Power distribution network harmonic state estimation method, system, equipment and medium

Abstract

The invention belongs to the technical field of power systems, and discloses a power distribution network harmonic state estimation method, system and device and a medium, and the method comprises the steps: dividing a power distribution network into a core measurement region and local regions based on the topological characteristics and node distribution of a radial power grid, and arranging the local regions with a node connection relation according to numbers; based on establishment and derivation of a measurement equation, harmonic state estimation is carried out on a core measurement area, a weight value of an estimation result is updated in real time, data estimation is carried out on nodes lacking quantity measurement in a local area according to historical harmonic state estimation data of the nodes, and node supplementary data is obtained; and performing harmonic state estimation on each local area by integrating the harmonic state estimation result of the core measurement area, the measurement data in the local area and the node supplementary data. The method is suitable for a common radial power distribution network and a power distribution network of a cloud side end collaborative architecture, and the calculation workload and the calculation time are greatly reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a method, system, device and medium for estimating harmonic state of a distribution network. Background Art

[0002] With the investment and development of new energy research, the large-scale access of new energy to the power distribution network has made the harmonic pollution problem of the system more prominent, which is in sharp contradiction with the high requirements of the intelligent active distribution network for power quality. Harmonic state estimation can use the limited measurement equipment in the system to estimate the harmonic state of the entire power system, so as to find the source of harmonic pollution. Therefore, it is particularly important to use harmonic state estimation to clarify the harmonic characteristics of different positions in the system for power grids with prominent harmonic problems, find the harmonic source through the harmonic characteristics, and then carry out targeted treatment of harmonics.

[0003] At present, scholars at home and abroad have done a lot of research on harmonic state estimation algorithms, which can be roughly divided into static algorithms and dynamic algorithms. Static algorithms refer to methods that use the quantity measurements at a certain moment to estimate the state quantity at that moment. Dynamic algorithms refer to methods that use the quantity measurements at a certain moment to estimate the state quantity at the next moment. In addition, many corresponding harmonic state estimation algorithms have also been proposed to address problems such as asynchronous quantity measurements and unobservable measurement matrices.

[0004] In the above-mentioned research on harmonic state estimation, both dynamic and static algorithms often estimate a power grid as a whole. However, with the acceleration of urbanization, the power grid is constantly expanding and upgrading to meet the growing energy demand. The amount of calculation required to estimate the harmonic state of an overly large power grid as a whole is huge. And in actual distribution networks, for economic reasons, measurement devices are generally not installed on every node in the distribution network. In order to achieve better configuration of the measurement device, it will be installed at nodes that have a greater impact on the operation of the distribution network or at other key nodes. However, if the harmonic state is estimated for the whole, the power grid area where the measurement is relatively scarce will take away the measurement redundancy of the key node concentrated area, resulting in an increase in the error of the harmonic state estimation result in the area where the measurement points are concentrated.

[0005] Therefore, how to provide a method, system, device and medium for estimating the harmonic state of a distribution network is a problem that needs to be solved urgently. Summary of the invention

[0006] The embodiments of the present invention provide a method, system, device and medium for estimating the harmonic state of a distribution network to solve the above-mentioned technical problems existing in the prior art.

[0007] In order to have a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not intended to be a general review, nor is it intended to identify key / important components or to delineate the scope of protection of these embodiments. Its only purpose is to present some concepts in a simple form as a preface to the detailed description that follows.

[0008] According to a first aspect of an embodiment of the present invention, a method for estimating a harmonic state of a power distribution network is provided.

[0009] In one embodiment, the method for estimating the harmonic state of a power distribution network includes:

[0010] Based on the topological characteristics and node distribution of the radial power grid, the distribution network is divided into core measurement areas and local areas, and the local areas with node connection relationships are arranged by number;

[0011] Based on the establishment and derivation of the measurement equation, the harmonic state of the core measurement area is estimated, and the weight value of the estimation result is updated in real time. Based on the historical harmonic state estimation data of the node, the data of the nodes lacking measurement in the local area is estimated to obtain the node supplementary data;

[0012] By integrating the harmonic state estimation results of the core measurement area, the measurement data in the local area and the node supplementary data, the harmonic state estimation is performed on each local area.

[0013] In one embodiment, the distribution network is divided into core measurement areas and local areas based on the topological characteristics and node distribution of the radial power grid, and the local areas with node connection relationships are arranged by number, including:

[0014] Select node areas in the distribution network that are equipped with phasor measurement devices and have a connection relationship with each other, define them as measurement areas, compare the number of nodes in each measurement area, and take the measurement area with the largest number of nodes as the core measurement area;

[0015] The remaining nodes in the distribution network are sorted out after removing the core nodes in the core measurement area. The area composed of the remaining nodes is defined as the remaining area. The core nodes directly connected to the remaining area are then divided into the remaining area, which is defined as the local area.

[0016] Arrange all the local areas after division, select the local areas composed of nodes with direct connection relationship according to the node distribution, and arrange them in order.

[0017] In one embodiment, the local area includes nodes of the remaining area and core nodes of the core measurement area that are directly connected to the remaining area, and there are no nodes with a direct connection relationship between local areas with different numbers.

[0018] In one embodiment, the establishment and derivation of the measurement equation is used to estimate the harmonic state of the core measurement area, and the weight value of the estimation result is updated in real time. According to the historical harmonic state estimation data of the node, the data of the node lacking measurement in the local area is estimated, and the node supplementary data is obtained, including:

[0019] According to the measurement equation, the objective function under the weighted least squares criterion is established. By deriving the objective function, the harmonic state of the core measurement area is estimated to obtain the estimation result.

[0020] The inverse of the variance of the measured data is used as the weight of the measured data, and the weight update algorithm is used to update the weight value of the estimated result after the harmonic state estimation in the core measurement area;

[0021] The Holt exponential smoothing model is established using the Holt exponential smoothing method. By obtaining historical harmonic state estimation data, supplementary estimation is performed on the nodes that lack quantity measurements in the local area to obtain node supplementary data.

[0022] In one embodiment, the mathematical model of the measurement equation is:

[0023] Z=HX+η

[0024] Where Z is the m×1-dimensional known quantity measurement; H is the m×n-dimensional measurement matrix; X is the n×1-dimensional state variable; and η is the m×1-dimensional quantity measurement error phasor.

[0025] In one embodiment, for different types of measurement data, the measurement equations include a node voltage measurement equation, an injection current measurement equation, a zero injection current measurement equation, and a transformation measurement equation.

[0026] In one embodiment, the formula of the objective function is:

[0027] J(X)=(Z-HX) T W(Z-HX)

[0028] Where J() is the objective function; Z is the m×1-dimensional known quantity measurement; H is the m×n-dimensional measurement matrix; X is the n×1-dimensional state variable; W is the weight matrix; and T is the transposed matrix.

[0029] In one embodiment, the step of estimating the harmonic state of each local area by integrating the harmonic state estimation result of the core measurement area, the measurement data in the local area, and the node supplementary data includes:

[0030] Collecting measurement data in a local area through a measurement terminal, wherein the measurement terminal includes a power quality meter, a power management unit and an intelligent fusion terminal, and the measurement data includes harmonic voltage and harmonic current;

[0031] The harmonic state estimation results of the core measurement area after weight update and the node supplement data of the local area after measurement supplement are obtained. Combined with the measurement data and using the measurement equation, the harmonic state estimation is performed in all local areas in turn to obtain the estimation results of each local area.

[0032] According to a second aspect of an embodiment of the present invention, a distribution network harmonic state estimation system is provided.

[0033] In one embodiment, the power distribution network harmonic state estimation system includes:

[0034] The area division module is used to divide the distribution network into core measurement areas and local areas based on the topological characteristics and node distribution of the radial power grid, and arrange the local areas with node connection relationships by number;

[0035] The core measurement estimation module is used to perform harmonic state estimation on the core measurement area based on the establishment and derivation of the measurement equation, update the weight value of the estimation result in real time, and estimate the data of the nodes lacking measurement in the local area based on the historical harmonic state estimation data of the nodes to obtain node supplementary data;

[0036] The local measurement estimation module is used to estimate the harmonic state of each local area by integrating the harmonic state estimation results of the core measurement area, the measurement data in the local area and the node supplementary data.

[0037] In one embodiment, the region division module includes: a core division module, a local division module and a number arrangement module, wherein:

[0038] The core division module is used to select node areas in the distribution network that are equipped with phasor measurement devices and have a connection relationship with each other, define them as measurement areas, and compare the number of nodes in each measurement area, and take the measurement area with the largest number of nodes as the core measurement area;

[0039] The local partitioning module is used to sort out the remaining nodes in the distribution network excluding the core nodes in the core measurement area, define the area composed of the remaining nodes as the remaining area, and then divide the core nodes directly connected to the remaining area into the remaining area, which is defined as the local area;

[0040] The numbering arrangement module is used to sort out all the local areas after division, select the local areas composed of nodes with direct connection relationship according to the node distribution, and arrange them in order.

[0041] In one embodiment, the local area includes nodes of the remaining area and core nodes of the core measurement area that are directly connected to the remaining area, and there are no nodes with a direct connection relationship between local areas with different numbers.

[0042] In one embodiment, the core measurement estimation module includes: a derivation estimation module, a weight update module and a node supplement module, wherein:

[0043] The derivation and estimation module is used to establish an objective function under the weighted least squares criterion according to the measurement equation, and to perform harmonic state estimation on the core measurement area by deriving the objective function to obtain an estimation result;

[0044] The weight updating module is used to use the inverse of the variance of the measurement data as the weight of the measurement data, and use the weight updating algorithm to update the weight value of the estimation result after the harmonic state estimation in the core measurement area;

[0045] The node supplement module is used to establish a Holt exponential smoothing model using the Holt exponential smoothing method, and to obtain node supplement data by acquiring historical harmonic state estimation data to supplement the estimation of nodes that lack quantity measurements in the local area.

[0046] In one embodiment, for different types of measurement data, the measurement equations include a node voltage measurement equation, an injection current measurement equation, a zero injection current measurement equation, and a transformation measurement equation.

[0047] In one embodiment, the local measurement estimation module includes: a measurement acquisition module and an integrated estimation module, wherein:

[0048] The measurement acquisition module is used to collect measurement data in a local area through a measurement terminal, the measurement terminal includes a power quality meter, a power management unit and an intelligent fusion terminal, and the measurement data includes harmonic voltage and harmonic current;

[0049] The integrated estimation module is used to obtain the harmonic state estimation results of the core measurement area after weight update, and the node supplementary data of the local area after measurement supplementation. Combined with the measurement data, the measurement equation is used to perform harmonic state estimation in all local areas in turn to obtain the estimation results of each local area.

[0050] According to a third aspect of an embodiment of the present invention, a computer device is provided.

[0051] In some embodiments, the computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0052] According to a fourth aspect of embodiments of the present invention, a computer-readable storage medium is provided.

[0053] In one embodiment, the computer-readable storage medium stores a computer program, and the computer program implements the steps of the above method when executed by a processor.

[0054] The technical solution provided by the embodiment of the present invention may include the following beneficial effects: the present invention first performs harmonic state estimation on the area where the key nodes where the measuring devices are concentrated are located, thereby avoiding the increase of data estimation error caused by the joint participation of nodes lacking measurement in harmonic state estimation; achieving more accurate harmonic state estimation of key nodes; for non-key nodes lacking measurement in local areas, historical data is used to estimate the node measurement values, and then harmonic state estimation is performed on the local areas respectively, thereby achieving faster harmonic state estimation of non-key nodes. Compared with the traditional harmonic state estimation method, it is more suitable for ordinary radial distribution networks and distribution networks with cloud-edge collaborative architecture, and greatly reduces the computing workload and computing time.

[0055] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0057] Figure 1 is a flow chart of a method for estimating harmonic state of a distribution network according to an exemplary embodiment;

[0058] Figure 2 is a system principle block diagram of a distribution network harmonic state estimation system according to an exemplary embodiment;

[0059] Figure 3 is a structural schematic diagram of a computer device according to an exemplary embodiment;

[0060] Figure 4 is a schematic diagram of detailed steps of a harmonic state estimation method according to an exemplary embodiment;

[0061] Figure 5 is a zoning principle diagram of a radial distribution network according to an exemplary embodiment;

[0062] Figure 6 is a transformer and a transmission line equivalent circuit shown according to an exemplary embodiment;

[0063] Figure 7is a schematic diagram of a simple network topology structure according to an exemplary embodiment;

[0064] Figure 8 is one of the schematic diagrams of harmonic state estimation results based on the least squares algorithm according to an exemplary embodiment;

[0065] Fig. 9 is a second schematic diagram of a harmonic state estimation result based on a least squares algorithm according to an exemplary embodiment;

[0066] Fig.10 is one of the schematic diagrams of the result of harmonic state estimation based on partition coordination according to an exemplary embodiment;

[0067] Fig.11 This is the second schematic diagram of the result of harmonic state estimation based on partition collaboration according to an exemplary embodiment. DETAILED DESCRIPTION

[0068] The following description and accompanying drawings fully illustrate the specific embodiments of this article so that those skilled in the art can practice them. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. The scope of the embodiments of this article includes the entire scope of the claims, as well as all available equivalents of the claims. Herein, the terms "first", "second", etc. are only used to distinguish one element from another, without requiring or implying any actual relationship or order between these elements. In fact, the first element can also be called the second element, and vice versa. Moreover, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that the structure, device or equipment including a series of elements includes not only those elements, but also other elements that are not explicitly listed, or also include elements inherent to such structure, device or equipment. In the absence of more restrictions, the elements defined by the sentence "including one..." do not exclude the existence of other identical elements in the structure, device or equipment including the elements. Each embodiment is described in a progressive manner herein, and each embodiment focuses on the differences from other embodiments, and the same and similar parts between the embodiments can be referred to each other.

[0069] The terms "longitudinal", "lateral", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc. in this document indicate the orientation or position relationship based on the orientation or position relationship shown in the drawings, and are only for the convenience of describing this document and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In the description of this document, unless otherwise specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a mechanical connection or an electrical connection, it can also be the internal communication of two elements, it can be a direct connection, or it can be an indirect connection through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0070] As used herein, the term "plurality" means two or more than two, unless otherwise specified.

[0071] In this document, the character " / " indicates that the preceding and following objects are in an "or" relationship. For example, A / B means: A or B.

[0072] In this article, the term "and / or" is a description of the association relationship between objects, indicating that three relationships may exist. For example, A and / or B means: A or B, or, A and B.

[0073] It should be understood that, although the various steps in the flow chart are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the figure may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0074] Each module in the device or system of the present application can be implemented in whole or in part by software, hardware, or a combination thereof. The above modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to the above modules.

[0075] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0076] Figure 1An embodiment of a method for estimating harmonic state of a distribution network according to the present invention is shown.

[0077] In this optional embodiment, the method for estimating the harmonic state of the distribution network includes:

[0078] Step S101: based on the topological characteristics and node distribution of the radial power grid, the distribution network is divided into a core measurement area and local areas, and the local areas with node connection relationships are arranged by number;

[0079] Step S103: Based on the establishment and derivation of the measurement equation, the harmonic state of the core measurement area is estimated, the weight value of the estimation result is updated in real time, and the data of the nodes lacking measurement in the local area are estimated according to the historical harmonic state estimation data of the nodes to obtain the node supplementary data;

[0080] Step S105 , performing harmonic state estimation on each local area by integrating the harmonic state estimation result of the core measurement area, the measurement data in the local area and the node supplementary data.

[0081] In this optional embodiment, when the distribution network is divided into core measurement areas and local areas based on the topological characteristics and node distribution of the radial power grid, and the local areas with node connection relationships are arranged by number, node areas in the distribution network that are equipped with phase measurement devices and have connection relationships with each other can be selected and defined as measurement areas, and the number of nodes in each measurement area can be compared, and the measurement area with the largest number of nodes can be used as the core measurement area; the remaining nodes in the distribution network excluding the core nodes in the core measurement area are sorted out, and the area composed of the remaining nodes is defined as the remaining area, and the core nodes directly connected to the remaining area are divided into the remaining area, which is defined as the local area; all the local areas after division are sorted out, and according to the node distribution, the local areas composed of nodes with direct connection relationships are screened, and arranged in order by number.

[0082] In this optional embodiment, the local area includes the nodes of the remaining area and the core nodes of the core measurement area that are directly connected to the remaining area, and there are no nodes with a direct connection relationship between local areas with different numbers.

[0083] In this optional embodiment, based on the establishment and derivation of the measurement equation, the harmonic state estimation is performed on the core measurement area, the weight value of the estimation result is updated in real time, and the data of the nodes that lack measurement in the local area are estimated based on the historical harmonic state estimation data of the nodes. When the node supplementary data is obtained, an objective function under the weighted least squares criterion can be established according to the measurement equation, and the harmonic state estimation is performed on the core measurement area by deriving the objective function to obtain the estimation result; the inverse of the variance of the measurement data is used as the weight of the measurement data, and the weight update algorithm is used to update the weight value of the estimation result after the harmonic state estimation of the core measurement area; the Holt exponential smoothing method is used to establish the Holt exponential smoothing model, and the historical harmonic state estimation data is obtained to supplement the estimation of the nodes that lack measurement in the local area to obtain the node supplementary data.

[0084] In this optional embodiment, the mathematical model of the measurement equation is:

[0085] Z=HX+η

[0086] Where Z is the m×1-dimensional known quantity measurement; H is the m×n-dimensional measurement matrix; X is the n×1-dimensional state variable; and η is the m×1-dimensional quantity measurement error phasor.

[0087] In this optional embodiment, for different types of measurement data, the measurement equations include a node voltage measurement equation, an injection current measurement equation, a zero injection current measurement equation, and a transformation measurement equation.

[0088] In this optional embodiment, the formula of the objective function is:

[0089] J(X)=(Z-HX) T W(Z-HX)

[0090] Where J() is the objective function; Z is the m×1-dimensional known quantity measurement; H is the m×n-dimensional measurement matrix; X is the n×1-dimensional state variable; W is the weight matrix; and T is the transposed matrix.

[0091] In this optional embodiment, when the harmonic state estimation is performed on each local area by integrating the harmonic state estimation result of the core measurement area, the measurement data in the local area and the node supplementary data, the measurement data in the local area can be collected through the measurement terminal, the measurement terminal includes a power quality meter, a power management unit and an intelligent fusion terminal, and the measurement data includes harmonic voltage and harmonic current; the harmonic state estimation result of the core measurement area after weight update and the node supplementary data of the local area after measurement supplement are obtained, and the measurement data are combined with the measurement equation to perform harmonic state estimation on all local areas in turn to obtain the estimation result of each local area.

[0092] Figure 2 An embodiment of a power distribution network harmonic state estimation system of the present invention is shown.

[0093] In this optional embodiment, the power distribution network harmonic state estimation system includes:

[0094] The area division module 201 is used to divide the distribution network into core measurement areas and local areas based on the topological characteristics and node distribution of the radial power grid, and arrange the local areas with node connection relationships according to numbers;

[0095] The core measurement estimation module 203 is used to perform harmonic state estimation on the core measurement area based on the establishment and derivation of the measurement equation, update the weight value of the estimation result in real time, and perform data estimation on the nodes lacking measurement in the local area according to the historical harmonic state estimation data of the nodes to obtain node supplementary data;

[0096] The local measurement estimation module 205 is used to estimate the harmonic state of each local area by integrating the harmonic state estimation result of the core measurement area, the measurement data in the local area and the node supplementary data.

[0097] In this optional embodiment, the area division module 201 includes: a core division module, a local division module and a number arrangement module, wherein the core division module is used to select node areas in the distribution network that are equipped with phase measurement devices and have a connection relationship with each other, define them as measurement areas, and compare the number of nodes in each measurement area, and take the measurement area with the largest number of nodes as the core measurement area; the local division module is used to sort out the remaining nodes in the distribution network except the core nodes in the core measurement area, define the area composed of the remaining nodes as the remaining area, and then divide the core nodes directly connected to the remaining area into the remaining area, which is defined as the local area; the number arrangement module is used to sort out all the local areas after division, and according to the node distribution, screen the local areas composed of nodes with a direct connection relationship, and arrange them in order.

[0098] In this optional embodiment, the local area includes the nodes of the remaining area and the core nodes of the core measurement area that are directly connected to the remaining area, and there are no nodes with a direct connection relationship between local areas with different numbers.

[0099] In this optional embodiment, the core measurement estimation module 203 includes: a derivation estimation module, a weight update module and a node supplement module, wherein the derivation estimation module is used to establish an objective function under the weighted least squares criterion according to the measurement equation, and to perform harmonic state estimation on the core measurement area by deriving the objective function to obtain an estimation result; the weight update module is used to use the inverse of the variance of the measurement data as the weight of the measurement data, and to use the weight update algorithm to update the weight value of the estimation result after the harmonic state estimation of the core measurement area; the node supplement module is used to establish a Holt exponential smoothing model using the Holt exponential smoothing method, and to perform supplementary estimation on the nodes that lack measurement in the local area by acquiring historical harmonic state estimation data to obtain node supplementary data.

[0100] In this optional embodiment, for different types of measurement data, the measurement equations include a node voltage measurement equation, an injection current measurement equation, a zero injection current measurement equation, and a transformation measurement equation.

[0101] In this optional embodiment, the local measurement estimation module 205 includes: a measurement acquisition module and an integrated estimation module, wherein the measurement acquisition module is used to collect measurement data in the local area through a measurement terminal, the measurement terminal includes a power quality meter, a power management unit and an intelligent fusion terminal, and the measurement data includes harmonic voltage and harmonic current; the integrated estimation module is used to obtain the harmonic state estimation result of the core measurement area after the weight is updated, and the node supplementary data of the local area after the measurement is supplemented, and in combination with the measurement data, the measurement equation is used to perform harmonic state estimation on all local areas in turn to obtain the estimation result of each local area.

[0102] A method for estimating the harmonic state of a distribution network constructed by the present invention is described in detail below in conjunction with specific embodiments and drawings.

[0103] The idea of ​​the overall solution of the present invention is:

[0104] The distribution network is divided into regions: the node areas in the distribution network that are equipped with phase measurement devices and have a connection relationship with each other are called measurement areas. The number of nodes in several measurement areas is compared, and the measurement area with the most nodes is selected as the core measurement area. The area composed of the remaining nodes in the power grid except the nodes in the core measurement area is called the remaining area. The core measurement area nodes directly connected to the remaining area are divided into the remaining area, which is called the local area, that is, the local area includes the nodes of the remaining area and the nodes of the core measurement area that are directly connected to the remaining area. Due to the topological characteristics of the radial power grid, the nodes in the local area are not all directly connected to each other. The areas composed of nodes with a direct connection relationship are arranged by number and are called local area 1, local area 2, and so on.

[0105] Based on the phase data measured in the core measurement area, the harmonic state of the core measurement area is estimated based on weighted least squares. After the harmonic state is estimated, the weight update algorithm is used to update the weights of the estimated node data.

[0106] For nodes lacking quantity measurements in local areas, the Holt exponential smoothing method is used to supplement the node data using historical harmonic state estimation result data.

[0107] The harmonic state estimation results of the coordinated core measurement area, the phasor measurement data of the local area and the supplementary data are used to estimate the harmonic state of each local area.

[0108] like Figure 1 As shown, this embodiment provides a method for estimating the harmonic state of a distribution network, including: Figure 4 Steps shown:

[0109] Step 1: Divide the distribution network into regions: The node areas in the distribution network that are equipped with phase measurement devices and have a connection relationship with each other are called measurement areas. Compare the number of nodes in several measurement areas and select the measurement area with the most nodes as the core measurement area. The area composed of the remaining nodes in the power grid except the nodes in the core measurement area is called the remaining area. The core measurement area nodes directly connected to the remaining area are divided into the remaining area, which is called the local area, that is, the local area includes the nodes of the remaining area and the nodes of the core measurement area that are directly connected to the remaining area. Due to the topological characteristics of the radial power grid, the nodes in the local area are not all connected to each other. The areas composed of nodes with a connection relationship are arranged by number and are called local area 1, local area 2, and so on.

[0110] As one or more embodiments, in step 1, the node areas in the distribution network that are equipped with phase measurement devices and have a direct connection relationship with each other are called measurement areas. Figure 5Only nodes 1, 2, 3, and 4 are measurement nodes and they are directly connected to each other. Therefore, nodes 1, 2, 3, and 4 are divided into the core measurement area. The area composed of the remaining nodes in the power grid except the nodes in the core measurement area is called the remaining area. That is, the remaining area is the area composed of the remaining power grid nodes except nodes 1, 2, 3, and 4. The core measurement area nodes directly connected to the remaining area are divided into the remaining area, which is called the local area. Nodes 2, 3, and 4 are core measurement area nodes and are directly connected to the remaining area, so nodes 2, 3, and 4 are divided into the remaining area, which is called the local area. Due to the topological characteristics of the radial power grid, not all nodes in the local area are connected to each other. The areas composed of nodes with connection relationships are arranged by number and are called local area 1, local area 2, and so on. From Figure 5 It can be seen that the local area can be divided into local area 1, local area 2, and local area 3. There is no direct connection between the three areas. Local area 1 contains nodes 2, 5, and 6; local area 2 contains nodes 3, 7, and 8; local area 3 contains nodes 4, 9, 10, 11, 12, 13, and 14.

[0111] Step 2: Estimating the harmonic state of the core measurement area;

[0112] As one or more embodiments, in step 2, performing harmonic state estimation on the core measurement area specifically includes:

[0113] When the topology and system parameters of the power network are determined, the measurement equation in harmonic state estimation can be described by the following mathematical model:

[0114] Z=HX+η (1)

[0115] Where: Z is the m×1-dimensional known quantity measurement; H is the m×n-dimensional measurement matrix; X is the n×1-dimensional state variable, and η is the m×1-dimensional quantity measurement error phasor, also called the error matrix.

[0116] For different measurement values, the measurement equation also has corresponding changes. The following introduces the specific solution process of several different measurement equations.

[0117] 1. Node voltage measurement equation

[0118] For the hth harmonic voltage phasor at node i, the measurement equation is as follows:

[0119] U m,i (h) = IU T,i (h)+ε i (h) (2)

[0120] Among them, U m,i(h) is the hth measured value of harmonic voltage at node i; U T,i (h) is the h-th state quantity of the harmonic voltage at the i-th node; I is the unit matrix; ε i (h) is the error phasor of the hth measurement value of the harmonic voltage at the i node.

[0121] 2. Injection Current Measurement Equation

[0122] The injection current measurement equation refers to the measurement equation established by measuring the current phasor at the node with load or generator in the power system, which is described as follows:

[0123]

[0124] Among them, I m,i (h) is the hth harmonic node injection current measurement value of node i; U T,j (h) is the h-th harmonic voltage phasor state quantity of node j; Y ij (h) is the element in the harmonic admittance matrix; n is the total number of system nodes.

[0125] 3. Zero injection current measurement equation

[0126] The zero current injection equation is slightly different from the ordinary node injection current measurement equation. You only need to replace the right side of the above equation with I m,i (h) can be set to zero, so the zero injection current measurement equation is a special form of the node injection measurement equation, which is described as follows:

[0127]

[0128] Where n is the number of nodes in the power system; ε i (h) is the error phasor of the hth measurement value of the harmonic current at the i node.

[0129] 4. Transformation Measurement Equation

[0130] The equivalent circuit of transformer and transmission line is as follows Figure 6 If there are current phasor and voltage phasor measurements on the branch node i side, the measurement equation is:

[0131] I m,i (h)-Y Lij (h)U m,i (h) = Y ij (h)U T,j (h)+ε i (h) (5)

[0132] Among them, I m,i (h) represents the measured value of the hth harmonic branch current on the i side of the branch, U m,i(h) represents the measured value of the harmonic voltage of order h on the i side of branch circuit, U T,j (h) represents the hth harmonic voltage phasor state quantity on the i and j sides of the branch, Y ij (h) is the custom branch admittance matrix Y L The elements in the custom branch admittance matrix are:

[0133] Y Lij (h) = Y i0 (h)+Y ij (h). ε i (h) is the node current measurement error phasor.

[0134] Define variables:

[0135] I TM,i (h)=I m,i (h)-Y Lij (h)U m,i (h) (6)

[0136] In the formula, I TM,i The subscript TM in (h) indicates the transformed measurement, I TM,i (h) is I m,i (h) and U m,i (h) Transformation combination.

[0137] Therefore, formula (6) can be written as:

[0138] I TM,i (h) = Y ij (h)U T,j (h)+ε i (h) (7)

[0139] This is the transformation measurement equation.

[0140] According to the measurement equation Z = HX + η, the objective function under the weighted least squares criterion is established:

[0141] J(X)=(Z-HX) T W(Z-HX) (8)

[0142] Where W is the weight matrix. In the actual power system, the accuracy of each measurement value is different, and the importance of the measurement values ​​obtained at different nodes is not the same. Data with smaller measurement variance indicates higher measurement accuracy, so the weight should be higher; data with larger measurement variance indicates lower measurement accuracy, so the weight should be lower. In this way, data with lower accuracy will not affect the overall estimation effect, and high-precision data can also play a greater role in system state estimation. In the current power system, the inverse of the variance of the measurement is generally used as the weight value of the measurement data.

[0143] Derivative of the objective function:

[0144]

[0145] You can get an estimated value

[0146]

[0147] Since the variance of the estimated result after the harmonic state estimation in the core measurement area is generally smaller than the variance before estimation, and the weights of the weighted least squares method are formulated based on the variance, the weights of the estimated result need to be modified accordingly. When the measured quantity is harmonic current and the state quantity is harmonic voltage, the following weight update algorithm can be used to update the weight:

[0148] like Figure 7 As shown, take the topology of a simple network as an example. x, y, z represent the estimated values ​​of harmonic currents, satisfying equation (11). Assume that the corresponding harmonic current measurement average values ​​are x0, y0, z0, and the measurement variances are D(x0), D(y0), and D(z0), respectively. The measurement results are optimized by weighted least squares method, that is, the state quantities x, y, and z are calculated to minimize equation (12):

[0149] z=x+y (11)

[0150] f=a(x-x0) 2 +b(y-y0) 2 +c(z-z0) 2 (12)

[0151] Where a, b, and c are the weights of points X, Y, and Z, respectively. In the current power system, the inverse of the variance of the measured value is generally used as the initial value of the measured data weight.

[0152] Solving equations (11) and (12) together yields:

[0153]

[0154] Formula (13) is the estimation result. According to the definition of weights, the variances of x0, y0, and z0 are known to be a, b, and c respectively; and the properties of variances are also known: if X and Y are independent, then:

[0155] D(aX±bY)=a 2 D(X)+b 2 D(Y) (14)

[0156] Therefore, we can get:

[0157]

[0158] After correction, the weights of the three points X, Y, and Z become

[0159] The weight update algorithm is used to update the results of harmonic state estimation in the core measurement area so that the harmonic state estimation of the local area can be performed later.

[0160] Step 3: Based on the historical harmonic state estimation data of the nodes, estimate the nodes in the remaining area that lack quantity measurements.

[0161] As one or more embodiments, in step 3, estimating nodes in the remaining area that lack quantity measurements specifically includes:

[0162] Holt exponential smoothing is used to estimate nodes that lack quantity measurements.

[0163] The calculation formula of Holt exponential smoothing model is:

[0164] S t =αy t-1 +(1-α)(S t-1 +b t-1 ) 0≤α≤1 (16)

[0165] b t =γ(S t -S t-1 )+(1-γ)b t-1 0≤γ≤1 (17)

[0166] Among them, S t is the smoothed value at time t, b t is the trend value at time t, y t-1 is the actual observed value at time t-1, and α and γ are smoothing parameters. There are many schemes for S in the Holt exponential smoothing model. t and b t Set the initial value. Usually set S t =y1, b1=y2-y1.

[0167] The prediction equation of the Holt exponential smoothing model is as follows:

[0168] F t+1 =S t +b t (18)

[0169] In the formula, F t+1 is the predicted value of the observed data at time t+1.

[0170] Step 4: Use the core measurement area state estimation results, local area measurement data, and Holt exponential smoothing method supplementary data to perform harmonic state estimation for each local area.

[0171] As one or more embodiments, in step 4, the harmonic state of the local area is estimated using the state estimation result of the core measurement area, the limited measurement data of the local area, and the Holt exponential smoothing method supplementary data, specifically including: in step 2, the harmonic state of the core measurement area is estimated based on the weighted least squares method. Since all nodes in the core measurement area are equipped with measurement devices, the redundancy of this estimation is very high, and the estimation result is relatively accurate, which can be used as the final estimation result of each node in the core measurement area.

[0172] In order to utilize the high-precision estimation results of the harmonic state estimation in the core measurement area and improve the redundancy of the subsequent harmonic state estimation based on weighted least squares in the local area, it is necessary to update the weights of the results after the harmonic state estimation. Harmonic state estimation is performed on each local area using the results of the weight update in step 2, the limited measurement data in the local area, and the node data supplemented by the Holt exponential smoothing method in step 3.

[0173] exist Figure 3 In the example of local area 3, the data of node 4 is the result of weight update after the core measurement area state estimation, nodes 11 and 12 are limited measurement data in local area 3, and the node data of the remaining nodes are supplementary data generated by the Holt exponential smoothing method based on the historical harmonic state estimation results. Based on these data, the harmonic state of local area 3 can be estimated.

[0174] Due to the complexity of power grid operation, the estimation of node measurement values ​​using historical data of nodes may result in a small number of estimated values ​​being significantly different from the true values. Figure 5 The Holt exponential smoothing method estimate at node 8 adds a larger error to simulate the phenomenon that the estimated value may differ greatly from the true value. Figure 5 The radial power grid shown in the figure is used to estimate the harmonic state of the partition. At the same time, the 14 nodes of the entire power grid are uniformly estimated by least squares. The estimation results of the two estimation methods are compared, as shown in Figure 8 , Fig. 9 , Fig.10 and Fig.11 shown.

[0175] As can be seen from the figure, when there is a large error between the estimated value of node 8 and the true value, if the least squares estimation algorithm is used, the data of node 8 will cause a large error in the overall estimation result, making the overall estimation accuracy worse. When the partitioned weighted least squares harmonic state estimation algorithm is used, only the local area 2 where node 8 is located has a large estimation error, and it has almost no effect on the estimation results of the local areas where other non-critical nodes exist and the core measurement areas where critical nodes exist. This shows the advancement of partitioned collaborative state estimation compared to traditional least squares estimation.

[0176] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 3 As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store static information and dynamic information data. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the steps in the above method embodiment are implemented.

[0177] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0178] In addition, the present invention also provides a computer device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiment when executing the computer program.

[0179] In addition, the present invention further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method embodiment are implemented.

[0180] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0181] The present invention is not limited to the structures which have been described above and shown in the drawings, and various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A method for estimating harmonic state of a distribution network, characterized in that: include: Based on the topological characteristics and node distribution of the radial power grid, the distribution network is divided into core measurement areas and local areas, and the local areas with node connection relationships are arranged by number; Based on the establishment and derivation of the measurement equation, the harmonic state of the core measurement area is estimated, and the weight value of the estimation result is updated in real time. Based on the historical harmonic state estimation data of the node, the data of the nodes lacking measurement in the local area is estimated to obtain the node supplementary data; By integrating the harmonic state estimation results of the core measurement area, the measurement data in the local area and the node supplementary data, the harmonic state estimation is performed on each local area.

2. The method for estimating harmonic state of a distribution network according to claim 1, characterized in that: Based on the topological characteristics and node distribution of the radial power grid, the distribution network is divided into core measurement areas and local areas, and the local areas with node connection relationships are arranged by number, including: Select node areas in the distribution network that are equipped with phasor measurement devices and have a connection relationship with each other, define them as measurement areas, compare the number of nodes in each measurement area, and take the measurement area with the largest number of nodes as the core measurement area; The remaining nodes in the distribution network are sorted out after removing the core nodes in the core measurement area. The area composed of the remaining nodes is defined as the remaining area. The core nodes directly connected to the remaining area are then divided into the remaining area, which is defined as the local area. Arrange all the local areas after division, select the local areas composed of nodes with direct connection relationship according to the node distribution, and arrange them in order.

3. The method for estimating harmonic state of a distribution network according to claim 2, characterized in that: The local area includes nodes of the remaining area and core nodes of the core measurement area that are directly connected to the remaining area, and there are no nodes with a direct connection relationship between local areas with different numbers.

4. The method for estimating harmonic state of a distribution network according to claim 1, characterized in that: Based on the establishment and derivation of the measurement equation, the harmonic state of the core measurement area is estimated, the weight value of the estimation result is updated in real time, and the data of the nodes lacking measurement in the local area are estimated according to the historical harmonic state estimation data of the nodes, and the node supplementary data includes: According to the measurement equation, the objective function under the weighted least squares criterion is established. By deriving the objective function, the harmonic state of the core measurement area is estimated to obtain the estimation result. The inverse of the variance of the measured data is used as the weight of the measured data, and the weight update algorithm is used to update the weight value of the estimated result after the harmonic state estimation in the core measurement area; The Holt exponential smoothing model is established using the Holt exponential smoothing method. By obtaining historical harmonic state estimation data, supplementary estimation is performed on the nodes that lack quantity measurements in the local area to obtain node supplementary data.

5. The method for estimating the harmonic state of a distribution network according to claim 4, characterized in that: The mathematical model of the measurement equation is: Z=HX+η Where Z is the m×1 dimensional known quantity measurement; H is the m×n dimensional measurement matrix; X is the n×1 dimensional state variable; η is the m×1-dimensional measurement error phasor.

6. The method for estimating the harmonic state of a distribution network according to claim 5, characterized in that: For different types of measurement data, the measurement equations include node voltage measurement equations, injection current measurement equations, zero injection current measurement equations, and transformation measurement equations.

7. The method for estimating harmonic state of a distribution network according to claim 4, characterized in that: The objective function formula is: J(X)=(Z-HX) T W(Z-HX) Where J() is the objective function; Z is the m×1-dimensional known quantity measurement; H is the m×n-dimensional measurement matrix; X is the n×1-dimensional state variable; W is the weight matrix; and T is the transposed matrix.

8. The method for estimating harmonic state of a distribution network according to claim 2, characterized in that: The harmonic state estimation for each local area by integrating the harmonic state estimation result of the core measurement area, the measurement data in the local area and the node supplementary data includes: Collecting measurement data in a local area through a measurement terminal, wherein the measurement terminal includes a power quality meter, a power management unit and an intelligent fusion terminal, and the measurement data includes harmonic voltage and harmonic current; The harmonic state estimation results of the core measurement area after weight update and the node supplement data of the local area after measurement supplement are obtained. Combined with the measurement data and using the measurement equation, the harmonic state estimation is performed in all local areas in turn to obtain the estimation results of each local area.

9. A distribution network harmonic state estimation system, characterized in that: include: The area division module is used to divide the distribution network into core measurement areas and local areas based on the topological characteristics and node distribution of the radial power grid, and arrange the local areas with node connection relationships by number; The core measurement estimation module is used to perform harmonic state estimation on the core measurement area based on the establishment and derivation of the measurement equation, update the weight value of the estimation result in real time, and estimate the data of the nodes lacking measurement in the local area based on the historical harmonic state estimation data of the nodes to obtain node supplementary data; The local measurement estimation module is used to estimate the harmonic state of each local area by integrating the harmonic state estimation results of the core measurement area, the measurement data in the local area and the node supplementary data.

10. The power distribution network harmonic state estimation system according to claim 9, characterized in that: The area division module includes: a core division module, a local division module and a numbering arrangement module, wherein: The core division module is used to select node areas in the distribution network that are equipped with phasor measurement devices and have a connection relationship with each other, define them as measurement areas, and compare the number of nodes in each measurement area, and take the measurement area with the largest number of nodes as the core measurement area; The local partitioning module is used to sort out the remaining nodes in the distribution network excluding the core nodes in the core measurement area, define the area composed of the remaining nodes as the remaining area, and then divide the core nodes directly connected to the remaining area into the remaining area, which is defined as the local area; The numbering arrangement module is used to sort out all the local areas after division, select the local areas composed of nodes with direct connection relationship according to the node distribution, and arrange them in order.

11. The power distribution network harmonic state estimation system according to claim 10, characterized in that: The local area includes nodes of the remaining area and core nodes of the core measurement area that are directly connected to the remaining area, and there are no nodes with a direct connection relationship between local areas with different numbers.

12. The power distribution network harmonic state estimation system according to claim 9, characterized in that: The core measurement estimation module includes: a derivation estimation module, a weight update module and a node supplement module, wherein: The derivation and estimation module is used to establish an objective function under the weighted least squares criterion according to the measurement equation, and to perform harmonic state estimation on the core measurement area by deriving the objective function to obtain an estimation result; The weight updating module is used to use the inverse of the variance of the measurement data as the weight of the measurement data, and use the weight updating algorithm to update the weight value of the estimation result after the harmonic state estimation in the core measurement area; The node supplement module is used to establish a Holt exponential smoothing model using the Holt exponential smoothing method, and to obtain node supplement data by acquiring historical harmonic state estimation data to supplement the estimation of nodes that lack quantity measurements in the local area.

13. The power distribution network harmonic state estimation system according to claim 12, characterized in that: For different types of measurement data, the measurement equations include node voltage measurement equations, injection current measurement equations, zero injection current measurement equations, and transformation measurement equations.

14. The power distribution network harmonic state estimation system according to claim 9, characterized in that: The local measurement estimation module includes: a measurement acquisition module and an integrated estimation module, wherein: The measurement acquisition module is used to collect measurement data in a local area through a measurement terminal, the measurement terminal includes a power quality meter, a power management unit and an intelligent fusion terminal, and the measurement data includes harmonic voltage and harmonic current; The integrated estimation module is used to obtain the harmonic state estimation results of the core measurement area after weight update, and the node supplementary data of the local area after measurement supplementation. Combined with the measurement data, the measurement equation is used to perform harmonic state estimation in all local areas in turn to obtain the estimation results of each local area.

15. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

16. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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