Prediction method in distribution transformer heavy and overload supply areas with relevance analyze and machine learning

Guobin ZHANG; Xiaorong WANG; Chunyu DENG

doi:10.11959/j.issn.2096-0271.2018011

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Prediction method in distribution transformer heavy and overload supply areas with relevance analyze and machine learning

COLUMN：2017 Top10 PRACTICES OF BIG DATA APPLICATION | 更新时间：2024-06-03

- Prediction method in distribution transformer heavy and overload supply areas with relevance analyze and machine learning
- Big Data Research Vol. 4, Issue 1, Pages: 2018011(2018)
- 作者机构：
- 作者简介：
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- DOI：10.11959/j.issn.2096-0271.2018011
  CLC： TP393
- Online First：2018-01，
  
  Published：15 January 2018
- 稿件说明：
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Guobin ZHANG, Xiaorong WANG, Chunyu DENG. Prediction method in distribution transformer heavy and overload supply areas with relevance analyze and machine learning[J]. Big Data Research, 2018, 4(1): 2018011.
DOI：

Guobin ZHANG, Xiaorong WANG, Chunyu DENG. Prediction method in distribution transformer heavy and overload supply areas with relevance analyze and machine learning[J]. Big Data Research, 2018, 4(1): 2018011. DOI： 10.11959/j.issn.2096-0271.2018011.

摘要

针对配电网运行中长期存在的台区重过载问题，提出基于关联规则挖掘的重过载影响因素分析方法，从设备和用户属性、自然环境、短期负荷特性中挖掘针对各类重过载事件的关联规则。从关联项中提取重过载影响因素，并基于机器学习模型，建立重过载事件预测模型，实现对重过载事件的短期预测。最后利用业务系统实际数据，对所提方法进行了效果验证。算例结果表明，新方法能够更为系统、全面地刻画重过载事件，提出的重过载预测模型在命中率和准确率方面表现良好。

Abstract

Aiming at the heavy and over load issue in operation of distribution network

a heavy overload forecasting model to achieve short-term forecast of heavy and overload events was established.Using the actual data of the business system

the proposed method was validated.The results show that the proposed method can describe the heavy and overload events more systematically and comprehensively

and the heavy overload forecasting model based on association rules performs well in the hit rate and accuracy rate.This method provides a new technical means that has certain practical value to enhance the distribution network management which could be seen as important experience of attempts in grid big data as well.

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references

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