Study On Mechanical Fault Diagnosis Of Large-scale Wind Turbines Based On Chaos And Fractal Theory

Wind power, as a kind of green energy, draws wide attention, and China has retainedits crown as the world’s largest wind manufacturing and wind farm building nation inrecent years. At the same time, wind power industy faces the problems of how to improveoperational stability and decrease fault ratio, which is the the bottleneck restricting furtherdevelopment to a certain extent. Wind power industy development shifts from scale andspeed to quality and efficiency in China, so it is an important content in the research ofincipient fault diagnosis and condition monitoring for wind power equipments. Every rigidconnecting part of wind power equipments is prone to various faults due to abominableworking environment, the big alternant loads produced by changing vane rotating based onwind speed. Some research work shows that the vibration fault in transmission system hashigher proportion compared to other wind turbine parts, so the condition monitoring to thetransmission system is critical. Currently lots of commercial systems can obtain the windturbine operating data in real time and provide the early warning and alarming function.However, these systems are lack of vibration monitoring and relative analysis. Traditionalfault diagnosis technology is also inefficient to the wind turbine equipments because ofstrong noise, instability and nonlinearity, especially to incipient fault diagnosis. Thereforethis paper selects the transmission system as the keystone research subject, and utilze chaosand fractal theory to analyze the vibration signals fault diagnosis and running stateprognosis. The main research work and conclusion are as follows:Vibration signal characteristics of wind turbines are analyzed in the paper. And at arelatively lower signal-to-noise ratio (SNR) the accuracy of fault signal feature extractionis greatly improved, which is effective proved by experimental results, because Duffingoscillator is high sensitive to weak periodical signals and immune to noise. Sampling integraltechnology is applied to improve the signal tracking ability, and maximum Lyapunov exponentis adopted as the quantitative judgment of fault signals. Angular resampling algorithm for applying in conditions monitoring of speed variability, as occurs in wind turbines, can changethe signals from time domain to angle domain in order to remove the speed fluctuations. Then,Duffing oscillator is utilized to extact the angle domain characteristic signals.Fractal dimensions of vibration signals are calculated to distinguish wind turbine differentworking state. Box dimension, correlation dimension and multifractal dimension of windturbine vibration signals are calculated separately. The method of monitor conditioning basedon fractal dimension is modified combined with bi-scale characteristic of vibration signal andthe wavelet packet transformation. Fractal dimension can be used as an efficient measure ofwind turbine different working state or fault by comparing results of dimension distance of themeasured signals to different known state.Based on maximum Lyapunov exponent，the local chaotic time series prediction model ispresented to forecast the wind turbine working state. As the tool of recognizing system chaoticbehavior, reconstructing phase space and nonlinear function approximation technique presentsdynamic model. The prediction model is directly from objective laws of time series, whichavoids the defects of human subjectivity and improve the precision and reliability of forecastresults.