A pca-knn based converter fault identification method for wind power generation system
A technology for wind power generation system and fault identification, applied in the direction of measuring electrical variables, instruments, measuring electricity, etc., can solve problems such as converter fault identification, improve accuracy and efficiency, reduce redundancy, and reduce classification time Effect
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[0057] Below in conjunction with accompanying drawing and specific embodiment the present invention is described in further detail:
[0058] The present invention provides a fault identification method for converters of wind power generation systems based on PCA-kNN, which extracts the characteristics of the time domain, frequency domain and time-frequency domain of the DC side DC voltage of the converters of wind power generation systems, and utilizes the dimensionality reduction capability of PCA to analyze these The features are dimensionally reduced, and finally the kNN algorithm is used to classify the samples to be tested. The overall algorithm principle flow process of the present invention is as figure 1 As shown, the specific steps are as follows:
[0059] Step 1: Detect the DC side output voltage signal of the back-to-back three-phase PWM rectifier in the wind power generation system under various open circuit faults;
[0060] The various open-circuit faults of the...
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