基于ICEM与NM-TDO-BP的变电站气象灾害风险评估

LI Yuan; YIN Chunya; SHANG Qiaoyan

doi:10.13336/j.1003-6520.hve.20250764

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基于ICEM与NM-TDO-BP的变电站气象灾害风险评估

更新时间：2025-12-15

- 基于ICEM与NM-TDO-BP的变电站气象灾害风险评估
- Vol. 51, Issue 10, Pages: 5103-5113(2025)
- 作者机构：
  
  1. 国网浙江省电力有限公司龙游县供电公司,衢州,324400
  2. 新疆大学电气工程学院,乌鲁木齐,830017
  3. 国网浙江省电力有限公司义乌市供电公司,金华,322000
- 作者简介：
- 基金信息：
- DOI：10.13336/j.1003-6520.hve.20250764
  CLC：
- Published：2025
- 稿件说明：
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LI Yuan, YIN Chunya, SHANG Qiaoyan. 基于ICEM与NM-TDO-BP的变电站气象灾害风险评估[J]. 2025, 51(10): 5103-5113.
DOI：

LI Yuan, YIN Chunya, SHANG Qiaoyan. 基于ICEM与NM-TDO-BP的变电站气象灾害风险评估[J]. 2025, 51(10): 5103-5113. DOI： 10.13336/j.1003-6520.hve.20250764.

摘要

气象灾害的发生将影响变电站安全运行。为提高变电站预防气象灾害水平，提出一种基于改进组合赋权法(improving combinatorial empowerment method，ICEM)与小生境技术-袋獾优化-BP神经网络(niching methods-tasmanian devil optimization-BP，NM-TDO-BP)的变电站气象灾害风险评估模型。首先，梳理分析变电站气象灾害风险因素，提出构建全方位的风险评估指标体系。基于零均值化处理、归一化和构成比率，组合运用熵权法和变异系数法实现对指标赋权。其次，将融合了小生境技术的改进袋獾优化算法引入BP神经网络算法，构建NM-TDO-BP模型，实现对变电站气象灾害风险评估。最后，通过算例分析验证了所提模型的可行性与优越性。

Abstract

The occurrence of meteorological disasters will affect the safe operation of substations. To improve the level of meteorological disaster prevention in substations

a meteorological disaster risk assessment model for substations based on the improved combined empowerment method (ICEM) and niching meth-tasmanian devil optimization-BP (NM-TDO-BP) is proposed. Firstly

the risk factors of meteorological disasters in substations are sorted out and analyzed

and it is proposed to construct an all-round risk assessment index system. Based on zero-mean processing

normalization

and composition ratio

the entropy weight method and coefficient of variation method are combined to achieve the weighting of indicators. Secondly

the improved tasmanian devil optimization (TDO) algorithm integrating niche technology is introduced into the BP neural network algorithm to construct the NM-TDO-BP model to achieve the risk assessment of meteorological disasters in substations. Finally

the feasibility and superiority of the proposed model are verified through case analysis.

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