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 powe...
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| creator | ZHANG CHU ZHANG XINRONG PENG TIAN CHEN JIE WANG YIWEI CHEN SHUAI WANG ZHENG GE YIDA CHEN JIALEI |
| description | 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 |
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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</description><language>chi ; eng</language><subject>CALCULATING ; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTINGELECTRIC POWER ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER ; 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| subjects | CALCULATING CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTINGELECTRIC POWER COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING CONVERSION OR DISTRIBUTION OF ELECTRIC POWER COUNTING DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES ELECTRIC DIGITAL DATA PROCESSING ELECTRICITY GENERATION PHYSICS SYSTEMS FOR STORING ELECTRIC ENERGY SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR |
| title | Wind power plant climbing event prediction method based on data enhancement |
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