A power network parameter identification method and terminal based on electrical quantity amplitude measurement

Abstract

A power network parameter identification method and terminal based on electrical quantity amplitude measurement, the method comprising: simultaneously acquiring voltage amplitudes and current injection amplitudes of a bus connected with a power source and voltage amplitudes of the remaining buses; taking voltage phases and current injection phases of the bus connected with the power source as unknown state quantities, and constructing bus voltage vectors and bus current injection vectors together with the voltage amplitudes and the current injection amplitudes; substituting the constructed bus voltage vectors and bus current injection vectors into a parameter identification final model to obtain a parameter identification model based on electrical quantity amplitude measurement; and solving the parameter identification model based on electrical quantity amplitude measurement by using multiple groups of snapshots and Newton method, so that the power network parameters and the voltage phases and the current injection phases modeled as unknown state quantities are identified together. Even if the voltage and current vector phases cannot be acquired, the power network parameters can still be accurately identified, and the number of line synchronous phasor measurement devices is effectively reduced.

Description

Technical Field

[0001] This invention belongs to the field of power network parameter identification technology, and particularly relates to a power network parameter identification method and terminal based on electrical quantity amplitude measurement. Background Technology

[0002] Power networks are a crucial component of power systems. Accurate power network parameters are typically required for applications such as fault location (FL), optimal power flow (OPF), transmission line protection (TLP), and state estimation (SE). However, in practice, real-world power network parameters often deviate from existing parameters in databases. Furthermore, line parameters are susceptible to environmental factors such as temperature, weather, human activity, and line usage duration. Therefore, real-world line parameters can sometimes deviate from standard values ​​in databases by as much as 25%-30%. This can severely impact the reliability of subsequent applications. Therefore, accurate and reliable power network parameter identification methods are of paramount importance.

[0003] In existing technologies, power network parameter identification methods still rely on the acquisition of phase measurements. If the voltage and current vector phases of the bus connected to the power source cannot be obtained, parameter identification is impossible. Therefore, it is necessary to install synchronous phasor measurement devices (TPMS) at a few critical buses (including those connected to the power source) or all buses to measure the voltage and current vector phases, and simultaneously measure the voltage amplitudes of the remaining buses without TPMS. This presents a challenge for large power network systems, as installing TPMS on all critical buses may still be difficult, making existing methods unsuitable for practical power network systems. Therefore, existing technologies also propose acquiring the voltage and current synchronization vectors of the bus connected to the power source, as well as the voltage amplitudes of other types of buses. Based on the acquired voltage, current, and voltage synchronization vectors, and a pre-defined hybrid measurement estimation model, the distribution network parameter estimation results are obtained, thereby reducing the need for TPMS installation on the lines. Although this technology reduces some synchronous phasor measurement devices, it still relies on phase measurement of voltage and current injection into the power supply bus. Since the injected current into the power supply bus is non-zero, other bus measurement methods cannot be used. This means that in practical applications, synchronous phasor measurement devices must be installed on every power supply bus. Furthermore, inaccurate phase measurement will affect the parameter identification results.

[0004] Therefore, there is an urgent need to propose a power network parameter identification method and terminal that is completely independent of voltage vector phase and current vector phase measurements. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a power network parameter identification method and terminal based on electrical quantity amplitude measurement. By simultaneously modeling the voltage vector phase and the current injection vector phase as unknown state quantities for power network parameter identification, only the voltage amplitude and current amplitude need to be measured, thereby avoiding the dependence of power network parameter identification on phase measurement.

[0006] The present invention adopts the following technical solution.

[0007] This invention proposes a method for identifying power network parameters based on electrical quantity amplitude measurement, comprising:

[0008] Step 1: Connect the bus voltage vector and the bus current injection vector using the admittance matrix to obtain the basic model of the power network;

[0009] Step 2: Decompose the admittance matrix according to graph theory, and substitute the decomposed admittance matrix into the basic power network model to obtain the power network parameter identification model.

[0010] Step 3: By decomposing the real and imaginary parts of the power network parameter identification model, the final parameter identification model is obtained;

[0011] Step 4: Simultaneously acquire the voltage amplitude and current injection amplitude of the bus connected to the power supply, as well as the voltage amplitude of the other buses; use the voltage phase and current injection phase of the bus connected to the power supply as unknown state variables, and construct the bus voltage vector and bus current injection vector together with the voltage amplitude and current injection amplitude; substitute the constructed bus voltage vector and bus current injection vector into the final parameter identification model to obtain the parameter identification model based on electrical quantity amplitude measurement.

[0012] Step 5: Solve the parameter identification model based on electrical quantity amplitude measurement using multiple snapshots and Newton's method. The power network parameters, along with the voltage phase and current injection phase modeled as unknown state quantities, are identified together.

[0013] Preferably, in step 1, the basic model of the power network satisfies the following relationship:

[0014]

[0015] In the formula,

[0016] Y AM Let be the admittance matrix.

[0017] This is the bus voltage vector, containing the voltage phasors of all buses. Each voltage phasor includes voltage magnitude and voltage phase.

[0018] The bus current injection vector contains the current injection phasors of all buses. Each current injection phasor includes the current injection magnitude and the current injection phase.

[0019] Preferably, step 2 includes:

[0020] Step 2.1, according to graph theory, decompose the admittance matrix into the following relation:

[0021]

[0022] In the formula,

[0023] A IM This is the correlation matrix, containing the static topology parameters of the entire power grid system.

[0024] (Y AM ) L This is the first diagonal matrix, and its diagonal elements correspond to the reciprocals of the series impedance of each distribution network segment.

[0025] (Y AM ) B This is the second diagonal matrix, and the diagonal elements of the second diagonal matrix correspond to the parallel admittance of each bus.

[0026] Step 2.2: Substitute the decomposed admittance matrix into the basic power network model to obtain the following relationship:

[0027]

[0028] Step 2.3: Perform an equivalent transformation on the relational expression obtained in Step 2.2 to obtain the power network parameter identification model, which satisfies the following relational expression:

[0029]

[0030] In the formula,

[0031] diag(·) represents expanding the column vector (·) into a diagonal matrix diag(·).

[0032] col(·) means extracting the diagonal matrix (·) into a column vector col(·).

[0033] Preferably, in step 3, the final model for parameter identification satisfies the following relationship:

[0034]

[0035] in,

[0036] The first real diagonal matrix C real satisfy:

[0037] The first imaginary diagonal matrix C imag satisfy:

[0038] The second real part diagonal matrix D real satisfy:

[0039] The second imaginary diagonal matrix D imag satisfy:

[0040] Real part column vector X G Satisfy: X G =col(real((Y AM ) L ));

[0041] The first imaginary column vector X B Satisfy: X B =col(imag((Y AM ) L ));

[0042] The second imaginary column vector X W Satisfy: X W =col(imag((Y AM ) B ));

[0043] In the above formula, real(·) represents extracting the real part of the phasor in complex form, and imag(·) represents extracting the imaginary part of the phasor in complex form.

[0044] Preferably, step 4 includes:

[0045] Step 4.1: Using the measured voltage amplitude and the voltage phase (as an unknown state quantity), construct the bus voltage vector according to the following relationship:

[0046]

[0047] Simultaneously, using the measured current injection amplitude and the current phase (as an unknown state quantity), the bus current injection vector is constructed according to the following relationship:

[0048]

[0049] In the formula,

[0050] Mag(·) represents the magnitude of each corresponding element in the vector (·). To measure the obtained voltage amplitude, To measure the obtained current injection amplitude,

[0051] θ V The unknown voltage phase vector contains the voltage phases of all buses.

[0052] θ I For unknown current injection phases, including current injection phases for all buses.

[0053] ⊙ represents the Hadamarda accumulation;

[0054] Step 4.2: Substitute the constructed bus voltage vector and bus current injection vector into the final parameter identification model to obtain the parameter identification model based on electrical quantity amplitude measurement, which satisfies the following relationship:

[0055] M Con ·X=I

[0056] In the formula,

[0057] M Con For identification matrix,

[0058] The first vector X satisfies: X = [X G X B X W ] T ,

[0059] The second vector I satisfies:

[0060] Preferably, M Con The following relationship must be satisfied:

[0061]

[0062] θ V and θ I As unknown state variables, they are all embedded into the identification matrix M. Con middle.

[0063] Preferably, in step 4.1,

[0064] Voltage phase vector θ V The following relationship must be satisfied:

[0065]

[0066] In the formula, Indicates the voltage phase of the first bus. This indicates the voltage phase of the second bus, and so on. This represents the voltage phase of the nth bus, where n is the number of buses;

[0067] Current injection phase vector θ I The following relationship must be satisfied:

[0068]

[0069] In the formula, This indicates the current injection phase of the first busbar. This indicates the current injection phase of the second bus, and so on. This indicates the current injection phase of the nth bus.

[0070] Preferably, the first busbar is used as the reference busbar, satisfying the following conditions:

[0071] Preferably, in step 5, each snapshot corresponds to a set of bus voltage vectors and bus current injection vectors, and multiple snapshots are obtained within a set time period to satisfy the following relationship:

[0072]

[0073] In the formula,

[0074] This represents the first snapshot of the identification matrix. This represents the second snapshot of the identification matrix, and so on. This represents the p-th snapshot of the identification matrix;

[0075] I (1) I represents the first snapshot of the second vector. (2) This represents the second snapshot of the second vector, and so on, I (p) This represents the p-th snapshot of the second vector;

[0076] p is the number of snapshots.

[0077] Preferably, in step 5, an optimization function for the state vector x to be estimated is constructed, satisfying the following relationship:

[0078]

[0079] In the formula, ||·||2 represents the L2 norm of the vector;

[0080] Where f(x) satisfies the following relationship:

[0081]

[0082] In the formula, I S =[I (1) |I (2) |…|I (p) ].

[0083] Preferably, the optimization function is solved iteratively using Newton's method, yielding the following relationship:

[0084] x υ+1 =x υ -(H T H) -1 H T (x υ )·F(x)

[0085] In the formula,

[0086] x υ+1 The value obtained in the (υ+1)th iteration of the state vector to be estimated.

[0087] x υ Let be the value obtained in the υth iteration of the state vector to be estimated.

[0088] H is the Jacobian matrix of f(x) that satisfies:

[0089] Preferably, H is calculated using a numerical difference scheme, yielding the following relationship:

[0090]

[0091] In the formula, Δx is the disturbance quantity.

[0092] In another aspect, the present invention proposes a power network parameter identification terminal based on electrical quantity amplitude measurement, comprising: a data acquisition module, a vector construction module, an identification model module, and an identification module.

[0093] The acquisition module is used to simultaneously acquire the voltage amplitude and current injection amplitude of the power supply bus and the voltage amplitude of the other buses;

[0094] The vector construction module is used to construct the bus voltage vector and the bus current injection vector together with the voltage phase and current injection phase of the bus connected to the power supply as unknown state variables, and the voltage amplitude and current injection amplitude acquired by the acquisition module.

[0095] The identification model module is used to substitute the constructed bus voltage vector and bus current injection vector into the final parameter identification model to obtain a parameter identification model based on electrical quantity amplitude measurement.

[0096] The identification module is used to solve the parameter identification model based on electrical quantity amplitude measurement using multiple sets of snapshots and Newton's method; the identification module outputs power network parameters and voltage phase and current injection phase modeled as unknown state quantities.

[0097] The identification model module includes a basic power network model unit, a power network parameter identification model unit, and a final parameter identification model unit; among which...

[0098] The basic model unit of the power network is used to connect the bus voltage vector and the bus current injection vector through the admittance matrix to obtain the basic model of the power network.

[0099] The power network parameter identification model unit is used to decompose the admittance matrix according to graph theory, and then substitute the decomposed admittance matrix into the basic power network model to obtain the power network parameter identification model.

[0100] The parameter identification final model unit is used to obtain the parameter identification final model by decomposing the real and imaginary parts of the power network parameter identification model.

[0101] The beneficial effect of this invention is that, compared with the prior art, the power network parameter identification method proposed in this invention can still accurately identify power network parameters even when the voltage vector phase and current vector phase cannot be obtained.

[0102] Using the power network parameter identification method and terminal proposed in this invention, the number of synchronous phasor measurement devices installed in the line can be effectively reduced.

[0103] Moreover, the power network parameter identification method and terminal proposed in this invention can reduce the impact of phase measurement errors on the accuracy and reliability of power network parameter identification results and have good robustness against measurement noise.

[0104] Meanwhile, using the power network parameter identification method and terminal proposed in this invention, in addition to obtaining the power network parameter identification results, it is also possible to obtain the identification results of the voltage phase and current injection phase of the power supply connection bus, which expands the applicable scenarios of the terminal and has better applicability to general power networks, thus gaining more functional expansion. Attached Figure Description

[0105] Figure 1 This is a flowchart of a power network parameter identification method based on electrical quantity amplitude measurement proposed in this invention;

[0106] Figure 2 This is an example diagram of a WSCC 9-node system in one embodiment of the present invention;

[0107] Figure 2 The annotations in the accompanying drawings are explained as follows:

[0108] 1-First busbar, 2-Second busbar, 3-Third busbar, 4-First voltage amplitude measurement point, 5-Second voltage amplitude measurement point, 6-Third voltage amplitude measurement point, 7-Fourth voltage amplitude measurement point, 8-Fifth voltage amplitude measurement point, 9-Sixth voltage amplitude measurement point;

[0109] Figure 3This is a schematic diagram illustrating the absolute estimation error of the real part of the admittance matrix of a WSCC 9-node system in one embodiment of the present invention.

[0110] Figure 4 This is a schematic diagram of the absolute estimation error of the imaginary part of the admittance matrix of a WSCC 9-node system in one embodiment of the present invention. Detailed Implementation

[0111] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of this invention. The embodiments described in this application are merely some embodiments of this invention, and not all embodiments. Based on the spirit of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of this invention.

[0112] Example 1.

[0113] This invention proposes a method for identifying power network parameters based on electrical quantity amplitude measurement, such as... Figure 1 As shown, it includes:

[0114] Step 1: Connect the bus voltage vector and the bus current injection vector using the admittance matrix to obtain the basic model of the power network.

[0115] For a power network system with n buses and m lines, define a vector. For a voltage phasor containing all buses, vector Let this be a current injection phasor containing all buses. These two vectors can be derived from the admittance matrix. Connect and obtain the basic model of the power network. In step 1, the basic model of the power network satisfies the following relationship:

[0116]

[0117] In the formula,

[0118] Y AM Let be the admittance matrix.

[0119] This is the bus voltage vector, containing the voltage phasors of all buses. Each voltage phasor includes voltage magnitude and voltage phase.

[0120] The bus current injection vector contains the current injection phasors of all buses. Each current injection phasor includes the current injection magnitude and the current injection phase.

[0121] Given an n×1 vector set, It is an n×n vector set.

[0122] Step 2: Decompose the admittance matrix according to graph theory, and substitute the decomposed admittance matrix into the basic power network model to obtain the power network parameter identification model.

[0123] Specifically, step 2 includes:

[0124] Step 2.1, according to graph theory, decompose the admittance matrix into the following relation:

[0125]

[0126] In the formula,

[0127] A IM This is the correlation matrix, containing the static topology parameters of the entire power grid system.

[0128] (Y AM ) L This is the first diagonal matrix, and its diagonal elements correspond to the reciprocals of the series impedance of each distribution network segment.

[0129] (Y AM ) B This is the second diagonal matrix, and its diagonal elements correspond to the parallel admittance of each bus.

[0130] Let m be a set of n×m matrices. Let m be a set of m×m matrices. It is a set of n×n matrices.

[0131] Step 2.2: Substitute the decomposed admittance matrix into the basic power network model to obtain the following relationship:

[0132]

[0133] Step 2.3: Perform an equivalent transformation on the relational expression obtained in Step 2.2 to obtain the power network parameter identification model, which satisfies the following relational expression:

[0134]

[0135] In the formula,

[0136] diag(·) represents expanding the column vector (·) into a diagonal matrix diag(·).

[0137] col(·) means extracting the diagonal matrix (·) into a column vector col(·).

[0138] Step 3: By decomposing the real and imaginary parts of the power network parameter identification model, the final parameter identification model is obtained.

[0139] Specifically, in step 3, the final model for parameter identification satisfies the following relationship:

[0140]

[0141] in,

[0142] The first real diagonal matrix C real satisfy:

[0143] The first imaginary diagonal matrix C imag satisfy:

[0144] The second real part diagonal matrix D real satisfy:

[0145] The second imaginary diagonal matrix D imag satisfy:

[0146] Real part column vector X G Satisfy: X G =col(real((Y AM ) L ));

[0147] The first imaginary column vector X B Satisfy: X B =col(imag((Y AM ) L ));

[0148] The second imaginary column vector X W Satisfy: X W =col(imag((Y AM ) B ));

[0149] In the above formula, real(·) represents extracting the real part of the phasor in complex form, and imag(·) represents extracting the imaginary part of the phasor in complex form.

[0150] Step 4: Simultaneously acquire the voltage amplitude and current injection amplitude of the bus connected to the power supply, as well as the voltage amplitude of the other buses; use the voltage phase and current injection phase of the bus connected to the power supply as unknown state variables, and construct the bus voltage vector and bus current injection vector together with the voltage amplitude and current injection amplitude; substitute the constructed bus voltage vector and bus current injection vector into the final parameter identification model to obtain the parameter identification model based on electrical quantity amplitude measurement.

[0151] Preferably, step 4 includes:

[0152] Step 4.1: Using the measured voltage amplitude and the voltage phase (as an unknown state quantity), construct the bus voltage vector according to the following relationship:

[0153]

[0154] Simultaneously, using the measured current injection amplitude and the current phase (as an unknown state quantity), the bus current injection vector is constructed according to the following relationship:

[0155]

[0156] In the formula,

[0157] Mag(·) represents the magnitude of each corresponding element in the vector (·). To measure the obtained voltage amplitude, To measure the obtained current injection amplitude,

[0158] θ V The unknown voltage phase vector contains the voltage phases of all buses.

[0159] θ I For unknown current injection phases, including current injection phases for all buses.

[0160] ⊙ represents the Hadamarda accumulation;

[0161] Step 4.2: Substitute the constructed bus voltage vector and bus current injection vector into the final parameter identification model to obtain the parameter identification model based on electrical quantity amplitude measurement, which satisfies the following relationship:

[0162] M Con ·X=I

[0163] In the formula,

[0164] M Con For identification matrix,

[0165] The first vector X satisfies: X = [X G X B X W ] T ,

[0166] The second vector I satisfies:

[0167] Preferably, M Con The following relationship must be satisfied:

[0168]

[0169] θ V and θ I As unknown state variables, they are all embedded into the identification matrix M. Con middle.

[0170] Preferably, in step 4.1,

[0171] Voltage phase vector θ V The following relationship must be satisfied:

[0172]

[0173] In the formula, Indicates the voltage phase of the first bus. This indicates the voltage phase of the second bus, and so on. This represents the voltage phase of the nth bus, where n is the number of buses;

[0174] Current injection phase vector θ I The following relationship must be satisfied:

[0175]

[0176] In the formula, This indicates the current injection phase of the first busbar. This indicates the current injection phase of the second bus, and so on. This indicates the current injection phase of the nth bus.

[0177] Preferably, the first busbar is used as the reference busbar, satisfying the following conditions:

[0178] Step 5: Solve the parameter identification model based on electrical quantity amplitude measurement using multiple snapshots and Newton's method. The power network parameters, along with the voltage phase and current injection phase modeled as unknown state quantities, are identified together.

[0179] Preferably, in step 5, the number of unknown state variables is greater than the number of measurements. To facilitate problem solving, each snapshot corresponds to a set of bus voltage vectors and bus current injection vectors. Multiple snapshots are obtained within a set time period, satisfying the following relationship:

[0180]

[0181] In the formula,

[0182] This represents the first snapshot of the identification matrix. This represents the second snapshot of the identification matrix, and so on. This represents the p-th snapshot of the identification matrix;

[0183] I(1) I represents the first snapshot of the second vector. (2) This represents the second snapshot of the second vector, and so on, I (p) This represents the p-th snapshot of the second vector;

[0184] p is the number of snapshots.

[0185] Preferably, in step 5, an optimization function for the state vector x to be estimated is constructed, satisfying the following relationship:

[0186]

[0187] In the formula, ||·||2 represents the L2 norm of the vector;

[0188] Where f(x) satisfies the following relationship:

[0189]

[0190] In the formula, I S =[I (1) |I (2) |…|I (p) ].

[0191] Preferably, the optimization function is solved iteratively using Newton's method, yielding the following relationship:

[0192] x υ+1 =x υ -(H T H) -1 H T (x υ )·F(x)

[0193] In the formula,

[0194] x υ+1 The value obtained in the (υ+1)th iteration of the state vector to be estimated.

[0195] x υ Let be the value obtained in the υth iteration of the state vector to be estimated.

[0196] H is the Jacobian matrix of f(x) that satisfies:

[0197] Preferably, H is calculated using a numerical difference scheme, yielding the following relationship:

[0198]

[0199] In the formula, Δx is the disturbance quantity.

[0200] The power network parameter identification method proposed in this invention can still accurately identify power network parameters even when the voltage vector phase and current vector phase cannot be obtained. Using the power network parameter identification method and terminal proposed in this invention effectively reduces the number of synchronous phasor measurement devices installed in the line. Furthermore, using the power network parameter identification method and terminal proposed in this invention can reduce the impact of phase measurement errors on the accuracy and reliability of power network parameter identification results, and it has good robustness against measurement noise. Simultaneously, using the power network parameter identification method and terminal proposed in this invention, in addition to obtaining the power network parameter identification results, it can also obtain the identification results of the voltage phase and current injection phase of the power supply connection bus, expanding the applicable scenarios of the terminal and providing better applicability to general power networks, thus achieving more functional expansion.

[0201] In another aspect, the present invention proposes a power network parameter identification terminal based on electrical quantity amplitude measurement, comprising: a data acquisition module, a vector construction module, an identification model module, and an identification module.

[0202] The acquisition module is used to simultaneously acquire the voltage amplitude and current injection amplitude of the power supply bus and the voltage amplitude of the other buses;

[0203] The vector construction module is used to construct the bus voltage vector and the bus current injection vector together with the voltage phase and current injection phase of the bus connected to the power supply as unknown state variables, and the voltage amplitude and current injection amplitude acquired by the acquisition module.

[0204] The identification model module is used to substitute the constructed bus voltage vector and bus current injection vector into the final parameter identification model to obtain a parameter identification model based on electrical quantity amplitude measurement.

[0205] The identification module is used to solve the parameter identification model based on electrical quantity amplitude measurement using multiple sets of snapshots and Newton's method; the identification module outputs power network parameters and voltage phase and current injection phase modeled as unknown state quantities.

[0206] The identification model module includes a basic power network model unit, a power network parameter identification model unit, and a final parameter identification model unit; among which...

[0207] The basic model unit of the power network is used to connect the bus voltage vector and the bus current injection vector through the admittance matrix to obtain the basic model of the power network.

[0208] The power network parameter identification model unit is used to decompose the admittance matrix according to graph theory, and then substitute the decomposed admittance matrix into the basic power network model to obtain the power network parameter identification model.

[0209] The parameter identification final model unit is used to obtain the parameter identification final model by decomposing the real and imaginary parts of the power network parameter identification model.

[0210] Example 2.

[0211] To verify the effectiveness of this invention, a 60Hz WSCC 9-node three-phase test system is used as an example. Figure 2 As shown in the diagram, this system has three generator buses (i.e., bus 1, bus 2, and bus 3), three loads, six branches, and three transformers. Specifically, bus 1, bus 2, and bus 3 do not have PMUs installed; only voltage amplitude and current injection amplitude measurements are acquired. Voltage amplitude measurement points 4, 5, 6, 7, 8, and 9 only acquire voltage amplitude measurements.

[0212] To ensure redundancy, this example uses 6 snapshots.

[0213] To verify the performance of this invention, two metrics are introduced below for evaluation:

[0214] The first indicator is to compare the estimated and actual values ​​of the line parameters.

[0215] The second criterion is that the absolute estimation errors of the real and imaginary parts satisfy the following relationship:

[0216]

[0217]

[0218] Where Re AM Error(%) = 0 and Im AM Error(%) = 0 represent a perfect estimate; Est. represents the estimated value, Act. represents the true value, re(AM) represents the real part of AM, and im(AM) represents the imaginary part of AM.

[0219] Based on the first indicator, the comparison results between the true and estimated values ​​of the line parameters are shown in Tables 1 and 2. Based on the second indicator, the absolute estimation errors of the real and imaginary parts of the admittance matrix are as follows: Figure 2 and Figure 3 As shown.

[0220] Table 1 Comparison of the actual and estimated values ​​of the real part of the admittance matrix.

[0221] Matrix Index True value estimated value Matrix Index True value estimated value <![CDATA[re(Y AM ) 1，4 ]]> 0.0000 0.0011 <![CDATA[re(Y AM ) 1，1 ]]> 0.0000 0.0020 <![CDATA[re(Y AM ) 2，7 ]]> 0.0000 0.0016 <![CDATA[re(Y AM ) 2，2 ]]> 0.0000 0.0031 <![CDATA[re(Y AM ) 3，9 ]]> 0.0000 0.0014 <![CDATA[re(Y AM ) 3，3 ]]> 0.0000 0.0026 <![CDATA[re(Y AM ) 4，5 ]]> -1.9422 -1.9431 <![CDATA[re(Y AM ) 4，4 ]]> 3.3074 3.3072 <![CDATA[re(Y AM ) 4，6 ]]> -1.3652 -1.3677 <![CDATA[re(Y AM ) 5，5 ]]> 3.2242 3.2253 <![CDATA[re(Y AM ) 5，7 ]]> -1.2820 -1.2809 <![CDATA[re(Y AM ) 6，6 ]]> 2.4371 2.4397 <![CDATA[re(Y AM ) 6，9 ]]> -1.1876 -1.1868 <![CDATA[re(Y AM ) 7，7 ]]> 2.7722 2.7717 <![CDATA[re(Y AM ) 7，8 ]]> -1.6171 -1.6184 <![CDATA[re(Y AM ) 8，8 ]]> 2.8047 2.8080 <![CDATA[re(Y AM ) 8，9 ]]> -1.1551 -1.1539 <![CDATA[re(Y AM ) 9，9 ]]> 2.5528 2.5539

[0222] In Table 1, re(Y) AM ) i，jLet represent the real part of the branch admittance matrix between node i and node j. Nodes numbered 1 to 3 correspond to the first bus 1, the second bus 2, and the third bus 3, respectively. Nodes numbered 4 to 9 correspond to the first voltage amplitude measurement point 4, the second voltage amplitude measurement point 5, the third voltage amplitude measurement point 6, the fourth voltage amplitude measurement point 7, the fifth voltage amplitude measurement point 8, and the sixth voltage amplitude measurement point 9, respectively. i, j = 1, 2, ..., 9.

[0223] Table 2 Comparison of the actual and estimated values ​​of the imaginary part of the admittance matrix.

[0224] Matrix Index True value estimated value Matrix Index True value estimated value <![CDATA[im(Y AM ) 1，4 ]]> 17.361 17.362 <![CDATA[im(Y AM ) 1，1 ]]> -17.361 -17.377 <![CDATA[im(Y AM ) 2，7 ]]> 16.000 16.005 <![CDATA[im(Y AM ) 2，2 ]]> -16.000 -16.003 <![CDATA[im(Y AM ) 3，9 ]]> 17.065 17.050 <![CDATA[im(Y AM ) 3，3 ]]> -17.065 -17.081 <![CDATA[im(Y AM ) 4，5 ]]> 10.511 10.505 <![CDATA[im(Y AM ) 4，4 ]]> -39.301 -39.327 <![CDATA[im(Y AM ) 4，6 ]]> 11.604 11.582 <![CDATA[im(Y AM ) 5，5 ]]> -15.841 -15.854 <![CDATA[im(Y AM ) 5，7 ]]> 5.5882 5.5906 <![CDATA[im(Y AM ) 6，6 ]]> -32.154 -32.191 <![CDATA[im(Y AM ) 6，9 ]]> 5.9751 5.9773 <![CDATA[im(Y AM ) 7，7 ]]> -23.303 -23.315 <![CDATA[im(Y AM ) 7，8 ]]> 13.698 13.718 <![CDATA[im(Y AM ) 8，8 ]]> -35.445 -35.462 <![CDATA[im(Y AM ) 8，9 ]]> 9.7842 9.7866 <![CDATA[im(Y AM ) 9，9 ]]> -17.338 -17.360

[0225] In Table 2, im(Y) AM ) i，j Let represent the imaginary part of the branch admittance matrix between node i and node j. Nodes numbered 1 to 3 correspond to the first bus 1, the second bus 2, and the third bus 3, respectively. Nodes numbered 4 to 9 correspond to the first voltage amplitude measurement point 4, the second voltage amplitude measurement point 5, the third voltage amplitude measurement point 6, the fourth voltage amplitude measurement point 7, the fifth voltage amplitude measurement point 8, and the sixth voltage amplitude measurement point 9, respectively. i, j = 1, 2, ..., 9.

[0226] Figure 3 and 4 These figures illustrate the absolute estimation error of the real part of the admittance matrix of the WSCC 9-node system and the absolute estimation error of the imaginary part of the admittance matrix of the WSCC 9-node system, respectively. It can be seen that the parameter estimation method in this embodiment can obtain accurate system line parameters.

[0227] This disclosure can be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of this disclosure.

[0228] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination of the foregoing. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0229] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0230] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent 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 the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving 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., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing state information from the computer-readable program instructions to implement various aspects of this disclosure.

[0231] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A method for identifying power network parameters based on electrical quantity amplitude measurement, characterized in that, The method includes: Step 1: Connect the bus voltage vector and the bus current injection vector using the admittance matrix to obtain the basic model of the power network; Step 2: Decompose the admittance matrix according to graph theory, and substitute the decomposed admittance matrix into the basic power network model to obtain the power network parameter identification model. Step 3: By decomposing the real and imaginary parts of the power network parameter identification model, the final parameter identification model is obtained; Step 4: Simultaneously acquire the voltage amplitude and current injection amplitude of the bus connected to the power supply, as well as the voltage amplitude of the other buses; use the voltage phase and current injection phase of the bus connected to the power supply as unknown state variables, and construct the bus voltage vector and bus current injection vector together with the voltage amplitude and current injection amplitude; substitute the constructed bus voltage vector and bus current injection vector into the final parameter identification model to obtain the parameter identification model based on electrical quantity amplitude measurement. Step 4 includes: Step 4.1: Using the measured voltage amplitude and the voltage phase (as an unknown state quantity), construct the bus voltage vector according to the following relationship: Simultaneously, using the measured current injection amplitude and the current phase (as an unknown state quantity), the bus current injection vector is constructed according to the following relationship: In the formula, Representing vectors The magnitude of each corresponding element in the equation. To measure the obtained voltage amplitude, To measure the obtained current injection amplitude, The unknown voltage phase vector contains the voltage phases of all buses. For unknown current injection phases, including current injection phases for all buses. It represents the Hadamardi (or Hadama) stack; This indicates the extraction of the real part of the phasor in complex form. This indicates the extraction of the imaginary part of the phasor in complex form; Step 4.2: Substitute the constructed bus voltage vector and bus current injection vector into the final parameter identification model to obtain the parameter identification model based on electrical quantity amplitude measurement, which satisfies the following relationship: In the formula, For the identification matrix, the first vector satisfy: The second vector satisfy: ; Real part column vector satisfy: ; First imaginary column vector satisfy: ; Second imaginary column vector satisfy: ; This indicates that the diagonal matrix Extract as a column vector ; This is the first diagonal matrix, and its diagonal elements correspond to the reciprocals of the series impedance of each distribution network segment. This is the second diagonal matrix, and the diagonal elements of the second diagonal matrix correspond to the parallel admittance of each bus. Step 5: Solve the parameter identification model based on electrical quantity amplitude measurement using multiple snapshots and Newton's method. The power network parameters, along with the voltage phase and current injection phase modeled as unknown state quantities, are identified together.

2. The power network parameter identification method based on electrical quantity amplitude measurement according to claim 1, characterized in that, In step 1, the basic model of the power network satisfies the following relationship: In the formula, Let be the admittance matrix. This is the bus voltage vector, containing the voltage phasors of all buses. Each voltage phasor includes voltage magnitude and voltage phase. The bus current injection vector contains the current injection phasors of all buses. Each current injection phasor includes the current injection magnitude and the current injection phase.

3. The power network parameter identification method based on electrical quantity amplitude measurement according to claim 2, characterized in that, Step 2 includes: Step 2.1, according to graph theory, decompose the admittance matrix into the following relation: In the formula, This is the correlation matrix, containing the static topology parameters of the entire power grid system. This is the first diagonal matrix, and its diagonal elements correspond to the reciprocals of the series impedance of each distribution network segment. This is the second diagonal matrix, and the diagonal elements of the second diagonal matrix correspond to the parallel admittance of each bus. Step 2.2: Substitute the decomposed admittance matrix into the basic power network model to obtain the following relationship: Step 2.3: Perform an equivalent transformation on the relational expression obtained in Step 2.2 to obtain the power network parameter identification model, which satisfies the following relational expression: In the formula, This indicates that the column vector Expand to a diagonal matrix , This indicates that the diagonal matrix Extract as a column vector .

4. The power network parameter identification method based on electrical quantity amplitude measurement according to claim 3, characterized in that, In step 3, the final model for parameter identification satisfies the following relationship: in, First real part diagonal matrix satisfy: ; First imaginary diagonal matrix satisfy: ; The second real part diagonal matrix satisfy: ; The second imaginary part diagonal matrix satisfy: ; Real part column vector satisfy: ; First imaginary part column vector satisfy: ; Second imaginary part column vector satisfy: ; In the above formula, This indicates the extraction of the real part of the phasor in complex form. This indicates the extraction of the imaginary part of the phasor in complex form.

5. The power network parameter identification method based on electrical quantity amplitude measurement according to claim 1, characterized in that, The following relationship must be satisfied: and As a result, all unknown state variables are embedded into the identification matrix. middle.

6. The power network parameter identification method based on electrical quantity amplitude measurement according to claim 1, characterized in that, In step 4.1, Voltage phase vector The following relationship must be satisfied: In the formula, Indicates the voltage phase of the first bus. This indicates the voltage phase of the second bus, and so on. Indicates the first The voltage phase of the busbar, where This refers to the number of busbars; Current injection phase vector The following relationship must be satisfied: In the formula, This indicates the current injection phase of the first busbar. This indicates the current injection phase of the second bus, and so on. Indicates the first Current injection phase of the busbar.

7. The power network parameter identification method based on electrical quantity amplitude measurement according to claim 6, characterized in that, The first busbar is used as the reference busbar, satisfying... .

8. The power network parameter identification method based on electrical quantity amplitude measurement according to claim 1, characterized in that, In step 5, each snapshot corresponds to a set of bus voltage vectors and bus current injection vectors. Multiple snapshots are obtained within a set time period, satisfying the following relationship: In the formula, This represents the first snapshot of the identification matrix. This represents the second snapshot of the identification matrix, and so on. The first element of the identification matrix is... Next snapshot; This represents the first snapshot of the second vector. This represents the second snapshot of the second vector, and so on. Represents the second vector's first... Next snapshot; It represents the number of snapshots.

9. The power network parameter identification method based on electrical quantity amplitude measurement according to claim 8, characterized in that, In step 5, the state vector to be estimated is constructed. The optimization function satisfies the following relationship: In the formula, Representing vectors Norm; in, The following relationship must be satisfied: In the formula, , .

10. The power network parameter identification method based on electrical quantity amplitude measurement according to claim 9, characterized in that, The optimization function is solved iteratively using Newton's method, yielding the following relationship: In the formula, The first state vector to be estimated The value obtained in the next iteration The first state vector to be estimated The value obtained in the next iteration yes The Jacobian matrix satisfies: .

11. The power network parameter identification method based on electrical quantity amplitude measurement according to claim 10, characterized in that, Numerical difference scheme for The calculation yields the following relationship: In the formula, It is a disturbance quantity.

12. A power network parameter identification terminal based on electrical quantity amplitude measurement using the method described in any one of claims 1-11, characterized in that, The identification terminal includes: a data acquisition module, a vector construction module, an identification model module, and an identification module; The acquisition module is used to simultaneously acquire the voltage amplitude and current injection amplitude of the power supply bus and the voltage amplitude of the other buses; The vector construction module is used to construct the bus voltage vector and the bus current injection vector together with the voltage phase and current injection phase of the bus connected to the power supply as unknown state variables, and the voltage amplitude and current injection amplitude acquired by the acquisition module. The identification model module is used to substitute the constructed bus voltage vector and bus current injection vector into the final parameter identification model to obtain a parameter identification model based on electrical quantity amplitude measurement. The identification module is used to solve the parameter identification model based on electrical quantity amplitude measurement using multiple sets of snapshots and Newton's method; the identification module outputs power network parameters and voltage phase and current injection phase modeled as unknown state quantities.

13. The power network parameter identification terminal based on electrical quantity amplitude measurement according to claim 12, characterized in that, The identification model module includes a basic power network model unit, a power network parameter identification model unit, and a final parameter identification model unit; among which... The basic model unit of the power network is used to connect the bus voltage vector and the bus current injection vector through the admittance matrix to obtain the basic model of the power network. The power network parameter identification model unit is used to decompose the admittance matrix according to graph theory, and then substitute the decomposed admittance matrix into the basic power network model to obtain the power network parameter identification model. The parameter identification final model unit is used to obtain the parameter identification final model by decomposing the real and imaginary parts of the power network parameter identification model.

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