Power grid fault first-aid repair duration prediction method based on multi-model fusion

Abstract

The invention provides a power grid fault first-aid repair duration prediction method based on multi-model fusion, and the method comprises the steps: cleaning inputted feature data before the prediction of fault first-aid repair duration, and eliminating abnormal data; carrying out feature engineering construction by using the cleaned data, selecting feature data, and respectively inputting the feature data into a prediction model for modeling; in the modeling process, adjusting and optimizing single model parameters, and outputting an optimal prediction result; and performing weighted summation on the prediction result, and outputting a final prediction value. Modeling is carried out by using multiple machine learning methods, a prediction result is weighted, and the advantage characteristics of multiple models are fused, so that the prediction result which is more accurate than that of a single model is obtained. According to the method, the fault repair time can be accurately estimated, and better support is provided for automation and intelligence of power grid fault repair.

Description

technical field [0001] The invention belongs to the field of model prediction, and in particular relates to a method for predicting the duration of emergency repair of power grid faults based on multi-model fusion. Background technique [0002] The analysis and prediction of power grid faults is an important part of power grid automation and intelligence. Due to the variety of power grid faults and the complex reasons, the analysis and prediction of power grid faults are more difficult, especially the prediction of the duration of emergency repair of power grid faults. With the construction of smart grid, the level of dispatching automation has been continuously improved, and the functions of the information collection system have been continuously strengthened. Many scholars have carried out research on power grid fault prediction. Reference [1] summarizes the fault diagnosis methods that are widely used in power grid fault diagnosis combined with a variety of intelligent ...

AI Technical Summary

Problems solved by technology

Existing studies mostly use the information collected inside the power grid as the basis for prediction, such as voltage, current, power and other indicators. These indicators reflect the operating status of the power grid to some extent, but ignore external factors such as weather and man-made sabotage.

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