融合PMU和PQMD量测数据的区间型抗差谐波状态估计

doi:10.19595/j.cnki.1000-6753.tces.231815

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摘要

在相量测量单元（PMU）配置数量不足以满足谐波状态估计的可观性条件时,可将电能质量监测装置（PQMD）作为数据补充源。本文针对多源量测数据存在的同步性差异和量测偏差等问题,提出了一种融合PMU与PQMD量测数据的区间型抗差谐波状态估计方法。首先,根据PQMD检测起始时刻不同步的特征,提出了基于重叠度指标的PMU与PQMD量测数据融合方法;其次,采用量测变换从PQMD功率量测数据得到等效电流相量量测,构建区间型混合量测全集;之后,采用投影统计法和改进Huber权函数计算量测权重,对重叠度低且残差大的量测赋予较小的权重以抑制量测偏差的影响,并根据权重大小优选测点,得到非同步量测偏差最小的量测子集;最后,通过迭代重加权最小二乘法求解状态估计模型,在IEEE30系统验证了本文方法的可行性与有效性。

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关键词 ：同步相量量测, 电能质量监测装置, 多源量测数据融合, 区间算法, 谐波状态估计

Abstract：

Harmonic state estimation is a key part of power system operation management, which can be used to monitor the harmonic in the power grid and provide an important reference for the stable operation of the power grid. At present, the number of phasor measurement unit (PMU) configurations are difficult to satisfy the observability requirements of state estimation. It is necessary to adapt power quality monitoring device (PQMD) data to improve the redundancy of measurement and make the harmonic state estimation possible. However, the non-synchronized monitoring data characteristics of PQMD make the fused measurement data still have deviations, which will lead to a large error in harmonic estimation state. Fusing PMU and PQMD measurement data and minimizing the asynchronous measurement bias of PQMD measurement data, as well as suppressing the influence of this measurement bias in state estimation, will provide a more effective means for grid harmonic analysis. Therefore, the paper proposes an interval robust harmonic state estimation method based on PMU and PQMD measurement data fusion.
Firstly, the detection period of PQMD is selected with the overlap index, and the reference period is selected with the maximum overlap as the target. The selected reference period is used as the measurement buffer of PMU, and the PQMD measurement data in this period is fused to form an interval mixed measurement set. The harmonic power measurements of PQMD are converted into equivalent harmonic current phasor measurement by the measurement transformation strategy, which is updated with state quantity in iterative solution. Secondly, the projection statistical method is used to calculate the initial weight of the measurement, and the overlap index is introduced into the Huber weight function to adjust the measurement weight. The measurement with low overlap and large residual is given a small weight to suppress the influence of measurement deviation, and further improve the robustness of the algorithm. Finally, the measurement points are preferring according to the weights, and the measurement subset with the least deviation of non-synchronous measurement is obtained. The harmonic state estimation model is solved by iterative reweighted least square method, and the harmonic state range of the whole network is obtained. The simulation results show that when the load fluctuation is 10% and the average overlap degree is 0.85, the estimated error of the proposed method is 1.92% in the upper bound and 3.24% in the lower bound. The error of phase angle upper bound estimation is 2.27%, and the error of lower bound estimation is 4.22%. When the overlap degree is reduced to 0.6, the average error of amplitude and phase angle of the proposed algorithm is less than 6%. When the level of load fluctuation increases to 40%, the average estimation error of state quantity is less than 5%.
The following conclusions can be obtained through simulation analysis: 1) The overlap index is used to quantify the measurement deviation of PQMD to improve the weight matrix in the state estimation, which can effectively suppress the influence of the measurement deviation on state estimation. In addition, the interval mixed measurement subset is obtained by preferring the measurement points according to the weight coefficient, which can further reduce the non-synchronous measurement deviation of the measurement set, improve the reliability of the interval mixed measurement set and the accuracy of state estimation. 2) Converting the interval weight matrix and Jacobian matrix into a definite value can further reduce the conservatism of the solution interval. 3) The proposed algorithm can effectively reduce the estimation error under different measurement deviations and different load fluctuation sizes which has robustness.

Key words： Phasor measurement units power quality monitoring device multi-source measurement data fusion interval algorithm harmonic state estimation

收稿日期: 2023-10-31

PACS:

TM711

基金资助:

国家自然科学基金资助项目（52377087）

通讯作者: 邵振国男,1970年生,教授,博士生导师,研究方向为高渗透率新能源电网建模与仿真、电能质量分析与治理等。E-mail：shao.zg@fzu.edu.cn

作者简介: 陈艺煌男,1998年生,硕士研究生,研究方向电能质量分析与治理、谐波状态估计等。E-mail：1484984430@qq.com

引用本文:

陈艺煌, 邵振国, 林俊杰, 张嫣, 陈飞雄. 融合PMU和PQMD量测数据的区间型抗差谐波状态估计[J]. 电工技术学报, 2024, 0(0): 6-. Chen Yihuang, Shao Zhenguo, Lin Junjie, Zhang Yan, Chen Feixiong. Interval Robust Harmonic State Estimation Method Based on PMU and PQMD Measurement Data Fusion. Transactions of China Electrotechnical Society, 2024, 0(0): 6-.

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