Abstract
The traditional wind turbine fault monitoring is often based on a single monitoring signal without considering the overall correlation between signals. A global condition monitoring method based on Copula function and autoregressive neural network is proposed for this problem. Firstly, the Copula function was used to construct the binary joint probability density function of the power and wind speed in the fault-free state of the wind turbine. The function was used as the data fusion model to output the fusion data, and a fault-free condition monitoring model based on the auto-regressive neural network in the faultless state was established. The monitoring model makes a single-step prediction of wind speed and power, and statistical analysis of the residual values of the prediction determines whether the value is abnormal, and then establishes a fault warning mechanism. The experimental results show that this method can provide early warning and effectively realize the monitoring of wind turbine condition.
Highlights
Wind energy has played an increasingly important role in the world energy structure in recent years, which results in the rapid development of wind power equipment
This paper proposes a wind turbine condition monitoring method based on Copula function and auto-regressive neural network, and uses Copula function to construct a joint probability density function data fusion model of wind speed and power binary monitoring parameters
The non-linear auto-regression (NAR) and other dynamic networks can feed back the output signal to the input end, so that the output signal can participate in the iterative training with memory function, so it can better describe the characteristics of time-varying systems with non-stationary, nonlinear and other complex mapping relationships [9], and overcome the shortcomings of the auto-regressive moving average (ARMA) model that can only be modelled for stationary linear signals
Summary
Wind energy has played an increasingly important role in the world energy structure in recent years, which results in the rapid development of wind power equipment. BP neural network was applied to turbine fault forecasting, and a multi-agent method was proposed to give comprehensive evaluation of the overall operating status [3]. Nonlinear State Estimate Technology (NEST) was deployed to detect and early-warn the temperature of gearbox bearings in wind turbines [5]. An artificial neural network approach was applied to the gearbox bearing and cooling oil temperature condition monitoring modelling [6]. Condition monitoring technology has developed greatly, but it is still based on single parameters (vibration, temperature, etc.) at present to detect turbine status and carry out fault early warning. This paper proposes a wind turbine condition monitoring method based on Copula function and auto-regressive neural network, and uses Copula function to construct a joint probability density function data fusion model of wind speed and power binary monitoring parameters
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