| 引用本文: | 吴龙腾,徐春华,邱泽坚,等.考虑双侧量测误差的配电网拓扑识别及参数联合估计方法[J].电力系统保护与控制,2023,51(16):149-157.[点击复制] |
| WU Longteng,XU Chunhua,QIU Zejian,et al.Topology identification and joint parameter estimation of a distribution network considering bilateral measurement errors[J].Power System Protection and Control,2023,51(16):149-157[点击复制] |
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| 摘要: |
| 配电网参数估计和拓扑识别是配电网规划、运行分析和安全控制的基础，传统线性回归方法对量测数据误差或噪声数据具有较高要求，只有在无噪声情况下，估计才是准确的。然而实际输入测量值(如电压幅值和相位角)和输出测量值(如有功和无功功率)均存在噪声数据，对于拓扑估计，即使量测误差很小，回归方法也无法得到准确拓扑。针对上述问题，首先构建了配电网参数估计的基本模型，并定量分析了量测误差对线路参数估计和拓扑识别的影响。在此基础上，建立了考虑双侧量测误差的线路参数估计模型。针对其非凸导致的难以求解的问题，基于拉格朗日函数进行等价转化，得到易于求解的最小化瑞利熵问题。最后，基于IEEE 8节点系统进行仿真分析，并与传统线性回归、最小二乘法进行对比，证明所提方法在量测误差达到10%时，依然具有良好的估计精度。 |
| 关键词: 配电网 拓扑识别 参数估计 双侧量测误差 等价模型 |
| DOI：10.19783/j.cnki.pspc.221694 |
| 投稿时间：2022-10-25修订日期：2023-02-06 |
| 基金项目:国家自然科学基金项目资助(52077196)；中国南方电网公司科技项目资助(031900KK52200015) |
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| Topology identification and joint parameter estimation of a distribution network considering bilateral measurement errors |
| WU Longteng1,XU Chunhua1,QIU Zejian2,CHENG Tao2,CHEN Fengchao2 |
| (1. Guangdong Power Grid Corp, Guangzhou 510000, China; 2. Dongguan Power Supply Bureau,
Guangdong Power Grid Co., Ltd., Dongguan 523000, China) |
| Abstract: |
| Distribution network parameter estimation and topology identification are the basis of network planning, operational analysis and security control. The traditional linear regression method has high requirements for measurement data error or noise data, and the estimation is accurate only when there is no noise. However, the actual input measurement values (such as voltage amplitude and phase angle) and output measurement values (such as active and reactive power) have noise data. For topology estimation, even if the measurement error is small, the regression method cannot get accurate topology. Given this, the basic model of distribution network parameter estimation is constructed first, then the influence of measurement error on line parameter estimation and topology identification is quantitatively analyzed. A line parameter estimation model considering the bilateral measurement error is established. It is difficult to analyze the model because of its non-convexity, the minimum Rayleigh entropy problem is obtained by equivalent transformation based on the Lagrange function. Finally, simulation analysis based on the IEEE 8-node system is carried out, and compared with traditional linear regression and the least squares method. This proves that the proposed method has good estimation accuracy even when the measurement error reaches 10%. |
| Key words: distribution network topology identification parameter estimation bilateral measurement error equivalent model |
