两级线性迭代策略下的混合状态估计的研究
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摘要
基于卡尔曼滤波及线性迭代基本原理,针对当前电力系统混合状态估计精度低、滤波效果差及收敛能力低等问题,提出了一种基于两级线性迭代的电力系统混合状态估计的研究策略:第1级利用相量测量单元(PMU)的量测数据进行线性估计;第2级将其与传统量测值相结合用于状态估计,并利用PMU的高频特性对两级的量测数据进行多次迭代采样。将其在IEEE 14和IEEE 57节点测试系统进行测试,并将结果与其他混合模型比较,结果表明,该策略的估计精度、数据收敛度及量测参数误差均优于其他混合模型。
A study strategy of hybrid state estimation for power system based on two-stage linear iteration and basic principles of Kalman filter and linear iteration was proposed, it is designed to solve the problems of lower accuracy of power system mixed state estimation, poorer filtering effect and weaker convergence ability, etc. In the first stage, the measurement data of phasor measurement unit(PMU) were used for linear estimation. In the second stage, where the content of the first stage was combined with the traditional measurement value to estimate the state, and the high frequency characteristic of PMU was used to sample the two-stage measurement data iteratively. Which was tested in IEEE 14 and IEEE 57 bus test system, and the results were compared with other hybrid models. The results showed that the estimation accuracy, data convergence and measurement parameter error of this strategy were better than those of other hybrid models.
A study strategy of hybrid state estimation for power system based on two-stage linear iteration and basic principles of Kalman filter and linear iteration was proposed, it is designed to solve the problems of lower accuracy of power system mixed state estimation, poorer filtering effect and weaker convergence ability, etc. In the first stage, the measurement data of phasor measurement unit(PMU) were used for linear estimation. In the second stage, where the content of the first stage was combined with the traditional measurement value to estimate the state, and the high frequency characteristic of PMU was used to sample the two-stage measurement data iteratively. Which was tested in IEEE 14 and IEEE 57 bus test system, and the results were compared with other hybrid models. The results showed that the estimation accuracy, data convergence and measurement parameter error of this strategy were better than those of other hybrid models.
引文
[1] 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 SH F, LIU G Y, HUANG R L, 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.
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[5] 安军,杨振瑞,周毅博,等. 基于平方根容积卡尔曼滤波的发电机动态状态估计[J]. 电工技术学报,2017,32(12):234-240.AN J, YANG ZH R, ZHOU Y B, et al. Dynamic state estimator for synchronous-machines based on square root cubature Kalman filter[J]. Transactions of China Electrotechnical Society,2017,32(12):234-240.
[6] 程瑛颖,肖冀,周峰,等. 广义安全域模型及其在电表状态估计中的应用[J]. 电子测量技术,2016,39(7):174-177.CHENG Y Y, XIAO Y, ZHOU F, et al. General model of security region and its application in energy meters’ status estimation [J]. Transactions of China Electrotechnical Society, 2016,39(7):174-177.
[7] 尹忠刚,肖鹭,孙向东,等. 基于粒子群优化的感应电机模糊扩展卡尔曼滤波器转速估计方法[J]. 电工技术学报,2016,31(6):55-65.YIN ZH G, XIAO L, SUN X D, et al. A speed estimation method of fuzzy extended Kalman filter for induction motors based on particle swarm optimization[J]. Transactions of China Electrotechnical Society,2016,31(6):55-65.
[8] 张海波,易文飞. 基于异步迭代模式的电力系统分布式状态估计方法[J]. 电力系统自动化,2014,38(9):125-131.ZHANG H B, YI W F. Distributed state estimation method for power systems based on asynchronous iteration mode[J]. Automation of Electric Power Systems, 2014,38(9):125-131.
[9] 马平川,沈沉,陈颖,等. 基于伪量测型协调变量的分布式状态估计算法[J].中国电机工程学报,2014,34(19):3170-3177.MA P CH, SHEN CH, CHEN Y, et al. Distributed state estimation based on the pseudo-measurement coordination variables[J]. Proceedings of the CSEE,2014,34(19):3170-3177.
[10] 牛胜锁,张达,梁志瑞,等. 基于抗差总体最小二乘法的电力系统谐波状态估计[J]. 电力系统保护与控制,2014,42(11):106-111.NIU SH S, ZHANG D, LIANG ZH R, et al. Power system harmonic state estimation based on robust total least squares[J]. Power System Protection and Control,2014,42(11):106-111.
[11] 赵洪山,田甜. 基于自适应无迹卡尔曼滤波的电力系统动态状态估计[J]. 电网技术,2014,38(1):188-192.ZHAO H SH, TIAN T. Dynamic state estimation for power system based on an adaptive unscented Kalman filter[J]. Power System Technology,2014,38(1):188-192.
[12] 邓松,岳东,朱力鹏,等. 电力大数据智能化高效分析挖掘技术框架[J].电子测量与仪器学报,2016,30(11):1679-1686.DENG S, YUE D, ZHU L P, et al. Framework of intelligent and efficient analysis and mining technology for power big data[J]. Journal of Electronic Measurement and Instrumentation,2016,30(11):1679-1686.
[13] 贾宏杰,吕英辉,曾沅,等. PMU在电力系统中的优化配置方法[J]. 电力科学与技术学报,2010,25(1):54-59,66.JIA H J, LV Y H, ZENG Y, et al. Optimal PMU placement in power system[J]. Journal of Electric Power Science and Technology,2010,25(1):54-59,66.
[14] 李滨,杜孟远,祝云,等. 基于准实时数据的智能配电网状态估计[J]. 电工技术学报,2016,31(1):34-44.LI B, DU M Y, ZHU Y, et al. A state estimator for smart distribution networks with quasi-real time data[J]. Transactions of China Electrotechnical Society,2016,31(1):34-44.
[15] 李从善,刘天琪,李兴源,等. 用于电力系统状态估计的WAMS/SCADA混合量测数据融合方法[J]. 高电压技术,2013,39(11):2686-2691.LI C SH, LIU T Q, LI X Y, et al. Data fusion method of WAMS/SCADA hybrid measurements in power system state estimation[J]. High Voltage Engineering, 2013,39(11):2686-2691.
[2] 常鲜戎,樊瑞. 计及零注入节点约束的混合量测分区状态估计方法[J]. 电网技术,2015,39(8):2253-2257.CHANG X R, FAN R. A Mixed measurement partition state estimation method taking zero injection node constraint into account[J]. Power System Technology,2015,39(8):2253-2257.
[3] 李江,王义伟,魏超,等. 卡尔曼滤波理论在电力系统中的应用综述[J]. 电力系统保护与控制,2014,42(6):135-144.LI J,WANG Y W,WEI CH,et al. A survey on the application of Kalman filtering method in power system[J]. Power System Protection and Control,2014,42(6):135-144.
[4] 朱晓青,马定寰,李圣清,等. 基于BP神经网络的微电网蓄电池荷电状态估计[J]. 电子测量与仪器学报,2017,31(12):2042-2048.ZHU X Q, MA D Y, LI SH Q, et al. Estimation of state of charge for micro grid battery based on BP neural network[J]. Journal of Electronic Measurement and Instrumentation, 2017,31(12):2042-2048.
[5] 安军,杨振瑞,周毅博,等. 基于平方根容积卡尔曼滤波的发电机动态状态估计[J]. 电工技术学报,2017,32(12):234-240.AN J, YANG ZH R, ZHOU Y B, et al. Dynamic state estimator for synchronous-machines based on square root cubature Kalman filter[J]. Transactions of China Electrotechnical Society,2017,32(12):234-240.
[6] 程瑛颖,肖冀,周峰,等. 广义安全域模型及其在电表状态估计中的应用[J]. 电子测量技术,2016,39(7):174-177.CHENG Y Y, XIAO Y, ZHOU F, et al. General model of security region and its application in energy meters’ status estimation [J]. Transactions of China Electrotechnical Society, 2016,39(7):174-177.
[7] 尹忠刚,肖鹭,孙向东,等. 基于粒子群优化的感应电机模糊扩展卡尔曼滤波器转速估计方法[J]. 电工技术学报,2016,31(6):55-65.YIN ZH G, XIAO L, SUN X D, et al. A speed estimation method of fuzzy extended Kalman filter for induction motors based on particle swarm optimization[J]. Transactions of China Electrotechnical Society,2016,31(6):55-65.
[8] 张海波,易文飞. 基于异步迭代模式的电力系统分布式状态估计方法[J]. 电力系统自动化,2014,38(9):125-131.ZHANG H B, YI W F. Distributed state estimation method for power systems based on asynchronous iteration mode[J]. Automation of Electric Power Systems, 2014,38(9):125-131.
[9] 马平川,沈沉,陈颖,等. 基于伪量测型协调变量的分布式状态估计算法[J].中国电机工程学报,2014,34(19):3170-3177.MA P CH, SHEN CH, CHEN Y, et al. Distributed state estimation based on the pseudo-measurement coordination variables[J]. Proceedings of the CSEE,2014,34(19):3170-3177.
[10] 牛胜锁,张达,梁志瑞,等. 基于抗差总体最小二乘法的电力系统谐波状态估计[J]. 电力系统保护与控制,2014,42(11):106-111.NIU SH S, ZHANG D, LIANG ZH R, et al. Power system harmonic state estimation based on robust total least squares[J]. Power System Protection and Control,2014,42(11):106-111.
[11] 赵洪山,田甜. 基于自适应无迹卡尔曼滤波的电力系统动态状态估计[J]. 电网技术,2014,38(1):188-192.ZHAO H SH, TIAN T. Dynamic state estimation for power system based on an adaptive unscented Kalman filter[J]. Power System Technology,2014,38(1):188-192.
[12] 邓松,岳东,朱力鹏,等. 电力大数据智能化高效分析挖掘技术框架[J].电子测量与仪器学报,2016,30(11):1679-1686.DENG S, YUE D, ZHU L P, et al. Framework of intelligent and efficient analysis and mining technology for power big data[J]. Journal of Electronic Measurement and Instrumentation,2016,30(11):1679-1686.
[13] 贾宏杰,吕英辉,曾沅,等. PMU在电力系统中的优化配置方法[J]. 电力科学与技术学报,2010,25(1):54-59,66.JIA H J, LV Y H, ZENG Y, et al. Optimal PMU placement in power system[J]. Journal of Electric Power Science and Technology,2010,25(1):54-59,66.
[14] 李滨,杜孟远,祝云,等. 基于准实时数据的智能配电网状态估计[J]. 电工技术学报,2016,31(1):34-44.LI B, DU M Y, ZHU Y, et al. A state estimator for smart distribution networks with quasi-real time data[J]. Transactions of China Electrotechnical Society,2016,31(1):34-44.
[15] 李从善,刘天琪,李兴源,等. 用于电力系统状态估计的WAMS/SCADA混合量测数据融合方法[J]. 高电压技术,2013,39(11):2686-2691.LI C SH, LIU T Q, LI X Y, et al. Data fusion method of WAMS/SCADA hybrid measurements in power system state estimation[J]. High Voltage Engineering, 2013,39(11):2686-2691.