Research On Fault Trend Prediction Method Of Wind Turbine Based On Data

As more and more domestic wind turbine rotor is, how to reduce operating costs, improve operational efficiency, prolong the service life of the fan to become an important topic of the operating units. At present, wind power equipment maintenance technology maturity to keep pace with the development of wind turbine speed, fan operation process often appear gear box, main shaft, leaf, generator and transformer is damaged, or even a wind turbine collapse accident. Gearbox is the key equipment during the operation and maintenance of doubly-fed wind turbines, and not only has a high failure rate, but needs relatively longer repair time. Once a fault occurs, which can lead to huge economic losses.This paper conducts research on fault trend prediction method of wind turbine gearbox, aiming to judge the future running state of gearbox, predict its development trend and estimate the remaining life, which will provide important reference and basis for the operation and maintenance of wind turbines.Firstly, a common structure for wind turbine gearboxes brief overview of typical fault type gearbox is described based on the four most common gearbox failure mode (mode fatigue, wear mode, overload mode and corrosion mode).Secondly, based on the description and prediction theory premise assumptions, using trend prediction method (BP neural network) data based on a set of fault simulation to predict signal to identify the most appropriate network parameters and structure of BP by comparative analysis.Finally, the optimization algorithm and genetic algorithm particle swarm optimization of BP neural network, making the prediction model to rationalize been improved prediction accuracy is verified PSO and genetic algorithm optimization of the feasibility and effectiveness.