| 引用本文: | 张越,单连飞,余建明,等.基于e-模糊树方法的电力系统状态估计[J].电力系统保护与控制,2019,47(5):138-144. |
| ZHANG Yue,SHAN Lianfei,YU Jianming,et al.Power system state estimation based on e- fuzzy tree method[J].Power System Protection and Control,2019,47(5):138-144 |
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| 基于e-模糊树方法的电力系统状态估计 |
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张越1,2,单连飞1,2,余建明1,2,张佳楠1,2,张文广3
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(1.南瑞集团有限公司(国网电力科学研究院有限公司)，江苏 南京 211106;2.北京科东电力控制系统 有限责任公司，北京 100192;3.华北电力大学新能源电力系统国家重点实验室，北京 102206)
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| 摘要: |
| 电力系统状态估计在能量管理系统中起着重要的作用。为了提高状态估计的整体性能，提出了基于e-模糊树方法(e-FT)的电力系统状态估计方法。以福州电网500 kV母线为研究对象，通过网络结构分析，对各量测量进行信息提取和变量选择，将最优的变量子集作为e-FT的输入变量，建立了各母线电压幅值的e-FT模型，并与其他方法进行了对比。随后，在量测量中加入不良数据，验证所提状态估计方法的鲁棒性。结果表明，该方法能够有效地抵抗量测量中的不良数据，具有较高的估计精度和较强的鲁棒性，并且能将不良数据限制在局部，减少对整个电网状态估计的影响。 |
| 关键词: 电力系统 状态估计 e-模糊树方法 变量选择 鲁棒性 |
| DOI：10.7667/PSPC180289 |
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| 基金项目:国网千人计划研究专项资助(5206001600A3) |
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| Power system state estimation based on e- fuzzy tree method |
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ZHANG Yue1,2,SHAN Lianfei1,2,YU Jianming1,2,ZHANG Jianan1,2,ZHANG Wenguang3
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(1. NARI Group Corporation Co., Ltd., (State Grid Electric Power Research Institute Co., Ltd.,), Nanjing 211106, China;2. Beijing KeDong Electric Power Control System Co., Ltd., Beijing 100192, China;3. State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China)
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| Abstract: |
| Power system state estimation plays an important role in Energy Management System (EMS). To improve the performance of state estimation, a power system state estimation method based on -fuzzy tree ( -FT) is proposed. Taken 500 kV bus of Fuzhou power grid as the research target, the network structure is analyzed to extract the measurement variables information and select the variables, the obtained feature matrix is used as the input of -FT to establish the -FT model of 500 kV bus voltage amplitude, which is made a comparison with other modeling methods. Then, the bad data of measurement variables is used to validate the robustness of the proposed state estimation method. The results show that the method can effectively resist the bad data of measurement variables, and have higher prediction accuracy and robustness. Besides, the bad data can be limited locally to reduce the impact on the entire power grid state estimation. This work is supported by “Thousand Talents Plan” Special Research Grant of State Grid Corporation of China (No. 5206001600A3). |
| Key words: power system state estimation -fuzzy tree variable selection robustness |
