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Method and device for predicting power failure number of power system station, equipment and medium

A technology of electric power system and prediction method, applied in the field of electric power system, can solve the problem of low model prediction accuracy, and achieve the effect of improving local search ability, improving accuracy and simplifying complexity

Pending Publication Date: 2021-11-12
ELECTRIC POWER RESEARCH INSTITUTE, CHINA SOUTHERN POWER GRID CO LTD +1
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Problems solved by technology

[0004] The present invention provides a method, device, equipment and medium for predicting the number of power outages at a power system site, so as to solve the problem in the prior art that the input hyperparameters are random and lead to low prediction accuracy of the model, thereby improving the accuracy of the model prediction

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  • Method and device for predicting power failure number of power system station, equipment and medium
  • Method and device for predicting power failure number of power system station, equipment and medium

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Embodiment Construction

[0049] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0050] see figure 1 , is a schematic flowchart of a method for predicting the number of outages in power system sites provided by an embodiment of the present invention, the method includes steps S11 to S13:

[0051]S11. Obtain the power parameters of the power system after preprocessing;

[0052] S12. Establish a prediction model based on the XGBoost algorithm, and optimize hyperparameters of the prediction model to obtain an optimal hyperparameter combination of...

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Abstract

The invention provides a method and device for predicting the power failure number of a power system station, equipment and a medium. An initial population can be generated through a Monte Carlo method, so that the problem of low algorithm search capability caused by non-uniform solution distribution due to random population initialization of most algorithms is avoided; and the scaling factor and the crossover probability are dynamically adjusted in the evolution process through the adaptive differential evolution algorithm, so that the local search capability of the algorithm is improved. By adopting the embodiment of the invention, hyper-parameter optimization can be carried out on the XGBoost classification algorithm based on the Monte Carlo method and the adaptive differential evolution algorithm, so that the accuracy of a model prediction result is improved, additional manual adjustment and optimization are not needed, and the complexity of parameter adjustment and optimization of the combined model is simplified.

Description

technical field [0001] The present invention relates to the technical field of power systems, in particular to a method, device, equipment and medium for predicting the number of power outages at power system sites. Background technique [0002] In the power system, compared with the power transmission equipment and power consumption equipment, the distribution network equipment is mainly exposed to the outdoor environment. Under different weather conditions, the faults of the power distribution equipment are easily affected to varying degrees, thus affecting the normal operation of the power system. run. Therefore, for different weather conditions, it is of great significance to predict the number of power outages at the site in advance for timely warning and processing, and to increase the self-adaptation of the power grid to natural disasters. [0003] In recent years, a large number of domestic and foreign scholars have used machine learning algorithms to predict the nu...

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Application Information

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IPC IPC(8): G06Q10/04G06N3/08G06N5/00G06Q50/06
CPCG06Q10/04G06N3/086G06Q50/06G06N5/01
Inventor 苏寅生周挺辉周保荣赵利刚甄鸿越黄冠标王长香吴小珊徐原翟鹤峰涂思嘉
Owner ELECTRIC POWER RESEARCH INSTITUTE, CHINA SOUTHERN POWER GRID CO LTD
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