Artificial intelligence prediction model construction method applying power distribution network full-service big data

A predictive model and artificial intelligence technology, applied in the field of power grids, can solve problems such as frequent load changes, complex operation and maintenance services, complex and changeable distribution network faults, etc.

Pending Publication Date: 2021-03-19
STATE GRID LIAONING ELECTRIC POWER RES INST
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AI Technical Summary

Problems solved by technology

[0002] With the increasing complexity of the distribution network, the overall coverage area is wide, the operation and maintenance business is complex, the equipment changes f

Method used

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Embodiment Construction

[0021] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0022] An artificial intelligence forecasting model construction method applying distribution network full-service big data is characterized in that it includes the following steps:

[0023] 1) Data preprocessing: Select the business data of the distribution network business for analysis and verification. The overall data set is divided into a training set and a test set. The characteristics are transaction data and technical indicators. The transaction data needs to be normali...

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Abstract

The invention relates to the technical field of power grids, and discloses an artificial intelligence prediction model construction method applying full-service big data of a power distribution network, which is characterized by comprising the following steps of: 1) data preprocessing: selecting service data of power distribution network services for analysis and verification, and dividing an overall data set into a training set and a test set, wherein the characteristics are transaction data and technical indexes, the transaction data needs to be subjected to normalization processing, the technical indexes are obtained through calculation of the transaction data, and a normalization process is included in a calculation process. According to the artificial intelligence prediction model construction method applying the power distribution network full-service big data, power distribution network reliability weak points, high-loss points and potential safety hazard points can be accurately positioned and graded, power distribution network fatigue aging and potential quality hazard equipment are rapidly sorted, operation and maintenance key lines and equipment are comprehensively mastered, and a guidance basis is provided for a power distribution network technical improvement scheme.

Description

technical field [0001] The invention relates to the technical field of power grids, in particular to an artificial intelligence prediction model construction method using full-service big data of distribution networks. Background technique [0002] With the increasing complexity of the distribution network, the overall coverage area is wide, the operation and maintenance business is complex, the equipment changes frequently, the network connection is diverse, and the operation mode is changeable, which leads to complex and changeable distribution network faults and frequent load changes. . In order to better analyze key links such as distribution network load changes and equipment failures, it is particularly urgent to provide effective power supply reliability analysis methods. Power supply reliability is affected by system load, component reliability performance, component electrical performance, and grid structure. Through research, if the four aspects of operating data ...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q10/06G06Q30/02G06Q50/06G06F16/215G06F16/25G06K9/62G06N3/04G06N3/08
CPCG06Q10/04G06Q10/06393G06Q10/0635G06Q10/067G06Q30/0201G06Q30/0202G06Q50/06G06F16/215G06F16/254G06N3/08G06N3/045G06F18/24G06F18/214
Inventor 鲁海威陈冬梅孟威厉雨邵天龙王浩解霄博崔冰房迪李威蒋和军于春洋丛培元李昊臻肖丹邓昕孟令俐梁鹏康琳琳孟蕾熊瑞杨鹏跃
Owner STATE GRID LIAONING ELECTRIC POWER RES INST
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