit cycle oscillation (LCO), the larger amplitude of which is likely to cause fracture failure of tower body. A novel fuzzy control method with adjustable quantization gain and BPNN control strategy are investigated in order to effectively eliminate LCO (leading to direct convergence of the system) or reduce the amplitude of LCO. The obvious effects of fuzzy control and BPNN control are illustrated by numerical comparisons of vibration suppression from nonlinear time response, amplitude of LCO and frequency spectrum analysis. An experimental platform is built based on hardware-in-the-loop simulation by way of PLC-OPC technology in order to test the real-time performance of the control algorithm. The feasibility of the control algorithm is demonstrated by the experimental results displayed by touch-screen hardware.

The limit cycle oscillation of divergent instability control based on classical flutter of blade section