Pmu control method and system based on fault triggering
By adopting a fault-triggered PMU control method, the problems of PMU data transmission delay and communication pressure were solved, enabling timely and accurate transmission of fault information and integrity of power grid data, and reducing network burden.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- STATE GRID SHANGHAI MUNICIPAL ELECTRIC POWER CO
- Filing Date
- 2023-04-06
- Publication Date
- 2026-05-12
AI Technical Summary
Existing PMUs suffer from packet loss and latency issues during data transmission, preventing the power grid master station from obtaining fault information in a timely manner. Furthermore, the power system, which spans a vast territory, requires a large number of PMUs, resulting in excessive pressure on the communication network.
A fault-triggered PMU control method is adopted. By setting a fault trigger index γt, data is sent to the estimator only when the fault trigger condition is met, and otherwise stored locally. Combined with Bayesian rules and particle filtering, the accuracy and integrity of the data are ensured.
This reduces the burden on network communication, ensures the effective transmission and accuracy of fault information, lowers communication pressure, and guarantees the integrity and reliability of power grid data.
Smart Images
Figure CN116455067B_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The application belongs to the field of wide-area measurement technology of power systems, and particularly relates to a PMU control method and system based on fault triggering. BACKGROUND
[0002] A synchronous phasor measurement device (PMU) is a phasor measurement unit configured by using a global positioning system (GPS) second pulse as a synchronous clock. The PMU can be used in the fields of dynamic monitoring, system protection, system analysis and prediction of a power system, and is an important equipment for ensuring safe operation of a power grid. Hundreds of PMUs have been installed and used worldwide. Field tests, operation and application research results show that the synchronous phasor measurement technology has application or application prospects in state estimation and dynamic monitoring, stability prediction and control, model verification, relay protection, fault location and the like of a power system. The PMU based on a GPS clock can measure phasor data such as voltage phase and current phase of a power system hub point, and transmit the data to a monitoring master station through a communication network. The monitoring master station determines how to split, cut off machines and cut off loads of the system according to the phase and amplitude of different points when the system is disturbed, so as to prevent further expansion of the accident and even collapse of the power grid.
[0003] However, the PMU still has defects such as data transmission packet loss and delay during data transmission. This will result in that the power grid master station cannot obtain information related to the fault state in time, cannot take timely rescue measures, and brings huge economic losses.
[0004] In addition, China has a vast territory, and power systems are widely distributed in different geographical locations. The structural characteristics determine that a large number of PMUs need to be added. This will also generate a large amount of data that needs to be transmitted through a communication network, which will bring specific pressure to the existing communication network. SUMMARY
[0005] In order to solve the above problems, the application 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.
[0006] In order to achieve the above purpose, the application adopts the following technical scheme
[0007] A PMU control method based on fault triggering, comprising the following steps:
[0008] S1: data modeling according to signals collected by a distribution network PMU:
[0009]
[0010] Where x(t), u(t), ω(t), and y(t) are the state vector, control input vector, external disturbance vector, and output vector of the power grid, respectively, and A, B, and B are the external disturbance vector and output vector, respectively. ω The system parameter matrix is of appropriate dimension; C and D are weighting matrices; g(t, x(t)) is a continuous nonlinear vector function; t is time.
[0011] S2, Set fault trigger index γ t ,
[0012] Where, γ t =0 indicates that the fault triggering condition is not met and the corresponding PMU measurement data has not been sent to the estimator. t =1 indicates that the fault triggering condition is met, and the corresponding distribution network PMU measurement data is sent to the estimator. Let it be a random variable;
[0013] f(.) represents the fault trigger function, which determines the information available to the estimator at the moment of failure; the fault trigger function is a Gaussian kernel function, i.e.
[0014] Where Σ is a non-singular positive definite weighting matrix, which determines the shape of the Gaussian kernel; y t y represents the vector transmitted by the PMU at time t; it This represents the vector sent by the PMU at time it, which is the last piece of information that satisfies the fault triggering condition;
[0015] S3. The information received by the estimator is represented as follows: The final estimator receives measurement data as I t ={y1, y2, ... y t-1 y t}
[0016] In one embodiment of the present invention, the estimator is an RMSE estimator.
[0017] In one embodiment of the present invention, the distribution network PMU is directly connected to the power grid master station via Ethernet or the Internet. When the fault triggering conditions are not met, the corresponding distribution network PMU stores the measurement data locally. Only when needed will the distribution network PMU directly send the measurement data to the power grid master station.
[0018] In one 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.
[0019] Furthermore, S4 includes the following steps: obtaining the posterior probability density function of this information using Bayesian rules: p(x t|I t The density function is approximated using the Dirac-Delta function and particles to obtain the normalized weights. The normalized weight is compared with a pre-set threshold to determine whether the fault information meets the estimation accuracy.
[0020] The present invention also provides a fault-triggered PMU control system, which includes multiple distribution network PMUs, each of which is installed at various nodes in the distribution network; each distribution network PMU includes a fault-triggered controller; the fault-triggered controller is used to execute the above-described control method; the distribution network PMU is connected to an estimator via a communication network.
[0021] In one embodiment of the present invention, the estimator is an RMSE estimator.
[0022] In one embodiment of the present invention, the distribution network PMU is directly connected to the power grid master station via Ethernet or the Internet. When the fault triggering conditions are not met, the corresponding distribution network PMU stores the measurement data locally. Only when needed will the distribution network PMU directly send the measurement data to the power grid master station.
[0023] In one embodiment of the present invention, the estimator sends the received data to the server of the power grid monitoring master station; the power grid monitoring master station processes the data received by the estimator to obtain the estimation accuracy of the fault information.
[0024] Furthermore, the power grid monitoring master station processes the data received by the estimator in the following steps: It uses Bayesian rules to obtain the posterior probability density function: p(x t |I t The density function is processed using the Dirac-Delta function and particle filtering to obtain the normalized weights. By comparing the normalized weight with a pre-set threshold, it can be determined whether the fault information meets the estimation accuracy requirements.
[0025] The technical solution provided by this invention reduces the network communication burden and saves limited network bandwidth while ensuring the effective transmission of fault information.
[0026] Even when the fault triggering conditions are not met, the corresponding PMU can still store the measurement data locally. When needed (e.g., for data backtracking or power grid big data analysis), the distribution network PMU directly sends the measured data to the power grid master station, ensuring the integrity of distribution network data.
[0027] This invention employs open-loop fault triggering, further reducing the communication pressure on the power grid.
[0028] The present invention further verifies the fault data of the PMU received at the end to ensure the accuracy of the fault information. Attached Figure Description
[0029] Figure 1 This is a schematic diagram of the main process of the present invention.
[0030] Figure 2 This is a schematic diagram of the circuit structure of the present invention. Detailed Implementation
[0031] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0032] A fault-triggered PMU control method includes the following steps: S1: Data modeling based on signals acquired by the PMU:
[0033]
[0034] Where x(t), u(t), ω(t), and y(t) are the state vector, control input vector, external disturbance vector, and output vector of the power grid, respectively, and A, B, and B are the external disturbance vector and output vector, respectively. ω The system parameter matrix is of appropriate dimension and is related to the power grid; C and D are weighting matrices; g(t, x(t)) is a continuous nonlinear vector function; t is time.
[0035] S2, Set fault trigger index γ t ,
[0036] Where, γ t =0 indicates that the fault triggering condition is not met and the corresponding PMU measurement data has not been sent to the estimator. t =1 indicates that the fault triggering condition is met, and the corresponding distribution network PMU measurement data is sent to the estimator. Let it be a random variable; f(.) represents the fault-triggered function, which determines the information available to the estimator in the fault-free state; y t y represents the vector transmitted by the PMU at time t; it This represents the vector sent by the PMU at time it, which is the last information received by the estimator that meets the triggering condition.
[0037] In one specific embodiment of the present invention, the estimator is an RMSE estimator.
[0038] The fault trigger function can be a Gaussian kernel function, i.e. Where ∑ is a non-singular positive definite weighted matrix, which determines the shape of the Gaussian kernel.
[0039] Fault triggering methods can generally be divided into closed-loop and open-loop methods, which have random or deterministic fault targets. In closed-loop methods, since the fault triggering conditions are related to the newly acquired signal, feedback from the estimator to the power management unit (PMU) is required at certain times. Because feedback communication makes this method too costly, this invention employs open-loop fault triggering to further reduce the communication burden on the power grid.
[0040] S3. The information received by the estimator is represented as follows: The final estimator receives measurement data as I t ={y1, y2, ... y t-1 y t};
[0041] S4. The power grid monitoring master station processes the data received by the estimator to obtain the estimation accuracy of the fault information and ensure the accuracy of the fault information.
[0042] In one specific embodiment of the present invention, when the fault triggering conditions are not met, the corresponding PMU will store the measurement data locally. When needed (e.g., for data backtracking or power grid big data analysis), the distribution network PMU will directly send the measured data to the power grid master station.
[0043] Because the data acquired by the PMU in this invention is only transmitted to the estimator when the fault triggering conditions are met, the measurement information received by the estimator is incomplete, posing a significant challenge to further processing of the fault signal.
[0044] In a specific embodiment of the present invention, the server of the power grid monitoring master station can perform particle filtering processing on the data received by the estimator to obtain the accuracy of fault information estimation. Specifically, the following steps are included:
[0045] The posterior probability density function is obtained using Bayes' rule: p(x) t |I t The density function is approximated using the Dirac-Delta function and particles to obtain the normalized weights. By comparing the normalized weight with a pre-set threshold, it can be determined whether the fault information meets the estimation accuracy requirements.
[0046] Normalized weights It can be expressed by the following formula:
[0047] Where N is the total number of particles in the swarm. These represent the weights of the i-th and j-th particles, respectively.
[0048] Obtained from the Dirac-Delta function:
[0049] See the main flowchart of this invention. Figure 1 .
[0050] The present invention also provides a fault-triggered PMU control system, which includes multiple distribution network PMUs, each of which is installed at various nodes in the distribution network; each distribution network PMU includes a fault-triggered controller; the fault-triggered controller is used to execute the above-described control method; the distribution network PMU is connected to an estimator via a communication network.
[0051] In one embodiment of the present invention, the estimator is an RMSE estimator.
[0052] In one embodiment of the invention, the distribution network PMU is directly connected to the power grid master station via Ethernet or the Internet. When the fault triggering conditions are not met, the corresponding distribution network PMU stores the measurement data locally. Only when needed does the distribution network PMU directly send the measured data to the power grid master station. This can alleviate the pressure on the communication network.
[0053] In one embodiment of the present invention, the estimator sends the received data to the server of the power grid monitoring master station; the power grid monitoring master station processes the data received by the estimator to obtain the estimation accuracy of the information.
[0054] Furthermore, the power grid monitoring master station processes the data received by the estimator in the following steps: It uses Bayesian rules to obtain the posterior probability density function: p(x t |I t The density function is processed using the Dirac-Delta function and particle filtering to obtain the normalized weights. By comparing the normalized weight with a pre-set threshold, it can be determined whether the fault information meets the estimation accuracy requirements.
[0055] See the schematic diagram of the main structure of this invention. Figure 2 .
[0056] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. The apparatuses, devices, and computer-readable storage media disclosed in the embodiments are described simply because they correspond to the methods disclosed in the embodiments; relevant details can be found in the method section.
[0057] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in connection with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can implement the described functions using different methods for each specific application, but such implementation should not be considered beyond the scope of this application. The steps of the methods or algorithms described in connection with the embodiments disclosed herein can be implemented directly in hardware, software modules executed by a processor, or a combination of both. Software modules can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disks, removable disks, CD-ROMs, or any other form of storage medium known in the art.
[0058] The control method and system provided by this invention have been described in detail above. Specific embodiments have been used to illustrate the principles and implementation methods of this invention. The descriptions of the embodiments above are only for the purpose of helping to understand the method and core ideas of this invention. It should be noted that those skilled in the art can make various improvements and modifications to this invention without departing from the principles of this invention, and these improvements and modifications also fall within the protection scope of the claims of this invention.
Claims
1. A fault-triggered PMU control method, characterized in that: Includes the following steps: S1: Data modeling based on signals collected by the distribution network PMU: Where x(t), u(t), ω(t), and y(t) are the state vector, control input vector, external disturbance vector, and output vector of the power grid, respectively, and A, B, and B are the external disturbance vector and output vector, respectively. ω The system parameter matrix is of appropriate dimension; C and D are weighting matrices; g(t, x(t)) is a continuous nonlinear vector function; t is time. S2, Set fault trigger index γ t , Where, γ t =0 indicates that the fault triggering condition is not met and the corresponding PMU measurement data has not been sent to the estimator. t =1 indicates that the fault triggering condition has been met, and the corresponding PMU measurement data is sent to the estimator. Let it be a random variable; f(.) represents the fault trigger function, which determines the information available to the estimator at the moment of failure; the fault trigger function is a Gaussian kernel function, i.e. Where ∑ is a non-singular positive definite weighted matrix, which determines the shape of the Gaussian kernel; y t y represents the vector transmitted by the PMU at time t; it This represents the vector sent by the PMU at time it, which is the last piece of information that satisfies the fault triggering condition; S3. The information received by the estimator is represented as follows: The final estimator receives measurement data as I t ={y1, y2, ... y t-1 y t } 2. The fault-triggered PMU control method according to claim 1, characterized in that: The estimator is an RMSE estimator.
3. The fault-triggered PMU control method according to claim 1, characterized in that: The distribution network PMU communicates directly with the power grid master station via Ethernet or the Internet. When the fault triggering conditions are not met, the corresponding distribution network PMU stores the measurement data locally. Only when needed will the distribution network PMU directly send the measurement data to the power grid master station.
4. The fault-triggered PMU control method according to claim 1, characterized in that: It also includes S4, where the power grid monitoring master station processes the data received by the estimator to obtain the estimation accuracy of fault information.
5. The fault-triggered PMU control method according to claim 4, characterized in that: S4 includes the following steps: obtaining the posterior probability density function p(x) using Bayes' rule. t |I t The density function is processed using the Dirac-Delta function and particle filtering to obtain the normalized weights. By comparing the normalized weight with a pre-set threshold, it can be determined whether the fault information meets the estimation accuracy requirements.
6. A fault-triggered PMU control system, characterized in that: Multiple distribution network PMUs are included, and the multiple distribution network PMUs are respectively installed at various nodes in the distribution network; Each distribution network PMU includes a fault trigger controller; the fault trigger controller is used to execute the control method as described in claim 1. The distribution network PMU communicates with the estimator via a communication network.
7. The fault-triggered PMU control system according to claim 6, characterized in that: The estimator is an RMSE estimator.
8. The fault-triggered PMU control system according to claim 6, characterized in that: The distribution network PMU communicates directly with the power grid master station via Ethernet or the Internet. When the fault triggering conditions are not met, the corresponding distribution network PMU stores the measurement data locally. Only when needed will the distribution network PMU directly send the measurement data to the power grid master station.
9. The fault-triggered PMU control system according to claim 6, characterized in that: The estimator sends the received data to the server of the power grid monitoring master station; the power grid monitoring master station processes the data received by the estimator to obtain the estimation accuracy.
10. The fault-triggered PMU control system according to claim 9, characterized in that: The power grid monitoring master station processes the data received by the estimator in the following steps: It uses Bayesian rules to obtain the posterior probability density function: p(x t |I t The density function is processed using the Dirac-Delta function and particle filtering to obtain the normalized weights. By comparing the normalized weight with a pre-set threshold, it can be determined whether the fault information meets the estimation accuracy requirements.