Optimal sensing equipment configuration information acquisition method and device for power distribution network

Abstract

The invention provides a power distribution network-oriented optimal sensing equipment configuration information acquisition method and device, and relates to the technical field of power distribution networks. The method comprises the following steps: acquiring electric energy quality data of a power distribution network monitoring point, and acquiring a power distribution network operation state estimation result according to the electric energy quality data; constructing a power distribution network optimal configuration model according to the power distribution network operation state estimation result, the voltage out-of-limit probability, the DPMU installation cost and the node voltage parameters; and solving the optimal configuration model of the power distribution network by adopting an optimized fruit fly algorithm to obtain optimal sensing equipment configuration information. The device executes the method. According to the optimal sensing equipment configuration information acquisition method and device for the power distribution network provided by the embodiment of the invention, the multi-target optimal configuration of the sensing equipment of the power distribution network is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of power distribution networks, and in particular to a method and device for acquiring optimal sensing device configuration information for power distribution networks. Background Art

[0002] With the continuous development of smart grid technology, the complexity and scale of distribution networks have also expanded. As the core link of power grid operation, the accuracy and reliability of state estimation have been increasingly valued. The emergence of smart distribution networks is an upgrade and transformation of traditional distribution networks, aiming to improve the safety, reliability and self-healing ability of power grids through advanced measurement technology, communication technology and the access of distributed power sources. In particular, with the connection of large-scale distributed power sources and the increase of user-side interaction, the operation form of smart distribution networks has become more complex. How to accurately obtain its operating status has become the key to the stable operation of the power grid. Therefore, the application of state estimation technology in smart distribution networks is becoming more and more important. However, when facing complex smart distribution networks, traditional distribution network measurement systems and state estimation methods can no longer meet their fast and accurate monitoring needs. This puts higher requirements on the state estimation of smart distribution networks and provides an opportunity for the application of new measurement equipment.

[0003] Based on the real-time measurement information of DPMU, online monitoring of the operating status of the distribution network can be realized. By reasonably arranging DPMU, the state of the distribution network can be estimated, the sensitivity of the feeder voltage to the reactive voltage regulating equipment can be analyzed, and a basis can be provided for the coordinated participation of various reactive voltage regulating equipment in a wide area in rapid voltage control. With the introduction of DPMU, the distribution network can obtain the operating status of the system in real time, thereby effectively improving the perception of the operating status of the power grid, which helps the power system to achieve a higher level of automation and intelligence. However, although DPMU has been widely used in transmission networks, its promotion and application in distribution networks still face many challenges. First, the number of nodes and branches in the distribution network is huge. If DPMU is to be fully configured, the cost will increase significantly. At the same time, the distribution network lines are relatively short, the node voltage phase angle difference is small, and higher measurement accuracy is required. In addition, when there are more distributed power sources connected, the uncertainty of power grid operation increases, which brings more challenges to the application of DPMU. Summary of the invention

[0004] In view of the problems in the prior art, an embodiment of the present invention provides a method and apparatus for acquiring optimal sensing device configuration information for a distribution network, which can at least partially solve the problems in the prior art.

[0005] On the one hand, the present invention proposes a method for acquiring optimal sensing device configuration information for a distribution network, comprising:

[0006] Acquire power quality data of a distribution network monitoring point, and obtain an estimated result of the distribution network operation status based on the power quality data;

[0007] Constructing an optimal configuration model of the distribution network according to the distribution network operation status estimation result, voltage over-limit probability, DPMU installation cost and node voltage parameters;

[0008] The optimized fruit fly algorithm is used to solve the optimal configuration model of the distribution network and obtain the optimal sensing device configuration information.

[0009] Wherein, obtaining the estimated result of the distribution network operation status according to the power quality data includes:

[0010] The power quality data is used as an independent variable of a pre-established hybrid measurement state estimation model of the distribution network, and a least squares solution to the state estimation problem is obtained;

[0011] A least squares model of a nonlinear equation group is constructed based on the least squares solution, and the nonlinear equation group is solved to obtain an estimation result of the distribution network operation state.

[0012] Wherein, the node voltage parameters include an average node voltage amplitude error, a maximum node voltage amplitude error, an average node voltage phase angle error, and a maximum node voltage phase angle error; accordingly, the optimal configuration model of the distribution network is constructed according to the distribution network operation state estimation result, voltage over-limit probability, DPMU installation cost, and node voltage parameters, including:

[0013] The optimal configuration model F of the distribution network is expressed as follows:

[0014] F=ω1F obj1 +ω2F obj2 +ω3F obj3

[0015]

[0016]

[0017] Among them, F obj1 is the optimal configuration sub-model of the first distribution network, F obj2 is the optimal configuration sub-model for the second distribution network, F obj3 is the optimal configuration sub-model for the third distribution network, ω1, ω2 and ω3 are related to F obj1 、F obj2 and F obj3 The corresponding sub-model weights, N is the total number of nodes in the optimal configuration model of the distribution network, x i =1 means that DPMU is configured at node i to measure all branch current phasors and node voltage phasors associated with node i, xi =0 means that DPMU is not configured at node i, w i is the voltage limit-exceeding probability of node i, used as the probability weight, M e is the preset adjustment factor to ensure Indicates the total number of configured DPMUs, C i represents the DPMU installation cost corresponding to node i, is the average node voltage amplitude error, is the maximum node voltage amplitude error, is the average node voltage phase angle error, is the maximum node voltage phase angle error, α1, α2, β1 and β2 are related to and The corresponding voltage parameter weights respectively;

[0018] A N×N is the connection matrix, whose element A ij It can be expressed as:

[0019] X N×1 Indicated by x i The matrix composed of.

[0020] Wherein, obtaining the optimized fruit fly algorithm includes:

[0021] A mixed strategy is used to initialize the population to expand the search range of individuals, so as to optimize the existing fruit fly algorithm.

[0022] Wherein, after the step of obtaining the optimal sensing device configuration information, the method for obtaining the optimal sensing device configuration information for the distribution network further includes:

[0023] The optimal sensing device configuration information is evaluated.

[0024] Wherein, the evaluating the optimal sensing device configuration information includes:

[0025] The node voltage amplitude error is used to evaluate the optimal sensing device configuration information.

[0026] Wherein, the evaluating the optimal sensing device configuration information includes:

[0027] The node voltage phase angle error is used to evaluate the optimal sensing device configuration information.

[0028] On the one hand, the present invention proposes an optimal sensing device configuration information acquisition device for a distribution network, comprising:

[0029] A first acquisition unit is used to acquire power quality data of a monitoring point of the distribution network, and acquire an estimation result of the distribution network operation state according to the power quality data;

[0030] A construction unit, used to construct an optimal configuration model of the distribution network according to the distribution network operation state estimation result, voltage over-limit probability, DPMU installation cost and node voltage parameters;

[0031] The second acquisition unit is used to solve the optimal configuration model of the distribution network by using the optimized fruit fly algorithm to obtain the optimal sensing device configuration information.

[0032] In another aspect, an embodiment of the present invention provides an electronic device, including: a processor, a memory, and a bus, wherein:

[0033] The processor and the memory communicate with each other via the bus;

[0034] The memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute the following method:

[0035] Acquire power quality data of a distribution network monitoring point, and obtain an estimated result of the distribution network operation status based on the power quality data;

[0036] Constructing an optimal configuration model of the distribution network according to the distribution network operation status estimation result, voltage over-limit probability, DPMU installation cost and node voltage parameters;

[0037] The optimized fruit fly algorithm is used to solve the optimal configuration model of the distribution network and obtain the optimal sensing device configuration information.

[0038] An embodiment of the present invention provides a non-transitory computer-readable storage medium, including:

[0039] The non-transitory computer-readable storage medium stores computer instructions, which cause the computer to execute the following method:

[0040] Acquire power quality data of a distribution network monitoring point, and obtain an estimated result of the distribution network operation status based on the power quality data;

[0041] Constructing an optimal configuration model of the distribution network according to the distribution network operation status estimation result, voltage over-limit probability, DPMU installation cost and node voltage parameters;

[0042] The optimized fruit fly algorithm is used to solve the optimal configuration model of the distribution network and obtain the optimal sensing device configuration information.

[0043] The method and device for acquiring optimal sensing device configuration information for a distribution network provided in an embodiment of the present invention acquire power quality data of a monitoring point of the distribution network, and acquire an estimated result of the distribution network operation state according to the power quality data; construct an optimal configuration model of the distribution network according to the estimated result of the distribution network operation state, voltage over-limit probability, DPMU installation cost and node voltage parameters; and use an optimized fruit fly algorithm to solve the distribution network optimal configuration model to obtain optimal sensing device configuration information, thereby realizing multi-objective optimal configuration of the distribution network sensing device. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0045] Figure 1 It is a flow chart of a method for acquiring optimal sensing device configuration information for a distribution network provided in one embodiment of the present invention.

[0046] Figure 2 It is a schematic diagram for illustrating the comparison of different model errors of system node voltage amplitudes provided by an embodiment of the present invention.

[0047] Figure 3 It is a schematic diagram for illustrating the comparison of different model errors of system node voltage phase angles provided by an embodiment of the present invention.

[0048] Figure 4 It is a structural diagram of an optimal sensing device configuration information acquisition device for a distribution network provided by an embodiment of the present invention.

[0049] Figure 5 A schematic diagram of the physical structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical scheme and advantages of the embodiments of the present invention more clear, the embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention. It should be noted that, in the absence of conflict, the embodiments in this application and the features in the embodiments can be combined with each other arbitrarily.

[0051] Figure 1 FIG. 1 is a flow chart of a method for acquiring optimal sensing device configuration information for a distribution network provided by an embodiment of the present invention. Figure 1As shown, the method for acquiring optimal sensing device configuration information for a distribution network provided by an embodiment of the present invention includes:

[0052] Step S1: Acquire power quality data of a distribution network monitoring point, and acquire an estimation result of the distribution network operation status according to the power quality data.

[0053] Step S2: constructing a distribution network optimal configuration model according to the distribution network operation status estimation result, voltage over-limit probability, DPMU installation cost and node voltage parameters.

[0054] Step S3: using the optimized fruit fly algorithm to solve the optimal configuration model of the distribution network and obtain the optimal sensing device configuration information.

[0055] In the above step S1, the device obtains the power quality data of the distribution network monitoring point, and obtains the estimated result of the distribution network operation status according to the power quality data. The device may be a computer device that executes the method, for example, it may include a server. It should be noted that the acquisition and analysis of data involved in the embodiment of the present invention are authorized by the user. The distribution network monitoring point refers to the node in the distribution network used to monitor the voltage value of the power system and assess the voltage quality. These monitoring points are usually selected at the main load points that can reflect the voltage quality of the power grid and are generally representative of the voltage level of the power grid. As long as the voltage quality of the monitoring point meets the requirements, the voltage quality of other points can basically meet the requirements. The method of obtaining the estimated result of the distribution network operation status according to the power quality data includes:

[0056] The power quality data is used as an independent variable of a pre-established hybrid measurement state estimation model of a distribution network, and the least squares solution of the state estimation problem is obtained; the explanation is as follows:

[0057] Based on the weighted least squares method, a hybrid measurement state estimation model of the distribution network is established, and the distribution network measurement equation can be expressed as:

[0058] z=h(x)+ε

[0059] Where z = [z s ,z p ,z PMU ,z AMI ]∈R m Represents all power quality data, z s SCADA system data; p is the constructed pseudo measurement data; PMU is the DPMU measurement data; AMI is the measurement system data, x=[V,θ]∈R nrepresents the system state quantity, V is the node voltage amplitude, θ is the node voltage phase angle, h(x) is the nonlinear equation of the distribution network, ε~N(0,W) is the measurement error, and W is the covariance matrix of the error.

[0060] The objective function of distribution network state estimation can be expressed as:

[0061]

[0062] The iterative process of solving the objective function of distribution network state estimation is:

[0063] Δx k =G(x k )H T (x k )W -1 (zh(x k ))

[0064] x k+1 =x k +Δx k

[0065] Among them, H(x k ) is h(x k ) corresponding to the Jacobian matrix, G(x k ) is the gain matrix, G(x k )=[H T (x k )W -1 H(x k )] -1 .

[0066] By continuously iterating according to the iterative process, the least squares solution to the state estimation problem can be finally obtained.

[0067] Based on the least squares solution, a least squares model of the nonlinear equation group is constructed, and the nonlinear equation group is solved to obtain the distribution network operation state estimation result. This step can be achieved by reasonably selecting the adaptive damping factor to ensure the local second-order convergence of the adaptive LM method, as described below:

[0068] The least squares model of the nonlinear equation system can be expressed as:

[0069]

[0070] Put f(x) at the kth iteration point x k The first-order Taylor expansion transforms the least squares model into:

[0071]

[0072] J(x k ) is f(xk ) is the Jacobian matrix corresponding to .

[0073] By adding the adaptive damping factor μ k , ensuring that the adaptive LM has the robustness and rapid descent characteristics of the steepest descent method at the beginning of the iteration, and has the good convergence characteristics of the Newton method at the end of the iteration, avoiding the problem of oscillating convergence of the steepest descent method.

[0074] Δx k =-[J(x k ) T W -1 J(x k )+μ k I] -1 J(x k ) T W -1 f(x k )

[0075] I is the identity matrix.

[0076] In the above step S2, the device constructs an optimal configuration model of the distribution network according to the distribution network operation state estimation result, voltage over-limit probability, DPMU installation cost and node voltage parameters.

[0077] The node voltage parameters include an average node voltage amplitude error, a maximum node voltage amplitude error, an average node voltage phase angle error, and a maximum node voltage phase angle error; accordingly, the optimal configuration model of the distribution network is constructed according to the distribution network operation state estimation result, voltage over-limit probability, DPMU installation cost, and node voltage parameters, including:

[0078] The optimal configuration model F of the distribution network is expressed as follows:

[0079] F=ω1F obj1 +ω2F obj2 +ω3F obj3

[0080]

[0081]

[0082] Among them, F obj1 is the optimal configuration sub-model of the first distribution network, F obj2 is the optimal configuration sub-model for the second distribution network, F obj3 is the optimal configuration sub-model for the third distribution network, ω1, ω2 and ω3 are related to F obj1 、F obj2 and F obj3 The corresponding sub-model weights, N is the total number of nodes in the optimal configuration model of the distribution network, xi =1 means that DPMU is configured at node i to measure all branch current phasors and node voltage phasors associated with node i, x i =0 means that DPMU is not configured at node i, w i is the voltage limit-exceeding probability of node i, used as the probability weight, M e is the preset adjustment factor to ensure Indicates the total number of configured DPMUs, C i represents the DPMU installation cost corresponding to node i, is the average node voltage amplitude error, is the maximum node voltage amplitude error, is the average node voltage phase angle error, is the maximum node voltage phase angle error, α1, α2, β1 and β2 are related to and The corresponding voltage parameter weights respectively;

[0083] A N×N is the connection matrix, whose element A ij It can be expressed as:

[0084] X N×1 Indicated by x i The matrix composed of.

[0085] The optimal configuration model of the distribution network contains N nodes and L branches. The goal is to solve the x that minimizes F under the premise of satisfying the constraint condition st. i The value represented by (0 or 1).

[0086] In the above step S3, the device uses the optimized fruit fly algorithm to solve the optimal configuration model of the distribution network to obtain the optimal sensing device configuration information. Obtaining the optimized fruit fly algorithm includes:

[0087] The hybrid strategy is used to initialize the population to expand the search range of individuals in order to optimize the existing fruit fly algorithm. The hybrid strategy is used to initialize the population to improve the efficiency of the algorithm; two perturbation operators, exchange (randomly exchange two sensing devices in different positions in a solution) and binding insertion (randomly delete two sensing devices from the solution and connect them to other nodes as a whole) are constructed to expand the search range of individuals.

[0088] After the step of obtaining the optimal sensing device configuration information, the method for obtaining the optimal sensing device configuration information for the distribution network further includes:

[0089] Evaluating the optimal sensing device configuration information. The evaluating the optimal sensing device configuration information includes:

[0090] The node voltage amplitude error is used to evaluate the optimal sensing device configuration information. The average node voltage amplitude error is:

[0091]

[0092] in, represents the estimated value of the voltage amplitude at node i, V i Represents the true value of the voltage amplitude at node i.

[0093] The maximum node voltage amplitude error is:

[0094]

[0095] The evaluating the optimal sensing device configuration information includes:

[0096] The node voltage phase angle error is used to evaluate the optimal sensing device configuration information.

[0097] The average node voltage phase angle error is:

[0098]

[0099] in, represents the estimated value of the voltage phase angle at node i, θ i Represents the true value of the voltage phase angle at node i, both expressed in radians.

[0100] The maximum node voltage phase angle error is:

[0101]

[0102] The average and maximum node voltage amplitude errors and the average and maximum node voltage phase angle errors are indicators used to measure the accuracy of the perception device configuration in estimating the distribution network state. The better the configuration, the smaller the error is usually, thereby improving the transparent perception of the distribution network operation state. The goal of the optimal configuration is to reduce these errors while optimizing the cost, so as to achieve the optimal accuracy and economy of the state estimation.

[0103] The effect of the method provided by the present invention is as follows Figure 2 and Figure 3 The data comparison is shown in Table 1:

[0104] Table 1

[0105]

[0106] The method provided by the present invention has the following characteristics: by establishing a hybrid measurement state estimation model and using the adaptive LM method to solve the nonlinear equation group, the accuracy of the distribution network state estimation is significantly improved; in the traditional power grid monitoring process, only the topological observable constraints and the economy of the configuration are considered. The present invention constructs a multi-objective distribution network DPMU optimal configuration model that considers state estimation accuracy, node voltage over-limit weights and economy, and realizes the multi-objective optimal configuration of the distribution network sensing equipment. By initializing the population and a variety of disturbance operators through a hybrid strategy, the optimization efficiency and search range of the fruit fly optimization algorithm in the distribution network DPMU optimal configuration model are improved. Through indicators such as the average and maximum node voltage amplitude errors, the average and maximum node voltage phase angle errors, etc., the accuracy of the distribution network state estimation results under different sensing device configurations is comprehensively evaluated.

[0107] The optimal sensing device configuration information acquisition method for distribution network provided by the embodiment of the present invention realizes high precision and economic optimal configuration of distribution network state estimation by comprehensively using weighted least squares method, adaptive LM method, multi-objective optimization strategy and fruit fly optimization algorithm. First, the hybrid measurement state estimation model of intelligent distribution network is established by weighted least squares method to provide a basis for subsequent optimization. Secondly, the nonlinear equations of state estimation problem are solved by adaptive LM method, which improves the efficiency and accuracy of solution. Then, a multi-objective optimal configuration model is constructed, which takes into account factors such as state estimation accuracy, node voltage over-limit weight and economy, and realizes the multi-objective optimal configuration of distribution network sensing devices. In the solution process, the population is initialized by hybrid strategy, and the perturbation operator is constructed to expand the search range, which improves the optimization efficiency of fruit fly optimization algorithm. Finally, the state estimation effect under different scenarios is comprehensively evaluated by evaluation indicators such as average node voltage amplitude and phase angle error. By comparing with traditional methods, it is proved that the method of the present invention has significant advantages in improving the state estimation accuracy of distribution network and optimizing the configuration of sensing devices. In summary, the method of the present invention not only improves the accuracy of distribution network state estimation, but also optimizes the configuration of sensing equipment, verifies its effectiveness and superiority in practical applications, and proves that the method proposed in the present invention has good engineering practical significance.

[0108] The method for acquiring optimal sensing device configuration information for a distribution network provided in an embodiment of the present invention acquires power quality data of a monitoring point of the distribution network, and acquires an estimated result of the distribution network operation state according to the power quality data; constructs an optimal configuration model of the distribution network according to the estimated result of the distribution network operation state, voltage over-limit probability, DPMU installation cost and node voltage parameters; and uses an optimized fruit fly algorithm to solve the distribution network optimal configuration model to obtain optimal sensing device configuration information, thereby realizing multi-objective optimal configuration of the distribution network sensing device.

[0109] Further, obtaining the distribution network operation status estimation result according to the power quality data includes:

[0110] The power quality data is used as an independent variable of a pre-established hybrid measurement state estimation model of the distribution network, and a least square solution to the state estimation problem is obtained by solving the problem; the above-mentioned embodiment can be referred to for description and will not be described in detail.

[0111] A least squares model of a nonlinear equation group is constructed based on the least squares solution, and the nonlinear equation group is solved to obtain the distribution network operation state estimation result. The above-mentioned embodiments can be referred to for explanation and will not be described in detail.

[0112] Further, the node voltage parameters include an average node voltage amplitude error, a maximum node voltage amplitude error, an average node voltage phase angle error, and a maximum node voltage phase angle error; accordingly, the optimal configuration model of the distribution network is constructed according to the distribution network operation state estimation result, voltage over-limit probability, DPMU installation cost, and node voltage parameters, including:

[0113] The optimal configuration model F of the distribution network is expressed as follows:

[0114] F=ω1F obj1 +ω2F obj2 +ω3F obj3

[0115]

[0116]

[0117] Among them, F obj1 is the optimal configuration sub-model of the first distribution network, F obj2 is the optimal configuration sub-model for the second distribution network, F obj3 is the optimal configuration sub-model for the third distribution network, ω1, ω2 and ω3 are related to F obj1 、F obj2 and F obj3 The corresponding sub-model weights, N is the total number of nodes in the optimal configuration model of the distribution network, x i =1 means that DPMU is configured at node i to measure all branch current phasors and node voltage phasors associated with node i, x i =0 means that DPMU is not configured at node i, w i is the voltage limit-exceeding probability of node i, used as the probability weight, M e is the preset adjustment factor to ensure Indicates the total number of configured DPMUs, C i represents the DPMU installation cost corresponding to node i, is the average node voltage amplitude error, is the maximum node voltage amplitude error, is the average node voltage phase angle error, is the maximum node voltage phase angle error, α1, α2, β1 and β2 are related to and The corresponding voltage parameter weights respectively can be referred to the above embodiment description and will not be described in detail.

[0118] A N×N is the connection matrix, whose element A ij It can be expressed as:

[0119] X N×1 Indicated by x i The matrix composed of can be referred to the above embodiment for description, which will not be described in detail.

[0120] Further, obtaining the optimized fruit fly algorithm includes:

[0121] The hybrid strategy is used to initialize the population to expand the search range of individuals, so as to optimize the existing fruit fly algorithm.

[0122] Furthermore, after the step of obtaining the optimal sensing device configuration information, the method for obtaining the optimal sensing device configuration information for the distribution network further includes:

[0123] Evaluating the optimal sensing device configuration information. Please refer to the above embodiment for description, which will not be repeated here.

[0124] Further, the evaluating the optimal sensing device configuration information includes:

[0125] The node voltage amplitude error is used to evaluate the optimal sensing device configuration information. The above-mentioned embodiments can be referred to for explanation and will not be described in detail.

[0126] Further, the evaluating the optimal sensing device configuration information includes:

[0127] The node voltage phase angle error is used to evaluate the optimal sensing device configuration information. The above-mentioned embodiments can be referred to for explanation and will not be described in detail.

[0128] Figure 4 is a schematic diagram of the structure of an optimal sensing device configuration information acquisition device for a distribution network provided by an embodiment of the present invention, such as Figure 4 As shown, the optimal sensing device configuration information acquisition device for the distribution network provided by the embodiment of the present invention includes a first acquisition unit 401, a construction unit 402 and a second acquisition unit 403, wherein:

[0129] The first acquisition unit 401 is used to acquire the power quality data of the distribution network monitoring point, and obtain the distribution network operation status estimation result according to the power quality data; the construction unit 402 is used to construct the distribution network optimal configuration model according to the distribution network operation status estimation result, voltage over-limit probability, DPMU installation cost and node voltage parameters; the second acquisition unit 403 is used to solve the distribution network optimal configuration model by using the optimized fruit fly algorithm to obtain the optimal sensing device configuration information.

[0130] Specifically, the first acquisition unit 401 in the device is used to obtain the power quality data of the distribution network monitoring point, and obtain the distribution network operation status estimation result according to the power quality data; the construction unit 402 is used to construct the distribution network optimal configuration model according to the distribution network operation status estimation result, voltage over-limit probability, DPMU installation cost and node voltage parameters; the second acquisition unit 403 is used to solve the distribution network optimal configuration model by using the optimized fruit fly algorithm to obtain the optimal sensing device configuration information.

[0131] The method and device for acquiring optimal sensing device configuration information for a distribution network provided in an embodiment of the present invention acquire power quality data of a monitoring point of the distribution network, and acquire an estimated result of the distribution network operation state according to the power quality data; construct an optimal configuration model of the distribution network according to the estimated result of the distribution network operation state, voltage over-limit probability, DPMU installation cost and node voltage parameters; and use an optimized fruit fly algorithm to solve the distribution network optimal configuration model to obtain optimal sensing device configuration information, thereby realizing multi-objective optimal configuration of the distribution network sensing device.

[0132] The embodiment of the present invention provides an embodiment of an optimal sensing device configuration information acquisition device for a distribution network, which can be specifically used to execute the processing flow of the above-mentioned method embodiments. Its functions are not repeated here, and reference can be made to the detailed description of the above-mentioned method embodiments.

[0133] Figure 5 A schematic diagram of the physical structure of an electronic device provided in an embodiment of the present invention, such as Figure 5 As shown, the electronic device includes: a processor (processor) 501, a memory (memory) 502 and a bus 503;

[0134] The processor 501 and the memory 502 communicate with each other via a bus 503;

[0135] The processor 501 is used to call the program instructions in the memory 502 to execute the methods provided by the above method embodiments, for example, including:

[0136] Acquire power quality data of a distribution network monitoring point, and obtain an estimated result of the distribution network operation status based on the power quality data;

[0137] Constructing an optimal configuration model of the distribution network according to the distribution network operation status estimation result, voltage over-limit probability, DPMU installation cost and node voltage parameters;

[0138] The optimized fruit fly algorithm is used to solve the optimal configuration model of the distribution network and obtain the optimal sensing device configuration information.

[0139] This embodiment discloses a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer can perform the methods provided by the above method embodiments, for example, including:

[0140] Acquire power quality data of a distribution network monitoring point, and obtain an estimated result of the distribution network operation status based on the power quality data;

[0141] Constructing an optimal configuration model of the distribution network according to the distribution network operation status estimation result, voltage over-limit probability, DPMU installation cost and node voltage parameters;

[0142] The optimized fruit fly algorithm is used to solve the optimal configuration model of the distribution network and obtain the optimal sensing device configuration information.

[0143] This embodiment provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, wherein the computer program enables the computer to execute the methods provided by the above method embodiments, for example, including:

[0144] Acquire power quality data of a distribution network monitoring point, and obtain an estimated result of the distribution network operation status based on the power quality data;

[0145] Constructing an optimal configuration model of the distribution network according to the distribution network operation status estimation result, voltage over-limit probability, DPMU installation cost and node voltage parameters;

[0146] The optimized fruit fly algorithm is used to solve the optimal configuration model of the distribution network and obtain the optimal sensing device configuration information.

[0147] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0148] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0149] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0150] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0151] In the description of this specification, the description with reference to the terms "one embodiment", "a specific embodiment", "some embodiments", "for example", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0152] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for acquiring optimal sensing device configuration information for a distribution network, characterized in that: include: Acquire power quality data of a distribution network monitoring point, and obtain an estimated result of the distribution network operation status based on the power quality data; Constructing an optimal configuration model of the distribution network according to the distribution network operation status estimation result, voltage over-limit probability, DPMU installation cost and node voltage parameters; The optimized fruit fly algorithm is used to solve the optimal configuration model of the distribution network and obtain the optimal sensing device configuration information.

2. The method for acquiring optimal sensing device configuration information for a distribution network according to claim 1, characterized in that: The obtaining of the distribution network operation status estimation result according to the power quality data comprises: The power quality data is used as an independent variable of a pre-established hybrid measurement state estimation model of the distribution network, and a least squares solution to the state estimation problem is obtained; A least squares model of a nonlinear equation group is constructed based on the least squares solution, and the nonlinear equation group is solved to obtain an estimation result of the distribution network operation state.

3. The method for acquiring optimal sensing device configuration information for a distribution network according to claim 1, characterized in that: The node voltage parameters include an average node voltage amplitude error, a maximum node voltage amplitude error, an average node voltage phase angle error, and a maximum node voltage phase angle error; accordingly, the optimal configuration model of the distribution network is constructed according to the distribution network operation state estimation result, voltage over-limit probability, DPMU installation cost, and node voltage parameters, including: The optimal configuration model F of the distribution network is expressed as follows: F=ω1F obj1 +ω2F obj2 +ω3F obj3 Among them, F obj1 is the optimal configuration sub-model of the first distribution network, F obj2 is the optimal configuration sub-model for the second distribution network, F obj3 is the optimal configuration sub-model for the third distribution network, ω1, ω2 and ω3 are related to F obj1 、F obj2 and F obj3 The corresponding sub-model weights, N is the total number of nodes in the optimal configuration model of the distribution network, x i =1 means that DPMU is configured at node i to measure all branch current phasors and node voltage phasors associated with node i, x i =0 means that DPMU is not configured at node i, w i is the voltage limit-exceeding probability of node i, used as the probability weight, M e is the preset adjustment factor to ensure Indicates the total number of configured DPMUs, C i represents the DPMU installation cost corresponding to node i, is the average node voltage amplitude error, is the maximum node voltage amplitude error, is the average node voltage phase angle error, is the maximum node voltage phase angle error, α1, α2, β1 and β2 are related to and The corresponding voltage parameter weights respectively; A N×N is the connection matrix, whose element A ij It is expressed as: X N×1 Indicated by x i The matrix composed of.

4. The method for acquiring optimal sensing device configuration information for a distribution network according to claim 1, characterized in that: Obtaining the optimized fruit fly algorithm, including: A mixed strategy is used to initialize the population to expand the search range of individuals, so as to optimize the existing fruit fly algorithm.

5. The method for acquiring optimal sensing device configuration information for a distribution network according to any one of claims 1 to 4, characterized in that: After the step of obtaining the optimal sensing device configuration information, the method for obtaining the optimal sensing device configuration information for the distribution network further includes: The optimal sensing device configuration information is evaluated.

6. The method for acquiring optimal sensing device configuration information for a distribution network according to claim 5, characterized in that: The evaluating the optimal sensing device configuration information includes: The node voltage amplitude error is used to evaluate the optimal sensing device configuration information.

7. The method for acquiring optimal sensing device configuration information for a distribution network according to claim 5, characterized in that: The evaluating the optimal sensing device configuration information includes: The node voltage phase angle error is used to evaluate the optimal sensing device configuration information.

8. An optimal sensing device configuration information acquisition device for a distribution network, characterized in that: include: A first acquisition unit is used to acquire power quality data of a monitoring point of the distribution network, and acquire an estimation result of the distribution network operation state according to the power quality data; A construction unit, used to construct an optimal configuration model of the distribution network according to the distribution network operation state estimation result, voltage over-limit probability, DPMU installation cost and node voltage parameters; The second acquisition unit is used to solve the optimal configuration model of the distribution network by using the optimized fruit fly algorithm to obtain the optimal sensing device configuration information.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. 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 7 are implemented.

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