Power grid frequency situation prediction method based on transfer learning
The invention discloses a power grid frequency situation prediction method based on transfer learning, which can adapt to frequency situation prediction requirements when a power grid operation mode and a topological structure are changed, and improves accuracy and reliability of a model. The method...
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| creator | WANG GUOSONG YUAN XIAOQING ZHANG DAN TANG JIANXING YAO YAO ZHU LINGZI HE XIANQIANG MA TANFENG LIU MINGSHUN WANG YIN QIN HAI OUYANG KEFENG FAN XIANG CHEN RUI CAO JIE |
| description | The invention discloses a power grid frequency situation prediction method based on transfer learning, which can adapt to frequency situation prediction requirements when a power grid operation mode and a topological structure are changed, and improves accuracy and reliability of a model. The method comprises the following steps: firstly, constructing a post-fault frequency situation prediction model based on a convolutional neural network (CNN), then obtaining enough samples for training by utilizing a transfer learning method, and correcting parameters of the CNN frequency situation prediction model, thereby obtaining a more accurate prediction model, and improving the precision of a system frequency prediction result. The precision of the frequency situation prediction model can be remarkably improved, the frequency change situation of the disturbed system can be predicted more accurately, corresponding control measures such as generator tripping, load shedding and direct-current emergency power control ar |
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The method comprises the following steps: firstly, constructing a post-fault frequency situation prediction model based on a convolutional neural network (CNN), then obtaining enough samples for training by utilizing a transfer learning method, and correcting parameters of the CNN frequency situation prediction model, thereby obtaining a more accurate prediction model, and improving the precision of a system frequency prediction result. 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The method comprises the following steps: firstly, constructing a post-fault frequency situation prediction model based on a convolutional neural network (CNN), then obtaining enough samples for training by utilizing a transfer learning method, and correcting parameters of the CNN frequency situation prediction model, thereby obtaining a more accurate prediction model, and improving the precision of a system frequency prediction result. The precision of the frequency situation prediction model can be remarkably improved, the frequency change situation of the disturbed system can be predicted more accurately, corresponding control measures such as generator tripping, load shedding and direct-current emergency power control ar</abstract><oa>free_for_read</oa></addata></record> |
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| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES PHYSICS SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR |
| title | Power grid frequency situation prediction method based on transfer learning |
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