Configuration method and device of power quality management equipment for intelligent distribution network

Abstract

The present application relates to a method, device, computer equipment, storage medium and computer program product for configuring power quality management equipment of a smart distribution network. The method comprises: obtaining monitoring data of each power quality indicator of each node in a historical time period in the smart distribution network as a monitoring data set; determining a comprehensive evaluation index of each power quality indicator in the smart distribution network according to the monitoring data set; judging whether the comprehensive evaluation index of each power quality indicator meets the national standard, and if the comprehensive evaluation index of any power quality indicator does not meet the national standard, determining the objective function of the corresponding power quality indicator, and calculating the sensitivity coefficient of each node in the smart distribution network according to the objective function; determining the installation location of the management equipment according to the sensitivity coefficients of all nodes. The use of this method can improve management efficiency and reduce management costs.

Description

Technical Field

[0001] The present application relates to the technical field of power quality optimization and management of distribution networks, and in particular to a method and device for configuring power quality management equipment of intelligent distribution networks. Background Art

[0002] With the gradual improvement of the advanced measurement system of the smart distribution network, the massive monitoring data stored in the monitoring system has not been fully utilized in the analysis, evaluation and management of the power quality of the smart distribution network. On the other hand, with the gradual improvement of the industrial electrification degree of the smart distribution network and the gradual increase in the scale of new energy power generation access, the number of various nonlinear loads in the smart distribution network has continued to increase, resulting in increasingly serious power quality problems in the distribution network; in addition, a large number of sensitive loads containing new processes and technologies are connected to the distribution network, and users' demand for optimizing and improving the power quality of the distribution network has gradually emerged. In the relevant technology, the most common configuration scheme for power quality management equipment is point-to-point nearby compensation. When there are a large number of decentralized nonlinear loads in the power grid, this management scheme has problems such as low compensation efficiency and high management cost. Therefore, there is an urgent need for a configuration method for power quality management equipment for smart distribution networks. Summary of the invention

[0003] Based on this, it is necessary to provide a method, device, computer equipment, computer-readable storage medium and computer program product for configuring power quality management equipment in an intelligent distribution network to address the above-mentioned technical problems.

[0004] In a first aspect, the present application provides a method for configuring power quality management equipment for a smart distribution network. The method comprises:

[0005] Acquire monitoring data of each power quality indicator of each node in the smart distribution network in a historical period as a monitoring data set;

[0006] Determine the comprehensive evaluation index of each power quality indicator in the smart distribution network based on the monitoring data set;

[0007] Determine whether the comprehensive evaluation index of each power quality indicator meets the national standard. If the comprehensive evaluation index of any power quality indicator does not meet the national standard, determine the objective function of the corresponding power quality indicator, and calculate the sensitivity coefficient of each node in the smart distribution network based on the objective function;

[0008] Determine the installation location of the governance equipment based on the sensitivity coefficients of all nodes.

[0009] In one embodiment, a comprehensive evaluation index of each power quality indicator in the smart distribution network is determined based on the monitoring data set, including:

[0010] Perform regularization transformation processing on each data in the monitoring data set to obtain a transformed data set;

[0011] Determine characteristic data of each power quality indicator of each node according to the transformed data set;

[0012] According to the characteristic data of each power quality indicator of all nodes, the comprehensive evaluation index of each power quality indicator in the smart distribution network is determined.

[0013] In one embodiment, the transformed data includes output data; accordingly, characteristic data of each power quality indicator of each node is determined according to the transformed data set, including:

[0014] Determine the efficiency evaluation index of each power quality indicator of each node according to the transformed data set;

[0015] Determine the evaluation coefficient of each power quality indicator of each node according to the efficiency evaluation index of each power quality indicator of each node and the transformation data;

[0016] Characteristic data of each power quality indicator of each node are determined according to the output data and the evaluation coefficient of each power quality indicator of each node.

[0017] In one embodiment, a comprehensive evaluation index of each power quality indicator in the smart distribution network is determined based on characteristic data of each power quality indicator of all nodes, including:

[0018] The characteristic data of each power quality indicator of each node are processed based on the half-rise Cauchy distribution function to obtain the membership coefficient of each power quality indicator of each node;

[0019] According to the membership coefficient and characteristic data of each power quality indicator of each node, the comprehensive evaluation index of each power quality indicator in the smart distribution network is determined.

[0020] In one embodiment, determining an objective function corresponding to a power quality indicator and calculating a sensitivity coefficient of each node in the smart distribution network according to the objective function includes:

[0021] According to the corresponding power quality index, determining characteristic data of the corresponding power quality index of each node;

[0022] According to the characteristic data of the corresponding power quality index of each node and the objective function, the sensitivity coefficient of each node in the smart distribution network is determined.

[0023] In one embodiment, determining the installation location of the governance device according to the sensitivity coefficients of all nodes includes:

[0024] Determine the absolute value of the sensitivity coefficient of each node, and use the node corresponding to the sensitivity coefficient with the largest absolute value among all the nodes' sensitivity coefficients as the installation location of the governance device.

[0025] In a second aspect, the present application also provides a device for configuring power quality management equipment in an intelligent distribution network.

[0026] The device comprises:

[0027] An acquisition module, used to acquire monitoring data of each power quality indicator of each node in the smart distribution network within a historical time period as a monitoring data set;

[0028] A first determination module is used to determine a comprehensive evaluation index of each power quality indicator in the smart distribution network according to a monitoring data set;

[0029] A judgment module is used to judge whether the comprehensive evaluation index of each power quality indicator meets the national standard. If the comprehensive evaluation index of any power quality indicator does not meet the national standard, the objective function of the corresponding power quality indicator is determined, and the sensitivity coefficient of each node in the smart distribution network is calculated according to the objective function;

[0030] The second determination module is used to determine the installation location of the management equipment according to the sensitivity coefficients of all nodes.

[0031] In a third aspect, the present application further provides a computer device. The computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the following steps when executing the computer program:

[0032] Acquire monitoring data of each power quality indicator of each node in the smart distribution network in a historical period as a monitoring data set;

[0033] Determine the comprehensive evaluation index of each power quality indicator in the smart distribution network based on the monitoring data set;

[0034] Determine whether the comprehensive evaluation index of each power quality indicator meets the national standard. If the comprehensive evaluation index of any power quality indicator does not meet the national standard, determine the objective function of the corresponding power quality indicator, and calculate the sensitivity coefficient of each node in the smart distribution network based on the objective function;

[0035] Determine the installation location of the governance equipment based on the sensitivity coefficients of all nodes.

[0036] In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0037] Acquire monitoring data of each power quality indicator of each node in the smart distribution network in a historical period as a monitoring data set;

[0038] Determine the comprehensive evaluation index of each power quality indicator in the smart distribution network based on the monitoring data set;

[0039] Determine whether the comprehensive evaluation index of each power quality indicator meets the national standard. If the comprehensive evaluation index of any power quality indicator does not meet the national standard, determine the objective function of the corresponding power quality indicator, and calculate the sensitivity coefficient of each node in the smart distribution network based on the objective function;

[0040] Determine the installation location of the governance equipment based on the sensitivity coefficients of all nodes.

[0041] In a fifth aspect, the present application further provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0042] Acquire monitoring data of each power quality indicator of each node in the smart distribution network in a historical period as a monitoring data set;

[0043] Determine the comprehensive evaluation index of each power quality indicator in the smart distribution network based on the monitoring data set;

[0044] Determine whether the comprehensive evaluation index of each power quality indicator meets the national standard. If the comprehensive evaluation index of any power quality indicator does not meet the national standard, determine the objective function of the corresponding power quality indicator, and calculate the sensitivity coefficient of each node in the smart distribution network based on the objective function;

[0045] Determine the installation location of the governance equipment based on the sensitivity coefficients of all nodes.

[0046] The above-mentioned intelligent distribution network power quality management equipment configuration method, device, computer equipment, storage medium and computer program product obtain the monitoring data of each power quality indicator of each node in the intelligent distribution network in the historical time period as a monitoring data set; determine the comprehensive evaluation index of each power quality indicator in the intelligent distribution network according to the monitoring data set; judge whether the comprehensive evaluation index of each power quality indicator meets the national standard. If the comprehensive evaluation index of any power quality indicator does not meet the national standard, determine the objective function of the corresponding power quality indicator, and calculate the sensitivity coefficient of each node in the intelligent distribution network according to the objective function; determine the installation location of the management equipment according to the sensitivity coefficients of all nodes. The number of installed management equipment can be reduced, thereby improving management efficiency and reducing management costs, and it has strong engineering application value and can produce good economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 A schematic diagram of a flow chart of a method for configuring power quality management equipment for a smart distribution network in one embodiment;

[0048] Figure 2 A schematic diagram of input data and output data in one embodiment;

[0049] Figure 3 A schematic diagram of an IEEE-18 node intelligent distribution network simulation system containing distributed nonlinear loads in one embodiment;

[0050] Figure 4 A schematic diagram of a configuration scheme for power quality management equipment of an IEEE-18 node intelligent distribution network simulation system in one embodiment;

[0051] Figure 5 A schematic diagram of comprehensive evaluation indexes of some power quality indicators of a smart distribution network simulation system when no treatment equipment is installed in one embodiment;

[0052] Figure 6 A schematic diagram of comprehensive evaluation indexes of some power quality indicators of a smart distribution network simulation system after installing a treatment device in one embodiment;

[0053] Figure 7 It is a structural block diagram of a configuration device for power quality management equipment of a smart distribution network in one embodiment;

[0054] Figure 8 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0056] It is understood that the terms "first", "second", etc. used in this application can be used in this article to describe various professional terms, but unless otherwise specified, these professional terms are not limited by these terms. These terms are only used to distinguish one professional term from another professional term. For example, without departing from the scope of this application, the third preset threshold and the fourth preset threshold can be the same or different.

[0057] It should be noted that the number of "multiple" etc. mentioned in the embodiments of the present application refers to the number of "at least two", for example, "multiple" refers to "at least two".

[0058] In one embodiment, Figure 1As shown, a method for configuring power quality management equipment for a smart distribution network is provided. This embodiment uses the method applied to a terminal as an example. It can be understood that the method can also be applied to a server, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0059] 101. Obtain monitoring data of each power quality indicator of each node in the smart distribution network in a historical time period as a monitoring data set;

[0060] 102. Determine the comprehensive evaluation index of each power quality indicator in the smart distribution network based on the monitoring data set;

[0061] 103. Determine whether the comprehensive evaluation index of each power quality indicator meets the national standard. If the comprehensive evaluation index of any power quality indicator does not meet the national standard, determine the objective function of the corresponding power quality indicator, and calculate the sensitivity coefficient of each node in the smart distribution network according to the objective function;

[0062] 104. Determine the installation location of the management equipment based on the sensitivity coefficients of all nodes.

[0063] In the above step 101, the historical time period refers to a certain time period or multiple time periods in the past in the smart distribution network. Each data in the monitoring data set represents the data at each moment. For example, the monitoring data of two power quality indicators are obtained in the historical time period t1 to t4, and the monitoring data of power quality indicator A are: the monitoring data obtained at time t1 is M1, the monitoring data obtained at time t2 is M2, the monitoring data obtained at time t3 is M3, and the monitoring data obtained at time t4 is M4; the monitoring data of power quality indicator B is: the monitoring data obtained at time t1 is N1, the monitoring data obtained at time t2 is N2, the monitoring data obtained at time t3 is N3, and the monitoring data obtained at time t4 is N4. Among them, t1, t2, t3, and t4 are the four moments in the historical time period t1 to t4 respectively.

[0064] The power quality index refers to the data in the smart distribution network that can reflect the distribution network conditions. In this embodiment, the power quality index includes voltage deviation, three-phase voltage imbalance, h-order harmonic content, total harmonic distortion rate and frequency deviation, among which the h-order harmonic content rate and the total harmonic distortion rate can reflect the harmonic voltage conditions of the smart distribution network, and h is an integer not less than 1.

[0065] In the above step 102, the comprehensive evaluation index is a comprehensive score of various electric energy indicators obtained by compressing the monitoring data set through Analytic Hierarchy Process (AHP).

[0066] In the above step 103, the objective function refers to the objective function of the optimal configuration of the power quality management equipment in the smart distribution network. Among them, the h-order harmonic content rate and the total harmonic distortion rate both reflect the harmonic voltage situation of the distribution network. Therefore, the h-order harmonic content rate and the total harmonic distortion rate correspond to the same objective function of the optimal configuration of the power quality management equipment, which is expressed as:

[0067]

[0068] In formula (1), U k,new is the harmonic voltage at node k after installing the control equipment, is the hth harmonic voltage at node k after installing the treatment equipment, To install the control equipment before the h-order harmonic voltage of the k-node, is the harmonic impedance between nodes k and q, is the output current of the control device installed at the node q, where the control device can be a parallel type power quality control device, and H is the highest order term of the harmonic voltage.

[0069] In addition, in order to simplify the solution process of the above formula (1), the single harmonic voltage formula in the above formula (1) is: After transformation, the corresponding convex optimization equation is obtained, as shown in formula (2):

[0070]

[0071] In formula (2), γ h ,as well as Represents the phase as well as The phase angle of .

[0072] Specifically, it is possible to compare whether the comprehensive evaluation index of each power quality indicator is less than the national standard. If it is less than, it is necessary to determine the objective function of the optimal configuration of the corresponding power quality indicator management equipment, and determine the sensitivity coefficient of each node in the smart distribution network according to the objective function, so as to determine the installation location of the management equipment in the smart distribution network. For example, if the comprehensive evaluation index of the voltage deviation is less than the national standard, the objective function of the optimal configuration of the voltage deviation management equipment is determined; if the comprehensive evaluation index of any power quality indicator such as the h-order harmonic content rate or the total harmonic distortion rate is less than the national standard, the objective function of the optimal configuration of the harmonic management equipment is determined; if the comprehensive evaluation index of the frequency deviation is less than the national standard, the objective function of the optimal configuration of the frequency deviation management equipment is determined; if the three-phase voltage imbalance is less than the national standard, the objective function of the optimal configuration of the three-phase voltage imbalance management equipment is determined.

[0073] In addition, after determining the installation location of the treatment equipment, the compensation capacity of the treatment equipment at the installation location will be determined. Taking the treatment of harmonic voltage as an example, the Lagrange multiplier method is used to solve formula (2), and the optimal output current of the treatment equipment can be obtained as shown in formula (3). The optimal output current formula is the compensation capacity of the treatment equipment at the installation location.

[0074]

[0075] In formula (3), and They are The real and imaginary parts of and They are The real and imaginary parts of and They are The real and imaginary parts of .

[0076] The method provided in the embodiment of the present invention processes a large amount of monitoring data in the advanced measurement system in the smart distribution network, introduces the comprehensive evaluation index of the power quality index in the smart distribution network as a weight coefficient into the objective function of the optimal configuration of the management equipment, and determines the optimal installation position and compensation capacity of the management equipment. Compared with the traditional point-to-point nearby compensation scheme, this embodiment can reduce the number of installed management equipment, thereby improving the management efficiency of power quality and reducing the management cost.

[0077] In combination with the content of the above embodiments, in one embodiment, determining the comprehensive evaluation index of each power quality indicator in the smart distribution network according to the monitoring data set includes:

[0078] 201. Perform regularization transformation processing on each data in the monitoring data set to obtain a transformed data set;

[0079] 202. Determine characteristic data of each power quality indicator of each node according to the transformed data set;

[0080] 203. Determine the comprehensive evaluation index of each power quality indicator in the smart distribution network based on the characteristic data of each power quality indicator of all nodes.

[0081] In the above step 201, the regularization transformation process is shown in formula (4):

[0082] data new =1 / data old (data old ≠0) (4);

[0083] In formula (4), data oldTo monitor data, data new To transform data.

[0084] It is worth mentioning that if the acquired monitoring data is 0, the monitoring data will be changed to the minimum accuracy of the corresponding monitoring device, where the monitoring device refers to the device that obtains the monitoring data of each power quality indicator of each node in the smart distribution network within the historical time period.

[0085] In the above step 202, the feature data is obtained by processing the data in the transformed data set through the CCR (A. Charnes & W. W. Cooper & E. Rhodes) evaluation model, wherein the data in the transformed data set serves as decision making units (DMUs) of the CCR model.

[0086] Specifically, since each data in the monitoring data set represents data at each moment, each data in the transformation data set is also represented as data at each moment. However, the characteristic data obtained after the transformation data set is processed by the CCR evaluation model no longer has the concept of time, and each characteristic data represents the characteristic data of each power quality indicator of each node. Then, each characteristic data of all nodes is processed to obtain the comprehensive evaluation index of each power quality indicator in the smart distribution network. The comprehensive evaluation index of each power quality indicator represents the comprehensive evaluation index of the same power quality indicator corresponding to all nodes in the smart distribution network.

[0087] The method provided by the embodiment of the present invention obtains characteristic data of each power quality indicator of each node by compressing the data in the monitoring data set in the time dimension, and then compresses the indicator dimension of each power quality indicator of all nodes, so as to obtain the comprehensive evaluation index of each power indicator corresponding to all nodes in the smart distribution network.

[0088] In combination with the content of the above embodiment, in one embodiment, the transformed data includes output data; accordingly, according to the transformed data set, characteristic data of each power quality indicator of each node is determined, including:

[0089] 301. Determine the efficiency evaluation index of each power quality indicator of each node according to the transformed data set;

[0090] 302. Determine an evaluation coefficient of each power quality indicator of each node according to the efficiency evaluation index of each power quality indicator of each node and the transformation data;

[0091] 303. Determine characteristic data of each power quality indicator of each node according to the output data and the evaluation coefficient of each power quality indicator of each node.

[0092] In this embodiment, the monitoring data of six power quality indicators, including voltage deviation, three-phase voltage imbalance, 5th harmonic content, 7th harmonic content, total harmonic distortion rate and frequency deviation at node k are taken as an example. It is worth mentioning that the voltage deviation, three-phase voltage imbalance, 5th harmonic content, 7th harmonic content, total harmonic distortion rate and frequency deviation correspond to the first power quality indicator, the second power quality indicator, the third power quality indicator, the fifth power quality indicator, the fourth power quality indicator and the sixth power quality indicator, respectively.

[0093] In this embodiment, the transformed data is divided into input data and output data, such as Figure 2 As shown. Among them, x k,1 、x k,2 、x k,3 、x k,4 、x k,5 、x k,6 The data of voltage deviation, three-phase voltage unbalance, 5th harmonic content, 7th harmonic content, total harmonic distortion and frequency deviation at time T1 are input data; k,1,1 ,y k,2,1 ,y k,3,1 ,y k,4,1 ,y k,5,1 ,y k,6,1 They are the data of voltage deviation, three-phase voltage unbalance, 5th harmonic content, 7th harmonic content, total harmonic distortion and frequency deviation at time T2, respectively. k,1,n ,y k,2,n ,y k,3,n ,y k,4,n ,y k,5,1 ,y k,6,n They are voltage deviation, three-phase voltage unbalance, 5th harmonic content, 7th harmonic content, total harmonic distortion and frequency deviation at T (n+1) The data at each moment is output data.

[0094] x k,1 、x k,2 、x k,3 、x k,4 、x k,5 、x k,6 As the input data of the decision-making units DMU1 to DMU6 of the CCR evaluation model, y k,1,1 ,y k,2,1 ,y k,3,1 ,y k,4,1 ,y k,5,1 ,y k,6,1 ,.....,y k,1,n ,y k,2,n ,y k,3,n,y k,4,n ,y k,5,n ,y k,6,n As the output data of the decision-making unit of the CCR evaluation model.

[0095] The efficiency evaluation index of each power quality indicator of each node is based on the decision-making unit DMU j The efficiency evaluation index (j=1,2,3,4,5,6) indicates that DMU j The efficiency evaluation index h k,j The expression of is shown in formula (5):

[0096]

[0097] Among them, I k,j =v1x k,j , v1 is x k,j The input coefficient of the j-th decision unit of node k, O k,j =u k1 y k,j,1 +u k2 y k,j,2 +...+u kn y k,j,n ,u kn for y k,j,n The nth output coefficient of the jth decision unit of node k.

[0098] Among them, v1 and u kn is the unknown coefficient. In formula (5), by adjusting v1 and u kn , so that h k,j The value is close to 1, with h k,j Close to 1 hour k,j v1 and u corresponding to the maximum value kn As the final coefficient of formula (5), v1 and u at this time kn is the optimal coefficient in the input and output coefficient group. Under the constraints of all decision-making unit efficiency evaluation indexes, the linear programming equation is obtained through Charnes-Cooper transformation, as shown in formula (6):

[0099]

[0100] In formula (6), γ = 1 / x k,j , μ k,j =u / x k,j =[μ k,1,j ,μ k,2,j ,μ k,3,j ,...,μ k,n,j ], μ k,j The transposed matrix, y k,j =[yk,j,1 ,y k,j,2 ,...,y k,j,n ], where u=[u k1 ,u k2 ,...,u kn ]. When h k,j When it is the maximum value, replace γ and μ in the linear programming equation (6) k,j The solutions are γ* and As the jth decision making unit DMU of node k j The optimal weight vector of Perform normalization processing, as shown in formula (7), to obtain the evaluation coefficient of the j-th decision unit of node k;

[0101]

[0102] In formula (7), is the evaluation index of the jth decision unit of node k. Through the evaluation coefficient of each decision unit of node k, the evaluation coefficient of each power quality indicator of node k can be determined.

[0103] Finally, the evaluation coefficient of each power quality index of each node is combined with the corresponding transformation data to calculate the characteristic data of each power quality index of each node. The calculation formula is shown in formula (8):

[0104]

[0105] In formula (8), y k,j,new is the characteristic data of a power quality indicator of node k, y k,j =[y k,j,1 ,y k,j,2 ,...,y k,j,n ], for The transposed matrix of .

[0106] The method provided by the embodiment of the present invention can determine the efficiency evaluation index of each power quality indicator of each node by transforming the data set, thereby determining the evaluation coefficient of each power quality indicator of each node, and further determining the characteristic data of each power quality indicator of each node.

[0107] In combination with the content of the above embodiments, in one embodiment, a comprehensive evaluation index of each power quality indicator in the smart distribution network is determined according to the characteristic data of each power quality indicator of all nodes, including:

[0108] 401. Processing characteristic data of each power quality indicator of each node based on a half-lift Cauchy distribution function to obtain a membership coefficient of each power quality indicator of each node;

[0109] 402. Determine a comprehensive evaluation index of each power quality indicator in the smart distribution network according to the membership coefficient and characteristic data of each power quality indicator of each node.

[0110] Specifically, the characteristic data of each power quality indicator of each node is processed based on the half-rise Cauchy distribution function to determine the membership fitting function, as shown in formula (9):

[0111]

[0112] In formula (9), λ k,j is the membership coefficient of the characteristic data of the jth power quality index of node k, a j With b j are the mapping coefficients in the half-lift Cauchy distribution function, y k,j,new is the characteristic data of the j-th power quality indicator of node k.

[0113] In determining the mapping coefficient a j With b j Before, first, we need to determine the standard value y of the characteristic data k,j,new,s.t , standard value of power quality index y k,j,new,s.t The corresponding membership coefficient (the standard membership coefficient, i.e. 0.6) is used. Secondly, the maximum membership coefficient (i.e. 1) and the minimum scoring accuracy λ need to be determined. min,j The difference is generally taken as 0.95, according to (1-λ min,j , 0.95) and (y k,j,new,s.t , 0.6) can be solved to get a j With b j , thereby determining the membership fitting function and then obtaining the membership coefficient of each power quality indicator of each node.

[0114] Before determining the comprehensive evaluation index of each power quality indicator in the smart distribution network according to the membership coefficient and characteristic data of each power quality indicator of each node, it is also necessary to determine the objective weight of each power quality indicator of each node according to the membership coefficient of each power quality indicator of each node, and calculate the objective weight of each power quality indicator of each node as shown in formula (10):

[0115]

[0116] In formula (10), Si k,jis the objective weight of the j-th power quality indicator of node k, and m is the total number of nodes included in the smart distribution network.

[0117] In combination with the above contents, according to the membership coefficient and characteristic data of each power quality indicator of each node, the comprehensive evaluation index of each power quality indicator in the smart distribution network is determined, including:

[0118] According to the objective weight and characteristic data of each power quality indicator of each node, the comprehensive evaluation index of each power quality indicator in the smart distribution network is determined, as shown in formula (11):

[0119]

[0120] In formula (11), For Si j The transposed matrix, S j It is the comprehensive evaluation index of the j-th power quality indicator.

[0121] The method provided in the embodiment of the present invention can obtain the membership coefficient of each power quality indicator of each node through the characteristic data of each power quality indicator of each node, thereby determining the comprehensive evaluation index of each power quality indicator in the smart distribution network.

[0122] In combination with the content of the above embodiments, in one embodiment, determining the objective function corresponding to the power quality index, and calculating the sensitivity coefficient of each node in the smart distribution network according to the objective function, includes:

[0123] 501. Determine characteristic data of the corresponding power quality index of each node according to the corresponding power quality index;

[0124] 502. Determine the sensitivity coefficient of each node in the smart distribution network according to the characteristic data of the corresponding power quality index of each node and the objective function.

[0125] Specifically, after the objective function is determined, the sensitivity coefficient of each node in the smart distribution network can be determined according to the characteristic data of the corresponding power quality index of each node and the slope of the objective function.

[0126] Taking the objective function of total harmonic distortion as an example, the objective function of total harmonic distortion is the same as that of the hth harmonic content rate, as shown in formula (1). Formula (2) is obtained after the transformation of formula (1). At this time, the calculation formula of the sensitivity coefficient of each node is shown in formula (12):

[0127]

[0128] In formula (11), Sc qis the sensitivity coefficient of node q, H is the highest order term of the most harmonic voltage, m is the total number of nodes in the smart distribution network, γ h ,as well as Represents the phase as well as The phase angle, To install the control equipment before the h-order harmonic voltage of the k-node, is the harmonic impedance between nodes k and q, A j =[y 1,j,new ,y 2,j,new ,...,y m,j,new ].

[0129] The method provided in the embodiment of the present invention can determine the sensitivity coefficient of each node in the smart distribution network through the characteristic data of the corresponding power quality index of each node and the objective function.

[0130] In combination with the content of the above embodiments, in one embodiment, determining the installation location of the management device according to the sensitivity coefficients of all nodes includes:

[0131] Determine the absolute value of the sensitivity coefficient of each node, and use the node corresponding to the sensitivity coefficient with the largest absolute value among all the nodes' sensitivity coefficients as the installation location of the governance device.

[0132] Specifically, before the installation location of the governance equipment is determined, all nodes in the smart distribution network are candidate nodes for the installation location of the governance equipment. After the sensitivity coefficients of all nodes are determined, the absolute values ​​of the sensitivity coefficients of all nodes are obtained to obtain the absolute sensitivity coefficients of all nodes. Finally, the node corresponding to the largest absolute sensitivity coefficient is selected from all the absolute sensitivity coefficients as the installation location of the governance equipment.

[0133] The method provided in the embodiment of the present invention can determine the installation location of the management equipment by determining the absolute value of the sensitivity coefficient of each node. Compared with the traditional point-to-point installation of management equipment, this method can improve the management efficiency of the management equipment and reduce the number of installed management equipment.

[0134] In one embodiment, an IEEE-18 node intelligent distribution network simulation system containing distributed nonlinear loads is constructed, such as Figure 3 As shown. Nodes 3, 5, and 7 in the system are equipped with three-phase nonlinear loads. Combined with the content of the above embodiment, the system is calculated to determine Figure 3 The configuration scheme of the power quality management equipment of the IEEE-18 node intelligent distribution network simulation system is as follows: Figure 4 shown.

[0135] Figure 5It is the comprehensive evaluation index of some power quality indicators of the intelligent distribution network simulation system when the control equipment is not installed. Figure 6 It is a comprehensive evaluation index of some power quality indicators of the smart distribution network simulation system after the installation of management equipment.

[0136] The method provided in the embodiment of the present invention is verified by an IEEE-18 node smart distribution network simulation system containing distributed nonlinear loads. After the management equipment is installed, the comprehensive evaluation index of the power quality indicators of the smart distribution network simulation system can be optimized, thereby proving the feasibility of the method and having certain engineering application value.

[0137] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments 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 explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these 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 part of the steps or stages in other steps.

[0138] Based on the same inventive concept, the embodiment of the present application also provides a smart distribution network power quality management equipment configuration device for implementing the smart distribution network power quality management equipment configuration method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more smart distribution network power quality management equipment configuration device embodiments provided below can refer to the limitations of the smart distribution network power quality management equipment configuration method above, and will not be repeated here.

[0139] In one embodiment, Figure 7 As shown, a device for configuring power quality management equipment for a smart distribution network is provided, comprising: an acquisition module 701, a first determination module 702, a judgment module 703, and a second determination module 704, wherein:

[0140] An acquisition module 701 is used to acquire monitoring data of each power quality indicator of each node in the smart distribution network in a historical time period as a monitoring data set;

[0141] A first determination module 702 is used to determine a comprehensive evaluation index of each power quality indicator in the smart distribution network according to the monitoring data set;

[0142] The judgment module 703 is used to judge whether the comprehensive evaluation index of each power quality indicator meets the national standard. If the comprehensive evaluation index of any power quality indicator does not meet the national standard, the objective function of the corresponding power quality indicator is determined, and the sensitivity coefficient of each node in the smart distribution network is calculated according to the objective function;

[0143] The second determination module 704 is used to determine the installation location of the management device according to the sensitivity coefficients of all nodes.

[0144] In one embodiment, the first determining module 702 includes:

[0145] The transformation submodule is used to perform regularization transformation processing on each data in the monitoring data set to obtain a transformed data set;

[0146] A first determination submodule is used to determine characteristic data of each power quality indicator of each node according to the transformed data set;

[0147] The second determination submodule is used to determine the comprehensive evaluation index of each power quality indicator in the smart distribution network according to the characteristic data of each power quality indicator of all nodes.

[0148] In one embodiment, the first determining submodule includes:

[0149] A first determination unit, configured to determine an efficiency evaluation index of each power quality indicator of each node according to the transformed data set;

[0150] a second determining unit, for determining an evaluation coefficient of each power quality indicator of each node according to the efficiency evaluation index of each power quality indicator of each node and the transformation data;

[0151] The third determining unit is used to determine characteristic data of each power quality indicator of each node according to the output data and the evaluation coefficient of each power quality indicator of each node.

[0152] In one embodiment, the second determining submodule includes:

[0153] A processing unit, used for processing characteristic data of each power quality indicator of each node based on a half-lift Cauchy distribution function to obtain a membership coefficient of each power quality indicator of each node;

[0154] The fourth determination unit is used to determine the comprehensive evaluation index of each power quality indicator in the smart distribution network according to the membership coefficient and characteristic data of each power quality indicator of each node.

[0155] In one embodiment, the determination module 703 includes:

[0156] A third determination submodule is used to determine characteristic data of the corresponding power quality indicator of each node according to the corresponding power quality indicator;

[0157] The fourth determination submodule is used to determine the sensitivity coefficient of each node in the smart distribution network according to the characteristic data of the corresponding power quality index of each node and the objective function.

[0158] In one embodiment, the second determining module 704 includes:

[0159] The fifth determination submodule is used to determine the absolute value of the sensitivity coefficient of each node, and the node corresponding to the sensitivity coefficient with the largest absolute value among the sensitivity coefficients of all nodes is used as the installation location of the management device.

[0160] Each module in the above-mentioned intelligent distribution network power quality management equipment configuration device can be implemented in whole or in part by software, hardware and their combination. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0161] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 8 As shown. The computer device includes a processor, a memory and a communication interface connected via 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 and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a method for configuring a smart distribution network power quality management device is implemented.

[0162] Those skilled in the art will understand that Figure 8 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application 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.

[0163] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0164] Acquire monitoring data of each power quality indicator of each node in the smart distribution network in a historical period as a monitoring data set;

[0165] Determine the comprehensive evaluation index of each power quality indicator in the smart distribution network based on the monitoring data set;

[0166] Determine whether the comprehensive evaluation index of each power quality indicator meets the national standard. If the comprehensive evaluation index of any power quality indicator does not meet the national standard, determine the objective function of the corresponding power quality indicator, and calculate the sensitivity coefficient of each node in the smart distribution network based on the objective function;

[0167] Determine the installation location of the governance equipment based on the sensitivity coefficients of all nodes.

[0168] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0169] Perform regularization transformation processing on each data in the monitoring data set to obtain a transformed data set;

[0170] Determine characteristic data of each power quality indicator of each node according to the transformed data set;

[0171] According to the characteristic data of each power quality indicator of all nodes, the comprehensive evaluation index of each power quality indicator in the smart distribution network is determined.

[0172] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0173] Determine the efficiency evaluation index of each power quality indicator of each node according to the transformed data set;

[0174] Determine the evaluation coefficient of each power quality indicator of each node according to the efficiency evaluation index of each power quality indicator of each node and the transformation data;

[0175] Characteristic data of each power quality indicator of each node are determined according to the output data and the evaluation coefficient of each power quality indicator of each node.

[0176] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0177] The characteristic data of each power quality indicator of each node are processed based on the half-rise Cauchy distribution function to obtain the membership coefficient of each power quality indicator of each node;

[0178] According to the membership coefficient and characteristic data of each power quality indicator of each node, the comprehensive evaluation index of each power quality indicator in the smart distribution network is determined.

[0179] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0180] According to the corresponding power quality index, determining characteristic data of the corresponding power quality index of each node;

[0181] According to the characteristic data of the corresponding power quality index of each node and the objective function, the sensitivity coefficient of each node in the smart distribution network is determined.

[0182] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0183] Determine the absolute value of the sensitivity coefficient of each node, and use the node corresponding to the sensitivity coefficient with the largest absolute value among all the nodes' sensitivity coefficients as the installation location of the governance device.

[0184] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0185] Acquire monitoring data of each power quality indicator of each node in the smart distribution network in a historical period as a monitoring data set;

[0186] Determine the comprehensive evaluation index of each power quality indicator in the smart distribution network based on the monitoring data set;

[0187] Determine whether the comprehensive evaluation index of each power quality indicator meets the national standard. If the comprehensive evaluation index of any power quality indicator does not meet the national standard, determine the objective function of the corresponding power quality indicator, and calculate the sensitivity coefficient of each node in the smart distribution network based on the objective function;

[0188] Determine the installation location of the governance equipment based on the sensitivity coefficients of all nodes.

[0189] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0190] Perform regularization transformation processing on each data in the monitoring data set to obtain a transformed data set;

[0191] Determine characteristic data of each power quality indicator of each node according to the transformed data set;

[0192] According to the characteristic data of each power quality indicator of all nodes, the comprehensive evaluation index of each power quality indicator in the smart distribution network is determined.

[0193] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0194] Determine the efficiency evaluation index of each power quality indicator of each node according to the transformed data set;

[0195] Determine the evaluation coefficient of each power quality indicator of each node according to the efficiency evaluation index of each power quality indicator of each node and the transformation data;

[0196] Characteristic data of each power quality indicator of each node are determined according to the output data and the evaluation coefficient of each power quality indicator of each node.

[0197] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0198] The characteristic data of each power quality indicator of each node are processed based on the half-rise Cauchy distribution function to obtain the membership coefficient of each power quality indicator of each node;

[0199] According to the membership coefficient and characteristic data of each power quality indicator of each node, the comprehensive evaluation index of each power quality indicator in the smart distribution network is determined.

[0200] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0201] According to the corresponding power quality index, determining characteristic data of the corresponding power quality index of each node;

[0202] According to the characteristic data of the corresponding power quality index of each node and the objective function, the sensitivity coefficient of each node in the smart distribution network is determined.

[0203] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0204] Determine the absolute value of the sensitivity coefficient of each node, and use the node corresponding to the sensitivity coefficient with the largest absolute value among all the nodes' sensitivity coefficients as the installation location of the governance device.

[0205] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:

[0206] Acquire monitoring data of each power quality indicator of each node in the smart distribution network in a historical period as a monitoring data set;

[0207] Determine the comprehensive evaluation index of each power quality indicator in the smart distribution network based on the monitoring data set;

[0208] Determine whether the comprehensive evaluation index of each power quality indicator meets the national standard. If the comprehensive evaluation index of any power quality indicator does not meet the national standard, determine the objective function of the corresponding power quality indicator, and calculate the sensitivity coefficient of each node in the smart distribution network based on the objective function;

[0209] Determine the installation location of the governance equipment based on the sensitivity coefficients of all nodes.

[0210] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0211] Perform regularization transformation processing on each data in the monitoring data set to obtain a transformed data set;

[0212] Determine characteristic data of each power quality indicator of each node according to the transformed data set;

[0213] According to the characteristic data of each power quality indicator of all nodes, the comprehensive evaluation index of each power quality indicator in the smart distribution network is determined.

[0214] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0215] Determine the efficiency evaluation index of each power quality indicator of each node according to the transformed data set;

[0216] Determine the evaluation coefficient of each power quality indicator of each node according to the efficiency evaluation index of each power quality indicator of each node and the transformation data;

[0217] Characteristic data of each power quality indicator of each node are determined according to the output data and the evaluation coefficient of each power quality indicator of each node.

[0218] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0219] The characteristic data of each power quality indicator of each node are processed based on the half-rise Cauchy distribution function to obtain the membership coefficient of each power quality indicator of each node;

[0220] According to the membership coefficient and characteristic data of each power quality indicator of each node, the comprehensive evaluation index of each power quality indicator in the smart distribution network is determined.

[0221] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0222] According to the corresponding power quality index, determining characteristic data of the corresponding power quality index of each node;

[0223] According to the characteristic data of the corresponding power quality index of each node and the objective function, the sensitivity coefficient of each node in the smart distribution network is determined.

[0224] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0225] Determine the absolute value of the sensitivity coefficient of each node, and use the node corresponding to the sensitivity coefficient with the largest absolute value among all the nodes' sensitivity coefficients as the installation location of the governance device.

[0226] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0227] 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 the memory, database or other medium used in the embodiments provided in the present application 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, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. 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). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0228] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0229] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for configuring power quality management equipment for a smart distribution network, characterized in that: The method comprises: Acquire monitoring data of each power quality indicator of each node in the smart distribution network in a historical time period as a monitoring data set, wherein the power quality indicators include voltage deviation, three-phase voltage imbalance, h-order harmonic content rate, total harmonic distortion rate and frequency deviation; Determining a comprehensive evaluation index of each power quality indicator in the smart distribution network according to the monitoring data set; Determine whether the comprehensive evaluation index of each power quality indicator meets the national standard. If any comprehensive evaluation index of the power quality indicator does not meet the national standard, determine the objective function of the corresponding power quality indicator, and calculate the sensitivity coefficient of each node in the smart distribution network according to the objective function; Determine the installation location of the governance equipment based on the sensitivity coefficients of all nodes; Determining the installation location of the management device according to the sensitivity coefficients of all nodes includes: Determine the absolute value of the sensitivity coefficient of each node, and use the node corresponding to the sensitivity coefficient with the largest absolute value among all the nodes' sensitivity coefficients as the installation location of the governance device.

2. The method according to claim 1, characterized in that Determining the comprehensive evaluation index of each power quality indicator in the smart distribution network according to the monitoring data set includes: Performing regularization transformation processing on each data in the monitoring data set to obtain a transformed data set; Determining characteristic data of each power quality indicator of each node according to the transformed data set; According to the characteristic data of each power quality indicator of all nodes, a comprehensive evaluation index of each power quality indicator in the smart distribution network is determined.

3. The method according to claim 2, characterized in that The transformed data includes output data; accordingly, determining characteristic data of each power quality indicator of each node according to the transformed data set includes: Determining the efficiency evaluation index of each power quality indicator of each node according to the transformed data set; Determining an evaluation coefficient of each power quality indicator of each node according to the efficiency evaluation index of each power quality indicator of each node and the transformation data; Characteristic data of each power quality indicator of each node are determined according to the output data and the evaluation coefficient of each power quality indicator of each node.

4. The method according to claim 3, characterized in that Determining the comprehensive evaluation index of each power quality indicator in the smart distribution network according to the characteristic data of each power quality indicator of all nodes includes: The characteristic data of each power quality indicator of each node are processed based on the half-rise Cauchy distribution function to obtain the membership coefficient of each power quality indicator of each node; According to the membership coefficient and characteristic data of each power quality indicator of each node, a comprehensive evaluation index of each power quality indicator in the smart distribution network is determined.

5. The method according to claim 4, characterized in that Before determining the comprehensive evaluation index of each power quality indicator in the smart distribution network according to the membership coefficient and characteristic data of each power quality indicator of each node, the method includes: According to the membership coefficient of each power quality indicator of each node, the objective weight of each power quality indicator of each node is determined; According to the objective weight and characteristic data of each power quality indicator of each node, a comprehensive evaluation index of each power quality indicator in the smart distribution network is determined.

6. The method according to claim 3, characterized in that The determining of the objective function corresponding to the power quality index and calculating the sensitivity coefficient of each node in the smart distribution network according to the objective function include: Determining characteristic data of the corresponding power quality indicator of each node according to the corresponding power quality indicator; The sensitivity coefficient of each node in the smart distribution network is determined according to the characteristic data of the power quality index corresponding to each node and the objective function.

7. A configuration device for power quality management equipment of a smart distribution network, characterized in that: The device comprises: An acquisition module, used to acquire monitoring data of each power quality indicator of each node in the smart distribution network in a historical time period as a monitoring data set, wherein the power quality indicators include voltage deviation, three-phase voltage unbalance, h-order harmonic content rate, total harmonic distortion rate and frequency deviation; A first determination module, configured to determine a comprehensive evaluation index of each power quality indicator in the smart distribution network according to the monitoring data set; A judgment module, used to judge whether the comprehensive evaluation index of each power quality indicator meets the national standard. If the comprehensive evaluation index of any power quality indicator does not meet the national standard, determine the objective function of the corresponding power quality indicator, and calculate the sensitivity coefficient of each node in the smart distribution network according to the objective function; The second determination module is used to determine the installation location of the governance device according to the sensitivity coefficients of all nodes; The second determination module is further used to determine the absolute value of the sensitivity coefficient of each node, and the node corresponding to the sensitivity coefficient with the largest absolute value among the sensitivity coefficients of all nodes is used as the installation location of the management device.

8. 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 6 are implemented.

9. 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 6 are implemented.

10. A computer program product, comprising a computer program, 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 6 are implemented.

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