Optimal configuration method and system for power distribution network measurement equipment

Abstract

The invention provides a power distribution network measurement equipment optimal configuration method and system, and the method comprises the steps: carrying out the state estimation of a power distribution network according to an obtained network topology parameter, historical power flow data and an initial measurement equipment configuration scheme, and obtaining a voltage amplitude and a phase angle corresponding to each node, determining the observability of the voltage amplitude and the phase angle of each node, and determining the observability of the voltage state of the power distribution network based on the time dimension and the space dimension based on the observability of the voltage amplitude and the phase angle; and establishing a measurement equipment optimal configuration model by taking the configuration cost of the power distribution network measurement equipment as a target, and iterating the initial measurement equipment configuration scheme in the process of solving the measurement equipment optimal configuration model based on an adaptive simulated annealing algorithm to obtain a target measurement equipment optimal configuration scheme. Accurate evaluation of observability of measurement data is realized, and a measurement equipment configuration result with excellent observability is obtained.

Description

Technical Field

[0001] The present invention relates to the field of distribution network measurement configuration, and in particular to distribution network measurement configuration optimization. Background Art

[0002] The structure of the distribution network is complex and the measurement units are insufficiently configured, resulting in weak observability of the operating status. Therefore, it is of great significance to reasonably select the installation location and number of measurement units to improve the accuracy of distribution network operating status analysis and the economy of measurement configuration.

[0003] The existing measurement equipment optimization configuration method mainly focuses on increasing the direct measurement range and reducing the state estimation error.

[0004] 1. The method of increasing the measurement range determines whether the data to be monitored is measurable through the characteristics of the measuring equipment. On this basis, the topological adjacency matrix and the binary vector corresponding to the configuration scheme are combined to construct the target and establish an optimization configuration model. This can ensure that the obtained configuration scheme can collect enough required data, but this method lacks quantitative evaluation of the quality of the collected data.

[0005] 2. The method to reduce the error of state estimation results is to increase the redundancy of measurement by screening the measurement equipment points and thus improve the accuracy of state estimation. This type of method often combines the configuration scheme to perform point estimation to obtain the voltage state of the entire network and then calculate the relevant data. Reducing the error of state estimation results can ensure the accuracy of the subsequent data required for calculation and realize quantitative evaluation of data, but most of them lack consideration of the randomness of measurement errors.

[0006] In addition, with the large-scale involvement of distributed power sources in the distribution network, the large amount of measurement data introduced by state estimation to improve measurement redundancy contains high uncertainty. This makes it more difficult to determine the measurement error, and the state estimation results contain higher randomness. Therefore, when configuring measurement equipment based on the state estimation results, it is necessary to fully consider the uncertainty in the measurement stage.

[0007] Therefore, it is of great significance to solve the problem of insufficient consideration of errors in the measurement phase, which leads to poor observability of configuration results. Summary of the invention

[0008] In order to solve the problem that the prior art distribution network measurement optimization configuration lacks quantitative evaluation of data quality and there is insufficient consideration of errors in the measurement stage, resulting in poor observable performance of the configuration results, the present invention proposes an optimization configuration method and system for distribution network measurement equipment to solve the above problems.

[0009] In a first aspect, a method for optimizing configuration of distribution network measurement equipment is provided, comprising:

[0010] Obtain network topology parameters, historical flow data, and initial measurement equipment configuration plan;

[0011] Perform state estimation according to the network topology parameters, the historical power flow data and the initial measurement equipment configuration scheme to obtain the voltage amplitude and phase angle corresponding to each node;

[0012] Determine the observability of the voltage amplitude and phase angle of each node, and determine the observability of the voltage state of the distribution network based on the time dimension and the space dimension based on the observability of the voltage amplitude and phase angle;

[0013] Taking the observability of the time dimension and the space dimension as constraints and the configuration cost of the distribution network measurement equipment as the target, a measurement equipment optimization configuration model is established, and in the process of solving the measurement equipment optimization configuration model based on an adaptive simulated annealing algorithm, the initial measurement equipment configuration scheme is iterated to obtain the target measurement equipment optimization configuration scheme.

[0014] Preferably, the determining the observability of the voltage amplitude and phase angle of each node comprises:

[0015] Acquire the power flow data at the current moment, and calculate the difference between the power flow data at the current moment and the voltage amplitude and phase angle corresponding to each node;

[0016] The observability of the voltage amplitude and phase angle of each node is determined based on the difference.

[0017] Preferably, the observability of the voltage amplitude and phase angle of each node is determined by the following formula:

[0018]

[0019] Among them, x lc is the measured value of the state variable calculated by state estimation, x zs is the true value of the system state variable at that moment, and β is the preset observable judgment threshold.

[0020] Preferably, determining the observability of the distribution network voltage state based on the time dimension based on the observability of the voltage amplitude and phase angle comprises:

[0021]

[0022] Among them, r t is the observability of the time dimension, m is the total number of time sections, n is the number of nodes in the topological network, j is the time section number, and i is the node number.

[0023] Preferably, determining the observability of the voltage state of the distribution network based on the spatial dimension based on the observability of the voltage amplitude and phase angle comprises:

[0024] Divide the topological network into multiple sub-areas with even numbers of nodes;

[0025] Obtain the measurement results at any time section, and determine the observability of the spatial dimension based on the measurement results according to the spatial dimension observability formula.

[0026] Preferably, the spatial dimension observability formula is as follows:

[0027]

[0028] Among them, r s is the observability of the spatial dimension, N is the number of state variables in the sub-region, K is the number of sub-regions in the entire network, b is the number of state variables, and a is the sub-region number.

[0029] Preferably, the measurement equipment optimization configuration model is as follows:

[0030]

[0031] Among them, e is the total cost of the configuration plan, n dev_i is the total number of configurations for the i-th type of measurement equipment; a i is the cost factor of the i-th measurement equipment; r t.set is the observability indicator under the set time dimension; r s.set is the observability index under the set spatial dimension; r t is the observability indicator of the time dimension; r s It is the observability indicator of the spatial dimension.

[0032] Preferably, in the process of solving the measurement equipment optimization configuration model based on the adaptive simulated annealing algorithm, the initial measurement equipment configuration scheme is iterated to obtain the target measurement equipment optimization configuration scheme, including:

[0033] In the process of iterating the initial measurement equipment configuration scheme by using an adaptive simulated annealing algorithm with an adaptive factor, the configuration costs of the initial measurement equipment configuration scheme and the next measurement equipment configuration scheme are compared, and the measurement equipment configuration scheme with a lower configuration cost is used as the initial measurement equipment configuration scheme;

[0034] When a preset iteration condition is met, a temperature adjustment method is determined, and when a preset temperature threshold is met after the temperature adjustment method is executed, the initial measurement device configuration scheme is used as the target measurement device configuration scheme.

[0035] Preferably, the adaptive simulated annealing algorithm is as follows:

[0036] L=l×α m

[0037]

[0038] Among them, L is the length of each update scheme encoding in the algorithm, l is the control step amplitude, which is proportional to the vector length, and j i is the current iteration number, j max is the total number of iterations, α m is the adaptive factor, α max The value is 0.9, α r is a random step factor on [-0.1, 0.1], and e is a natural base.

[0039] Preferably, the step of comparing the configuration costs of the initial measurement equipment configuration scheme and the next measurement equipment configuration scheme during the iteration process of the adaptive simulated annealing model to determine the initial measurement equipment configuration scheme includes:

[0040] If the next measurement equipment configuration scheme does not satisfy the observability constraint, the next measurement equipment configuration scheme is not accepted; or

[0041] If the next measurement equipment configuration scheme satisfies the observability constraint and the configuration cost is lower than the configuration cost of the initial measurement equipment configuration scheme, accept the next measurement equipment configuration scheme and use the next measurement equipment configuration scheme as the initial measurement equipment configuration scheme; or

[0042] When the next measurement equipment configuration scheme satisfies the observability constraint and the configuration cost is higher than the configuration cost of the initial measurement equipment configuration scheme, determine whether to accept the next measurement equipment configuration scheme according to the Metropolis criterion, and when it is determined to accept the next measurement equipment configuration scheme, use the next measurement equipment configuration scheme as the initial measurement equipment configuration scheme.

[0043] Preferably, the Metropolis criterion is calculated as shown below:

[0044]

[0045] Among them, cost is the configuration cost, k is the number of iterations, T is the real-time temperature during the annealing process, P is the probability of accepting the next measurement equipment configuration plan, and cost k+1 is the configuration cost corresponding to the current iteration process, cost k is the configuration cost corresponding to the previous iteration, and e is the natural base.

[0046] Preferably, the temperature adjustment method is as follows:

[0047]

[0048] Where n is the number of iterations, T k is the kth temperature, υ m is the set temperature rise boundary value, s is the cooling factor, υ is the ratio of the two temperature changes to the scheme, T k+1 is the temperature after the change, and f(υ) is the temperature change criterion.

[0049] Preferably, the calculation of the ratio of the two temperatures to the change of the scheme is as follows:

[0050]

[0051] Where A is the number of times the scheme is changed at the current temperature, S is the number of set iterations at the current temperature, n represents the nth temperature change, and A n A represents the number of scheme changes corresponding to the nth temperature. n-1 Indicates the number of scheme changes corresponding to the n-1th temperature, S n Indicates the number of iterations corresponding to the nth temperature, S n-1 Indicates the number of iterations corresponding to the n-1th temperature.

[0052] Preferably, performing state estimation according to the network topology parameters, the historical power flow data and the initial measurement equipment configuration scheme to obtain the voltage amplitude and phase angle corresponding to each node includes:

[0053] Based on the network topology parameters, the historical power flow data and the initial measurement device configuration scheme, calculating the injected power pseudo-measurement data of the node set not configured with the measurement device;

[0054] A measurement formula is generated based on the injected power pseudo-measurement data, and the measurement formula is solved to obtain the voltage amplitude and phase angle corresponding to each node.

[0055] Preferably, the injection power pseudo-measurement data is calculated as shown in the following formula:

[0056]

[0057] Among them, P mea,total , Q mea,total is the total active power and reactive power in the island calculated based on real-time measurements, P k , Q k Inject historical data of active power and reactive power into the node, P pse,k , Q pse,k Inject pseudo measurements of active and reactive power into the node, P total , Q total They are the historical data of the total active power and reactive power in the island respectively.

[0058] Preferably, the measurement formula is as follows:

[0059] z=h(x)+v

[0060] Wherein, z is the measurement vector, including the collected power and voltage, h(x) is the measurement function equation based on Kirchhoff's law, x represents the voltage amplitude and phase angle of each node, and v is the measurement error vector.

[0061] Preferably, the voltage amplitude and phase angle are calculated as shown below:

[0062]

[0063] Among them, x (k) is the system state variable after k iterations, HT represents the transpose of the Jacobian matrix, W -1 represents the measurement weight matrix, represents the partial derivative of the measurement equation, x (k+1) represents the updated state variable, Δx (k) Represents the correction amount of the state variable.

[0064] In a second aspect, a system for optimizing configuration of distribution network measurement equipment is provided, comprising:

[0065] An acquisition module is configured to acquire network topology parameters, historical flow data, and an initial measurement device configuration plan;

[0066] An estimation module is configured to perform state estimation according to the network topology parameters, the historical power flow data and the initial measurement equipment configuration scheme to obtain the voltage amplitude and phase angle corresponding to each node;

[0067] A determination module is configured to determine the observability of the voltage amplitude and phase angle of each node, and determine the observability of the distribution network voltage state based on the time dimension and the space dimension based on the observability of the voltage amplitude and phase angle;

[0068] The output module is configured to establish a measurement equipment optimization configuration model based on the observability of the time dimension and the space dimension and the configuration cost of the distribution network measurement equipment as a target, and to iterate the initial measurement equipment configuration scheme in the process of solving the measurement equipment optimization configuration model based on an adaptive simulated annealing algorithm to obtain a target measurement equipment optimization configuration scheme.

[0069] In a third aspect, the present application further provides an electronic device, comprising: at least one processor and a memory; the memory and the processor are connected via a bus;

[0070] The memory is used to store one or more programs;

[0071] When the one or more programs are executed by the at least one processor, the above-mentioned method for optimizing configuration of distribution network measurement equipment is implemented.

[0072] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed, the method for optimizing configuration of a distribution network measurement device as described above is implemented.

[0073] Compared with the prior art, the present invention has the following beneficial effects:

[0074] The present invention provides a method for optimizing the configuration of distribution network measurement equipment, including estimating the distribution network state according to the obtained network topology parameters, historical flow data and initial measurement equipment configuration scheme, obtaining the voltage amplitude and phase angle corresponding to each node, and determining the observability of the voltage amplitude and phase angle of each node, and then determining the voltage state of the distribution network based on the observability of the voltage amplitude and phase angle. Based on the observability of the time dimension and the space dimension, the measurement equipment optimization configuration model is established with the observability of the time dimension and the space dimension as constraints and the configuration cost of the distribution network measurement equipment as the target, and the initial measurement equipment configuration scheme is iterated in the process of solving the measurement equipment optimization configuration model based on the adaptive simulated annealing algorithm to obtain the target measurement equipment optimization configuration scheme. A data observability evaluation method under the time dimension and the space dimension is proposed to achieve accurate evaluation of the observability of the measurement data when the measurement data is subjected to random disturbances, and the model is constructed with the observability of the time dimension and the space dimension as constraints, and the initial measurement equipment configuration scheme is iterated by solving the model through the adaptive simulated annealing algorithm to obtain the target measurement equipment optimization configuration scheme with better observability performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 It is a flow chart of the method for optimizing configuration of distribution network measurement equipment of the present invention;

[0076] Figure 2 A specific flow chart of the method for optimizing configuration of distribution network measurement equipment of the present invention;

[0077] Figure 3 A schematic diagram of the relationship between the voltage amplitude and its observable threshold value in the method for optimizing the configuration of the distribution network measurement equipment of the present invention;

[0078] Figure 4 A schematic diagram of the relationship between the voltage phase angle and its observable threshold value in the method for optimizing configuration of the distribution network measurement equipment of the present invention;

[0079] Figure 5 A schematic diagram of sub-area division of the method for optimizing configuration of distribution network measurement equipment of the present invention;

[0080] Figure 6 It is a schematic diagram of iterative optimization configuration cost change of the optimization configuration method of the distribution network measurement equipment of the present invention;

[0081] Figure 7 It is a structural schematic diagram of the optimization configuration system of the power distribution network measurement equipment of the present invention;

[0082] Figure 8 The figure is a schematic diagram of the structure of an electronic device of the present invention. DETAILED DESCRIPTION

[0083] The present invention proposes an optimization configuration method for distribution network measurement equipment, which performs high-precision state estimation based on historical flow data, network topology parameters and initial measurement equipment configuration scheme, and proposes data observability evaluation methods in time dimension and space dimension, so as to achieve accurate evaluation of observability of measurement data when the measurement data is subject to random disturbance, and constructs a model with time dimension and space dimension observability as constraints, and iterates the initial measurement equipment configuration scheme by solving the model with an adaptive simulated annealing algorithm that introduces an adaptive factor, so as to obtain an optimized configuration scheme for target measurement equipment with better observability performance, so as to achieve the best configuration result with comprehensive configuration cost and data accuracy with a small number of iterations.

[0084] In order to better understand the present invention, the content of the present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0085] Embodiment 1:

[0086] A method for optimizing the configuration of distribution network measurement equipment, such as Figure 1 As shown, including:

[0087] Step 1: Obtain network topology parameters, historical flow data, and initial measurement equipment configuration plan;

[0088] Step 2: performing state estimation according to the network topology parameters, the historical power flow data and the initial measurement equipment configuration scheme to obtain the voltage amplitude and phase angle corresponding to each node;

[0089] Step 3: Determine the observability of the voltage amplitude and phase angle of each node, and determine the observability of the distribution network voltage state based on the time dimension and the space dimension based on the observability of the voltage amplitude and phase angle;

[0090] Step 4: Taking the observability of the time dimension and the space dimension as constraints and the configuration cost of the distribution network measurement equipment as the target, a measurement equipment optimization configuration model is established, and in the process of solving the measurement equipment optimization configuration model based on the adaptive simulated annealing algorithm, the initial measurement equipment configuration scheme is iterated to obtain the target measurement equipment optimization configuration scheme.

[0091] Among them, observability can be understood as a measure of a system's ability to infer its internal state from the knowledge of its external output; that is, if the outside of the system can be observed to determine what is happening inside it, then the system is observable.

[0092] like Figure 2 As shown, Figure 2 The specific flow chart of the method for optimizing the configuration of the distribution network measurement equipment of the present invention is as follows. Figure 2 The optimization configuration method of the distribution network measurement equipment of the present invention is described.

[0093] In one embodiment, step 1 specifically includes:

[0094] Step 1.1: Obtain network topology parameters, which include the adjacency matrix of the connection relationship between each node in the network and the impedance value of each branch of the distribution network. The network topology parameters are shown in the following formula:

[0095]

[0096] Where J represents the adjacency matrix of the network.

[0097] In one implementation, if the nodes are adjacent, then ij =1, if not adjacent, then j ij =0; on this basis, the elements with a value of 1 in the matrix J should also fill in the impedance value Z between adjacent branches ij , the impedance value is shown as follows:

[0098] Z ij =R ij +p*X ij

[0099] Among them, R ij is the equivalent resistance value on the distribution network branch, X ij is the equivalent reactance value on the distribution network branch, and p is the imaginary part of the complex number.

[0100] Step 1.2: Obtain the historical flow data of the distribution network to facilitate the subsequent generation of pseudo-measurements based on the historical flow data. The historical flow data is shown in the following formula:

[0101] {P i , Q i , P ij , Q ij , V i}

[0102] Among them, Pi is the active power injected into the node, Qi is the reactive power injected into the node, Vi is the voltage amplitude, P ijis the branch active power, Q ij is the branch reactive power.

[0103] Step 1.3: Set the initial measurement equipment configuration scheme, which is as follows:

[0104] Dec S =[1,6]

[0105] Dec F =[2,3,4,5]

[0106] Dec M =[18, 22, 26, 33]

[0107] Among them, Dec S Indicates that the intelligent converged terminal is configured on nodes 1 and 6. F Indicates that the feeder monitoring unit is configured on branches 2, 3, 4, and 5; Dec M Indicates that the smart meters are configured on nodes 18, 22, 26, and 33.

[0108] Step 1.4: Set the data collection type of the measurement equipment corresponding to the distribution network.

[0109] The measurement equipment includes an intelligent fusion terminal, an intelligent meter and a feeder detection unit. The intelligent fusion terminal is used to collect node voltage vectors, the intelligent meter is used to collect node injection power, and the feeder monitoring unit is used to collect branch power. The acquisition errors of the above measurement equipment are assumed to obey Gaussian distribution, and the acquisition accuracy of different devices is characterized by adjusting the standard deviation. The corresponding data acquisition is shown in the following formula:

[0110] x m,i =x i ×(1+α i ×randn)

[0111] Among them, x m,i is the data measurement value corresponding to the i-th type of equipment, x i is the true value of the data corresponding to the i-th type of equipment, α i is the standard deviation of the measured data, randn represents Gaussian noise, its mathematical expectation can be set to 0, and its variance can be set to 1.

[0112] In one implementation, the data types and sampling standard deviations collected by the measurement equipment are shown in Table 1 below:

[0113] Table 1 Equipment measurement parameters

[0114]

[0115] In this embodiment, in step 2, state estimation is performed according to the network topology parameters, the historical power flow data, and the initial measurement equipment configuration scheme to obtain the voltage amplitude and phase angle corresponding to each node, including:

[0116] Based on the network topology parameters, the historical power flow data and the initial measurement device configuration scheme, calculating the injected power pseudo-measurement data of the node set not configured with the measurement device;

[0117] A measurement formula is generated based on the injected power pseudo-measurement data, and the measurement formula is solved to obtain the voltage amplitude and phase angle corresponding to each node.

[0118] That is, step 2 specifically includes:

[0119] Step 2.1: Divide the connected nodes without measurement equipment into islands, and collect the total power flowing into the area through the island edge device. The total power in the island is calculated as follows:

[0120] S total =S in -S out =P total +jQ total

[0121] Among them, S total is the total power in the island, S in is the power flowing into the island, S out is the power flowing out of the island, P total , Q total They are the historical data of the total active power and reactive power in the island respectively.

[0122] Step 2.2: The proportional coefficient constructed by historical data is combined with real-time data to calculate the pseudo-measurement data of the injected power of the unconfigured measurement node. The calculation is shown in the following formula:

[0123]

[0124] Among them, P mea,total , Q mea,total are the total active power and reactive power in the island calculated based on real-time measurements, P k , Q k Inject historical data of active power and reactive power into the node, P pse,k , Q pse,k Inject pseudo measurements of active and reactive power into nodes.

[0125] Step 2.3: Generate a measurement equation based on the measurement data generated in step 2.2. The measurement equation is shown as follows:

[0126] z=h(x)+v

[0127] Among them, z is the measurement vector, including the collected power, voltage, etc.; v is the measurement error vector; h(x) is the measurement function equation based on Kirchhoff's law, and x represents the voltage and phase angle of each node, that is, the state variable of the entire system.

[0128] In addition, the measurement function equation based on Kirchhoff's law is calculated as follows:

[0129]

[0130] Among them, P i , Q i are the active power and reactive power injected into the node respectively; ij , Q ij are branch active power and reactive power respectively; V i 、V j are the voltage amplitudes of nodes i and j respectively; θ ij is the voltage phase angle difference between node i and node j; G ij , B ij are the real and imaginary parts of the corresponding admittance matrix elements respectively; g ij 、b ij are the conductance and susceptance between nodes i and j respectively; i and j represent the node numbers; n is the total number of nodes.

[0131] Step 2.4: Linearize the measurement function using the Newton iteration method and solve it using the following iterative form:

[0132]

[0133] Among them, x (k) is the system state variable after k iterations; H(x) is the Jacobian matrix of the measurement function, H T represents the transpose of the Jacobian matrix, W -1 represents the measurement weight matrix, represents the partial derivative of the measurement equation, x (k+1) represents the updated state variable, Δx (k) Represents the correction amount of the state variable.

[0134] In one implementation, the iteration convergence condition can be |x (k+1) -x (k) |<ε, where ε is the convergence accuracy, ε=1e-4. After the solution is completed, the voltage amplitude and voltage phase angle of each node in the network can be obtained.

[0135] In one embodiment, step 3 specifically includes:

[0136] Step 3.1, in the process of determining the observability of the voltage amplitude and phase angle of each node, includes:

[0137] Acquire the power flow data at the current moment, and calculate the difference between the power flow data at the current moment and the voltage amplitude and phase angle corresponding to each node;

[0138] The observability of the voltage amplitude and phase angle of each node is determined based on the difference.

[0139] That is, the voltage amplitude and phase angle are selected as state variables, and the error between the true value and the measured value is compared to determine whether it is observable. The calculation method is as follows:

[0140]

[0141] Among them, x lc is the measured value of the state variable calculated by state estimation; x zs is the true value of the system state variable at that moment; β is the set observable judgment threshold.

[0142] In one implementation, if the quantity measured is voltage, the observable judgment threshold of the voltage is 1% of the true value; if the quantity measured is a phase angle, the observable judgment threshold of the phase angle is 0.3°.

[0143] Step 3.2: Perform state estimation based on the measurement results of multiple time sections and calculate the observable times of the state variables at each time section. The observability calculation under the time dimension is shown as follows:

[0144]

[0145] Among them, r t is the observability of the time dimension, m is the number of state estimations within the set time period, and its value of 96 times means that state estimation is performed with a step size of 5 minutes within 24 hours, and n is the number of nodes in the network.

[0146] In this embodiment, the relationship between the voltage amplitude in the state estimation result and its corresponding observable threshold is as follows: Figure 3 As shown in the figure, the relationship between the voltage phase angle and its corresponding observable threshold in the state estimation result is as follows: Figure 4 shown.

[0147] Step 3.3: Divide the topological network into several sub-regions with uniform nodes, select a measurement result under a time section, and calculate the ratio of the number of nodes with observable state variables in each sub-region to the total number of nodes to express the observability of the spatial dimension. The observability calculation of the spatial dimension is shown in the following formula:

[0148]

[0149] Among them, r s is the observability of the spatial dimension, N is the number of state variables in the sub-region, K is the number of sub-regions in the entire network,

[0150] In one implementation, taking the IEEE 33-node system as an example, the entire distribution network is divided into three sub-areas with evenly distributed nodes, such as Figure 5 shown.

[0151] In one embodiment, step 4 specifically includes:

[0152] Step 4.1: Taking the configuration cost of the measurement equipment as the objective function and the observability of the time dimension and space dimension calculated in step 3 as constraints, the measurement equipment optimization configuration model is established as follows:

[0153]

[0154] Among them, e is the total cost of the configuration plan, n dev_i is the total number of configurations of the i-th measurement equipment, a i is the cost factor of the i-th measurement equipment, r t.set is the observability index under the set time dimension, r s.set is the observability index under the set spatial dimension, r t is the observability indicator of the time dimension; r s It is the observability indicator of the spatial dimension.

[0155] In one embodiment, the cost factors are 300,000, 120,000, and 50,000, respectively, corresponding to the three types of measurement equipment with high, medium, and low precision set in step 1.4; in addition, the temporal observability can be set to 90%, and the spatial observability can be set to 95%.

[0156] In this embodiment, in the process of solving the measurement equipment optimization configuration model based on the adaptive simulated annealing algorithm, the initial measurement equipment configuration scheme is iterated to obtain the target measurement equipment optimization configuration scheme, including:

[0157] In the process of solving the measurement equipment optimization configuration model and iterating the initial measurement equipment configuration scheme by introducing an adaptive simulated annealing algorithm with an adaptive factor, comparing the configuration costs of the initial measurement equipment configuration scheme and the next measurement equipment configuration scheme, and taking the measurement equipment configuration scheme with a low configuration cost as the initial measurement equipment configuration scheme;

[0158] When a preset iteration condition is met, a temperature adjustment method is determined, and when a preset temperature threshold is met after the temperature adjustment method is executed, the initial measurement device configuration scheme is used as the target measurement device configuration scheme.

[0159] That is, step 4.2: In the iterative process of the simulated annealing algorithm, an adaptive factor is introduced to adjust the coding length of the scheme change, and its calculation formula is as follows:

[0160] L=l×α m

[0161]

[0162] Where L is the length of each update scheme encoding in the algorithm, l is the control step amplitude, which is proportional to the vector length and has a value of 10; j i is the current iteration number; j max is the total number of iterations, α m is the adaptive factor and e is the natural base.

[0163] In one embodiment, the total number of iterations can be set to 10,000 times. max The value is 0.9, α r is a random step size factor on [-0.1, 0.1], which constrains the step size while maintaining the randomness of the algorithm, so that the algorithm can output a more stable optimal solution in the later stage.

[0164] In this embodiment, during the iteration process of the adaptive simulated annealing model, the configuration costs of the initial measurement equipment configuration scheme and the next measurement equipment configuration scheme are compared to determine the initial measurement equipment configuration scheme, including:

[0165] If the next measurement equipment configuration scheme does not satisfy the observability constraint, the next measurement equipment configuration scheme is not accepted; or

[0166] If the next measurement equipment configuration scheme satisfies the observability constraint and the configuration cost is lower than the configuration cost of the initial measurement equipment configuration scheme, accept the next measurement equipment configuration scheme and use the next measurement equipment configuration scheme as the initial measurement equipment configuration scheme; or

[0167] When the next measurement equipment configuration scheme satisfies the observability constraint and the configuration cost is higher than the configuration cost of the initial measurement equipment configuration scheme, determine whether to accept the next measurement equipment configuration scheme according to the Metropolis criterion, and when it is determined to accept the next measurement equipment configuration scheme, use the next measurement equipment configuration scheme as the initial measurement equipment configuration scheme.

[0168] That is, step 4.3: compare the configuration costs of different solutions in each iteration of the annealing simulation algorithm. If the new solution does not meet the set observability, then do not accept the new solution; if the new solution meets observability and has a lower configuration cost, then accept the new solution; if the new solution meets observability but has a higher configuration cost, then accept the new solution according to the Metropolis criterion. This criterion accepts the inferior solution according to the probability calculated by the following formula.

[0169]

[0170] Among them, cost is the configuration cost; k is the number of iterations; T is the real-time temperature during the annealing process; P is the probability of accepting the inferior solution, cost k+1 is the configuration cost corresponding to the current iteration process, cost k is the configuration cost corresponding to the previous iteration, and e is the natural base.

[0171] Step 4.4: After reaching the set number of iterations at one temperature, the cooling method is determined by the ratio of the two temperatures to the change in the indirect scheme, as shown in the following formula:

[0172]

[0173] Where n is the number of iterations; T k is the kth temperature; m is the set temperature rise boundary value; s is the cooling factor; υ is the ratio of the two temperature indirect changes to the scheme, T k+1 is the temperature after the change, and f(υ) is the temperature change criterion.

[0174] In this embodiment, the calculation ratio of the two temperature component acceptance scheme changes is shown in the following formula:

[0175]

[0176] Wherein, A is the number of times the scheme change is accepted at the current temperature; S is the number of iterations set at the current temperature; n represents the nth temperature change, which is determined by the number of current temperature changes in the actual iteration; preferably, this value is set to 100 in this embodiment.

[0177] The relevant parameter settings in the simulated annealing algorithm are shown in Table 2 below:

[0178] Table 2 Simulated annealing parameters

[0179] parameter Value Control step size l 10 <![CDATA[Total number of iterations j max > 10000 Initial temperature T 100℃ Cooling Factor 0.95 <![CDATA[Heating boundary υ m > 0.04

[0180] The above algorithm is used to solve the optimization configuration model. The change of configuration cost during iterative optimization is as follows: Figure 6After reaching the final temperature, the optimal measurement equipment optimization configuration scheme is output, including the number and type of each measurement equipment configured at the node and branch, and the comparison with the initial measurement equipment optimization configuration scheme is shown in Table 3 below.

[0181] Table 3 Comparison of solutions

[0182]

[0183] Embodiment 2:

[0184] The present invention based on the same inventive concept also provides an optimization configuration system for distribution network measurement equipment, such as Figure 7 As shown, including:

[0185] An acquisition module is configured to acquire network topology parameters, historical flow data, and an initial measurement device configuration plan;

[0186] An estimation module is configured to perform state estimation according to the network topology parameters, the historical power flow data and the initial measurement equipment configuration scheme to obtain the voltage amplitude and phase angle corresponding to each node;

[0187] A determination module is configured to determine the observability of the voltage amplitude and phase angle of each node, and determine the observability of the distribution network voltage state based on the time dimension and the space dimension based on the observability of the voltage amplitude and phase angle;

[0188] The output module is configured to establish a measurement equipment optimization configuration model based on the observability of the time dimension and the space dimension and the configuration cost of the distribution network measurement equipment as a target, and iterate the initial measurement equipment configuration plan in the process of solving the measurement equipment optimization configuration model based on an adaptive simulated annealing algorithm to obtain a target measurement equipment optimization configuration plan.

[0189] Preferably, the determination module is further configured to:

[0190] Acquire the power flow data at the current moment, and calculate the difference between the power flow data at the current moment and the voltage amplitude and phase angle corresponding to each node;

[0191] The observability of the voltage amplitude and phase angle of each node is determined based on the difference.

[0192] Preferably, the observability of the voltage amplitude and phase angle of each node is determined by the following formula:

[0193]

[0194] Among them, x lc is the measured value of the state variable calculated by state estimation, x zsis the true value of the system state variable at that moment, and β is the preset observable judgment threshold.

[0195] Preferably, determining the observability of the distribution network voltage state based on the time dimension based on the observability of the voltage amplitude and phase angle comprises:

[0196]

[0197] Among them, r t is the observability of the time dimension, m is the total number of time sections, n is the number of nodes in the topological network, j is the time section number, and i is the node number.

[0198] Preferably, determining the observability of the voltage state of the distribution network based on the spatial dimension based on the observability of the voltage amplitude and phase angle comprises:

[0199] Divide the topological network into multiple sub-areas with even numbers of nodes;

[0200] Obtain the measurement results at any time section, and determine the observability of the spatial dimension based on the measurement results according to the spatial dimension observability formula.

[0201] Preferably, the spatial dimension observability formula is as follows:

[0202]

[0203] Among them, r s is the observability of the spatial dimension, N is the number of state variables in the sub-region, K is the number of sub-regions in the entire network, b is the number of state variables, and a is the sub-region number.

[0204] Preferably, the measurement equipment optimization configuration model is as follows:

[0205]

[0206] Where e is the total cost of the configuration solution; n dev_i is the total number of configurations for the i-th type of measurement equipment; a i is the cost factor of the i-th measurement equipment; r t.set is the observability indicator under the set time dimension; r s.set is the observability index under the set spatial dimension; r t is the observability indicator of the time dimension; r s It is the observability indicator of the spatial dimension.

[0207] Preferably, the output module is further configured to:

[0208] In the process of solving the measurement equipment optimization configuration model and iterating the initial measurement equipment configuration scheme by introducing an adaptive simulated annealing algorithm with an adaptive factor, comparing the configuration costs of the initial measurement equipment configuration scheme and the next measurement equipment configuration scheme, and taking the measurement equipment configuration scheme with a low configuration cost as the initial measurement equipment configuration scheme;

[0209] When a preset iteration condition is met, a temperature adjustment method is determined, and when a preset temperature threshold is met after the temperature adjustment method is executed, the initial measurement device configuration scheme is used as the target measurement device configuration scheme.

[0210] Preferably, the adaptive simulated annealing algorithm is as follows:

[0211] L=l×α m

[0212]

[0213] Among them, L is the length of each update scheme encoding in the algorithm, l is the control step amplitude, which is proportional to the vector length, and j i is the current iteration number, j max is the total number of iterations, α m is the adaptive factor, α max The value is 0.9, α r is a random step factor on [-0.1, 0.1], and e is a natural base.

[0214] Preferably, the output module further includes:

[0215] The comparison acceptance module is configured as follows:

[0216] If the next measurement equipment configuration scheme does not satisfy the observability constraint, the next measurement equipment configuration scheme is not accepted; or

[0217] If the next measurement equipment configuration scheme satisfies the observability constraint and the configuration cost is lower than the configuration cost of the initial measurement equipment configuration scheme, accept the next measurement equipment configuration scheme and use the next measurement equipment configuration scheme as the initial measurement equipment configuration scheme; or

[0218] When the next measurement equipment configuration scheme satisfies the observability constraint and the configuration cost is higher than the configuration cost of the initial measurement equipment configuration scheme, determine whether to accept the next measurement equipment configuration scheme according to the Metropolis criterion, and when it is determined to accept the next measurement equipment configuration scheme, use the next measurement equipment configuration scheme as the initial measurement equipment configuration scheme.

[0219] Preferably, the Metropolis criterion is calculated as shown below:

[0220]

[0221] Among them, cost is the configuration cost, k is the number of iterations, T is the real-time temperature during the annealing process, P is the probability of accepting the next measurement equipment configuration plan, and cost k+1 is the configuration cost corresponding to the current iteration process, cost k is the configuration cost corresponding to the previous iteration, and e is the natural base.

[0222] Preferably, the temperature adjustment method is as follows:

[0223]

[0224] Where n is the number of iterations, T k is the kth temperature, υ m is the set temperature rise boundary value, s is the cooling factor, υ is the ratio of the two temperature changes to the scheme, T k+1 is the temperature after the change, and f(υ) is the temperature change criterion.

[0225] Preferably, the calculation of the ratio of the two temperatures to the change of the scheme is as follows:

[0226]

[0227] Where A is the number of times the scheme is changed at the current temperature, S is the number of set iterations at the current temperature, n represents the nth temperature change, and A n A represents the number of scheme changes corresponding to the nth temperature. n-1 Indicates the number of scheme changes corresponding to the n-1th temperature, S n Indicates the number of iterations corresponding to the nth temperature, S n-1 Indicates the number of iterations corresponding to the n-1th temperature.

[0228] Preferably, the estimation module is further configured to:

[0229] Based on the network topology parameters, the historical power flow data and the initial measurement device configuration scheme, calculating the injected power pseudo-measurement data of the node set not configured with the measurement device;

[0230] A measurement formula is generated based on the injected power pseudo-measurement data, and the measurement formula is solved to obtain the voltage amplitude and phase angle corresponding to each node.

[0231] Preferably, the injection power pseudo-measurement data is calculated as shown in the following formula:

[0232]

[0233] Among them, P mea,total , Q mea,total is the total active power and reactive power in the island calculated based on real-time measurements, P k , Q k Inject historical data of active power and reactive power into the node, P pse,k , Q pse,k Inject pseudo measurements of active and reactive power into the node, P total , Q total They are the historical data of the total active power and reactive power in the island respectively.

[0234] Preferably, the measurement formula is as follows:

[0235] z=h(x)+v

[0236] Wherein, z is the measurement vector, including the collected power and voltage, h(x) is the measurement function equation based on Kirchhoff's law, x represents the voltage amplitude and phase angle of each node, and v is the measurement error vector.

[0237] Preferably, the voltage amplitude and phase angle are calculated as shown below:

[0238]

[0239] Among them, x (k) is the system state variable after k iterations, H(x) is the Jacobian matrix of the measurement function, H T represents the transpose of the Jacobian matrix, W -1 represents the measurement weight matrix, represents the partial derivative of the measurement equation, x (k+1) represents the updated state variable, Δx (k) Represents the correction amount of the state variable.

[0240] Example 3

[0241] like Figure 8 As shown, the present invention also provides an electronic device, which may be a computer device, a single-chip device, an intelligent mobile device, etc. The electronic device in this embodiment may include a processor, a memory, a transceiver component, etc. The memory, the processor, and the transceiver component are connected via a bus; the memory may be used to store an execution program, and an exemplary execution program may include instructions; the processor is used to execute the instructions stored in the memory. The memory may also be used to store data, which may be called and / or modified when the instructions are executed.

[0242] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in a storage medium to implement the corresponding method flow or corresponding functions, so as to implement the steps of an optimization configuration method for a distribution network measurement device in the above-mentioned embodiment.

[0243] Example 4

[0244] Based on the same inventive concept, the present invention also provides a readable storage medium, specifically an electronic device readable storage medium (Memory), which is a memory device in an electronic device for storing programs and data. It can be understood that the storage medium here can include both built-in storage media in electronic devices and, of course, extended storage media supported by electronic devices. The storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions can be one or more execution programs (including program codes). It should be noted that the storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor loads and executes one or more instructions stored in the storage medium, which can implement the steps of an optimization configuration method for a distribution network measurement device in the above embodiment.

[0245] 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.

[0246] 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.

[0247] 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.

[0248] 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 The steps for the functions specified in one or more boxes.

[0249] The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are included in the scope of the claims of the present invention to be approved.

Claims

1. A method for optimizing configuration of distribution network measurement equipment, characterized in that: include: Obtain network topology parameters, historical flow data, and initial measurement equipment configuration plan; Perform state estimation according to the network topology parameters, the historical power flow data and the initial measurement equipment configuration scheme to obtain the voltage amplitude and phase angle corresponding to each node; Determine the observability of the voltage amplitude and phase angle of each node, and determine the observability of the voltage state of the distribution network based on the time dimension and the space dimension based on the observability of the voltage amplitude and phase angle; Taking the observability of the time dimension and the space dimension as constraints and the configuration cost of the distribution network measurement equipment as the target, a measurement equipment optimization configuration model is established, and in the process of solving the measurement equipment optimization configuration model based on an adaptive simulated annealing algorithm, the initial measurement equipment configuration scheme is iterated to obtain the target measurement equipment optimization configuration scheme.

2. The method according to claim 1, characterized in that The observability of the voltage amplitude and phase angle of each node is determined, including: Acquire the power flow data at the current moment, and calculate the difference between the power flow data at the current moment and the voltage amplitude and phase angle corresponding to each node; The observability of the voltage amplitude and phase angle of each node is determined based on the difference.

3. The method according to claim 2, characterized in that The observability of the voltage amplitude and phase angle of each node is determined by the following formula: Among them, x lc is the measured value of the state variable calculated by state estimation, x zs is the true value of the system state variable at that moment, and β is the preset observable judgment threshold.

4. The method according to claim 1, characterized in that: The observability of the time dimension is calculated as follows: Among them, r t is the observability of the time dimension, m is the total number of time sections, n is the number of nodes in the topological network, j is the time section number, and i is the node number.

5. The method according to claim 1, characterized in that Determining the observability of the voltage state of the distribution network based on the observability of the voltage amplitude and phase angle based on the observability of the spatial dimension includes: Divide the topological network into multiple sub-areas with even numbers of nodes; Obtain the measurement results at any time section, and determine the observability of the spatial dimension based on the measurement results according to the spatial dimension observability formula.

6. The method according to claim 5, characterized in that The spatial dimension observability formula is as follows: Among them, r s is the observability of the spatial dimension, N is the number of state variables in the sub-region, K is the number of sub-regions in the entire network, b is the number of state variables, and a is the sub-region number.

7. The method according to claim 1, characterized in that The measurement equipment optimization configuration model is as follows: Among them, e is the total cost of the configuration plan, n dev_i is the total number of configurations for the i-th type of measurement equipment; a i is the cost factor of the i-th measurement equipment; r t.set is the observability indicator under the set time dimension; r s.set is the observability index under the set spatial dimension; r t is the observability indicator of the time dimension; r s It is the observability indicator of the spatial dimension.

8. The method according to claim 1, characterized in that In the process of solving the measurement equipment optimization configuration model based on the adaptive simulated annealing algorithm, the initial measurement equipment configuration scheme is iterated to obtain the target measurement equipment optimization configuration scheme, including: In the process of solving the measurement equipment optimization configuration model and iterating the initial measurement equipment configuration scheme by introducing an adaptive simulated annealing algorithm with an adaptive factor, comparing the configuration costs of the initial measurement equipment configuration scheme and the next measurement equipment configuration scheme, and taking the measurement equipment configuration scheme with a low configuration cost as the initial measurement equipment configuration scheme; When a preset iteration condition is met, a temperature adjustment method is determined, and when a preset temperature threshold is met after the temperature adjustment method is executed, the initial measurement device configuration scheme is used as the target measurement device configuration scheme.

9. The method according to claim 8, characterized in that The adaptive simulated annealing algorithm is shown in the following formula: L＝l×α m Among them, L is the length of each update scheme encoding in the algorithm, l is the control step amplitude, which is proportional to the vector length, and j i is the current iteration number, j max is the total number of iterations, α m is the adaptive factor, α max The value is 0.9, α r is a random step factor on [-0.1, 0.1], and e is a natural base.

10. The method according to claim 8, characterized in that The step of comparing the configuration costs of the initial measurement equipment configuration scheme and the next measurement equipment configuration scheme during the iteration process of the adaptive simulated annealing model to determine the initial measurement equipment configuration scheme includes: Step 1: Determine whether the next measurement equipment configuration scheme satisfies the observability constraint. If not, the next measurement equipment configuration scheme is not accepted, otherwise, proceed to step 2; Step 2: Determine whether the configuration cost of the next measurement equipment configuration scheme that meets the observability constraint is lower than the configuration cost of the initial measurement equipment configuration scheme. If so, accept the next measurement equipment configuration scheme and use it as the initial measurement equipment configuration scheme. Otherwise, determine whether to accept the next measurement equipment configuration scheme according to the Metropolis criterion, and if it is determined to accept the next measurement equipment configuration scheme, use the next measurement equipment configuration scheme as the initial measurement equipment configuration scheme.

11. The method according to claim 10, characterized in that The Metropolis criterion is calculated as follows: Among them, cost is the configuration cost, k is the number of iterations, T is the real-time temperature during the annealing process, P is the probability of accepting the next measurement equipment configuration plan, and cost k+1 is the configuration cost corresponding to the current iteration process, cost k is the configuration cost corresponding to the previous iteration, and e is the natural base.

12. The method according to claim 9, characterized in that The temperature adjustment method is as follows: Where n is the number of iterations, T k is the kth temperature, υ m is the set temperature rise boundary value, s is the cooling factor, υ is the ratio of the two temperature changes to the scheme, T k+1 is the temperature after the change, and f(υ) is the temperature change criterion.

13. The method according to claim 12, characterized in that The calculation of the ratio of the two temperatures to the change of the scheme is shown in the following formula: Where A is the number of times the scheme is changed at the current temperature, S is the number of set iterations at the current temperature, n represents the nth temperature change, and A n A represents the number of scheme changes corresponding to the nth temperature. n-1 Indicates the number of scheme changes corresponding to the n-1th temperature, S n Indicates the number of iterations corresponding to the nth temperature, S n-1 Indicates the number of iterations corresponding to the n-1th temperature.

14. The method according to claim 1, characterized in that The state estimation is performed according to the network topology parameters, the historical power flow data and the initial measurement equipment configuration scheme to obtain the voltage amplitude and phase angle corresponding to each node, including: Based on the network topology parameters, the historical power flow data and the initial measurement device configuration scheme, calculating the injected power pseudo-measurement data of the node set not configured with the measurement device; A measurement formula is generated based on the injected power pseudo-measurement data, and the measurement formula is solved to obtain the voltage amplitude and phase angle corresponding to each node.

15. The method according to claim 14, characterized in that The injection power pseudo-measurement data is calculated as follows: Among them, P mea,total , Q mea,total are the total active power and reactive power in the island calculated based on real-time measurements, P k , Q k are historical data of active power and reactive power injected into the node, P pse,k , Q pse,k Pseudo measurements of active power and reactive power injected into the node, P total , Q total They are the historical data of the total active power and reactive power in the island respectively.

16. The method according to claim 14, characterized in that The measurement formula is as follows: z=h(x)+v Wherein, z is the measurement vector, h(x) is the measurement function equation based on Kirchhoff's law, x represents the voltage amplitude and phase angle of each node, and v is the measurement error vector.

17. The method according to claim 16, characterized in that The voltage amplitude and phase angle are calculated as follows: Among them, x (k) is the system state variable after k iterations, H T represents the transpose of the Jacobian matrix, W -1 represents the measurement weight matrix, It means to find the partial derivative of the measurement function equation, x (k+1) represents the updated state variable, Δx (k) Represents the correction amount of the state variable.

18. An optimization configuration system for distribution network measurement equipment, characterized in that: include: An acquisition module is configured to acquire network topology parameters, historical flow data, and an initial measurement device configuration plan; An estimation module is configured to perform state estimation according to the network topology parameters, the historical power flow data and the initial measurement equipment configuration scheme to obtain the voltage amplitude and phase angle corresponding to each node; A determination module is configured to determine the observability of the voltage amplitude and phase angle of each node, and determine the observability of the distribution network voltage state based on the time dimension and the space dimension based on the observability of the voltage amplitude and phase angle; The output module is configured to establish a measurement equipment optimization configuration model based on the observability of the time dimension and the space dimension and the configuration cost of the distribution network measurement equipment as a target, and iterate the initial measurement equipment configuration plan in the process of solving the measurement equipment optimization configuration model based on an adaptive simulated annealing algorithm, and output a target measurement equipment configuration plan.

19. An electronic device, characterized in that: include: at least one processor and memory; The memory and the processor are connected via a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, the optimization configuration method of the distribution network measurement equipment according to any one of claims 1 to 17 is implemented.

20. A readable storage medium, characterized in that: An execution program is stored thereon, and when the execution program is executed, the optimization configuration method of the distribution network measurement equipment according to any one of claims 1 to 17 is implemented.

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