| 引用本文: | 于 浩,张 健,朱广杰,等.基于二维水动力模型与数据融合的电力设施淹没风险动态评估及预警方法[J].电力系统保护与控制,2025,53(15):71-82.[点击复制] |
| YU Hao,ZHANG Jian,ZHU Guangjie,et al.Dynamic assessment and early warning method of power facility flood risk based on a 2D hydrodynamic model and data fusion[J].Power System Protection and Control,2025,53(15):71-82[点击复制] |
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| 基于二维水动力模型与数据融合的电力设施淹没风险动态评估及预警方法 |
| 于浩1,张健1,朱广杰2,瞿舜克3,肖克江2,4,雷兴炜2,陈亮2 |
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| (1.国网黑龙江省电力有限公司电力科学研究院, 黑龙江 哈尔滨 150030;2.华中师范大学人工智能教育学部，
湖北 武汉 430079;3.国网黑龙江省电力有限公司，黑龙江 哈尔滨 150090;
4.数字教育湖北省重点实验室(华中师范大学)，湖北 武汉 430079) |
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| 摘要: |
| 为应对暴雨引发的电力设施淹没导致电网系统故障和供电瘫痪的问题，提出了一种基于二维水动力模型与数据融合的动态评估与预警机制。首先，结合芝加哥降雨模型与二维水动力模型，精确模拟关键电力设施位置的积水深度，提高预测准确性。其次，考虑积水深度、设备类型和可能的故障模式等因素，开发了一个综合评价体系，以量化电力设施因淹没而发生故障的风险。此外，构建了融合变分模态分解(variational mode decomposition, VMD)和长短期记忆网络(long short-term memory, LSTM)的短期负荷预测模型VMD-LSTM，为暴雨期间的电网负荷变化提供精准预测，并作为风险评估的重要依据。最后，设计了一套风险预警等级方案，能够准确标记需预警的电网节点，为管理者提供科学决策支持。通过具体区域电网案例分析，展示了该方法的有效性和实用性，为防灾减灾提供了坚实的技术支撑。 |
| 关键词: 暴雨淹没 电力设施 风险评估 负荷预测 二维水动力模型 |
| DOI：10.19783/j.cnki.pspc.241005 |
| 投稿时间：2024-07-29修订日期：2025-01-16 |
| 基金项目:国家自然科学基金面上项目资助(62173158，62377023)；湖北省自然科学基金创新发展联合基金重点项目资助(2025AFD195)；中央高校基本科研业务费资助(CCNU25ai013)；数字教育湖北省重点实验室开放研究基金项目资助(F2024G01)；国家电网有限公司科技项目资助(52243723000D) |
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| Dynamic assessment and early warning method of power facility flood risk based on a 2D hydrodynamic model and data fusion |
| YU Hao1,ZHANG Jian1,ZHU Guangjie2,QU Shunke3,XIAO Kejiang2,4,LEI Xingwei2,CHEN Liang2 |
| (1. State Grid Heilongjiang Electric Power Company Limited Electric Power Research Institute, Harbin 150030, China;
2. Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China;
3. State Grid Heilongjiang Electric Power Company Limited, Harbin 150090, China; 4. Hubei Key
Laboratory of Digital Education (Central China Normal University), Wuhan 430079, China) |
| Abstract: |
| To address the issue of power system failures and power outages caused by the flooding of power facilities during rainstorm, this paper proposes a dynamic assessment and early warning mechanism based on a two-dimensional (2D) hydrodynamic model and data fusion. First, by combining the Chicago rainfall model with a 2D hydrodynamic model, the depth of water accumulation at key power facility locations is accurately simulated, significantly improving prediction accuracy. Next, a comprehensive evaluation system is developed to quantify the risk of power facility failures due to flooding, taking into account factors such as water depth, equipment type, and potential failure modes. In addition, a short-term load forecasting model (VMD-LSTM) integrating variational mode decomposition (VMD) and long short- term memory (LSTM) is constructed to provide accurate forecasting of grid load changes during rainstorm as an critical input for risk assessment. Finally, a risk warning level scheme is designed to accurately identify the power grid nodes requiring warning, providing scientific decision-making support for operators. Through the analysis of specific regional power grid cases, the effectiveness and practicality of the propsoed method are demonstrated, providing solid technical support for disaster prevention and mitigation. |
| Key words: storm flooding electric power facility risk assessment load forecasting 2D hydrodynamic model |
