Wind power plant climbing event prediction method based on data enhancement

The invention discloses a wind power plant climbing event prediction method based on data enhancement, and the method comprises the following steps: obtaining the historical power data of a wind power plant, and carrying out the data cleaning; carrying out segmented trend extraction on the wind power data obtained in the previous step by using an MK-sliding window detection method, and carrying out climbing detection; constructing a time series generative adversarial network TimeGAN, performing data enhancement on the detected wind power climbing data, and dividing the data into a training set, a verification set and a test set; establishing an ETSform model, and inputting the obtained training set and the verification set into the ETSform model for training; a Logistic chaotic mapping strategy and a Gaussian-Cauchy mixed variation strategy are adopted to improve an artificial bee bird algorithm AHA to obtain an IAHA algorithm, the IAHA algorithm is utilized to optimize hyper-parameters of an ETSform model, a