| 引用本文: | 全昊天,薛屹洵,常馨月,张佳惠,王煜尘,谷鹏飞,孙宏斌.基于环境自适应动态投影统计法的极地电-热综合能源系统可调鲁棒状态估计[J].电力自动化设备,2026,46(5):91-99 |
| Quan Haotian,Xue Yixun,Chang Xinyue,Zhang Jiahui,Wang Yuchen,Gu Pengfei,Sun Hongbin.Adjustable robust state estimation of integrated electricity-heat system in Antarctica based on environment-adaptive dynamic projection statistics[J].Electric Power Automation Equipment,2026,46(5):91-99 |
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| 摘要: |
| 极地区域电-热综合能源系统(IEHS)长期面临极端恶劣天气引发的异常数据与量测不确定性挑战，传统状态估计方法在适应性与鲁棒性方面存在不足。为此，提出一种环境自适应动态投影统计法(EADPS)，构建适用于极地环境的鲁棒状态估计框架。该方法基于南极秦岭站的历史气象数据，通过动态α分位数调整机制优化异常值识别阈值，实现对极端环境参数波动的自适应响应，提升状态估计稳定性与异常识别精度。以14节点配电网和6节点热网耦合的IEHS为算例，仿真结果表明，中高恶劣天气下EADPS显著改善了估计性能，高恶劣场景中的电网电压幅值均方根误差降低了26.7%，异常识别率提升了24个百分点，能为极地能源系统的稳定运行提供可靠的数据支撑。 |
| 关键词: 电-热综合能源系统 状态估计 极地环境 极端场景 环境自适应动态投影统计法 鲁棒性 |
| DOI：10.16081/j.epae.202511008 |
| 分类号:P941.61;N8;TK01;TM73 |
| 基金项目:国家自然科学基金资助项目(U23A20649,52321004)；山西省能源互联网研究院重大科研支撑专项(SXEI2024A001) |
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| Adjustable robust state estimation of integrated electricity-heat system in Antarctica based on environment-adaptive dynamic projection statistics |
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Quan Haotian1, Xue Yixun1, Chang Xinyue2, Zhang Jiahui2, Wang Yuchen1, Gu Pengfei1, Sun Hongbin1,3
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1.Key Laboratory of Cleaner Intelligent Control on Coal & Electricity of Ministry of Education, College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China;2.Shanxi Energy Internet Research Institute, Taiyuan 030006, China;3.Department of Electrical Engineering, Tsinghua University, Beijing 100084, China
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| Abstract: |
| The integrated electricity-heat energy system(IEHS) in Antarctica region has long been confronted with the challenges of abnormal data and measurement uncertainties caused by extremely harsh weather conditions, so traditional state estimation methods exhibit inherent limitations in terms of adaptability and robustness. To this end, an environment-adaptive dynamic projection statistics(EADPS) is proposed, based on which, a robust state estimation framework suitable for Antarctica environment is constructed. Based on the historical meteorological data of the Antarctic Qinling Station, the threshold for identifying outliers is optimized through a dynamic α-quantile adjustment mechanism, achieving an adaptive response to fluctuations in extreme environmental parameters and enhancing the stability of state estimation and the accuracy of anomaly identification. Taking the IEHS with a 14-node power distribution network and a 6-node heat network as an example, the simulative results show that, under medium and high severity weather conditions, EADPS significantly improves the estimation performance. In the high severity weather scenario, the root mean square error of power grid voltage amplitude is reduced by 26.7%,and the abnormal identification rate is increased by 24 percentage points, which can provide reliable data support for the stable operation of energy system in Antarctica. |
| Key words: integrated electricity-heat energy system state estimation polar environment extreme scenarios environment-adaptive dynamic projection statistics robustness |
