Method and system for estimating frequency of power system

Abstract

The invention discloses a power system frequency estimation method and system, and the method comprises the steps: obtaining a power grid model, remote signaling data, generator rotation speed and PMU frequency measurement, and forming a node branch model; forming an expanded node susceptance matrix according to the node branch model, reactance parameters in the generator and remote signaling data; performing node reordering on the expanded node susceptance matrix; forming a factor table of the reordered extended node susceptance matrix by adopting symbol decomposition and numerical decomposition methods; calculating a frequency participation factor matrix F by adopting a multi-thread parallel technology; obtaining a reduced frequency participation factor matrix F'according to the values of the elements in the frequency participation factor matrix F and a set threshold value; and calculating the node frequency by adopting a multi-thread parallel method. The method overcomes the defect that only the average frequency of the power grid can be calculated according to the inertia center frequency of the power grid, and has important reference significance for guiding a dispatching center to monitor selection of power grid generator frequency points.

Description

Technical Field

[0001] The invention belongs to the technical field of power system automation and relates to a method and system for estimating the frequency of a power system. Background Art

[0002] With the massive access of wind and solar energy, centralized / decentralized wind and solar energy and other new energy units that can provide frequency regulation capabilities are increasingly replacing traditional synchronous generators, resulting in a lower inertia level of the power system and increasingly obvious time and space characteristics of the power grid frequency. When an accident occurs in the system, the frequency and voltage of the power grid nodes change significantly. From the perspective of modeling and analysis, it is necessary to accurately describe the frequency characteristics of each busbar of the power grid. The traditional frequency simulation analysis and calculation based on the inertia center frequency analysis method is difficult to reproduce the node frequency deviation, frequency change rate and other indicators after the disturbance of the new power system. The obtained frequency analysis results are not accurate enough and it is difficult to take into account the grid-related performance of new energy power generation equipment. It can no longer adapt to the analysis of the new characteristics of high-proportion new energy power systems. The frequency analysis and control of the power system are facing severe challenges. At the same time, new energy uses additional control means to provide virtual inertia for the system, affecting the system inertia response process. It is becoming increasingly urgent to analyze the factors / weights of the influence of different power generation resources on the grid node frequency in the new power system.

[0003] The existing schemes for frequency estimation methods include: Patent with publication number CN114552572A provides a method and device for photovoltaic-supported grid frequency based on optimization control and prediction tracking, belonging to the field of grid support technology. The method is based on the output optimization model of the frequency of the grid-connected photovoltaic-supported AC system, obtains the optimal output curve of photovoltaic active power and converts it into the optimal output curve of active current, predicts the photovoltaic active current output within the time window, and selects the switch control signal when the photovoltaic predicted active current is closest to the optimal output curve of active current as the optimal control quantity to achieve the prediction and optimization tracking of photovoltaic active current. Patent with publication number CN118281907A provides a method, device and computer-readable storage medium for frequency regulation of wind storage system, involving the field of wind storage system technology. The method establishes a frequency regulation simulation model of the wind storage system, and determines the target control sequence at the current moment to regulate the frequency of the wind storage system according to the frequency regulation simulation model, the objective function and the constraints. After analysis, none of the above methods have a description of the estimation method of the node frequency of the power system. Summary of the invention

[0004] In order to solve the deficiencies in the prior art, the present invention discloses a method and system for estimating the frequency of a power system, which realizes the estimation and calculation of the node frequency in the power grid based on the electrical susceptance parameters of power grid lines, transformers and generators, real-time topological connection relationships, generator speed measurement, and PMU frequency measurement. The method takes into account the electrical distance between the node and the frequency measurement point, and estimates the node frequency by a linear combination of the generator speed and PMU frequency measurement, and obtains an analytical expression for the bus node frequency, which can more accurately reflect the local frequency at each bus node of the power grid, accurately characterize the spatiotemporal distribution characteristics of the power grid frequency, and provide guidance for the frequency monitoring, analysis and control of new power systems.

[0005] To achieve the above-mentioned object of the invention, the present invention adopts the following technical solution.

[0006] In one aspect, the present invention discloses a method for estimating a frequency of a power system, the method comprising the following steps:

[0007] Step 1: Obtain the telesignaling status of the target power grid model and the real-time monitoring system, as well as the generator speed measurement data and the PMU frequency measurement data, and determine whether the current telesignaling status of the target power grid has changed relative to the previous cycle. If it has changed, proceed to step 2 to recalculate the frequency participation factor matrix of the current cycle of the target power grid. Otherwise, directly use the frequency participation factor matrix of the previous calculation cycle of the target power grid and proceed to step 4.

[0008] Step 2: Perform topological analysis based on the power grid model and the current telesignaling status to form a node branch model, and form an extended node susceptance matrix B′ based on the node branch model and the internal reactance parameters of the online generator. BB , and decompose to obtain the expanded node susceptance matrix B′ BB Factor table LDL T , where L is a triangular matrix, L T is the transposed matrix of L, and D is a diagonal matrix;

[0009] Step 3, calculate the frequency participation factor matrix F, and obtain the reduced frequency participation factor matrix F′;

[0010] Step 4: Generate a speed vector ω based on the acquired generator speed measurement and PMU frequency measurement data G ;

[0011] Step 5: According to ω B =F′ω G Calculate the node frequency ω in the target power grid B ;

[0012] Step 6: According to the reduced frequency participation factor matrix F′, obtain the set of generators that have the greatest impact on all node frequencies in the target power grid, and the set of nodes where the generators that have the greatest impact on a certain node frequency are located.

[0013] More preferably,

[0014] In step 2, an extended node susceptance matrix B′ is formed based on the node branch model and the internal reactance parameters of the online generator. BB ,include:

[0015] The node susceptance matrix B formed according to the branch reactance between nodes BB , the full network susceptance matrix B formed by the internal reactance of the synchronous generator BS and contains only the generator internal susceptance matrix B formed according to the internal reactance of the generator BG ;

[0016] According to B BB and B BS Form the extended node susceptance matrix B′ BB , where B′ BB =B BB +B BS .

[0017] More preferably,

[0018] At the node connected to the generator, the node susceptance matrix B is corrected according to the internal reactance of the generator. BB Thus directly forming B′ BB ;

[0019] When considering the frequency measurement value measured by the PMU, the node where the PMU frequency measurement is located is calculated as the generator node. BG .

[0020] More preferably,

[0021] In step 2, the expanded node susceptance matrix B′ BB Before decomposition, the expanded node susceptance matrix B′ BB Reorder the node numbers to reduce the extended node susceptance matrix B′ BB The number of non-zero entries during the factorization process.

[0022] More preferably,

[0023] In step 2, the expanded node susceptance matrix B′ is formed by symbolic decomposition and numerical decomposition. BB The factor table.

[0024] More preferably,

[0025] In step 3, the expanded node susceptance matrix B′ BB Factor table LDL T and the generator internal susceptance matrix B BG According to (LDL T )F=B BG The frequency participation factor matrix F is calculated using OpenMP-based multi-threaded parallel technology;

[0026] According to the size of the element value in the frequency participation factor matrix F and the set threshold value, the node generators in F that have a small impact on the node frequency are ignored, and the reduced frequency participation factor matrix F′ is obtained.

[0027] Further preferably, the first method is used to obtain the reduced frequency participation factor matrix F′, that is, all elements in each row of the frequency participation factor matrix F are arranged in descending order, the first k largest elements of each row of the sorted matrix are taken, the remaining elements are set to zero, and the values ​​in the matrix are updated to obtain the reduced frequency participation factor matrix F′, where k is an integer value less than the number of columns of the frequency participation factor matrix F.

[0028] More preferably,

[0029] The second method is used to obtain the reduced frequency participation factor matrix F′, that is, to find the maximum element F in each row of the frequency participation factor matrix F row by row. max , according to the set threshold value |f i |<αF max The elements f in the matrix F are sorted row by row i Screening is performed, α is a parameter between (0, 1.0), and the value in F is updated to obtain the reduced frequency participation factor matrix F′.

[0030] More preferably,

[0031] In step 4, when considering the frequency measurement value measured by the PMU, the collected PMU frequency measurement is added to the speed vector ω G .

[0032] More preferably,

[0033] In step 5, when it is necessary to calculate all or multiple node frequencies in the power grid, the node frequency ω is calculated using matrix-vector multiplication based on OpenMP multithreading parallel technology. B .

[0034] More preferably,

[0035] In step 6, the number of valid non-zero elements in each column of the reduced frequency participation factor matrix F′ is counted by column, and the valid non-zero elements are averaged and arranged from large to small. The column with a larger average value has a greater impact on the frequency of each node in the power grid corresponding to the generator in the column;

[0036] The generator corresponding to the column with the largest value in each row of the reduced frequency participation factor matrix F′ has the greatest influence on the frequency of the node corresponding to that row, and the generator corresponding to the column with the smaller value has less influence on the frequency of the node corresponding to that row.

[0037] On the other hand, the present invention also discloses a power system frequency estimation system based on the above estimation method, comprising:

[0038] Grid model acquisition and parameter measurement module, remote signaling status judgment module, extended node susceptance matrix factor table calculation module, reduced frequency participation factor matrix calculation module, generator speed vector acquisition module, grid node frequency calculation module, and generator impact on node frequency analysis module;

[0039] The power grid model acquisition and parameter measurement module acquires the target power grid model and the telesignaling status of the real-time monitoring system as well as the generator speed measurement data and the PMU frequency measurement data;

[0040] The remote signal state judgment module judges whether the current remote signal state of the target power grid has changed compared with the previous cycle;

[0041] If the telesignaling status changes, then,

[0042] The extended node susceptance matrix factor table calculation module calculates the extended node susceptance matrix B′ according to the node branch model BB Factor table LDL T ;

[0043] The reduced frequency participation factor matrix calculation module calculates the frequency participation factor matrix F and obtains the reduced frequency participation factor matrix F′;

[0044] The generator speed vector acquisition module forms a speed vector ω based on the acquired generator speed measurement and PMU frequency measurement data. G ;

[0045] The grid node frequency calculation module calculates the frequency ω of each node according to the reduced frequency participation factor and speed vector B ;

[0046] The generator impact on node frequency analysis module analyzes and obtains the generator set that has the greatest impact on all node frequencies in the power grid, as well as the node set where the generator that has the greatest impact on a certain node frequency is located;

[0047] If the telesignaling status does not change, then,

[0048] The grid node frequency calculation module directly calculates the frequency ω of each node based on the frequency participation factor matrix calculated in the previous cycle and the generator speed vector obtained this time. B ,Then the generator impact on node frequency analysis module analyzes and obtains the set of generators that have the greatest impact on all node frequencies in the power grid, and the set of nodes where the generator that has the greatest impact on a certain node frequency is located.

[0049] The present invention also requests protection for an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program implements the method for estimating the frequency of a power system when loaded into the processor.

[0050] A computer-readable storage medium stores a computer program, wherein the computer program implements the method for estimating the frequency of a power system when executed by a processor.

[0051] The beneficial effects of the present invention are as follows:

[0052] 1. The present invention realizes the estimation of the node frequency of the power system. The method is based on the electrical susceptance parameters and real-time topological connection relationship of the power grid lines, transformers and generators. The node frequency is estimated and calculated through the linear combination of generator speed and PMU frequency measurement. The grid structure, generator parameters and the electrical distance between the frequency measurement point and the node are taken into consideration. The disadvantage that the grid inertia center frequency can only calculate the average frequency of the grid is overcome, and the local frequency at each node of the grid can be more accurately reflected, and the spatiotemporal distribution characteristics of the grid frequency are accurately characterized.

[0053] 2. The present invention realizes the calculation of the frequency participation factor matrix. By looking at the values ​​of each row of the reduced frequency participation factor matrix F' by row, the node where the generator with the greatest impact on the frequency of a certain node is located can be determined; at the same time, by counting the number of valid non-zero elements in each column of the reduced frequency participation factor matrix F' by column and calculating the average value, the generator with the greatest impact on the frequency of all nodes in the power grid can be obtained from the perspective of the average value. From the perspective of power grid operation monitoring, it is necessary to accurately measure the rotation speed of the generators at these nodes or accurately record the frequencies of these nodes based on the PMU device, which has important guiding significance for power grid frequency monitoring.

[0054] 3. The present invention can effectively reduce the non-zero element density of the frequency participation factor matrix by reducing the frequency participation factor matrix. The first method ensures the accuracy of frequency estimation of each node in the power grid, and is particularly suitable for dynamic or real-time frequency monitoring, control and application. The second method is suitable for simulation analysis and calculation research of large power systems, and has high calculation efficiency. In practical applications, the two methods can also be combined to reduce the amount of calculation while ensuring accuracy.

[0055] 4. The calculation of the frequency participation factor matrix of the present invention can be extended to nodes including asynchronous generators, such as nodes with frequency acquisition of PMU devices. At the same time, new energy power generation equipment connected to the grid through the power electronics technology interface generally adopts phase-locked loop (PLL) technology to collect its node frequency, which can also be added to the calculation of the frequency participation factor matrix. This patent has scalability. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 This is a flow chart of an embodiment of a method for estimating the frequency of a power system according to the present invention;

[0057] Figure 2 This is a schematic diagram of the L-array non-zero element structure analysis;

[0058] Figure 3 Schematic diagram of LU factorization j column. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical scheme and advantages of the present invention clearer, the technical scheme of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The embodiments described in this application are only embodiments of a part of the present invention, rather than all embodiments. Based on the spirit of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the protection scope of the present invention.

[0060] The present application discloses a method for estimating the frequency of a power system, the method comprising the following steps:

[0061] Step 1: Obtain the telesignaling status of the target power grid model and the real-time monitoring system, as well as the generator speed measurement data and the PMU frequency measurement data, and determine whether the current telesignaling status of the target power grid has changed compared with the previous cycle. If it has changed, proceed to step 2 to recalculate the frequency participation factor matrix of the current cycle of the target power grid. Otherwise, directly use the frequency participation factor matrix of the previous cycle of the target power grid and proceed to step 4.

[0062] Step 2: Perform topological analysis based on the power grid model and the current telesignaling status to form a node branch model. Based on the node branch model and the internal reactance parameters of the online generator, an extended node susceptance matrix B′ is formed. BB , and decompose to obtain the expanded node susceptance matrix B′ BB Factor table LDL T , where L is a triangular matrix, L T is the transposed matrix of L, and D is a diagonal matrix;

[0063] The extended node susceptance matrix B′ is formed according to the node branch model and the internal reactance parameters of the online generator. BB ,include:

[0064] The node susceptance matrix B formed according to the branch reactance between nodes BB , the full network susceptance matrix B formed by the internal reactance of the synchronous generator BS and contains only the generator internal susceptance matrix B formed according to the internal reactance of the generator BG ;

[0065] According to B BB and B BS Form the extended node susceptance matrix B′ BB , where B′ BB =B BB +B BS .

[0066] At the node connected to the generator, the node susceptance matrix B is corrected according to the internal reactance of the generator. BB Thus directly forming B′ BB ; When considering the frequency measurement value measured by the PMU, the node where the PMU frequency measurement is located is calculated as the generator node B BG .

[0067] In the extended node susceptance matrix B′ BB Before decomposition, the expanded node susceptance matrix B′ is sorted using Tinney-1 or Tinney-2 or approximate minimum degree sorting algorithm. BB Reorder the node numbers to reduce the extended node susceptance matrix B′ BB The number of non-zero entries in the factorization process. The extended node susceptance matrix B′ is formed by symbolic decomposition and numerical decomposition. BB The factor table.

[0068] Step 3: Calculate the frequency participation factor matrix F and obtain the reduced frequency participation factor matrix F′;

[0069] By expanding the node susceptance matrix B′ BB Factor table LDL T and the generator internal susceptance matrix BBG According to (LDL T )F=B BG The frequency participation factor matrix F is calculated using OpenMP-based multi-threaded parallel technology;

[0070] According to the size of the element value in the frequency participation factor matrix F and the set threshold value, the node generators in F that have a small impact on the node frequency are ignored, and the reduced frequency participation factor matrix F′ is obtained.

[0071] In a preferred embodiment of the present invention, the following two methods or a combination of the two methods may be used to obtain the reduced frequency participation factor matrix F′:

[0072] The first method is to arrange all the elements in each row of the frequency participation factor matrix F in descending order, take the first k largest elements of each row of the sorted matrix, set the remaining elements to zero, and update the values ​​in the matrix to obtain the reduced frequency participation factor matrix F′, where k is an integer value less than the number of columns of the frequency participation factor matrix F.

[0073] The second method is to find the maximum element F in each row of the frequency participation factor matrix F row by row. max , according to the set threshold value |f i |<αF max The elements f in the matrix F are sorted row by row i Screening is performed, α is a parameter between (0, 1.0), and the value in F is updated to obtain the reduced frequency participation factor matrix F′.

[0074] Step 4: Form the speed vector ω based on the acquired generator speed measurement and PMU frequency measurement data G ;

[0075] When considering the frequency measurement value measured by the PMU, the collected PMU frequency measurement is added to the speed vector ω G .

[0076] Step 5: According to ω B =F′ω G Calculate the node frequency ω B ;

[0077] When it is necessary to calculate the frequencies of all or multiple nodes in the power grid, the node frequency ω is calculated using matrix-vector multiplication based on OpenMP multithreading parallel technology. B .

[0078] Step 6: Obtain the set of generators that have the greatest impact on the frequencies of all nodes in the power grid, and the set of nodes where the generators that have the greatest impact on the frequency of a certain node are located;

[0079] The number of valid non-zero elements in each column of the reduced frequency participation factor matrix F′ is counted by column, and the valid non-zero elements are averaged and arranged from large to small. From the perspective of the average value, the set of generators with the greatest impact on the frequency of all nodes in the power grid is obtained. For example, if the average value of the i-th column is the largest, then the vector ω G The corresponding i-th generator has the greatest comprehensive impact on the frequency of each node in the power grid. According to this logic, the set of generators with the greatest impact on the frequency of all nodes in the power grid can be obtained.

[0080] From the perspective of rows, the values ​​of each row of the reduced frequency participation factor matrix F' determine the node set where the generator with the greatest impact on a node frequency is located. The generator of the node corresponding to the largest value in each row of F' has the greatest impact on the node frequency. The smaller the value, the smaller the impact of the generator on the node frequency. For example, the value of the i-th row and j-th column in F' is the largest, which means that the vector ω G The jth generator has the greatest impact on the frequency of node i.

[0081] This application also protects a power system frequency estimation system based on the above estimation method, including:

[0082] Grid model acquisition and parameter measurement module, remote signaling status judgment module, extended node susceptance matrix factor table calculation module, reduced frequency participation factor matrix calculation module, generator speed vector acquisition module, grid node frequency calculation module, and generator impact on node frequency analysis module;

[0083] The power grid model acquisition and parameter measurement module acquires the target power grid model and the telesignaling status of the real-time monitoring system as well as the generator speed measurement data and the PMU frequency measurement data;

[0084] The remote signal state judgment module judges whether the current remote signal state of the target power grid has changed compared with the previous cycle;

[0085] If the telesignaling status changes, then,

[0086] The extended node susceptance matrix factor table calculation module calculates the extended node susceptance matrix B′ according to the node branch model BB Factor table LDL T ;

[0087] The reduced frequency participation factor matrix calculation module calculates the frequency participation factor matrix F and obtains a reduced frequency participation factor matrix F′;

[0088] The generator speed vector acquisition module forms a speed vector ω based on the acquired generator speed measurement and PMU frequency measurement data. G ;

[0089] The grid node frequency calculation module calculates the frequency ω of each node according to the reduced frequency participation factor and speed vector B ;

[0090] The generator impact on node frequency analysis module analyzes and obtains the generator set that has the greatest impact on all node frequencies in the power grid, as well as the node set where the generator that has the greatest impact on a certain node frequency is located;

[0091] If the telesignaling status does not change, then,

[0092] The grid node frequency calculation module directly calculates the frequency ω of each node based on the frequency participation factor matrix calculated in the previous cycle and the generator speed vector obtained this time. B ,Then the generator impact on node frequency analysis module analyzes and obtains the set of generators that have the greatest impact on all node frequencies in the power grid, and the set of nodes where the generator that has the greatest impact on a certain node frequency is located.

[0093] Embodiment 1:

[0094] To more clearly describe the present invention, Figure 1 The embodiment 1 shown in the figure details a specific scheme of a method for estimating the frequency of a power system. In a preferred but non-limiting embodiment of the present invention, the method includes the following contents:

[0095] S1. Obtain the power grid model and the telesignal status of the real-time monitoring system, as well as the generator speed measurement and PMU frequency measurement, and perform topological analysis based on the obtained model and telesignal status to form a node branch model. Assume that the number of computing nodes after the power grid topology analysis is n, the number of branches is b, and the number of generators is m.

[0096] The acquisition method is based on the unified data service of the scheduling automation system, which can ensure that the acquisition method is simple and efficient.

[0097] In this step, the telemetry and telesignaling status of the power grid model and the real-time monitoring system, as well as the generator speed measurement and PMU frequency measurement are first obtained. The power grid model and telesignaling data obtained include the power grid line reactance parameters, transformer reactance parameters, generator speed and its internal reactance parameters, topological connection relationship between devices and the telesignaling status of the switch required for frequency calculation. If the frequency measurement collected by the phasor measurement unit (PMU) device in the power grid is available and the PMU frequency measurement is considered, the PMU frequency measurement data is also obtained.

[0098] Secondly, according to the obtained power grid model and the remote signal status of the switch, the power grid topology analysis is performed using the global topology or local topology method:

[0099] When the change between the current telesignal status and the telesignal status used in the last frequency calculation does not exceed the set range, the local topology method is used for topological analysis;

[0100] When the current telesignal status and the last calculated telesignal status change beyond the set range, a global topology method is used to perform topological analysis to form a node branch model.

[0101] S2, forming the node susceptance matrix B according to the node branch model and the internal reactance parameters of the online generator BB , B BS and B BG , where B BB is the high-dimensional sparse node susceptance matrix (n×n dimension) formed according to the reactance (ignoring resistance) of the branches between nodes in the power flow calculation, B BS is a high-dimensional sparse susceptance matrix (n×n dimension) formed according to the internal reactance of the synchronous generator, B BG The node susceptance matrix B is a high-dimensional sparse susceptance matrix (n×m dimensions) that only contains the reactance formed by the internal reactance of the generator. The node susceptance matrix B is formed by the branch superposition method based on the reactance of the line and transformer branch between nodes and the internal reactance of the generator. BB , B BS and B BG .

[0102] In the process of forming the node susceptance matrix by the branch superposition method, a balanced binary tree red-black tree is used to store the intermediate calculation process and the final node susceptance matrix. In the specific implementation, the associative container map container provided by C++STL (Standard Template Library) is used for storage.

[0103] Define the structure: typedef std::map<int,float>SparseRowVal is used to store a row of non-zero column numbers and value sets. The column number is represented by the key index of int type (integer variable) in the map, and the value is represented by the float type value corresponding to the key index.

[0104] Define the structure: typedef std::map<int,SparseRowVal>SparseMatrixValMap is used to store the non-zero elements of the sparse matrix node susceptance matrix, and the row number is represented by the int key index.

[0105] Traverse all branches in the power grid, starting from k and traversing from 1 to b in sequence, according to the branch reactance parameter x k and the first and last calculation nodes i and j correspond to B BB The value of .

[0106]

[0107] If the frequency measurement measured by the PMU device is taken into account, calculate B BG The node where the PMU frequency measurement is located needs to be treated as a generator node, so that the PMU frequency measurement is taken into account for calculation.

[0108] S3. According to B BB and B BS Calculate the extended node susceptance matrix B′ BB , where B′ BB =B BB +B BS At the node connected to the generator, the node susceptance matrix B is modified according to the internal reactance of the generator. BB Thus directly forming B′ BB , there is no need to perform the addition operation of two high-dimensional matrices during implementation.

[0109] S4. In order to reduce the extended node susceptance matrix B′ BB The number of non-zero elements filled in the factorization process is calculated by using Tinney-1 or Tinney-2 or Approximate Minimum Degree (AMD) algorithm to sort the matrix B′. BB Reorder the node numbers. After eliminating a vertex in the node sorting process, since updating the degrees of the vertex's adjacent vertices is the most time-consuming stage, in the approximate minimum degree sorting algorithm, instead of calculating the exact degree of the vertex, the upper limit of the vertex degree, which is easier to calculate, is used instead. The approximate degree of the vertex replaces the exact degree to reduce the time required for sorting.

[0110] In the node sorting process, only the node susceptance matrix B′ needs to be BB The non-zero structure does not use B′ BB The value of is numerically calculated to realize node reordering. In the implementation, a global n-dimensional permutation matrix perm[] is designed. Regardless of whether Tinney-1, Tinney-2 or AMD method is used, a global perm[] is formed. This makes it easy to use different node sorting methods to reorder B′ according to the scale of the power grid. BB Reorder nodes.

[0111] At the same time, two global n-dimensional node number correspondence tables oldnew[] and newold[] are designed, where newold[i] represents the new number of node i after topological analysis and oldnew[j] represents the node number (original number) after topological analysis. This avoids the matrix B′ after sorting. BB Rearrange the values ​​of the elements in each row.

[0112] S5. Use symbolic decomposition and numerical decomposition to form an extended node susceptance matrix B′ BB The factor table.

[0113] The extended node susceptance matrix B′ after reordering in S4 BB Using the symbolic decomposition method based on the G / P (Gilbert-Peierls) algorithm, complete B′ from the 1st column to the nth column BB =LDL T Analysis of the non-zero row number set of each column of the middle and lower triangular matrix L. During the calculation process, one column is analyzed each time, and the sparse structure Lp and elimination tree Parent of L are returned. The schematic diagram of the non-zero row number set analysis is as follows Figure 2 As shown. Symbolic analysis is based on the matrix B′ BB Calculate LDL from the set of row numbers where the non-zero elements in the kth column are located T The set of non-zero row numbers on the kth column after decomposition is completed.

[0114] The extended node susceptance matrix B′ after reordering in S4 BB According to the left-looking numerical decomposition calculation and analysis method, combined with the sparse structure Lp obtained by symbolic decomposition, the tree Parent and matrix B′ are eliminated BB The element values ​​are looped over k=1, 2, ..., n columns and look left to perform LDL T Numerical decomposition, get the value of L and the diagonal matrix D, so that B' is completed BB LDL T Decomposition. Looking to the left, the LU numerical decomposition method decomposes each column from the 1st column to the nth column in turn, that is, each time a column vector is calculated, and numerical calculations are performed based on the symbolic analysis results of this column to obtain the values ​​of all non-zero elements in the kth column. The schematic diagram of the factor decomposition process is shown in Figure 3 shown.

[0115] S6, by extending the node susceptance matrix B′ BB Factor table LDL T and B BG According to (LDL T )F=B BG The frequency participation factor matrix F is calculated using OpenMP-based multi-threaded parallel technology.

[0116] Since the calculation of each column element of the frequency participation factor matrix F is independent of each other, according to (LDL T )[F1 F2 …F m ]=[B BG1 B BG2 … B BGm ] format, each column is independent according to

[0117] (LDLT )F1B BG1

[0118] (LDL T )F2B BG2

[0119] …

[0120] (LDL T )F m B BGm

[0121] The OpenMP-based multi-threaded parallel computing is used to quickly calculate the elements of the participation factor matrix. F1 is the first column of the frequency participation factor matrix F, and the other columns are similar.

[0122] S7. Due to F is a dense matrix, and the calculated F is also a dense matrix. For power grids (especially large power grids), generators far away from the specified node will not significantly affect the frequency of the node. A large number of elements in the matrix F are very small / negligible, and those generator nodes in F that have little impact on the node frequency need to be ignored. Use one of the following two methods to filter the elements of F and get the reduced frequency participation factor matrix F′ again.

[0123] Method M1: Arrange all the elements in each row of F in descending order, take the first k largest elements of each row of the sorted matrix F, set the remaining elements to zero, and update the values ​​in F to obtain the reduced frequency participation factor matrix F′.

[0124] Method M2: Find the maximum element F in each row of F row by row max , according to the set threshold value |d i |<αF max The elements d in the matrix F are sorted row by row i Screening is performed, α is a parameter between (0, 1.0), and the value in F is updated to obtain the reduced frequency participation factor matrix F′.

[0125] S8, forming a speed vector ω according to the generator speed measurement obtained in S1 G If the frequency measurement collected by the Phasor Measurement Unit (PMU) device in the power grid is available, the collected PMU frequency measurement can also be added to the speed vector ω G If the frequency measurement measured by the PMU device is taken into account, B is calculated in the above steps 2 to 3 BG , B′ BB , the node where the PMU frequency measurement is located needs to be processed as a generator node, so as to take the PMU frequency measurement into account for calculation.

[0126] S9, according to ω B =F′ω G Calculate node frequency. When it is necessary to calculate all or multiple node frequencies in the power grid, the OpenMP-based multi-threaded parallel method is still used to quickly calculate the node frequency ω using matrix-vector multiplication. B .

[0127] When the telesignaling state obtained by this calculation is the same as the telesignaling state obtained last time, the node frequency is calculated directly using F′ formed by the last calculation and the latest generator speed and PMU frequency measurements.

[0128] S10. Count the number of valid non-zero elements in each column of the reduced frequency participation factor matrix F′ by column, and average the valid non-zero elements, arrange them from large to small, and obtain the set of generators that have the greatest impact on the frequency of all nodes in the power grid from the perspective of the average value. For example, if the average value of the i-th column is the largest, then the vector ω G The i-th generator corresponding to the ith generator has the greatest comprehensive impact on the frequency of each node in the power grid. According to this logic, the set of generators with the greatest impact on the frequency of all nodes in the power grid can be obtained. From the perspective of rows, the values ​​of each row of the reduced frequency participation factor matrix F′ can determine the set of nodes where the generator with the greatest impact on the frequency of a certain node is located. The value of the i-th row and j-th column in F′ is the largest, which means that the vector ω G The jth generator has the greatest impact on the frequency of node i.

[0129] The present disclosure may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0130] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples of computer-readable storage media (a non-exhaustive list) include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium is not to be interpreted as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0131] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0132] The computer program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be customized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0133] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for estimating power system frequency, characterized in that: The method comprises the following steps: Step 1: Obtain the telesignaling status of the target power grid model and the real-time monitoring system, as well as the generator speed measurement data and the PMU frequency measurement data, and determine whether the current telesignaling status of the target power grid has changed relative to the previous calculation cycle. If it has changed, proceed to step 2 to recalculate the frequency participation factor matrix of the current cycle of the target power grid. Otherwise, directly use the frequency participation factor matrix of the previous cycle of the target power grid and proceed to step 4. Step 2: Perform topological analysis based on the power grid model and the current telesignaling status to form a node branch model, and form an extended node susceptance matrix B′ based on the node branch model and the internal reactance parameters of the online generator. BB , and decompose to obtain the extended node susceptance matrix B′ BB Factor table LDL T , where L is a triangular matrix, L T is the transposed matrix of L, and D is a diagonal matrix; Step 3, calculate the frequency participation factor matrix F, and obtain the reduced frequency participation factor matrix F′; Step 4: Generate a speed vector ω based on the acquired generator speed measurement and PMU frequency measurement data G ; Step 5: According to ω B =F′ω G Calculate the frequency ω of each node in the target power grid B ; Step 6: According to the reduced frequency participation factor matrix F′, obtain the set of generators that have the greatest impact on all node frequencies in the target power grid, and the set of nodes where the generators that have the greatest impact on a certain node frequency are located.

2. The method for estimating the power system frequency according to claim 1, characterized in that: In step 2, an extended node susceptance matrix B′ is formed based on the node branch model and the internal reactance parameters of the online generator. BB ,include: The node susceptance matrix B formed according to the branch reactance between nodes BB , the full network susceptance matrix B formed by the internal reactance of the synchronous generator BS and contains only the generator internal susceptance matrix B formed according to the internal reactance of the generator BG ; According to B BB and B BS Form the extended node susceptance matrix B′ BB , where B′ BB =B BB +B BS .

3. The method for estimating the power system frequency according to claim 2, characterized in that: At the node connected to the generator, the node susceptance matrix B is corrected according to the internal reactance of the generator. BB Thus directly forming B′ BB ; When considering the frequency measurement value measured by the PMU, the node where the PMU frequency measurement is located is calculated as the generator node. BG .

4. The method for estimating the power system frequency according to claim 2, characterized in that: In step 2, the expanded node susceptance matrix B′ BB Before decomposition, the expanded node susceptance matrix B′ BB Reorder the node numbers to reduce the extended node susceptance matrix B′ BB The number of non-zero entries during the factorization process.

5. The method for estimating the power system frequency according to claim 2 or 4, characterized in that: In step 2, the expanded node susceptance matrix B′ is formed by symbolic decomposition and numerical decomposition. BB The factor table.

6. The method for estimating the power system frequency according to claim 2 or 3, characterized in that: In step 3, the expanded node susceptance matrix B′ BB Factor table LDL T and the generator internal susceptance matrix B BG According to (LDL T )F=B BG The frequency participation factor matrix F is calculated using OpenMP-based multi-threaded parallel technology; According to the size of the element value in the frequency participation factor matrix F and the set threshold value, the node generators in F that have a small impact on the node frequency are ignored, and the reduced frequency participation factor matrix F′ is obtained.

7. The method for estimating the power system frequency according to claim 6, characterized in that: Arrange all the elements in each row of the frequency participation factor matrix F in descending order, take the first k largest elements of each row of the sorted matrix, set the remaining elements to zero, and update the values ​​in the matrix to obtain the reduced frequency participation factor matrix F′, where k is an integer value less than the number of columns of the frequency participation factor matrix F.

8. The method for estimating the power system frequency according to claim 6, characterized in that: Find the maximum element F in each row of the frequency participation factor matrix F row by row max , according to the set threshold value |f i |<αF max The elements f in the matrix F are sorted row by row i Screening is performed, α is a parameter between (0, 1.0), and the value in F is updated to obtain the reduced frequency participation factor matrix F′.

9. The method for estimating the power system frequency according to claim 1, 7 or 8, characterized in that: In step 4, when considering the frequency measurement value measured by the PMU, the collected PMU frequency measurement is added to the speed vector ω G .

10. The method for estimating the frequency of a power system according to claim 9, characterized in that: In step 5, when it is necessary to calculate all or multiple node frequencies in the power grid, the node frequency ω is calculated using matrix-vector multiplication based on OpenMP multithreading parallel technology. B .

11. The method for estimating the frequency of a power system according to claim 1, characterized in that: In step 6, the number of valid non-zero elements in each column of the reduced frequency participation factor matrix F′ is counted by column, and the valid non-zero elements are averaged and arranged from large to small. The column with a larger average value has a greater impact on the frequency of each node in the power grid corresponding to the generator in the column. The generator corresponding to the column with the largest value in each row of the reduced frequency participation factor matrix F′ has the greatest influence on the frequency of the node corresponding to that row, and the generator corresponding to the column with the smaller value has less influence on the frequency of the node corresponding to that row.

12. A power system frequency estimation system based on the estimation method according to any one of claims 1 to 11, characterized in that: include: Grid model acquisition and parameter measurement module, remote signaling status judgment module, extended node susceptance matrix factor table calculation module, reduced frequency participation factor matrix calculation module, generator speed vector acquisition module, grid node frequency calculation module, and generator impact on node frequency analysis module; The power grid model acquisition and parameter measurement module acquires the target power grid model and the telesignaling status of the real-time monitoring system as well as the generator speed measurement data and the PMU frequency measurement data; The remote signal state judgment module judges whether the current remote signal state of the target power grid has changed compared with the previous cycle; If the telesignaling status changes, then, The extended node susceptance matrix factor table calculation module calculates the extended node susceptance matrix B′ according to the node branch model BB Factor table LDL T ; The reduced frequency participation factor matrix calculation module calculates the frequency participation factor matrix F and obtains a reduced frequency participation factor matrix F′; The generator speed vector acquisition module forms a speed vector ω based on the acquired generator speed measurement and PMU frequency measurement data. G ; The grid node frequency calculation module calculates the frequency ω of each node according to the reduced frequency participation factor and speed vector B ; The generator impact on node frequency analysis module analyzes and obtains the generator set that has the greatest impact on all node frequencies in the power grid, as well as the node set where the generator that has the greatest impact on a certain node frequency is located; If the telesignaling status does not change, then, The grid node frequency calculation module directly calculates the frequency ω of each node based on the frequency participation factor matrix calculated in the previous cycle and the generator speed vector obtained this time. B ,Then the generator impact on node frequency analysis module analyzes and obtains the set of generators that have the greatest impact on all node frequencies in the power grid, and the set of nodes where the generator that has the greatest impact on a certain node frequency is located.

13. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the computer program is loaded into a processor, the method for estimating the frequency of a power system according to any one of claims 1 to 11 is implemented.

14. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for estimating the frequency of a power system according to any one of claims 1 to 11 is implemented.

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