Method and system for predicting power grid defect materials through multi-model fusion
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A multi-model and defective technology, applied in the field of power grid material information processing, can solve the problems of low material storage efficiency, achieve the effects of reducing costs, enhancing reliability, and improving planning capabilities
Pending Publication Date: 2020-09-08
GUIZHOU POWER GRID CO LTD
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[0002] During the operation of the power grid, many devices will fail due to various reasons, such as load during operation, thunderstorm weather, heavy snow weather and landslides, etc. Some failures cannot be solved t
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Embodiment 1
[0039] refer to figure 1 and figure 2 , which is the first embodiment of the present invention, this embodiment provides a method for multi-model fusion to predict power grid defect materials, including:
[0040] S1: Construct regression model, negative feedback neural network model, gradient boosting tree GBDT model and XgBoost model sequentially based on the failure probability strategies of different regions, time and equipment. What needs to be explained is:
[0041] The regression model solves the optimization objective, as follows,
[0042]
[0043] Among them, n samples : number of samples, w: weight coefficient of the vector in each dimension of the sample, X: sample data, y: amount of material defects, α, β: regularization coefficient, ||w|| 1 ,||w|| 2 Respectively, the first-order norm and the second-order norm of the coefficient;
[0044]
[0045] in, The predicted value of defective materials (the unit is a piece, and the value is a positive real num...
Embodiment 2
[0091] refer to image 3 , which is the second embodiment of the present invention. This embodiment is different from the first embodiment in that it provides a system for multi-model fusion prediction of power grid defect materials, including:
[0092] The collection module 100 is used for collecting meteorological data and historical defect data and constructing a sample data set.
[0093]The data processing center module 200 is used to receive, calculate, store, and output data information to be processed, which includes a computing unit 201, a database 202, and an input and output management unit 203. The computing unit 201 is connected to the collection module 100 for receiving and collecting The data information obtained by the module 100 is used for calculation and processing, and the prediction results of each model and the fusion prediction results are calculated. The database 202 is connected to each module to store all the data information received, and provides dep...
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Abstract
The invention discloses a method and system for predicting power grid defect materials through multi-model fusion. The method comprises the steps: sequentially building a regression model, a negativefeedback neural network model, a gradient boosting tree GBDT model and an XgBoost model based on the fault probability strategies of different regions, time and equipment; uniformly inputting the acquired defective material data and the corresponding meteorological data into the regression model, the negative feedback neural network model, the gradient boosting tree GBDT model and the XgBoost model for training; respectively outputting prediction results corresponding to the models; and performing fusion processing on the trained multiple models by using a multi-model fusion strategy to form aprediction model, and solving an average value of the prediction result to obtain a final fusion prediction result. According to the invention, early storage of emergency repair material types, scales and places is guided, the prospective planning capability of material management of a power grid company is improved, and the cost of operation materials of a power grid enterprise is reduced whilethe operation reliability of a power grid is enhanced.
Description
technical field [0001] The invention relates to the technical field of information processing of electric power materials in a power grid, in particular to a method and system for predicting defective materials in a power grid through multi-model fusion. Background technique [0002] During the operation of the power grid, many equipment will fail due to various reasons, such as load during operation, thunderstorm weather, heavy snow weather and landslides, etc. Some failures cannot be solved through maintenance, so the materials need to be repaired Reserves, traditional material storage is relatively inefficient in terms of efficiency, that is, through some simple analysis and strategies to ensure the supply of materials. [0003] With the development and transformation of power grid enterprises, the efficiency of warehousing and scheduling has received attention. With the development of technologies such as big data, Internet of Things, and artificial intelligence, data ac...
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