考虑分布式光伏控制环节的配电网自适应动态状态估计方法

doi:10.19595/j.cnki.1000-6753.tces.260901

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摘要

随着配电网分布式光伏渗透率持续升高,其出力间歇性对系统状态感知提出了更高要求。为表征光伏动态特性,应对量测数据中的非高斯噪声,本文建立了含光伏控制环节的动态状态估计模型,提出一种基于最大相关熵准则的自适应容积卡尔曼滤波算法。首先,考虑光伏系统反馈控制结构,构建光伏侧微分方程以跟踪状态量的非线性传递过程;其次,将最大相关熵准则引入滤波框架,利用高斯核函数实现误差的非线性加权;进而,通过动态调整核宽参数与卡尔曼增益加权矩阵,提升算法在非高斯噪声环境下的鲁棒性;最后,基于IEEE 33节点系统仿真,从稳态性能、动态跟踪、故障响应、大测量误差鲁棒性和高光伏渗透率适应性五个维度验证。结果表明,该算法有效提升了复杂工况下的估计准确度,为新型配电网动态状态估计提供了可靠的技术支撑。

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关键词 ：配电网, 动态状态估计, 非高斯噪声, 最大相关熵准则, 自适应滤波

Abstract：

With the increasing penetration of distributed photovoltaic (PV) generation, distribution networks are increasingly affected by power fluctuations, operating-point variations, and non-Gaussian measurement disturbances, which impose higher requirements on the accuracy and robustness of dynamic state estimation. Conventional methods usually represent PV units as equivalent power injections and therefore cannot adequately describe the dynamic effects of their internal control loops. Moreover, Kalman-filter-based methods derived under Gaussian noise assumptions may suffer performance degradation in the presence of non-Gaussian noise and abnormal measurements. To address these issues, a dynamic state estimation model considering the control dynamics of distributed PV systems was established, and a maximum correntropy criterion-based adaptive cubature Kalman filter (MCC-ACKF) was proposed.
A two-stage three-phase distributed PV system was modeled by explicitly considering its feedback control structure. Differential equations were constructed to describe the dynamic evolution of PV-side states, which were then incorporated into the distribution-network state vector to form a unified nonlinear dynamic state estimation model. To improve robustness against non-Gaussian disturbances, the maximum correntropy criterion was introduced into the cubature Kalman filtering framework. An adaptive weighting matrix based on state prediction and measurement errors was constructed to adjust the Kalman gain, while a kernel-width adaptation strategy based on the median absolute deviation of residuals was introduced to update the kernel parameter online. These mechanisms reduced the influence of abnormal measurements and improved the adaptability of the filtering process.
The proposed method was evaluated on an IEEE 33-bus, 10 kV distribution network with distributed PV units. Five simulation cases were considered, including steady-state operation, load and irradiance variations, short-circuit faults with bad data, large measurement errors, and increasing PV penetration. The square-root cubature Kalman filter, the singular-value-decomposition-based adaptive cubature Kalman filter, and two ablation variants were used for comparison. Under steady-state conditions, the mean absolute errors of voltage magnitude and phase angle were reduced, respectively, compared with the square-root cubature Kalman filter. Under simultaneous load and irradiance variations, the proposed method maintained stable tracking and achieved the lowest overall estimation errors. When a short-circuit fault and 0.5% bad measurement data were introduced, the average voltage-magnitude error remained below 2% of the true value, and the average phase-angle error remained below 1°. The method also showed lower estimation errors under 10% large measurement disturbances.
As the PV penetration increased from 67.29% to 94.21%, the estimation errors increased but no sustained divergence occurred. At 94.21% penetration, the average voltage-magnitude error remained below 0.1%, and the phase-angle mean absolute error was 7.51×10^-3°. The average computation time per estimation step was 6.63-6.78 ms, which was shorter than the 10 ms sampling interval. The results show that the proposed MCC-ACKF provides accurate and robust dynamic state estimation under non-Gaussian disturbances and high PV penetration while satisfying the computational requirements of the tested system.

Key words： Distribution network dynamic state estimation non-Gaussian noise maximum correntropy criterion adaptive filtering

收稿日期: 2026-06-05

PACS:

TM711

基金资助:

国家科技重大专项资助（2026ZD0809902）

通讯作者: 毕天姝女,1973年生,博士,教授,研究方向为电力系统保护控制、广域同步相量测量技术及应用等。E-mail：tsbi@ncepu.edu.cn

作者简介: 刘灏男,1985年生,博士,教授,研究方向为广域同步相量测量技术。E-mail：hliu@ncepu.edu.cn

引用本文:

刘灏, 张林雁, 毕天姝, 王紫薇. 考虑分布式光伏控制环节的配电网自适应动态状态估计方法[J]. 电工技术学报, 0, (): 260901-. Liu Hao, Zhang Linyan, Bi Tianshu, Wang Ziwei. Adaptive Dynamic State Estimation for Distribution Networks Considering Distributed Photovoltaic Control. Transactions of China Electrotechnical Society, 0, (): 260901-.

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