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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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

Received: 31 October 2023

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TM711

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	Chen Yihuang
	Shao Zhenguo
	Lin Junjie
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	Chen Feixiong

Cite this article:

Chen Yihuang,Shao Zhenguo,Lin Junjie等. Interval Robust Harmonic State Estimation Method Based on PMU and PQMD Measurement Data Fusion[J]. Transactions of China Electrotechnical Society, 2024, 0(0): 6-.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.231815 OR https://dgjsxb.ces-transaction.com/EN/Y2024/V0/I0/6

[1] 杨权, 梁永昌, 魏建荣, 等. 多谐波源下分布式电源并网逆变器的谐波抑制策略[J]. 电工技术学报, 2023, 38(11): 2908-2920.
Yang Quan, Liang Yongchang, Wei Jianrong, et al.Research on harmonic suppression strategy of grid connected inverter under multi-harmonic sources[J]. Transactions of China Electrotechnical Society, 2023, 38(11): 2908-2920.
[2] 邵振国, 黄伟达. 考虑出力不确定性的分布式电源谐波传播计算[J]. 电工技术学报, 2019, 34(增刊2): 674-683.
Shao Zhenguo, Huang Weida.Harmonic propagation calculation of distributed generation considering output uncertainty[J]. Transactions of China Electrotechnical Society, 2019, 34(S2): 674-683.
[3] 周念成, 谭桂华, 何建森, 等. 基于统计方法的电网谐波状态估计误差分析[J]. 电工技术学报, 2009, 24(6): 109-114.
Zhou Niancheng, Tan Guihua, He Jiansen, et al.Error analysis in harmonic state estimation of power system based on the statistical approach[J]. Transactions of China Electrotechnical Society, 2009, 24(6): 109-114.
[4] 吴笃贵, 徐政. 基于相量量测的电力系统谐波状态估计(Ⅱ): 可观性、质量评估与算例研究[J]. 电工技术学报, 2004, 19(3): 76-81.
Wu Dugui, Xu Zheng.Power system harmonic state estimation based on phasor measurements(II)—observability analysis, quality evaluation algorithm and example studies[J]. Transactions of China Electrotechnical Society, 2004, 19(3): 76-81.
[5] 刘道伟, 谢小荣, 穆钢, 等. 基于同步相量测量的电力系统在线电压稳定指标[J]. 中国电机工程学报, 2005, 25(1): 13-17.
Liu Daowei, Xie Xiaorong, Mu Gang, et al.An on-line voltage stability index of power system based on synchronized phasor mesurement[J]. Proceedings of the CSEE, 2005, 25(1): 13-17.
[6] 王少芳, 刘广一, 黄仁乐, 等. 多采样周期混合量测环境下的主动配电网状态估计方法[J]. 电力系统自动化, 2016, 40(19): 30-36.
Wang Shaofang, Liu Guangyi, Huang Renle, et al.State estimation method for active distribution networks under environment of hybrid measurements with multiple sampling periods[J]. Automation of Electric Power Systems, 2016, 40(19): 30-36.
[7] 薛辉, 贾清泉, 王宁, 等. 基于PMU量测数据和SCADA数据融合的电力系统状态估计方法[J]. 电网技术, 2008, 32(14): 44-49.
Xue Hui, Jia Qingquan, Wang Ning, et al.A novel power system state estimation method based on merging PMU-measured data into SCADA data[J]. Power System Technology, 2008, 32(14): 44-49.
[8] 刘喆林, 王成山, 李鹏, 等. 多源量测数据融合的配电网状态估计及应用[J]. 中国电机工程学报, 2021, 41(8): 2605-2615.
Liu Zhelin, Wang Chengshan, Li Peng, et al.State estimation of distribution networks based on multi-source measurement data and its applications[J]. Proceedings of the CSEE, 2021, 41(8): 2605-2615.
[9] 黄天敏. 基于抗差状态估计的配电网运行状态诊断与分析[J]. 电力电容器与无功补偿, 2022, 43(6): 92-102.
Huang Tianmin.DiagnosisandAnalysisonOperationStateofDistributionNetworkBasedonRobustStateEstimation[J]. Power Capacitor & Reactive Power Compensation, 2022, 43(6): 92-102.
[10] 盛万兴, 方恒福, 沈玉兰, 等. 考虑量测时延时基于3种数据融合的配网状态估计[J]. 电力系统及其自动化学报, 2019, 31(12): 108-115.
Sheng Wanxing, Fang Hengfu, Shen Yulan, et al.Distribution state estimation based on fusion of three data sources considering measurement delay[J]. Proceedings of the CSU-EPSA, 2019, 31(12): 108-115.
[11] Zhang Qing, Chakhchoukh Y, Vittal V, et al.Impact of PMU measurement buffer length on state estimation and its optimization[J]. IEEE Transactions on Power Systems, 2013, 28(2): 1657-1665.
[12] Zhao Junbo, Mili L.A framework for robust hybrid state estimation with unknown measurement noise statistics[J]. IEEE Transactions on Industrial Informatics, 2018, 14(5): 1866-1875.
[13] Murugesan V, Chakhchoukh Y, Vittal V, et al.PMU data buffering for power system state estimators[J]. IEEE Power and Energy Technology Systems Journal, 2015, 2(3): 94-102.
[14] 国家能源局. DL/T 1608—2016 电能质量数据交换格式规范[S]. 北京: 中国电力出版社, 2016.
[15] 吴星, 刘天琪, 李兴源, 等. 基于WAMS/SCADA数据兼容和改进FCM聚类算法的PMU最优配置[J]. 电网技术, 2014, 38(3): 756-761.
Wu Xing, Liu Tianqi, Li Xingyuan, et al.Optimal configuration of PMU based on data compatibility of WAMS/SCADA and improved FCM clustering algorithm[J]. Power System Technology, 2014, 38(3): 756-761.
[16] 张逸, 林焱, 吴丹岳. 电能质量监测系统研究现状及发展趋势[J]. 电力系统保护与控制, 2015, 43(2): 138-147.
Zhang Yi, Lin Yan, Wu Danyue.Current status and development trend of power quality monitoring system[J]. Power System Protection and Control, 2015, 43(2): 138-147.
[17] IEEE recommended practice for the transfer of power quality data[J]. IEEE Std 1159 3-2003, 2004: 1-138.
[18] 肖先勇, 胡誉蓉, 王杨, 等. 基于非同步电能质量监测系统的谐波状态估计[J]. 中国电机工程学报, 2021, 41(12): 4121-4132.
Xiao Xianyong, Hu Yurong, Wang Yang, et al.Harmonic state estimation based on asynchronous power quality monitoring system[J]. Proceedings of the CSEE, 2021, 41(12): 4121-4132.
[19] 邵振国, 林洪洲, 陈飞雄, 等. 采用区间动态状态估计的局部不可观系统谐波源定位[J]. 电工技术学报, 2023, 38(9): 2391-2402.
Shao Zhenguo, Lin Hongzhou, Chen Feixiong, et al.Harmonic source location in the partial unobservable system based on interval dynamic state estimation[J]. Transactions of China Electrotechnical Society, 2023, 38(9): 2391-2402.
[20] 林晓婉, 代锋, 刘沈全, 等. 含LCC-HVDC的交直流混联电网统一谐波状态估计方法[J]. 电力系统自动化, 2022, 46(13): 94-103.
Lin Xiaowan, Dai Feng, Liu Shenquan, et al.Unified harmonic state estimation method for AC/DC hybrid power grid with LCC-HVDC[J]. Automation of Electric Power Systems, 2022, 46(13): 94-103.
[21] 徐俊俊, 吴在军, 张腾飞, 等. 融入多源量测数据的配电网分布式区间状态估计[J]. 中国电机工程学报, 2022, 42(24): 8888-8900.
Xu Junjun, Wu Zaijun, Zhang Tengfei, et al.A distributed interval state estimation framework of distribution networks based on multi-source measurements[J]. Proceedings of the CSEE, 2022, 42(24): 8888-8900.
[22] 林洪洲, 邵振国, 陈飞雄, 等. 采用区间型非同步监测数据的鲁棒动态谐波状态估计[J]. 电网技术, 2023, 47(4): 1701-1709.
Lin Hongzhou, Shao Zhenguo, Chen Feixiong, et al.Robust dynamic harmonic state estimation using interval asynchronous monitoring data[J]. Power System Technology, 2023, 47(4): 1701-1709.
[23] Bi Tianshu, Guo Jinrui, Xu Kai, et al.The impact of time synchronization deviation on the performance of synchrophasor measurements and wide area damping control[J]. IEEE Transactions on Smart Grid, 2017, 8(4): 1545-1552.
[24] 张逸, 杨洪耕. 海量电能质量数据交换格式文件快速解析方案[J]. 电力自动化设备, 2013, 33(12): 116-121, 127.
Zhang Yi, Yang Honggeng.Fast parsing of massive PQDIF files[J]. Electric Power Automation Equipment, 2013, 33(12): 116-121, 127.
[25] Zhao Junbo, Wang Shaobu, Mili L, et al.A robust state estimation framework considering measurement correlations and imperfect synchronization[J]. IEEE Transactions on Power Systems, 2018, 33(4): 4604-4613.
[26] Mili L, Cheniae M G, Vichare N S, et al.Robust state estimation based on projection statistics[of power systems[J]. IEEE Transactions on Power Systems, 1996, 11(2): 1118-1127.
[27] 徐艳春, 王格, 孙思涵, 等. 基于改进广义极大似然估计的配电网状态估计方法[J]. 南方电网技术, 2022, 16(6): 23-32.
Xu Yanchun, Wang Ge, Sun Sihan, et al.Distribution network state estimation method based on improved generalized maximum likelihood estimation[J]. Southern Power System Technology, 2022, 16(6): 23-32.
[28] 李超然, 肖飞, 樊亚翔, 等. 基于门控循环单元神经网络和Huber-M估计鲁棒卡尔曼滤波融合方法的锂离子电池荷电状态估算方法[J]. 电工技术学报, 2020, 35(9): 2051-2062.
Li Chaoran, Xiao Fei, Fan Yaxiang, et al.A hybrid approach to lithium-ion battery SOC estimation based on recurrent neural network with gated recurrent unit and huber-M robust Kalman filter[J]. Transactions of China Electrotechnical Society, 2020, 35(9): 2051-2062.
[29] 钱峰, 龚庆武. 基于IGG法的电力系统状态估计[J]. 电力系统自动化, 2005, 29(3): 36-39, 56.
Qian Feng, Gong Qingwu.State estimation of power system based on IGG method[J]. Automation of Electric Power Systems, 2005, 29(3): 36-39, 56.
[30] 秦晓辉, 毕天姝, 杨奇逊. 计及PMU的混合非线性状态估计新方法[J]. 电力系统自动化, 2007, 31(4): 28-32.
Qin Xiaohui, Bi Tianshu, Yang Qixun.A new method for hybrid non-linear state estimation with PMU[J]. Automation of Electric Power Systems, 2007, 31(4): 28-32.
[31] 朱茂林, 刘灏, 毕天姝. 考虑风电场量测相关性的双馈风力发电机鲁棒动态状态估计[J]. 电工技术学报, 2023, 38(3): 726-740.
Zhu Maolin, Liu Hao, Bi Tianshu.Robust dynamic state estimation of doubly-fed induction generator considering measurement correlation in wind farms[J]. Transactions of China Electrotechnical Society, 2023, 38(3): 726-740.