Harmonic state estimation method and system considering harmonic coupling

Abstract

The invention belongs to the technical field of power systems, and provides a harmonic state estimation method and system considering harmonic coupling, and the method comprises the steps: obtaining historical non-phasor data of a monitoring node of a power system; constructing a harmonic coupling matrix of the power monitoring nodes based on the acquired non-phasor data; according to the non-phasor data and the harmonic coupling matrix, constructing a Jacobian matrix of a harmonic network of the power monitoring node, and estimating power phasor data of each frequency of the power monitoring node; performing phase angle synchronization on the obtained estimated value of the power phasor data of each frequency of the power detection node; and according to the synchronized power phasor data, calculating voltage and current phasor data of a non-power monitoring node, and completing harmonic state estimation considering harmonic coupling. The method is suitable for estimating a large power grid with a large number of nodes, the calculation workload is greatly reduced, the calculation time is greatly shortened, the harmonic coupling characteristics of the nodes can be reflected, the dimensionality of the Jacobian matrix is effectively reduced, and the calculation efficiency is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power systems, and in particular relates to a harmonic state estimation method and system considering harmonic coupling. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] Against the backdrop of global energy transformation and changes in the power industry, new power systems with the characteristics of high efficiency, greenness, flexibility and reliability have emerged based on the concepts of clean energy, digital technology and intelligent management. However, the massive introduction of new energy sources has made the harmonic disturbance problem of new power systems more serious. With the widespread application of large-scale heterogeneous power electronic equipment in new power systems, new characteristics of strong coupling are gradually emerging between harmonics of different frequencies, resulting in an increasingly complex development trend of harmonic disturbance problems. Therefore, it is particularly important to estimate the harmonic state of the power system, study the coupling characteristics of harmonics of different frequencies, and then carry out targeted governance of harmonic pollution problems.

[0004] Harmonic state estimation uses the collected measurement information and the relationship between the measurement and the selected state variable to calculate the estimated value of the state variable through the statistical criteria corresponding to the selected method. At present, domestic and foreign scholars have conducted extensive research on harmonic state estimation algorithms, which can be mainly divided into two categories: static algorithms and dynamic algorithms: static harmonic state estimation refers to the method of using the measurement at a certain moment to estimate the state quantity at that moment; dynamic harmonic state estimation refers to the method of using the measurement at a certain moment to estimate the state quantity at the next moment according to the motion equation.

[0005] Traditional harmonic state estimation, whether static or dynamic, treats harmonics of different frequencies separately, without fully considering the mutual influence between harmonics, resulting in the estimation process being unable to effectively reflect the coupling relationship between harmonics. At the same time, most traditional algorithms are based on nonlinear weighted least squares algorithms (i.e., nonlinear algorithms) for estimation in node networks of specific harmonic frequencies; the dimension of the Jacobian matrix in the nonlinear weighted least squares algorithm is positively correlated with the number of nodes involved in the estimation. Therefore, the more nodes involved in the estimation, the higher the dimension of the Jacobian matrix, and the lower the computational efficiency of the algorithm, which greatly limits the application of harmonic state estimation algorithms in large-scale power grids. Summary of the invention

[0006] To solve the above problems, the present invention proposes a harmonic state estimation method and system considering harmonic coupling, which calculates the harmonic coupling matrix of the node based on the non-phasor data collected historically, establishes a Jacobian matrix based on the harmonic coupling matrix in the harmonic network of the node according to the obtained harmonic coupling matrix and the data collected by the monitoring node at the current moment, estimates the voltage and current phasor data of each frequency of the monitoring node, performs phase angle synchronization on the estimated data of the monitoring node, estimates the harmonic state of the non-monitoring node based on the phasor data of the synchronized monitoring node, and completes the harmonic state estimation considering harmonic coupling.

[0007] According to some embodiments, a first solution of the present invention provides a harmonic state estimation method considering harmonic coupling, which adopts the following technical solution: A harmonic state estimation method considering harmonic coupling includes: Obtain historical non-phasor data of power system monitoring nodes; Based on the acquired non-phasor data, a harmonic coupling matrix of the power monitoring node is constructed; According to the non-phasor data and harmonic coupling matrix, the Jacobian matrix of the harmonic network of the power monitoring node is constructed to estimate the power phasor data of each frequency of the power monitoring node; Performing phase angle synchronization on the estimated values ​​of the power phasor data of each frequency of the power detection node; According to the synchronized power phasor data, the voltage and current phasor data of the non-power monitoring nodes are calculated to complete the harmonic state estimation considering harmonic coupling.

[0008] As a further technical limitation, the Jacobian matrix of the constructed power monitoring node harmonic network is for ;in, , and Both dimensional matrix, m is the number of frequencies considered; and Both dimensional matrix; matrix Each element in the equation is a function of the voltage amplitude measurement on the state variable u The partial derivatives of and Each element in the equation is respectively injected into the state variable by the active and reactive power measurement function u The partial derivatives of and Each element in the equation is respectively injected into the state variable by the active and reactive power measurement function The partial derivatives of .

[0009] As a further technical limitation, in the process of phase angle synchronization, based on the estimated value of each frequency power phasor data of the power monitoring node, the non-phasor data of the monitoring node is converted into phasor data to increase the redundancy of the power grid; the fundamental wave is used as the reference voltage, and the voltage phase angle of the reference fundamental wave node is 0 o The reference angle is used to synchronize the estimated values ​​of the power phasor data of each frequency at the power detection node, thereby completing the synchronization of the estimated values ​​of the power phasor data of each frequency at the power detection node with the fundamental wave phase angle.

[0010] As a further technical limitation, based on the synchronized power phasor data obtained, a measurement phasor used to characterize the relationship between the measurement quantity and the state variable is obtained, and a phasor data objective function is constructed according to the obtained measurement phasor. When the obtained objective function is minimized, the objective function is solved to obtain the voltage and current phasor data of the non-power monitoring node.

[0011] As a further technical limitation, the harmonic coupling matrix of the constructed power monitoring node is ;in, express n The node h Subharmonic injection current phasor value; express n The node h Subharmonic voltage phasor values; Indicates h The subharmonic current and k The coupling relationship of subharmonic voltages, i.e., the elements of the harmonic coupling matrix; H Indicates the highest harmonic order considered in the model.

[0012] As a further technical limitation, the acquired historical non-phasor data of the power system monitoring node includes at least node voltage amplitude, injected power and branch power.

[0013] According to some embodiments, a second solution of the present invention provides a harmonic state estimation system considering harmonic coupling, which adopts the following technical solution: A harmonic state estimation system considering harmonic coupling, comprising: An acquisition module configured to acquire historical non-phasor data of a power system monitoring node; A construction module configured to construct a harmonic coupling matrix of a power monitoring node based on the acquired non-phasor data; A calculation module, which is configured to construct a Jacobian matrix of the harmonic network of the power monitoring node according to the non-phasor data and the harmonic coupling matrix, and estimate the power phasor data of each frequency of the power monitoring node; A synchronization module, configured to perform phase angle synchronization on the estimated values ​​of the power phasor data of each frequency of the power detection node; The estimation module is configured to calculate the voltage and current phasor data of the non-power monitoring node according to the synchronized power phasor data, and complete the harmonic state estimation considering the harmonic coupling.

[0014] According to some embodiments, a third solution of the present invention provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium stores a program thereon, which, when executed by a processor, implements the steps in a harmonic state estimation method considering harmonic coupling as described in the first solution of the present invention.

[0015] According to some embodiments, a fourth solution of the present invention provides an electronic device, which adopts the following technical solution: An electronic device comprises a memory, a processor and a program stored in the memory and running on the processor, wherein when the processor executes the program, the steps in a harmonic state estimation method considering harmonic coupling as described in the first solution of the present invention are implemented.

[0016] According to some embodiments, a fifth solution of the present invention provides a computer program product, which adopts the following technical solution: A computer program product comprises software codes, wherein the program in the software codes executes the steps in a harmonic state estimation method considering harmonic coupling as described in the first solution of the present invention.

[0017] Compared with the prior art, the present invention has the following beneficial effects: In the past, the calculation of the harmonic coupling matrix of the node required the measurement of the phasor data of the node. The present invention uses the non-phasor data measured by the monitoring node to realize the calculation of the harmonic coupling matrix of all nodes in the entire power grid; the present invention takes into account the strong coupling characteristics of each harmonic of the node, and the Jacobian matrix is ​​established in the harmonic network of each node when estimating, and the dimension of the Jacobian matrix is ​​related to the number of measurements and the number of harmonics to be estimated; because when establishing the Jacobian matrix, the measurements are the node voltage amplitude and injected active and reactive power of the fundamental wave of the node and each harmonic, and the measurements are fixed. For each additional harmonic of a frequency analyzed, three more measurements are made; the dimension of the Jacobian matrix is ​​only related to the number of harmonics to be estimated. In large-scale power grids, the proposed algorithm can simplify the calculation of high-dimensional Jacobian matrices into low-dimensional calculations. Compared with the traditional harmonic state estimation method, it is more suitable for large-scale power grids with a large number of estimation nodes, and greatly reduces the calculation workload and calculation time, can reflect the harmonic coupling characteristics of the node, effectively reduces the dimension of the Jacobian matrix, and improves the calculation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings in the specification that constitute a part of this embodiment are used to provide a further understanding of this embodiment. The schematic embodiments of this embodiment and their descriptions are used to explain this embodiment and do not constitute improper limitations on this embodiment.

[0019] Figure 1 This is a flow chart of a harmonic state estimation method considering harmonic coupling in Embodiment 1 of the present invention; Figure 2 This is a schematic diagram of the structure of the IEEE4 node in the first embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of an IEEE9 node in Embodiment 1 of the present invention; Figure 4 This is a schematic diagram of the estimation result in the first embodiment of the present invention; Figure 5 This is a structural block diagram of a harmonic state estimation system considering harmonic coupling in the second embodiment of the present invention. DETAILED DESCRIPTION

[0020] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0021] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present application belongs.

[0022] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0023] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0024] Embodiment 1 Embodiment 1 of the present invention introduces a harmonic state estimation method considering harmonic coupling.

[0025] In the past, the calculation of the harmonic coupling matrix of a node required the measurement of the phasor data of the node. This embodiment can utilize the non-phasor data measured by the monitoring node to calculate the harmonic coupling matrix of all nodes in the entire power grid.

[0026] In traditional harmonic state estimation, the Jacobian matrix is ​​established in the node network, and its dimension is related to the number of quantity measurements and the number of nodes to be estimated; when facing a large power grid, the Jacobian matrix established by the harmonic state estimation has a high dimension, which not only consumes a large amount of storage capacity, but also causes the convergence efficiency of the traditional method to be reduced during calculation, and increases the solution time. In this embodiment, the strong coupling characteristics of each harmonic of the node are taken into account. When estimating, the Jacobian matrix is ​​established in the harmonic network of each node, and the dimension of the Jacobian matrix is ​​related to the number of quantity measurements and the number of harmonics to be estimated. Since when establishing the Jacobian matrix, the quantity measurements are the node voltage amplitude and injected active and reactive power of the fundamental wave of the node and each harmonic, the quantity measurements are fixed. For each additional harmonic of one frequency analyzed, three more quantity measurements are made. Therefore, the dimension of the Jacobian matrix is ​​only related to the number of harmonics to be estimated. In large-scale power grids, the proposed algorithm can simplify the high-dimensional Jacobian matrix calculation to a low-dimensional calculation. This embodiment can reflect the harmonic coupling characteristics of the nodes, and effectively reduce the dimension of the Jacobian matrix, thereby improving the calculation efficiency; it is more suitable for estimating large power grids with a large number of nodes, and greatly reduces the calculation workload and calculation time; it provides a reference for the use of the coupling matrix in harmonic state estimation, and has strong practical engineering significance.

[0027] like Figure 1 A harmonic state estimation method considering harmonic coupling is shown, comprising: Obtain historical non-phasor data of power system monitoring nodes; Based on the acquired non-phasor data, a harmonic coupling matrix of the power monitoring node is constructed; According to the non-phasor data and harmonic coupling matrix, the Jacobian matrix of the harmonic network of the power monitoring node is constructed to estimate the power phasor data of each frequency of the power monitoring node; Performing phase angle synchronization on the estimated values ​​of the power phasor data of each frequency of the power detection node; According to the synchronized power phasor data, the voltage and current phasor data of the non-power monitoring nodes are calculated to complete the harmonic state estimation considering harmonic coupling.

[0028] As one or more embodiments, the specific process of acquiring historical non-phasor data of a power system monitoring node in this embodiment includes: Due to the high cost of phasor measurement equipment, most of the measurement equipment configured at the monitoring nodes in the current power grid can only measure non-phasor data, and the types of data that can be measured are: node voltage amplitude, injected power, branch power, etc. Therefore, it is necessary to use these measurement data to construct the node harmonic coupling matrix.

[0029] Based on the non-phasor data collected at each historical moment, the state of each harmonic and fundamental wave of the node at each historical moment is estimated using a nonlinear algorithm, that is, the node voltage amplitude and phase angle difference are used as state quantities, and the node voltage amplitude, injected power, branch power and other measurement data are used as measurement quantities to establish the measurement equation shown in the following formula: (1) in, z is the measurement vector; x is the state variable vector; h ( x ) is the measurement function, which is a nonlinear function; is the measurement error corresponding to the measured value.

[0030] The goal of the nonlinear algorithm is to find the state value that minimizes the weighted sum of squares of the measurement residuals, that is: (2) in, W is the weight matrix.

[0031] The estimated result is the minimum point of the objective function. Due to the nonlinearity of the measurement equation, it is impossible to directly obtain the extreme point by solving the equation. It is necessary to approach the solution of the equation through iteration. The iteration process can be repeated according to formula (3) and formula (4): (3) (4) in, H ( x ) is the Jacobian matrix of the measurement function vector with respect to the state variable vector, which is composed of the partial derivatives of the measurement function with respect to the state variable; Indicates k The approximation result of the iteration is also k +1 starting point for iteration; It is called the gain matrix; It is k The iteration increment of +1 iteration indicates the distance of this round of iteration to the true solution.

[0032] Formula (5) is used as the convergence criterion to determine whether it has converged.

[0033] (5) in, is the convergence index, which can generally be taken as 10 -6 ~10 -4 .

[0034] Based on formula (1), formula (2), formula (3), formula (4) and formula (5), the phasor values ​​of the fundamental voltage and each harmonic voltage at each node can be obtained; and the phasor value of the injected current at each node can be calculated according to formula (6); that is, (6) in, for h Subharmonic node injection current; Y for h Nodal admittance matrix of subharmonics; for h The node voltage of the subharmonic.

[0035] The calculated voltage and current phasor data can be used to calculate the node harmonic coupling matrix, that is: (7) in, express n The node h Subharmonic injection current phasor value; express n The node h Subharmonic voltage phasor values; Indicates h The subharmonic current and k The coupling relationship of subharmonic voltages is an element of the harmonic coupling matrix; H Indicates the highest harmonic order considered in the model.

[0036] Formula (7) can be simplified as: (8) The least squares algorithm can be used to solve formula (8) to obtain the harmonic coupling matrix of the node: It is assumed that the harmonic coupling matrix is ​​stable and unchanged. When the harmonic state of the power grid is estimated later, the harmonic coupling matrix can be used to perform a harmonic state estimation algorithm based on harmonic coupling (hereinafter referred to as the coupling estimation algorithm).

[0037] In this embodiment, the node admittance matrix Y h Reflects h The connection relationship between nodes of the subharmonic node network and the actual parameters of the power grid line. Similarly, the harmonic coupling matrix can be considered Y n Reflects the node nThe coupling relationship between each harmonic and the fundamental wave of the harmonic network. Traditional harmonic state estimation uses the node admittance matrix to establish the Jacobian matrix for estimation in the node network of different frequencies, which is difficult to reveal the coupling relationship between harmonics of different frequencies. By analogy, the Jacobian matrix can be established in the harmonic network of different nodes using the harmonic coupling matrix to estimate and realize the coupling estimation algorithm.

[0038] by Figure 2 Taking the 4-node system shown in the figure as an example, considering the 5th, 7th, and 11th harmonics, the coupling estimation algorithm is introduced. Set nodes 1, 2, and 3 as monitoring nodes. The monitoring nodes must be equipped with node injection active power (The superscript represents the node, and the subscript represents the frequency.), the injected reactive power And the voltage amplitude Based on the above, the coupling matrix of nodes 1, 2, and 3 can be calculated.

[0039] After obtaining the harmonic coupling matrix, the harmonic coupling matrix is ​​used to estimate the voltage of each frequency of the monitoring node in the harmonic network. Taking node 2 as an example, a measurement matrix is ​​established in the harmonic network of node 2. z With state quantity x As shown in the following formula: (9) (10) in, , , , , They are all in vector form, and their elements are as follows: (11) The fundamental voltage is set as the reference voltage, so the fundamental voltage phase angle is 0°. , , They are the phase angles of the 5th, 7th and 11th harmonic voltages at node 2 relative to the fundamental voltage.

[0040] The Jacobian matrix can be calculated based on the harmonic coupling matrix of node 2. The Jacobian calculation formula based on the harmonic coupling matrix is ​​as follows: (12) in, , and Both Dimensional matrix. m For the number of frequencies considered, node 2 considers the fundamental wave and the 5th, 7th, and 11th harmonics, so m is 4. and Both Dimensional matrix. Matrix Each element in the equation is a function of the voltage amplitude measurement on the state variable u The partial derivatives of and Each element in the equation is injected into the state variable by the active and reactive power measurement function u The matrix is ​​composed of partial derivatives of and Each element in the equation is injected into the state variable by the active and reactive power measurement function The elements in these matrices are solved by the following formula: (13) (14) (15) (16) (17) in, h is the harmonic order of the measurement function corresponding to the measured quantity, k is the harmonic order of the state quantity; and They are obtained from the harmonic coupling matrix of the node, as shown below: (18) in, For Node n The harmonic coupling matrix No. h Line k Column elements.

[0041] According to the harmonic coupling matrix of node 2 and the above formula, the Jacobian matrix of the harmonic network of node 2 can be established. It can be seen that the Jacobian matrix The dimension is , whose dimension is related to the number of frequencies considered and has nothing to do with the number of nodes in the power grid. Therefore, the increase in the number of nodes in the power grid will not affect the Jacobian matrix Increase in dimensions.

[0042] For a power grid with a large number of nodes, taking a 33-node power grid as an example, the traditional method is used to establish the Jacobian matrix in the node admittance matrix. Since the number of nodes is 33 and the state quantity is the voltage amplitude and phase angle of each node at the same frequency, the dimension of the Jacobian matrix established must be at least greater than 50 × 50. However, using the proposed algorithm, the fundamental wave and the 5th, 7th, and 11th harmonics are still considered, and 4 frequencies are considered, so the dimension of the Jacobian matrix established in the harmonic coupling matrix is ​​12 × 7, which is much smaller than the dimension of the Jacobian matrix in the traditional algorithm.

[0043] The Jacobian matrix established based on the harmonic coupling matrix can be used to estimate the harmonic voltage phasor values ​​of the fundamental wave, 5th, 7th, and 11th harmonics of node 2 in the harmonic network of node 2.

[0044] After obtaining the voltage phasor of node 2, the injected current phasor values ​​of the fundamental wave, 5th, 7th, and 11th harmonics of node 2 can be calculated using formula (7).

[0045] Similarly, the phasor values ​​of the node voltage and the injected current at each frequency of node 1 and node 4 can be calculated. Therefore, through the above estimation, the non-phasor data of the node can be , , Convert to phasor data , , which increases the redundancy of the power grid.

[0046] In this embodiment, the non-phasor data of the monitoring node , , Convert to phasor data , , which increases the redundancy of the power grid. However, the estimated results all use the fundamental wave as the reference voltage, and the fundamental wave node voltage phase angles of nodes 1, 2, and 3 are all 0°, so phase angle synchronization is required. In the fundamental wave node network, taking node 1 as the reference node, the fundamental wave phase angle differences of the remaining nodes relative to node 1 can be obtained by using formulas (1), (2), (3), (4), and (5) for state estimation. Based on these phase angle differences, the previously estimated harmonics can be synchronized with the fundamental wave phase angle.

[0047] In this embodiment, the voltage and current phasor data of the remaining non-monitoring nodes are calculated using the synchronized phasor data of the monitoring node, specifically including: Since node 4 is not equipped with a measuring device, the above steps cannot estimate the harmonic state of node 4. After the above steps, the harmonic node voltages and injected current phasor values ​​of nodes 1, 2, and 3 can be obtained. Therefore, these estimated data can be used as new measurement data to calculate the harmonic state of node 4 based on the linear weighted least squares algorithm (hereinafter referred to as the linear algorithm) in the node network of each harmonic.

[0048] For the phasor data after synchronization in step 3, the relationship between the quantity measurement and the state variable is expressed by the measurement equation shown in formula (16), that is: (19) in, Z h The measured phase refers to the phase of nodes 1, 2, and 3 after synchronization in step 3. h Subharmonic voltage and current phasor data; X h is the state phasor, which refers to the h Subharmonic voltage phasors; H h for h The measurement matrix established by the subharmonic network; h is the measurement error phasor.

[0049] Similar to formula (2), we can get formula (20): (20) in, W is the weight matrix.

[0050] Taking the derivative of the objective function and taking it equal to zero gives h The final estimate of the subharmonic network is: (twenty one) From the above analysis, it can be seen that the proposed algorithm needs to ensure that the node voltage amplitude and the injected power are configured at the same node.

[0051] When there is branch power measurement, it is necessary to ensure that the starting node of the branch measurement is configured with the node voltage amplitude measurement. The branch power is converted into the branch current phasor using formula (19), and the branch current phasor can be used as a quantity measurement in step 4 for estimation; that is, (twenty two) in, For Node i To Node j The branch current; For Node i To Node j Branch power; For Node i The node voltage amplitude.

[0052] When there are branch current phasor measurements, injected current phasor measurements, and node voltage phasor measurements, they can be calculated as quantity measurements in step 4. Therefore, the algorithm can also be applied to the case where the power grid has mixed measurements.

[0053] by Figure 3 Taking the IEEE 9-node system shown in FIG. 1 as an example, the algorithm proposed in this embodiment and the traditional nonlinear weighted least squares algorithm are used to estimate the harmonic state; the estimation results of the 5th harmonic are selected for display, as shown in FIG. Figure 4 The results show that the present embodiment is better than the traditional algorithm in terms of estimation error, that is, the coupled estimation algorithm uses non-phasor data and the harmonic coupling matrix to estimate in the harmonic network, and obtains the voltage and injected current phasor values ​​of the monitoring node after estimation. Based on this data, a harmonic state estimation is performed in the node network based on a linear algorithm; to some extent, after two estimations, the error is slightly better than the traditional method that only estimates once.

[0054] This embodiment takes into account the strong coupling characteristics of each harmonic of the node. When estimating, the Jacobian matrix is ​​established in the harmonic network of each node. The dimension of the Jacobian matrix is ​​related to the number of measurements and the number of harmonics to be estimated; because when establishing the Jacobian matrix, the measurements are the node voltage amplitude and injected active and reactive power of the fundamental wave of the node and each harmonic, the measurement is fixed. For each additional harmonic of a certain frequency analyzed, three more measurements are made; the dimension of the Jacobian matrix is ​​only related to the number of harmonics to be estimated. In large-scale power grids, the proposed algorithm can simplify the calculation of high-dimensional Jacobian matrices into low-dimensional calculations. Compared with traditional harmonic state estimation methods, it is more suitable for large power grids with a large number of estimation nodes, and greatly reduces the computational workload and calculation time, and can reflect the harmonics of the nodes.

[0055] Embodiment 2 Embodiment 2 of the present invention introduces a harmonic state estimation system taking harmonic coupling into consideration.

[0056] like Figure 5 A harmonic state estimation system considering harmonic coupling is shown, comprising: An acquisition module configured to acquire historical non-phasor data of a power system monitoring node; A construction module configured to construct a harmonic coupling matrix of a power monitoring node based on the acquired non-phasor data; A calculation module, which is configured to construct a Jacobian matrix of the harmonic network of the power monitoring node according to the non-phasor data and the harmonic coupling matrix, and estimate the power phasor data of each frequency of the power monitoring node; A synchronization module, configured to perform phase angle synchronization on the estimated values ​​of the power phasor data of each frequency of the power detection node; The estimation module is configured to calculate the voltage and current phasor data of the non-power monitoring node according to the synchronized power phasor data, and complete the harmonic state estimation considering the harmonic coupling.

[0057] The detailed steps are the same as those of the harmonic state estimation method considering harmonic coupling provided in the first embodiment, and will not be repeated here.

[0058] Embodiment 3 Embodiment 3 of the present invention provides a computer-readable storage medium.

[0059] A computer-readable storage medium stores a program thereon, and when the program is executed by a processor, the program implements the steps in a harmonic state estimation method considering harmonic coupling as described in Embodiment 1 of the present invention.

[0060] The detailed steps are the same as those of the harmonic state estimation method considering harmonic coupling provided in the first embodiment, and will not be repeated here.

[0061] Embodiment 4 A fourth embodiment of the present invention provides an electronic device.

[0062] An electronic device comprises a memory, a processor and a program stored in the memory and running on the processor, wherein when the processor executes the program, the steps in a harmonic state estimation method considering harmonic coupling as described in Embodiment 1 of the present invention are implemented.

[0063] The detailed steps are the same as those of the harmonic state estimation method considering harmonic coupling provided in the first embodiment, and will not be repeated here.

[0064] Embodiment 5 Embodiment 5 of the present invention provides a computer program product.

[0065] A computer program product includes software codes, wherein the program in the software codes executes the steps in a harmonic state estimation method considering harmonic coupling as described in Embodiment 1 of the present invention.

[0066] The detailed steps are the same as those of the harmonic state estimation method considering harmonic coupling provided in the first embodiment, and will not be repeated here.

[0067] The above description is only a preferred embodiment of the present embodiment and is not intended to limit the present embodiment. For those skilled in the art, the present embodiment may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present embodiment shall be included in the protection scope of the present embodiment.

Claims

1. A harmonic state estimation method considering harmonic coupling, characterized in that: include: Obtain historical non-phasor data of power system monitoring nodes; Based on the acquired non-phasor data, a harmonic coupling matrix of the power monitoring node is constructed; According to the non-phasor data and harmonic coupling matrix, the Jacobian matrix of the harmonic network of the power monitoring node is constructed to estimate the power phasor data of each frequency of the power monitoring node; Performing phase angle synchronization on the estimated values ​​of the power phasor data of each frequency of the power detection node; According to the synchronized power phasor data, the voltage and current phasor data of the non-power monitoring nodes are calculated to complete the harmonic state estimation considering harmonic coupling.

2. The harmonic state estimation method considering harmonic coupling as claimed in claim 1, characterized in that: Jacobian matrix of the constructed power monitoring node harmonic network for ;in, , and Both dimensional matrix, m is the number of frequencies considered; and Both dimensional matrix; matrix Each element in the equation is a function of the voltage amplitude measurement on the state variable u The partial derivatives of and Each element in the equation is respectively injected into the state variable by the active and reactive power measurement function u The partial derivatives of and Each element in the equation is respectively injected into the state variable by the active and reactive power measurement function The partial derivatives of .

3. The harmonic state estimation method considering harmonic coupling as claimed in claim 1, characterized in that: In the process of phase angle synchronization, based on the estimated values ​​of the power phasor data of each frequency of the power monitoring node, the non-phasor data of the monitoring node is converted into phasor data to increase the redundancy of the power grid; Take the fundamental wave as the reference voltage and the voltage phase angle of the reference fundamental wave node as 0 o The reference angle is used to synchronize the estimated values ​​of the power phasor data of each frequency at the power detection node, thereby completing the synchronization of the estimated values ​​of the power phasor data of each frequency at the power detection node with the fundamental wave phase angle.

4. The harmonic state estimation method considering harmonic coupling as claimed in claim 1, characterized in that: Based on the synchronized power phasor data obtained, the measured phasors used to characterize the relationship between the measured quantity and the state variables are obtained, and the phasor data objective function is constructed according to the obtained measured phasors. When the obtained objective function is minimized, the objective function is solved to obtain the voltage and current phasor data of the non-power monitoring node.

5. The harmonic state estimation method considering harmonic coupling as claimed in claim 1, characterized in that: The harmonic coupling matrix of the constructed power monitoring node is: ;in, express n The node h Subharmonic injection current phasor value; express n The node h Subharmonic voltage phasor values; Indicates h The subharmonic current and k The coupling relationship of subharmonic voltages, i.e., the elements of the harmonic coupling matrix; H Indicates the highest harmonic order considered in the model.

6. The harmonic state estimation method considering harmonic coupling as claimed in claim 1, characterized in that: The acquired historical non-phasor data of the power system monitoring nodes at least include node voltage amplitude, injected power and branch power.

7. A harmonic state estimation system considering harmonic coupling, characterized in that: include: An acquisition module configured to acquire historical non-phasor data of a power system monitoring node; A construction module configured to construct a harmonic coupling matrix of a power monitoring node based on the acquired non-phasor data; A calculation module, which is configured to construct a Jacobian matrix of the harmonic network of the power monitoring node according to the non-phasor data and the harmonic coupling matrix, and estimate the power phasor data of each frequency of the power monitoring node; A synchronization module, configured to perform phase angle synchronization on the estimated values ​​of the power phasor data of each frequency of the power detection node; The estimation module is configured to calculate the voltage and current phasor data of the non-power monitoring node according to the synchronized power phasor data, and complete the harmonic state estimation considering the harmonic coupling.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of a harmonic state estimation method considering harmonic coupling are implemented as described in any one of claims 1 to 6.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the program, the steps of a harmonic state estimation method considering harmonic coupling are implemented as described in any one of claims 1-6.

10. A computer program product comprising software code, characterized in that The program in the software code executes the steps of a harmonic state estimation method considering harmonic coupling as described in any one of claims 1 to 6.

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