INVESTIGATION OF POSSIBILITIES OF USING KERNELS OF LINEAR AR AND ARMA PROCESSES AS DIAGNOSTIC PARAMETERS OF TECHNICAL CONDITION OF ROTARY NODES OF GENERATORS OF WIND TURBINE
The paper considers some methods of diagnosing the technical condition of rotating units of wind turbine generators. It is proposed to use linear processes of autoregression (AR) and auto regression-moving average (ARMA) as mathematical models of vibrations of wind turbine generator units. Such processes belong to linear random processes with discrete time, which have infinitely divisible distribution laws. The peculiarities of such processes are that the autoregression and moving average coefficients are directly related to the kernel of linear random processes with discrete time. This makes it possible to construct recurrent algorithms for estimating the kernels of linear random processes with discrete time. As an example of the use of the proposed approach, the vibration signal of the rotating unit of the rolling bearing of the wind turbine generator USW 56-100 from the side of the main shaft mounted on the stand for testing wind turbines is considered. The speed of rotation of the main shaft was 72 rpm. For the study of vibration signals, a prototype of the wind generator diagnostic system developed at the Institute of electrodynamics of the National Academy of Sciences of Ukraine was used, with the help of which vibration signals were registered and estimates of the kernels of linear random processes were obtained. Different criteria were used to estimate the autoregression parameters, namely, the final prediction error (FPE) and the Henn-Quinn test (HQ). Some parameters of the kernels of linear AR processes are shown, which can be used as diagnostic signs of the technical condition of the units of wind turbine generators.
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