A power distribution system impedance identification method and device based on wave characteristic analysis

Abstract

The application provides a power distribution system impedance identification method and device based on fluctuation characteristics analysis, and the steps of the method comprise the following: obtaining a load impedance fluctuation and an equivalent potential fluctuation according to data at a measuring point respectively; obtaining a first impedance expression, i.e., an expression of impedance identified based on an equivalent potential steady-state value obtained based on covariance, according to data at the measuring point; obtaining a second impedance expression, i.e., an expression of impedance identified based on an equivalent potential fluctuation value obtained based on covariance, according to the load impedance fluctuation and the equivalent potential fluctuation; obtaining a fluctuation coefficient according to data at the measuring point based on covariance failure and the second impedance expression; obtaining a control coefficient according to the fluctuation coefficient; obtaining a power distribution system impedance identification expression and identifying the impedance of the power distribution system according to the first impedance expression, the second impedance expression and the control coefficient. The method is not affected by power source potential fluctuation on the system side and does not depend on any assumption, and has higher reliability.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power distribution network system impedance identification, and particularly relates to a power distribution system impedance identification method and device based on fluctuation characteristic analysis. BACKGROUND

[0002] With the continuous rise of new energy in the power system, the operation characteristics of the power distribution network have changed significantly. The access of distributed power sources has uncertainty and volatility, which makes it difficult for traditional fixed parameter current protection strategies to cope with system dynamics, and problems such as insufficient protection sensitivity or misoperation may occur.

[0003] At the same time, the amplitude and direction of the fault current of the power distribution network are jointly influenced by multiple factors such as network structure, distributed power source location and output, which brings greater technical challenges to the configuration of protection devices. In order to cope with these new problems, how to accurately identify the impedance of the power distribution system is particularly important, which will effectively improve the reliability and action accuracy of the protection system. SUMMARY

[0004] The present application provides a power distribution system impedance identification method based on fluctuation characteristic analysis, which is not affected by the fluctuation of the system side power potential and does not depend on any assumptions, and has high reliability.

[0005] To achieve the above purpose, the technical scheme adopted by the present application is as follows: a power distribution system impedance identification method based on fluctuation characteristic analysis, comprising the following steps:

[0006] Obtaining data at a measurement point; obtaining load impedance fluctuation and equivalent potential fluctuation from the data at the measurement point, respectively;

[0007] Obtaining a first impedance expression from the data at the measurement point, the first impedance expression being an expression of impedance identified based on the equivalent potential steady-state value obtained based on covariance;

[0008] Obtaining a second impedance expression from the load impedance fluctuation and the equivalent potential fluctuation, the second impedance expression being an expression of impedance identified based on the equivalent potential fluctuation value obtained based on covariance; obtaining a fluctuation coefficient from the data at the measurement point based on covariance failure and the second impedance expression;

[0009] Obtaining a control coefficient from the fluctuation coefficient; obtaining a power distribution system impedance identification expression from the first impedance expression, the second impedance expression and the control coefficient;

[0010] Identifying the impedance of the power distribution system according to the power distribution system impedance identification expression.

[0011] In some embodiments, the step of obtaining the first impedance expression from the data at the measurement points is:

[0012] Based on Thevenin theorem, a target function is constructed from the system-side equivalent potential expression, which is an expression of the system-side equivalent potential and the system-side equivalent impedance;

[0013] The first expression is obtained by solving the target function from at least three groups of the data at the measurement points using least square method, which is an intermediate expression of the first impedance;

[0014] The first impedance expression is obtained by expressing the first expression using covariance method.

[0015] In some embodiments, the data at the measurement points include voltage phasors and current phasors.

[0016] In some embodiments, the step of obtaining the first impedance expression from the first expression using covariance method is:

[0017] Based on at least three groups of the data at the measurement points, real and imaginary parts of the system-side equivalent potential, the system-side equivalent impedance, the voltage phasors and the current phasors are separated respectively, and current coefficient matrix, equivalent impedance and equivalent potential matrix and voltage coefficient matrix are constructed respectively;

[0018] A second expression is constructed from the current coefficient matrix, the equivalent impedance and equivalent potential matrix and the voltage coefficient matrix;

[0019] A third expression is obtained by solving the second expression using least square method, which is an expression of the equivalent impedance and equivalent potential matrix with respect to real part of voltage phasor, imaginary part of voltage phasor, real part of current phasor and imaginary part of current phasor;

[0020] A fourth expression is obtained by expressing the real part of voltage phasor, the imaginary part of voltage phasor, the real part of current phasor and the imaginary part of current phasor in the form of expected value and fluctuation respectively;

[0021] The first impedance expression is obtained by substituting the fourth expression into the third expression and simplifying.

[0022] In some embodiments, the step of obtaining the second impedance expression from the load impedance fluctuation and the equivalent potential fluctuation is:

[0023] A fifth expression is obtained by making arithmetic covariance of the load impedance fluctuation and the equivalent potential fluctuation;

[0024] Based on the failure of the "source-load" fluctuation uncorrelation principle, the fifth expression is simplified to obtain the second impedance expression.

[0025] In some embodiments, the second impedance expression is:

[0026]

[0027] wherein Z v is a second impedance, δ is a real value of a complex covariance function of a load impedance fluctuation and an equivalent potential fluctuation, Cov[ΔU v , ΔZ L ] is a complex covariance function of a voltage phasor fluctuation at a measurement point and a load impedance fluctuation, ΔU v is the voltage phasor fluctuation at the measurement point, ΔZ L is the load impedance fluctuation, Cov[ΔI v , ΔZ L ] is a complex covariance function of a current phasor fluctuation at the measurement point and the load impedance fluctuation, ΔI v is the current phasor fluctuation at the measurement point.

[0028] In some embodiments, the fluctuation coefficient is a ratio of a first value and a second value, the first value is a real value of a complex covariance function of the load impedance fluctuation and the equivalent potential fluctuation, and the second value is a complex covariance value of the voltage phasor fluctuation at the measurement point and the load impedance fluctuation.

[0029] In some embodiments, the control coefficient is:

[0030] λ m = exp(-μ|e m |);

[0031] wherein λ m is a control coefficient of the mth identification, m is an identification order, exp is an exponential function, μ is a decay coefficient, and e m is a fluctuation coefficient of the mth identification.

[0032] In some embodiments, the power distribution system impedance identification expression is:

[0033] Z th = (1-λ)Z c + λZ v ;

[0034] wherein Z th is a power distribution system impedance identification value, λ is a control coefficient, Z c is a first impedance, and Z v is a second impedance.

[0035] The application discloses a device for realizing the power distribution system impedance identification method based on fluctuation characteristics analysis, which comprises a data acquisition unit, a first processing unit, a second processing unit and an identification unit.

[0036] The data acquisition unit is used for acquiring data at a measuring point.

[0037] The first processing unit is used for obtaining a load impedance fluctuation amount and an equivalent potential fluctuation amount respectively according to the data at the measuring point.

[0038] The second processing unit is used for:

[0039] obtaining a first impedance expression according to the data at the measuring point.

[0040] obtaining a second impedance expression according to the load impedance fluctuation amount and the equivalent potential fluctuation amount.

[0041] obtaining a fluctuation coefficient according to the data at the measuring point based on covariance failure and the second impedance expression.

[0042] obtaining a control coefficient according to the fluctuation coefficient.

[0043] The identification unit is used for:

[0044] obtaining a power distribution system impedance identification expression according to the first impedance expression, the second impedance expression and the control coefficient.

[0045] identifying the impedance of the power distribution system according to the power distribution system impedance identification expression based on the data at the measuring point.

[0046] Compared with the prior art, the application has the following beneficial effects:

[0047] 1. The application adopts the data at the measuring point, obtains the impedance expression identified based on the covariance of the equivalent potential steady-state value and the impedance expression identified based on the equivalent potential fluctuation value, obtains the fluctuation coefficient based on the covariance failure and further obtains the control coefficient, constructs the power distribution system impedance identification expression based on the control coefficient, and identifies the impedance of the power distribution system through the power distribution system impedance identification expression, which is not affected by the fluctuation of the system-side power supply potential and does not depend on any assumption, and has high reliability.

[0048] 2. The power distribution system impedance identification method based on fluctuation characteristics analysis provided by the application can correctly identify the system impedance in the case that disturbance occurs on the system side, and solves the problem that the traditional identification method is difficult to correctly identify in the case that disturbance occurs on the system side. BRIEF DESCRIPTION OF DRAWINGS

[0049] Figure 1A flowchart of the power distribution system impedance identification method based on fluctuation characteristics analysis of the present application;

[0050] Figure 2 A fluctuation analysis graph based on equivalent potential steady-state value in an embodiment of the present application;

[0051] Figure 3 A fluctuation analysis graph based on equivalent potential fluctuation value in an embodiment of the present application;

[0052] Figure 4 An impedance identification graph of the distributed power supply access in an embodiment of the present application.

[0053] Figure 5 A device signal transmission schematic diagram for implementing the power distribution system impedance identification method based on fluctuation characteristics analysis in the present application. DETAILED DESCRIPTION

[0054] To clearly illustrate the technical features of the present application, the embodiments of the present application will be described in detail below with reference to the accompanying drawings and embodiments, so that the implementation process of how to apply technical means to solve technical problems and achieve corresponding technical effects can be fully understood and implemented. The embodiments of the present application and each feature in the embodiments can be combined with each other without conflict, and the formed technical solutions are within the protection scope of the present application.

[0055] Referring to Figure 1 The embodiment of the present application provides a power distribution system impedance identification method based on fluctuation characteristics analysis, comprising the following steps:

[0056] Obtaining data at the measuring point; obtaining load impedance fluctuation and equivalent potential fluctuation based on the data at the measuring point;

[0057] Obtaining a first impedance expression based on the data at the measuring point, the first impedance expression being an expression of the impedance identified based on the equivalent potential steady-state value obtained based on the covariance; obtaining a second impedance expression based on the load impedance fluctuation and the equivalent potential fluctuation, the second impedance expression being an expression of the impedance identified based on the equivalent potential fluctuation value obtained based on the covariance;

[0058] Based on the covariance failure and the second impedance expression, obtaining a fluctuation coefficient based on the data at the measuring point; obtaining a control coefficient based on the fluctuation coefficient;

[0059] Obtaining a power distribution system impedance identification expression based on the first impedance expression, the second impedance expression and the control coefficient;

[0060] Identifying the impedance of the power distribution system based on the power distribution system impedance identification expression.

[0061] The data at the measuring point is used to obtain an impedance expression identified based on the equivalent potential steady-state value and an impedance expression identified based on the equivalent potential fluctuation value, and then the fluctuation coefficient is obtained based on the covariance failure, and further the control coefficient is obtained, and an impedance identification expression of the power distribution system is constructed based on the control coefficient. The impedance of the power distribution system identified by the impedance identification expression of the power distribution system is not affected by the fluctuation of the system side power potential and does not depend on any assumption, and has high reliability.

[0062] Based on the Thevenin theorem, the system equivalent potential is decomposed into the superposition of the equivalent potential steady-state value and the equivalent potential fluctuation value by considering the fluctuation of the system equivalent potential:

[0063] E th = E c + ΔE v ;

[0064] In the formula, E th is the system equivalent potential, E c is the equivalent potential steady-state value, and ΔE v is the equivalent potential fluctuation value.

[0065] A new impedance identification mathematical model is constructed, which includes two parts, one part is the impedance Z c identified based on the equivalent potential steady-state value E c , and the other part is the impedance Z v identified based on the equivalent potential fluctuation value ΔE V .

[0066] In some embodiments, the step of obtaining a first impedance expression according to the data at the measuring point is:

[0067] Based on the Thevenin theorem, a target function is constructed according to the system side equivalent potential expression, and the target function is the expression of the system side equivalent potential and the system side equivalent impedance.

[0068] In some embodiments, the data at the measuring point includes voltage phasor and current phasor.

[0069] The step of constructing the target function is:

[0070] According to the Thevenin theorem, the equivalent potential steady-state value is:

[0071] E c = Z c I + U;

[0072] In the formula, E c is the equivalent potential steady-state value, Z c is the impedance identified based on the equivalent potential steady-state value, I is the current phasor value in the data at the measuring point, and U is the voltage phasor value in the data at the measuring point.

[0073] When identifying the impedance of a power distribution system using data from N (N>2) sets of measurement points, due to the unknowns (E... c and Z c The number of ) is less than the number of equations, therefore the objective function is established as:

[0074]

[0075] In the formula, min is the minimization function, f(Z) c E c ) for Z c E c The relevant function is defined as follows: N represents the total number of data sets from the measurement points required for one first impedance identification; typically, the sampling frequency is 4kHz (i.e., 4000 data sets from the measurement points can be obtained per second). If N = 200, then one first impedance identification is performed every 0.05 seconds. i represents the sequence number of the data sets from the measurement points required for one first impedance identification. i U represents the current phasor value at the i-th measurement point. i The voltage phasor value in the data at the i-th measurement point;

[0076] Using the least squares method, the objective function is solved based on data from at least three measurement points to obtain the first expression, which is an intermediate expression for the first impedance; the first expression is:

[0077] Z c =-(X) T X) -1 X T Y;

[0078] In the formula, X = [I1, I2, ..., I N ] T Y = [U1, U2, ..., U N ] T X represents the current phasor matrix in the data from N measurement points, and I N U represents the current phasor in the data at the Nth measurement point, Y represents the voltage phasor matrix in the data at the Nth measurement point, and U represents the voltage phasor matrix. N This represents the voltage phasor in the data at the Nth measurement point, indicated by the superscript. T Indicates transpose;

[0079] The first impedance expression is obtained by expressing the first expression using the covariance method.

[0080] The steps to obtain the first impedance expression using the covariance method are as follows:

[0081] Based on at least three groups of data at measuring points, real and imaginary parts of equivalent potential on system side, equivalent impedance on system side, voltage phasor and current phasor are separated respectively, and current coefficient matrix, equivalent impedance and equivalent potential matrix and voltage coefficient matrix are constructed respectively; the steps are:

[0082] Real and imaginary parts of equivalent potential on system side, equivalent impedance on system side, voltage phasor and current phasor are represented as:

[0083]

[0084] In the formula, E c,d is real part of steady-state value of equivalent potential, E c,q is imaginary part of steady-state value of equivalent potential, R c is resistance identified by steady-state value of equivalent potential, X c is reactance identified by steady-state value of equivalent potential, I c,d is real part of current phasor, I c,q is imaginary part of current phasor, U c,d is real part of voltage phasor, U c,q is imaginary part of voltage phasor;

[0085] Based on at least three groups of data at measuring points, current coefficient matrix A is:

[0086]

[0087] In the formula, is kth calculated value of real part of current phasor in data at the first group of measuring points, is kth calculated value of imaginary part of current phasor in data at the first group of measuring points, is kth calculated value of real part of current phasor in data at the Nth group of measuring points, is kth calculated value of imaginary part of current phasor in data at the Nth group of measuring points, k is calculation order, and total calculation times are same as total group number of measuring point data required for performing first impedance identification once;

[0088] Equivalent impedance and equivalent potential matrix X is:

[0089] X = [R c X c E c,d E c,q ] T ;

[0090] Voltage coefficient matrix B is:

[0091]

[0092] In the formula, is kth calculated value of real part of voltage phasor in data at the first group of measuring points, is the kth calculated value of the imaginary part of the voltage phasor in the data at the first group of measurement points, is the kth calculated value of the real part of the voltage phasor in the data at the Nth group of measurement points, is the kth calculated value of the imaginary part of the voltage phasor in the data at the Nth group of measurement points, and k is the calculation order;

[0093] The second expression is constructed according to the current coefficient matrix, the equivalent impedance and equivalent potential matrix, and the voltage coefficient matrix, and the second expression is:

[0094]

[0095] The third expression is obtained by solving the second expression by using the least square method, the third expression is an expression of the equivalent impedance and equivalent potential matrix with respect to the real part of the voltage phasor, the imaginary part of the voltage phasor, the real part of the current phasor, and the imaginary part of the current phasor; and the third expression is:

[0096]

[0097] wherein:

[0098]

[0099] The fourth expression is obtained by using the expected value and fluctuation amount to respectively represent the real part of the voltage phasor, the imaginary part of the voltage phasor, the real part of the current phasor, and the imaginary part of the current phasor; and the fourth expression is:

[0100]

[0101] wherein, is the expected value of the real part of the kth calculated current phasor, is the fluctuation amount of the real part of the kth calculated current phasor, is the expected value of the imaginary part of the kth calculated current phasor, is the fluctuation amount of the imaginary part of the kth calculated current phasor, is the expected value of the real part of the kth calculated voltage phasor, is the fluctuation amount of the real part of the kth calculated voltage phasor, is the expected value of the imaginary part of the kth calculated voltage phasor, is the fluctuation amount of the imaginary part of the kth calculated voltage phasor;

[0102] The first impedance expression is obtained by substituting the fourth expression into the third expression and simplifying, and the first impedance expression is:

[0103]

[0104] wherein, Cov is a complex covariance function, Cov[ΔI c ,ΔUc ] for ΔI c and ΔU c , Cov[ΔI c , ΔI c ] for ΔI c and ΔI c , Cov[ΔI c , ΔU c ] for the fluctuation of the steady-state value of the current phasor in the measured point data, and Cov[ΔU i , ΔU v ] for the fluctuation of the steady-state value of the voltage phasor in the measured point data;

[0105] Figure 2 is a fluctuation analysis diagram based on the steady-state value of the equivalent potential, and the red box part in the diagram is the sample data of the voltage phasor and the current phasor at N groups of measured points required for the first Thevenin equivalence, and the red box part is shown in detail, Δ v , ΔI v , ΔI v , and ΔI L respectively represent the fluctuation of the real part of the voltage phasor, the fluctuation of the imaginary part of the voltage phasor, the fluctuation of the real part of the current phasor, and the fluctuation of the imaginary part of the current phasor. Subsequently, the voltage phasor fluctuation and the current phasor fluctuation are used to perform the first Thevenin equivalence on the group of data, and the impedance identified by the steady-state value of the equivalent potential is calculated.

[0106] In some embodiments, the step of obtaining a second impedance expression according to the load impedance fluctuation and the equivalent potential fluctuation is:

[0107] According to the Thevenin theorem:

[0108]

[0109] In the formula, ΔE v is the fluctuation of the system-side Thevenin equivalent potential, that is, the equivalent potential fluctuation, Z v is the impedance identified based on the equivalent potential fluctuation, ΔI v is the fluctuation of the current phasor fluctuation in the measured point data, ΔU v is the fluctuation of the voltage phasor fluctuation in the measured point data, and ΔZ L is the load impedance fluctuation;

[0110] The load impedance fluctuation and the equivalent potential fluctuation are obtained by arithmetic covariance to obtain a fifth expression; the fifth expression is:

[0111] Cov[ΔE v , ΔZ L ] = Cov[ΔI v , ΔZ L ] Z v + Cov[ΔU v , ΔZ L ];

[0112] Based on the failure of the "source-load" fluctuation uncorrelation principle, the fifth expression is simplified to obtain the second impedance expression.

[0113] Generally, based on the "source-load" fluctuation uncorrelation principle, it is assumed that the load impedance fluctuation and the equivalent potential fluctuation are independent of each other, which can be expressed as: Cov[ΔE v ,ΔZ L ]=0, which is provided for the fifth expression, and after simplification, it can be obtained that:

[0114] However, both the impedance identification based on the equivalent potential steady-state value E c and the impedance identification based on the equivalent potential fluctuation ΔE v use fluctuation of electrical quantities for solving, but the fluctuation characteristics they aim at are not the same.

[0115] The former, i.e., the impedance identification based on the equivalent potential steady-state value E c , uses the fluctuation of the data deviating from the expected value at each group of measuring points, while the latter, i.e., the impedance identification based on the equivalent potential fluctuation ΔE v , uses the fluctuation of the data at the latter group of measuring points and the data at the former group of measuring points.

[0116] Therefore, the equivalent impedance is solved based on the equivalent potential steady-state value E c , which only uses one set of sample values, while the equivalent impedance is solved based on the equivalent potential fluctuation ΔE v , which uses two sets of sample values, which is one time of the former. Since the solution based on the equivalent potential steady-state value E c requires a large amount of sample data, when the number of samples used for each equivalent is small, the identification error of this method is large. At this time, the solution based on the equivalent potential fluctuation ΔE v uses adjacent sample data, which increases the sample quantity, and the identification result is more accurate. However, with the increase of the sample quantity, the "source-load" fluctuation uncorrelation principle assumed in the impedance identification based on the equivalent potential fluctuation ΔE v will fail, at which time an error will be generated: i.e., Cov[ΔE v ,ΔZ L ]≠0, which makes Cov[ΔE v ,ΔZ L ]=δ, i.e.:

[0117] The fifth expression can be expressed as: δ=Cov[ΔI v ,ΔZ L ]Z v +Cov[ΔU v ,ΔZ L ];

[0118] The second impedance expression can be obtained by simplifying:

[0119]

[0120] wherein Z v is the real value of the complex covariance function of the load impedance fluctuation and the equivalent potential fluctuation.

[0121] Figure 3 is the fluctuation analysis diagram based on the equivalent potential fluctuation value, wherein the upper layer data is the sample data of the voltage phasor, the current phasor and the impedance at the measuring point, and the lower layer is the fluctuation data required for each equivalent. The voltage phasor fluctuation, the current phasor fluctuation and the impedance fluctuation are obtained by subtracting the data at the corresponding positions of each group in the upper layer. Then, the Thevenin equivalent is performed on the group of data to calculate the impedance identified by the equivalent potential fluctuation value.

[0122] In some embodiments, the fluctuation coefficient is the ratio of a first value and a second value, the first value is the real value of the complex covariance function of the load impedance fluctuation and the equivalent potential fluctuation, and the second value is the complex covariance value of the voltage phasor fluctuation at the measuring point and the load impedance fluctuation; the fluctuation coefficient is:

[0123]

[0124] wherein e m is the fluctuation coefficient value identified for the mth time, δ m is the first value identified for the mth time, is the value of the voltage fluctuation in the measuring point data identified for the mth time, is the load impedance fluctuation value identified for the mth time, and m is the identification order;

[0125] In some embodiments, the control coefficient is:

[0126] λ m = exp(-μ|e m |);

[0127] wherein λ m is the control coefficient identified for the mth time, exp is the exponential function, and μ is the attenuation coefficient.

[0128] In some embodiments, the expression for identifying the impedance of the power distribution system is:

[0129] Z th = (1-λ)Z c + λZ v ;

[0130] wherein Z th is the identified value of the impedance of the power distribution system, and λ is the control coefficient.

[0131] Taking a sampling frequency of 4kHz as an example, when N = 200, that is, the first impedance and the second impedance are identified once every 0.05 seconds, then 1 / (0.05) = 20 identifications can be performed per second. This means that the first impedance and the second impedance can be identified 20 times per second, and there are 20 corresponding control coefficients, denoted as λ1, λ2, ..., λ... 20 ,but:

[0132] Z th1 =(1-λ1)Z c1 +λ1Z v1 ;

[0133] Z th2 =(1-λ2)Z c2 +λ2Z v2 ;

[0134] ...

[0135] Z th20 =(1-λ) 20 )Z c20 +λ 20 Z v20 ;

[0136] In the formula, Z th1 Z represents the first identification value of the power distribution system impedance, λ1 represents the control coefficient value of the first identification, and Z represents the first identification value. c1 Z is the first impedance value identified in the first instance. v1 Z is the second impedance value identified in the first instance. th2 Z represents the second identification value of the power distribution system impedance, λ2 is the control coefficient value for the second identification, and Z represents the second identification value. c2 Z is the first impedance value identified in the second identification. v2 Z is the second impedance value identified in the second identification. th20 λ is the 20th identification value of the power distribution system impedance. 20 Z represents the control coefficient value for the 20th identification. c20 Z is the first impedance value identified in the 20th identification. v20 This is the second impedance value identified on the 20th attempt.

[0137] like Figure 4 As shown, theoretically there should be 20 points within a 1-2 second time period, each representing one impedance identification value for the power distribution system. However, marking 20 points within a 1-second time period would be too dense. Figure 4 One point appears every three points. Therefore, there are 20 points in the time interval of 1 second to 4 seconds.

[0138] Combining the first impedance expression, the second impedance expression, and the control coefficient above, the specific expression for impedance identification of the power distribution system can be obtained as follows:

[0139]

[0140] Figure 4 For the impedance identification map of the distributed power access, the system side and the load side simultaneously access 2.6MW photovoltaic models, the equivalent impedance of the system side is 3.113Ω, and the equivalent impedance of the load side is 3.745Ω, wherein the load side fluctuates with an amplitude of ±5%, according to the above parameters, the power distribution network containing the distributed power is built in the RTDS to verify the power distribution system impedance identification method based on the fluctuation characteristic analysis proposed in the application, and it can be seen that the impedance of the power distribution system identified by the power distribution system impedance identification expression proposed in the application is close to the theoretical value, and the accuracy is high.

[0141] Referring to Figure 5 The embodiment of the application also provides an apparatus for realizing the power distribution system impedance identification method based on the fluctuation characteristic analysis, comprising a data acquisition unit, a first processing unit, a second processing unit and an identification unit.

[0142] The data acquisition unit is used for acquiring the data at the measuring point.

[0143] The first processing unit is used for obtaining the load impedance fluctuation and the equivalent potential fluctuation according to the data at the measuring point.

[0144] The second processing unit:

[0145] is used for obtaining the first impedance expression according to the data at the measuring point.

[0146] is used for obtaining the second impedance expression according to the load impedance fluctuation and the equivalent potential fluctuation.

[0147] is used for obtaining the fluctuation coefficient according to the second impedance expression.

[0148] is used for obtaining the control coefficient according to the fluctuation coefficient.

[0149] The identification unit:

[0150] is used for obtaining the power distribution system impedance identification expression according to the first impedance expression, the second impedance expression and the control coefficient.

[0151] is used for identifying the impedance of the power distribution system according to the power distribution system impedance identification expression based on the data at the measuring point.

[0152] Finally, it should be noted that the above content is only used to illustrate the technical solutions of the application, and is not a limitation on the protection scope of the application, and the simple modification or equivalent replacement of the technical solutions of the application by the ordinary skilled in the art does not deviate from the essence and scope of the technical solutions of the application.

Claims

1. A method for impedance identification of a power distribution system based on fluctuation characteristic analysis, characterized in that: Includes the following steps: Obtain data at the measurement points; Based on the data at the measurement points, the load impedance fluctuation and the equivalent potential fluctuation are obtained respectively. The first impedance expression is obtained based on the data at the measurement point. The first impedance expression is the expression of the impedance identified by the steady-state value of the equivalent potential obtained based on the covariance. A second impedance expression is obtained based on the load impedance fluctuation and the equivalent potential fluctuation. The second impedance expression is an expression for the impedance identified by the equivalent potential fluctuation value obtained based on the covariance. Based on the covariance failure and the second impedance expression, the fluctuation coefficient is obtained from the data at the measurement point. The control coefficient is obtained based on the fluctuation coefficient; the power distribution system impedance identification expression is obtained based on the first impedance expression, the second impedance expression, and the control coefficient; Based on the data at the measurement points, the impedance of the power distribution system is identified according to the power distribution system impedance identification expression. The fluctuation coefficient is the ratio of a first value to a second value. The first value is the true value of the complex covariance function of the load impedance fluctuation and the equivalent potential fluctuation, and the second value is the complex covariance value of the voltage phasor fluctuation and the load impedance fluctuation at the measurement point. The control coefficient is: ； In the formula, For the first Control coefficients for secondary identification In order to identify the order, It is an exponential function. The attenuation coefficient is... For the first The fluctuation coefficient of the sub-identification; The impedance identification expression for the power distribution system is: ； In the formula, For the impedance identification value of the power distribution system, For control coefficients, The first impedance, This is the second impedance.

2. The method for impedance identification of power distribution systems based on fluctuation characteristic analysis according to claim 1, characterized in that: The steps to obtain the first impedance expression based on the data at the measurement point are as follows: Based on Thevenin's theorem, an objective function is constructed according to the system-side equivalent potential expression. The objective function is the expression for the system-side equivalent potential and the system-side equivalent impedance. The objective function is solved using the least squares method based on data from at least three sets of measurement points to obtain a first expression, which is an intermediate expression for the first impedance. The first impedance expression is obtained by expressing the first expression using the covariance method.

3. The method for impedance identification of power distribution systems based on fluctuation characteristic analysis according to claim 2, characterized in that: The data at the measurement point includes voltage phasors and current phasors.

4. The method for impedance identification of power distribution systems based on fluctuation characteristic analysis according to claim 3, characterized in that: The steps to obtain the first impedance expression by expressing the first expression using the covariance method are as follows: Based on data from at least three sets of measurement points, the real and imaginary parts of the system-side equivalent potential, the system-side equivalent impedance, the voltage phasor, and the current phasor are separated, and the current coefficient matrix, equivalent impedance and equivalent potential matrix, and voltage coefficient matrix are constructed respectively. A second expression is constructed based on the current coefficient matrix, the equivalent impedance and equivalent potential matrix, and the voltage coefficient matrix; The third expression is obtained by solving the second expression using the least squares method. The third expression is the expression of the equivalent impedance and equivalent potential matrix with respect to the real part of the voltage phasor, the imaginary part of the voltage phasor, the real part of the current phasor, and the imaginary part of the current phasor. The fourth expression is obtained by expressing the real part of the voltage phasor, the imaginary part of the voltage phasor, the real part of the current phasor, and the imaginary part of the current phasor in the form of expected value and fluctuation amount, respectively. Substituting the fourth expression into the third expression and simplifying it, we obtain the first impedance expression.

5. The method for impedance identification of power distribution systems based on fluctuation characteristic analysis according to claim 4, characterized in that: The steps to obtain the second impedance expression based on the load impedance fluctuation and the equivalent potential fluctuation are as follows: The fifth expression is obtained by taking the arithmetic covariance of the load impedance fluctuation and the equivalent potential fluctuation. Based on the failure of the "source-charge" fluctuation uncorrelation principle, the fifth expression is simplified to obtain the second impedance expression.

6. The method for impedance identification of power distribution systems based on fluctuation characteristic analysis according to claim 5, characterized in that: The second impedance expression is: ； In the formula, For the second impedance, This represents the true value of the complex covariance function of the load impedance fluctuation and the equivalent potential fluctuation. Let be the complex covariance function of the voltage phasor fluctuation and the load impedance fluctuation at the measuring point. This refers to the voltage phasor fluctuation at the measurement point. This refers to the load impedance fluctuation. Let be the complex covariance function of the current phasor fluctuation and the load impedance fluctuation at the measuring point. This represents the phasor fluctuation of the current at the measuring point.

7. An apparatus for implementing the power distribution system impedance identification method based on fluctuation characteristic analysis as described in any one of claims 1-6, characterized in that: It includes a data acquisition unit, a first processing unit, a second processing unit, and an identification unit; The data acquisition unit is used to acquire data at the measurement point; The first processing unit is used to obtain the load impedance fluctuation and the equivalent potential fluctuation based on the data at the measurement point, respectively. Second processing unit: Used to obtain the first impedance expression based on the data at the measurement point; Used to obtain a second impedance expression based on the load impedance fluctuation and the equivalent potential fluctuation; Used to obtain the fluctuation coefficient based on the data at the measurement point, based on the covariance failure and the second impedance expression. Used to obtain the control coefficient based on the fluctuation coefficient; The identification unit: Used to obtain the power distribution system impedance identification expression based on the first impedance expression, the second impedance expression, and the control coefficient; This is used to identify the impedance of the power distribution system based on the data at the measurement point and according to the power distribution system impedance identification expression.

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