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CN-116455067-B - PMU control method and system based on fault triggering

CN116455067BCN 116455067 BCN116455067 BCN 116455067BCN-116455067-B

Abstract

The invention provides a PMU control method and system based on fault triggering. The method comprises the following steps of S1, carrying out data modeling according to signals collected by a distribution network PMU, S2, setting a fault trigger index gamma t , S3 and finally obtaining measurement data received by an estimator as I t ＝{y 1 ,y 2 ,...y t‑1 ,y t . The power grid monitoring main station can also process the data received by the estimator to obtain the estimation accuracy of the fault information. The invention reduces network communication burden and saves limited network bandwidth under the condition of ensuring effective transmission of fault information.

Inventors

  • LIU JINSONG
  • ZHOU MIN
  • LIU SHU
  • GU LI
  • LI HUIMIN
  • ZHANG XINHUI
  • LIANG DONG

Assignees

  • 国网上海市电力公司

Dates

Publication Date
20260512
Application Date
20230406

Claims (10)

  1. 1. A PMU control method based on fault triggering is characterized by comprising the following steps: S1, carrying out data modeling according to signals collected by a distribution network PMU: Wherein x (t), u (t), omega (t) and y (t) are respectively a state vector, a control input vector, an external interference vector and an output vector of the power grid, A, B, B ω is a system parameter matrix with proper dimension, C, D is a weighting matrix, g (t, x (t)) is a continuous nonlinear vector function, and t is time; S2, setting a fault trigger index gamma t , Where γ t =0 indicates that the fail-over condition is not met and no corresponding PMU measurement data is sent to the estimator, γ t =1 indicates that the fail-over condition is met, the corresponding PMU measurement data is sent to the estimator, Is a random variable; f (), denotes a fault trigger function which determines the information available to the estimator at the moment of the fault, the fault trigger function being a gaussian kernel function, i.e Wherein Sigma is a non-singular positive-definite weight matrix which determines the shape of a Gaussian kernel, y t represents a vector sent by the PMU at time t, y it represents a vector sent by the PMU at time it, and the vector is the last information meeting a fault triggering condition; S3, the information received by the estimator is expressed as follows: The measurement data received by the final estimator is I t ＝{y 1 ,y 2 ,...y t-1 ,y t .
  2. 2. The method of claim 1, wherein the estimator is an RMSE estimator.
  3. 3. The method for controlling the PMU based on the fault triggering of claim 1, wherein the PMU is directly connected with the power grid main station in a communication way through the Ethernet or the Internet, when the fault triggering condition is not met, the corresponding PMU stores the measured data locally, and the PMU directly transmits the measured data to the power grid main station when the measured data are needed.
  4. 4. The method of claim 1 further comprising S4, the power grid monitoring master station processing data received by the estimator to obtain the estimation accuracy of the fault information.
  5. 5. The method of PMU control based on fault triggering as claimed in claim 4, wherein S4 includes obtaining a posterior probability density function p (x t |I t ) using Bayesian rules, processing the density function using a Dirac-delta function and particle filtering, and finally obtaining normalized weights And comparing the normalized weight with a preset threshold value to judge whether the fault information meets the estimation accuracy requirement.
  6. 6. A PMU control system based on fault triggering is characterized in that a plurality of PMUs comprise distribution networks, and the distribution networks are respectively arranged at each node in a power distribution network; each distribution network PMU comprises a fault trigger controller, wherein the fault trigger controller is used for executing the control method according to claim 1; the distribution network PMU is in communication connection with the estimator through a communication network.
  7. 7. The fail-over based PMU control system of claim 6, wherein the estimator is an RMSE estimator.
  8. 8. The system of claim 6, wherein the distribution network PMU is directly connected with the power grid master station in a communication manner through the Ethernet or the Internet, and when the fault trigger condition is not met, the corresponding distribution network PMU locally stores the measured data, and the distribution network PMU directly transmits the measured data to the power grid master station when the measured data are needed.
  9. 9. The PMU control system based on fault triggering of claim 6, wherein the estimator sends the received data to a server of a power grid monitoring master station, and the power grid monitoring master station processes the data received by the estimator to obtain an estimation accuracy.
  10. 10. The PMU control system based on fault triggering of claim 9, wherein the power grid monitoring main station processes the data received by the estimator includes the steps of obtaining a posterior probability density function p (x t |I t ) through Bayesian rules, processing the density function through a Dirac-delta function and particle filtering, and finally obtaining normalized weights And comparing the normalized weight with a preset threshold value to judge whether the fault information meets the estimation accuracy requirement.

Description

PMU control method and system based on fault triggering Technical Field The invention belongs to the technical field of wide-area measurement of power systems, and particularly relates to a PMU control method and system based on fault triggering. Background The synchrophasor measurement device (PMU: phasor measurement unit) is a phasor measurement unit that is constructed using Global Positioning System (GPS) second pulses as a synchronizing clock. The method can be used in the fields of dynamic monitoring, system protection, system analysis, prediction and the like of the power system. Is an important device for guaranteeing the safe operation of the power grid. Hundreds of PMUs have been installed worldwide. The results of field tests, operation and application research show that the synchronous phasor measurement technology has application or application prospect in the aspects of power system state estimation and dynamic monitoring, stable prediction and control, model verification, relay protection, fault positioning and the like. The PMU based on the GPS clock can measure voltage phase, current phase and other quantity data of the junction point of the power system, the data are transmitted to the monitoring master station through the communication network, and the monitoring master station determines how to disconnect, cut off and cut off loads of the system when the system is disturbed according to the phase amplitudes of different points, so that further expansion of accidents and even power grid breakdown are prevented. However, PMUs have the defects of packet loss, delay and the like in data transmission. The method can lead the power grid master station to not obtain information related to the fault state in time, and can not make rescue measures in time, so that huge economic loss is caused. And the power system is widely distributed in different geographic positions, and the structural characteristics of the power system determine that a large number of PMUs need to be added. This will also create a huge amount of data to be transmitted over the communication network, bringing a specific pressure to the existing communication network. Disclosure of Invention In order to solve the above problems, the present invention provides a PMU control method and system based on fault triggering, which can reduce the communication burden on the network and ensure the stability of the system. In order to achieve the above purpose, the invention adopts the following technical scheme A fault trigger based PMU control method comprising the steps of: S1, carrying out data modeling according to signals collected by a distribution network PMU: Wherein x (t), u (t), omega (t) and y (t) are respectively a state vector, a control input vector, an external interference vector and an output vector of the power grid, A, B, B ω is a system parameter matrix with proper dimension, C, D is a weighting matrix, g (t, x (t)) is a continuous nonlinear vector function, and t is time; S2, setting a fault trigger index gamma t, Wherein γ t =0 indicates that the fail-over condition is not met and no corresponding PMU measurement data is sent to the estimator, γ t =1 indicates that the fail-over condition is met, the corresponding distribution network PMU measurement data is sent to the estimator,Is a random variable; f (), denotes a fault trigger function which determines the information available to the estimator at the moment of the fault, the fault trigger function being a gaussian kernel function, i.e Wherein Sigma is a non-singular positive-definite weight matrix which determines the shape of a Gaussian kernel, y t represents a vector sent by the PMU at time t, y it represents a vector sent by the PMU at time it, and the vector is the last information meeting a fault triggering condition; S3, the information received by the estimator is expressed as follows: The measurement data received by the final estimator is I t＝{y1,y2,...yt-1,yt. In one embodiment of the invention, the estimator is an RMSE estimator. In an embodiment of the invention, the distribution network PMU is directly in communication connection with the power grid master station through the Ethernet or the Internet, when the fault triggering condition is not satisfied, the corresponding distribution network PMU locally stores the measured data, and the distribution network PMU directly transmits the measured data to the power grid master station when the measured data are needed. In an embodiment of the present invention, the method further includes S4, where the power grid monitoring master station processes the data received by the estimator to obtain the estimation accuracy of the fault information. Further, S4 comprises the steps of obtaining a posterior probability density function of the information using Bayesian rules, p (x t|It), approximating the density function using a Dirac-delta function and particles, and finally obtaining normalized weightsAnd c