Cleaning method of wind turbine abnormal data
A technology for wind turbines and abnormal data, which is applied in the fields of electrical digital data processing, digital data information retrieval, and special data processing applications, etc. It can solve the problems of inability to exclude high-density curtailment data, omission of low-density sparse data, and poor adaptability to operating conditions and other issues to achieve the effect of reducing dependence, improving adaptability, and strong versatility
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[0023] The embodiments will be described in detail below in conjunction with the accompanying drawings.
[0024] The present invention proposes a method for cleaning abnormal data of wind turbines based on the combination of optimal intra-group variance and two-dimensional probability density, such as Figure 9 shown, including:
[0025] Step 1: Wind turbine operation data preprocessing;
[0026] Step 2: Use the optimal variance method within the group to eliminate the data in the power-limited area;
[0027] Step 3: Use the two-dimensional probability density estimation method to remove outliers with sparse density;
[0028] Step 4: Obtain normal operation data through the upper and lower boundary lines.
[0029] 1) Data preprocessing.
[0030] In the original data, there are many abnormal data or even empty data caused by reasons such as shutdown, failure, and sensor failure, that is, the wind speed is greater than the cut-out wind speed or less than 0, and the power is ...
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