| 引用本文: | 陈中,倪纯奕,蔡榕,潘俊迪,赵奇,罗玉春.基于σ-预算的主动配电网两阶段区间状态估计方法[J].电力自动化设备,2025,45(1):9-15，50 |
| CHEN Zhong,NI Chunyi,CAI Rong,PAN Jundi,ZHAO Qi,LUO Yuchun.Two-stage interval state estimation method for active distribution network based on σ-budget[J].Electric Power Automation Equipment,2025,45(1):9-15，50 |
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| 摘要: |
| 针对新型电力系统背景下主动配电网存在的实时量测数据少、量测不确定性强等问题，提出一种基于σ-预算的主动配电网两阶段区间状态估计方法。在第一阶段，构建混合整数线性规划模型，采用伪量测数据替代辨识出的量测不良数据，提高区间状态估计算法的鲁棒性；在第二阶段，基于广义方和根误差合成理论建立基于σ-预算的量测不确定性集，作为区间状态估计模型的输入可行域，以克服区间估计算法高保守性的问题；结合稀疏矩阵构建区间状态估计线性优化迭代模型，从而更加高效地求解状态变量的区间上下界。通过修改的IEEE 33节点系统、IEEE 118节点系统和江苏某市26节点配电网仿真验证了所提方法的有效性。 |
| 关键词: 主动配电网 区间状态估计 不良数据辨识 不确定性集 σ-预算 误差合成 |
| DOI：10.16081/j.epae.202409018 |
| 分类号:TM73 |
| 基金项目:国家电网公司总部科技项目(5108-202218280A-2-296-XG) |
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| Two-stage interval state estimation method for active distribution network based on σ-budget |
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CHEN Zhong1, NI Chunyi1, CAI Rong2, PAN Jundi1, ZHAO Qi2, LUO Yuchun3
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1.School of Electrical Engineering, Southeast University, Nanjing 210096, China;2.State Grid Suzhou Power Supply Company of Jiangsu Electric Power Co.,Ltd.,Suzhou 215004, China;3.State Grid Electric Power Research Institute Co.,Ltd.,Nanjing 211106, China
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| Abstract: |
| Aiming at the problems such as few real-time measurement data and strong measurement uncertainty of active distribution network under the background of new power system, a two-stage interval state estimation method based on σ-budget is proposed for active distribution network. In the first stage, a mixed integer linear programming model is constructed, and the pseudo measurement data is used to replace the identified bad measurement data for improving the robustness of interval state estimation algorithm. In the second stage, a σ-budget based measurement uncertainty set is constructed based on the gene-ralized square and root error synthesis theory, which is adopted as the input feasible domain of interval state estimation model to overcome the high conservatism problem of interval state estimation algorithm. A linear optimization iterative model of interval state estimation is constructed by combining the sparse matrix, so as to solve the interval upper and lower bounds of state variables more efficiently. The effectiveness of the proposed method is verified by the simulation of a modified IEEE 33-bus network, a modified IEEE 118-bus network, and an actual 26-bus distribution network in a city of Jiangsu. |
| Key words: active distribution network interval state estimation bad data identification uncertainty set σ-budget error synthesis |
