WO2016078477A1 - Procédé d'estimation d'état généralisé linéaire triphasé de sous-station de transformateur - Google Patents

Procédé d'estimation d'état généralisé linéaire triphasé de sous-station de transformateur Download PDF

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Publication number
WO2016078477A1
WO2016078477A1 PCT/CN2015/090854 CN2015090854W WO2016078477A1 WO 2016078477 A1 WO2016078477 A1 WO 2016078477A1 CN 2015090854 W CN2015090854 W CN 2015090854W WO 2016078477 A1 WO2016078477 A1 WO 2016078477A1
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measurement
switch
node
state estimation
phase
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PCT/CN2015/090854
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English (en)
Chinese (zh)
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张婷
翟明玉
张海滨
齐苗苗
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国电南瑞科技股份有限公司
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Publication of WO2016078477A1 publication Critical patent/WO2016078477A1/fr

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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16Z—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS, NOT OTHERWISE PROVIDED FOR
    • G16Z99/00—Subject matter not provided for in other main groups of this subclass
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation

Definitions

  • the power system state estimation uses the measured data collected by the substation to estimate or predict the operating state of the power system. State estimation is the basis of the information processing stage in the energy management system, which directly affects the decision-making of the control center and is related to the safe and stable operation of the power grid.
  • the traditional state estimation ignores the interaction between the analog data, the switch quantity and the network parameters. It is assumed that the switch state and the network parameters are correct before the state estimation. When these assumptions are not established, serious errors will result.
  • the generalized state estimation method of substation has the advantages of small network scale and high data redundancy.
  • the generalized state estimation of substation is used as the pre-processing stage before the state estimation of the control center. To provide more accurate basic data for the control center, it needs to have fast and reliable characteristics, and the existing generalized state estimation method is not available.
  • the present invention provides a three-phase linear generalized state estimation method for a substation, which has small calculation scale, low complexity, fast calculation, and improved reliability.
  • a three-phase linear generalized state estimation method for a substation comprising the following steps,
  • Step 3 ignoring the three-phase mutual inductance between the zero-impedance branches in the b-th voltage level, establishing a phase-separated zero-impedance network as a network model for the three-phase linear generalized state estimation of the substation, and proceeding to step 4;
  • Step 5 performing state estimation on the a-phase split zero impedance network, and proceeding to step 6;
  • Step 6 calculating the standardized residual of each measurement by using the state variable value obtained by the state estimation, and proceeding to step 7;
  • step 7 the absolute value of the absolute value of the residual is compared with the threshold of the detection. If the threshold value is exceeded, the data is determined to have bad data. If there is bad data, the amount corresponding to the residual is removed, and then steps 5 to 7 are repeated. , until the absolute value of the absolute value of the residual obtained by the loop is less than or equal to the detection threshold, and proceeds to step eight;
  • Step 8 judging whether the punctured quantity measurement includes a pseudo-measurement of the switch remote signal, if it is included, determining that the switch remote signal is incorrect, if not, the switch remote signal is correct, and the process proceeds to step IX;
  • the state estimation is based on the collected measurement data. On each phase-separated zero-impedance network, all the linear measurement equations are established by using the voltage of each node and the power flowing through each switch branch as state variables. Estimate the measurement equations and obtain the state variable values.
  • the measurement equation includes a node voltage measurement equation, a switching power measurement equation, a node injection power measurement equation, and a switch state pseudo-measurement equation.
  • the switching state pseudo-measurement equation is as follows,
  • r is the residual vector
  • r T is the transpose of the residual vector
  • R -1 is the weight matrix
  • z is the measurement vector
  • H is the coefficient matrix of the measurement equation
  • x is the state variable to be calculated
  • a three-phase linear generalized state estimation method for a substation includes the following steps:
  • Step 3 ignoring the three-phase mutual inductance between the zero-impedance branches in the b-th voltage level, and establishing a phase-separated zero-impedance network as a network model for the three-phase linear generalized state estimation of the substation, and moving to step four.
  • Step 5 Perform state estimation on the a-phase split zero impedance network, and go to step 6.
  • the state estimation is based on the collected measurement data. On each phase-separated zero-impedance network, the voltage of each node and the power flowing through each switch branch are used as state variables, and all linear measurement equations are established. Estimate the calculation and find the state variable value.
  • the measurement data includes data collected by the measurement and control device, data collected by the phasor measurement unit, and data collected by the protection device.
  • the data collected by the PMU includes the voltage amplitude of the node i. Voltage phase angle of node i Active power flowing over the switching branch ij Reactive power flowing over the switching branch ij Active power injected by node i Reactive power injected by node i And switch the remote signal value C pmu .
  • the data collected by the protection device includes the active power flowing through the switch branch ij Reactive power flowing over the switching branch ij And switch remote signal data C pro .
  • the established measurement equations include the node voltage measurement equation, the switching power measurement equation, the node injection power measurement equation, and the switch state pseudo-measurement equation.
  • the active power and the reactive power flowing through the switch branch ij are the active and reactive power pseudo-measures on the switch branch ij, respectively, and their values should be zero; for Measurement error, ⁇ oqij is Measurement error.
  • I is the identity matrix
  • a 1 , A 2 , A 3 , A 4 , A 5 , and A 6 are all coefficient matrices
  • x V , x ⁇ , x P , and x Q all represent various state variable vectors. They are all kinds of measurement vectors, ⁇ V , ⁇ ⁇ , ⁇ P , ⁇ Q , ⁇ op , ⁇ oq , ⁇ cv , ⁇ c ⁇ are measurement error vectors corresponding to various quantity measurements;
  • the objective function of the state estimation is to find the minimum of the following formula:
  • the estimated value of the state variable according to the optimization formula The state variable value can be calculated according to this formula.
  • Step 6 Calculate the standardized residual of each measurement by using the state variable value obtained by the state estimation, and go to step 7.
  • step 7 the absolute value of the absolute value of the residual is compared with the threshold of the detection. If the threshold value is exceeded, the data is determined to have bad data. If there is bad data, the amount corresponding to the residual is removed, and then steps 5 to 7 are repeated. Until the absolute value of the absolute value of the residual obtained by the loop is less than or equal to the detection threshold, go to step 8.
  • Step 8 Determine whether the cull measurement includes the pseudo-measurement of the switch remote signal. If it is included, judge the switch remote signal error. If not, the switch remote signal is correct, and go to step 9.
  • the state estimation of the above method is carried out in a phase-separated zero-impedance network with small calculation scale and low computational difficulty.
  • the method improves the state estimation by using three-phase multi-source real-time data from the measurement and control device, the phasor measurement unit and the protection device. Data redundancy and computational reliability; the measurement equations established by this method are linear, and the calculation does not require iteration, so the estimation calculation is simple and fast; the switch state pseudo-measurement equation established by the method participates in state estimation together, using the maximum residue
  • the difference method is used to identify bad data and realize the synchronous identification of bad data and topological errors. This method solves the bad data and topology errors in the substation, and improves the more accurate basic data for the control center, which significantly reduces the state estimation of the control center. The amount of calculation increases the reliability.

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  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Supply And Distribution Of Alternating Current (AREA)
  • Remote Monitoring And Control Of Power-Distribution Networks (AREA)

Abstract

L'invention concerne un procédé d'estimation d'état généralisé linéaire triphasé de sous-station de transformateur, comprenant les étapes suivantes : l'établissement d'une équation de mesure linéaire et d'une équation de mesure de pseudo-état de marche-arrêt sur la base d'un réseau à impédance zéro et à déphasage par l'utilisation de données en temps réel multisource triphasées provenant d'un dispositif de mesure et de commande, une unité de mesure et un dispositif de protection de phaseur destinés à participer ensemble à l'estimation d'état; l'identification de mauvaises données avec le procédé d'erreur résiduelle maximale et l'identification simultanée d'erreurs de topologie. Le procédé améliore la redondance de données et la fiabilité de calcul pour l'estimation d'états et élimine la nécessité d'un calcul itératif et résout les mauvaises données et l'erreur de topologie à l'intérieur de la sous-station de transformateur, fournissant ainsi des données de base plus précises et réduisant le calcul d'estimation d'états pour un centre de régulation et de commande.
PCT/CN2015/090854 2014-11-18 2015-09-26 Procédé d'estimation d'état généralisé linéaire triphasé de sous-station de transformateur WO2016078477A1 (fr)

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CN107016489A (zh) * 2017-03-09 2017-08-04 中国电力科学研究院 一种电力系统抗差状态估计方法和装置
CN108255951A (zh) * 2017-12-18 2018-07-06 国网上海市电力公司 基于数据挖掘的中低压配电网状态估计伪量测量确定方法
CN109193799A (zh) * 2018-09-07 2019-01-11 华北电力大学 一种基于图论的配电网多种量测量的最优配置方法
CN109888773A (zh) * 2019-02-25 2019-06-14 武汉大学 一种电力系统多区域分布式状态评估方法
CN110190600A (zh) * 2019-06-21 2019-08-30 国网天津市电力公司 一种基于ami量测近邻回归的三相配电网拓扑辨识方法
CN110210690A (zh) * 2019-06-21 2019-09-06 国网天津市电力公司 一种配电系统微型同步相量测量单元优化配置方法
CN112966358A (zh) * 2021-02-08 2021-06-15 贵州电网有限责任公司 一种基于数据修补的主动配电网状态感知方法
CN113553538A (zh) * 2021-05-14 2021-10-26 河海大学 一种递推修正混合线性状态估计方法
CN115249971A (zh) * 2022-07-27 2022-10-28 重庆大学 一种辐射网的加权平均快速前推回代抗差状态估计方法

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CN104332997B (zh) * 2014-11-18 2016-03-02 国电南瑞科技股份有限公司 一种变电站三相线性广义状态估计方法
CN113420405B (zh) * 2021-05-10 2023-01-31 中国南方电网有限责任公司 一种输电线路参数修正方法

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Cited By (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107016489A (zh) * 2017-03-09 2017-08-04 中国电力科学研究院 一种电力系统抗差状态估计方法和装置
CN108255951A (zh) * 2017-12-18 2018-07-06 国网上海市电力公司 基于数据挖掘的中低压配电网状态估计伪量测量确定方法
CN109193799A (zh) * 2018-09-07 2019-01-11 华北电力大学 一种基于图论的配电网多种量测量的最优配置方法
CN109888773B (zh) * 2019-02-25 2022-08-05 武汉大学 一种电力系统多区域分布式状态评估方法
CN109888773A (zh) * 2019-02-25 2019-06-14 武汉大学 一种电力系统多区域分布式状态评估方法
CN110190600B (zh) * 2019-06-21 2022-09-30 国网天津市电力公司 一种基于ami量测近邻回归的三相配电网拓扑辨识方法
CN110210690A (zh) * 2019-06-21 2019-09-06 国网天津市电力公司 一种配电系统微型同步相量测量单元优化配置方法
CN110190600A (zh) * 2019-06-21 2019-08-30 国网天津市电力公司 一种基于ami量测近邻回归的三相配电网拓扑辨识方法
CN110210690B (zh) * 2019-06-21 2022-12-06 国网天津市电力公司 一种配电系统微型同步相量测量单元优化配置方法
CN112966358A (zh) * 2021-02-08 2021-06-15 贵州电网有限责任公司 一种基于数据修补的主动配电网状态感知方法
CN113553538A (zh) * 2021-05-14 2021-10-26 河海大学 一种递推修正混合线性状态估计方法
CN113553538B (zh) * 2021-05-14 2023-12-01 河海大学 一种递推修正混合线性状态估计方法
CN115249971A (zh) * 2022-07-27 2022-10-28 重庆大学 一种辐射网的加权平均快速前推回代抗差状态估计方法

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