Risk Warning Methods for Weak Distribution Networks with High Penetration of Distributed Resources

ZHOU Jun; YE Xueshun; JIA Dongli; WANG Shuai

doi:10.13882/j.cnki.ncdqh.2604F003

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Risk Warning Methods for Weak Distribution Networks with High Penetration of Distributed Resources

更新时间：2026-06-08

- Risk Warning Methods for Weak Distribution Networks with High Penetration of Distributed Resources
- Rural Electrification Issue 5, Pages: 32-42, 71(2026)
- 作者机构：
  
  中国电力科学研究院有限公司，北京海淀,100192
- 作者简介：
- 基金信息：
- DOI：10.13882/j.cnki.ncdqh.2604F003
  CLC： TM732
- Published：2026
- 稿件说明：
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周俊, 叶学顺, 贾东梨, 王帅. 高比例分布式资源接入下薄弱配电网风险预警方法[J]. 农村电气化, 2026, (5): 32-42, 71.

ZHOU Jun, YE Xueshun, JIA Dongli, et al. Risk Warning Methods for Weak Distribution Networks with High Penetration of Distributed Resources[J]. Rural Electrification, 2026, (5): 32-42, 71.
周俊, 叶学顺, 贾东梨, 王帅. 高比例分布式资源接入下薄弱配电网风险预警方法[J]. 农村电气化, 2026, (5): 32-42, 71. DOI： 10.13882/j.cnki.ncdqh.2604F003.

ZHOU Jun, YE Xueshun, JIA Dongli, et al. Risk Warning Methods for Weak Distribution Networks with High Penetration of Distributed Resources[J]. Rural Electrification, 2026, (5): 32-42, 71. DOI： 10.13882/j.cnki.ncdqh.2604F003.

摘要

面对高比例分布式资源接入导致薄弱配电网运行风险加剧的现状，文章提出一种融合多源数据与混合驱动模型的风险预警方案。通过剖析双峰负荷及极端气象条件下分布式资源的波动规律与多维度风险耦合机制，建立基于无迹卡尔曼滤波（UKF）的多指标动态风险感知模型，实现对系统运行状态的实时监测与噪声抑制。与此同时，构建融合G-ReliefF特征筛选与AdaBoost增强决策树（Ada-DT）的停电预警模型，优化输入特征组合并提高分类预测的精准度。整合气象、量测、设备等各类多源数据，搭建起涵盖动态感知、特征优化、分级预警的完整技术体系。算例验证结果显示，该方法可有效提高状态估计的准确性，其中Ada-DT预警模型的综合准确率达到89.5%，在中高风险事件识别方面表现优异，为薄弱配电网的风险管控及供电韧性提升提供了可靠的技术支撑。

Abstract

To address the aggravated operational risks in weak distribution networks caused by the high penetration of distributed resources

this paper proposes a risk early warning scheme integrating multi-source data and a hybrid-driven model. By analyzing the fluctuation patterns of distributed resources under dual-peak load and extreme weather conditions

as well as the multi-dimensional risk coupling mechanism

a multi-indicator dynamic risk perception model based on the unscented Kalman filter (UKF) is established to realize real-time monitoring of system operating states and noise suppression. Meanwhile

an outage early warning model integrating G-ReliefF feature selection and an AdaBoost-enhanced decision tree (AdaBoost-DT) is constructed to optimize the input feature combination and improve classification prediction accuracy. By integrating multi-source data such as meteorological

measurement

and equipment data

a complete technical framework covering dynamic perception

feature optimization

and hierarchical early warning is developed. Case study results show that the proposed method can effectively improve the accuracy of state estimation

and the Ada-DT early warning model achieves an overall accuracy of 89.5%

with excellent performance in identifying medium- and high-risk events. This method provides reliable technical support for risk management and power supply resilience enhancement in weak distribution networks.

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references

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