A data-driven power quality monitoring point optimization method and device

Abstract

The present invention discloses a data-driven power quality monitoring point optimization method and device, the method comprising: obtaining power quality monitoring data of power grid nodes in a to-be-evaluated area; based on the sample size estimation principle, determining the optimal number of monitoring points according to the statistical characteristic values ​​of the power quality monitoring data; based on the hierarchical modeling method, constructing an optimization target model according to the optimal number of monitoring points, power grid node attributes, and preset power quality control requirements; based on the optimization target model, using an improved genetic algorithm to perform global optimization and solve, and obtain a Pareto solution set; determining the optimal monitoring points according to the Pareto solution set and a preset scenario bias weight matrix. By implementing the present invention, the monitoring optimization points are updated based on the existing monitoring data. The incremental demand for updating the monitoring points driven by the monitoring data is realized; in addition, the improved genetic algorithm is used to reduce the solution sample space while satisfying the monitoring point optimization architecture, thereby improving the optimization calculation efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of power system analysis, and in particular to a data-driven power quality monitoring point optimization method and device. Background Art

[0002] With the rapid development of China's economy, the scale of power system nodes has expanded rapidly, and a large number of nonlinear and fluctuating loads have been connected to the power grid, resulting in increasingly serious and complex power quality problems such as high and low voltage, harmonics, imbalance, and voltage sag. In order to timely discover, analyze and solve the power quality problems in the power grid, power companies generally build power quality monitoring systems. However, the current monitoring points of the power quality monitoring system are usually laid out based on human experience, with a single point distribution target, generally only set at the interference source load or important nodes of the power grid. The evaluation of the power quality level in each region lacks a benchmark basis for horizontal comparison. At the same time, due to the lack of data at some nodes, some power quality events cannot be traced. However, if monitoring devices are configured for all nodes of the power grid, the economic efficiency is poor and unnecessary. Therefore, studying the optimal configuration of power quality monitoring points is of great significance for reducing equipment investment, improving the economic benefits of the power system, and realizing lean management of power quality.

[0003] At present, the research on the optimal configuration of power quality monitoring points at home and abroad is mainly based on users and power grid parameters, taking into account the propagation law of power quality events, and using simulation calculations, mathematical programming and other methods, such as the voltage sag observable area (MRA) method, graph analysis method, singular value decomposition method, regression coefficient method, etc., to maximize the economic benefits of monitoring point layout, that is, to ensure that when an event that may cause power quality problems occurs at any point in the entire network, it can be recorded by at least one monitoring device, while ensuring that the number of monitoring points deployed is minimized.

[0004] The above method is mainly based on the propagation law of power quality events, static data and power grid parameters, and uses simulation calculation to establish an impact coverage model. It introduces a target planning method to find the optimal layout plan. However, it is difficult to obtain power grid parameters in actual situations, especially in distribution networks, where line topology and equipment parameters have uncertain and time-varying characteristics. Summary of the invention

[0005] In view of this, an embodiment of the present invention provides a data-driven power quality monitoring point optimization method and device to solve the technical problem in the prior art that it is difficult and has great uncertainty when using power grid parameters to optimize monitoring points.

[0006] The technical solution proposed by the present invention is as follows:

[0007] A first aspect of an embodiment of the present invention provides a method for optimizing power quality monitoring point layout, including: obtaining power quality monitoring data of power grid nodes in a to-be-evaluated area; based on a sample size estimation principle, determining the optimal number of monitoring points according to the statistical characteristic values ​​of the power quality monitoring data; based on a hierarchical modeling method, constructing an optimization target model according to the optimal number of monitoring points, power grid node attributes, and preset power quality control requirements; based on the optimization target model, using an improved genetic algorithm to perform global optimization solution to obtain a Pareto solution set; determining the optimal monitoring points according to the Pareto solution set and a preset scenario bias weight matrix.

[0008] Optionally, based on the sample size estimation principle, the optimal number of monitoring points is determined according to the statistical characteristic values ​​of the power quality monitoring data, including: calculating the statistical characteristic values ​​of the monitoring data according to the power quality monitoring data; based on the sample size estimation principle, calculating the statistical accuracy corresponding to different monitoring point scales according to the statistical characteristic values; drawing a trend chart according to the statistical accuracy corresponding to different monitoring point scales; and first determining the theoretical optimal number of monitoring points according to the trend chart and the statistical accuracy threshold.

[0009] Optionally, based on the sample size estimation principle, the optimal number of monitoring points is determined according to the statistical characteristic values ​​of the power quality monitoring data, and also includes: determining the optimal number of monitoring points according to the theoretical optimal number of monitoring points, the number of existing monitoring points and the number of monitoring points with installation conditions; and confirming the rationality based on the optimal number of monitoring points.

[0010] Optionally, based on a hierarchical modeling method, an optimization target model is constructed according to the optimal number of monitoring points, grid node attributes and preset power quality control requirements, including: marking grid node attributes according to whether installation conditions are met and whether a monitoring terminal has been installed; determining the optimization target according to the power quality control requirements; based on a hierarchical modeling method, the optimal number of monitoring points and the optimization target are used as objective functions, and grid node attributes are used as constraints to construct an optimization target model.

[0011] Optionally, the optimization objectives include: importance, weak area coverage and monitoring range coverage; the importance is determined based on interference coverage, coverage of important sensitive users and total short-circuit capacity; the weak area coverage is determined based on the average line loss rate of covered nodes and lines, the proportion of total power outage events at covered nodes, the proportion of total complaints at covered nodes and the proportion of equipment damage at covered nodes; the monitoring range coverage is determined based on the number of observable nodes and the number of power grid nodes in the area to be evaluated.

[0012] Optionally, based on the optimization target model, an improved genetic algorithm is used to perform global optimization solution to obtain a Pareto solution set, including: using an improved genetic algorithm crossover operator and mutation operator, based on the objective function and constraints, to perform global optimization solution to obtain a Pareto solution set; the crossover operator is determined by randomly selecting two crossover points and satisfying that the algebraic sum of the selected points of the two chromosomes is equal; the mutation operator is determined by the mutation points appearing in pairs and satisfying that the algebraic sum of the mutation points is unchanged before and after.

[0013] Optionally, the optimal monitoring points are determined according to the Pareto solution set and the preset scenario bias weight matrix, including: determining the scenario bias weight matrix according to the importance, weak area coverage, monitoring range coverage and preset scenarios; calculating the Pareto solution set score according to the scenario bias weight matrix and the Pareto solution set; and determining the optimal monitoring points for the preset scenario according to the Pareto solution set score.

[0014] A second aspect of an embodiment of the present invention provides an apparatus for optimizing power quality monitoring point layout, comprising: a data acquisition module for acquiring power quality monitoring data of power grid nodes in an area to be evaluated; a quantity calculation module for determining the optimal number of monitoring points based on the statistical characteristic values ​​of the power quality monitoring data based on the sample size estimation principle; a model construction module for constructing an optimization target model based on a hierarchical modeling method according to the optimal number of monitoring points, power grid node attributes, and preset power quality control requirements; a genetic algorithm solution module for performing global optimization solution based on the optimization target model using an improved genetic algorithm to obtain a Pareto solution set; and a monitoring point determination module for determining the optimal monitoring points based on the Pareto solution set and a preset scenario bias weight matrix.

[0015] A third aspect of an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the power quality monitoring point optimization method as described in the first aspect of the embodiment of the present invention and any one of the first aspects.

[0016] A fourth aspect of an embodiment of the present invention provides an electronic device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to execute the power quality monitoring point optimization method as described in the first aspect of the embodiment of the present invention and any one of the first aspects.

[0017] The technical solution provided by the present invention has the following effects:

[0018] The embodiment of the present invention provides a data-driven power quality monitoring point optimization method and device, which is based on the existing monitoring data to drive the update of monitoring optimization points. First, the fundamentals and fluctuation levels of power quality indicators are mastered through the existing monitoring system data information, and the optimal number of monitoring points that can cover the fundamentals and fluctuation probability characteristics is calculated based on the sample size estimation theory, so as to realize the incremental demand for updating monitoring points driven by monitoring data, and avoid the complex grid parameter acquisition and evaluation process compared with the traditional method; secondly, the method comprehensively considers the power quality control needs, clarifies the various optimization goals and constraints of the monitoring points, establishes the optimization target model, introduces the bias target weight matrix, realizes the centralized setting of bias parameters for various scenarios, and can dynamically adapt to the control needs of various typical operation scenarios, effectively improving the scenario applicability and flexibility of power quality monitoring point optimization; in addition, the improved genetic algorithm is adopted to reduce the solution sample space while meeting the monitoring point optimization architecture, and improve the optimization calculation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0020] Figure 1 is an overall flow chart of a method for optimizing power quality monitoring point layout according to an embodiment of the present invention;

[0021] Figure 2 is an overall flow chart of a method for optimizing power quality monitoring point layout according to an embodiment of the present invention;

[0022] Figure 3 is a flow chart of calculating the number of points for optimizing the power quality monitoring points according to an embodiment of the present invention;

[0023] Figure 4 is a flow chart of calculating the number of points for optimizing the power quality monitoring points according to another embodiment of the present invention;

[0024] Figure 5 is a flow chart of constructing an optimization target model of a method for optimizing power quality monitoring point layout according to an embodiment of the present invention;

[0025] Figure 6 is a flow chart of an improved genetic algorithm according to a method for optimizing power quality monitoring point layout according to an embodiment of the present invention;

[0026] Figure 7 is a statistical accuracy trend diagram of the power quality monitoring point optimization method according to an embodiment of the present invention;

[0027] Figure 8 is a schematic diagram of a scenario deployment scheme of a method for optimizing power quality monitoring deployment according to an embodiment of the present invention;

[0028] Fig. 9 is a structural block diagram of a device for optimizing power quality monitoring points according to an embodiment of the present invention;

[0029] Fig.10 is a schematic diagram of the structure of a computer-readable storage medium provided according to an embodiment of the present invention;

[0030] Fig.11 is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0031] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0032] The embodiment of the present invention provides a method for optimizing the deployment of power quality monitoring points. Figure 1 and Figure 2 As shown, the method comprises the following steps:

[0033] Step S101: Acquire power quality monitoring data of power grid nodes in the area to be evaluated.

[0034] Specifically, when optimizing the power quality monitoring points, a certain area can be selected as the area to be evaluated for optimization. After determining the area to be evaluated, monitoring data can be obtained based on the power quality system monitoring points that have been set up in the area, and temporary test data can also be obtained as monitoring data through temporarily set monitoring equipment.

[0035] When acquiring monitoring data, representative data can be extracted for a certain voltage level object in the regional power grid to be evaluated. The number of buses of this voltage level in the PMS substation of the regional power grid to be evaluated is extracted as the sample population. When extracting representative data, representative grid nodes are selected based on the principle of stratified sampling and power quality index data are extracted, or all-node monitoring data can be extracted as representative data. Among them, when the stratified sampling principle is adopted, the stratified sampling dimension can be load capacity or short-circuit capacity, etc. The extracted power quality indicators include but are not limited to: voltage, three-phase voltage unbalance, total harmonic voltage distortion rate or flicker, etc., and the extracted data granularity is at least minute-level data.

[0036] Step S102: Based on the sample size estimation principle, the optimal number of monitoring points is determined according to the statistical characteristic values ​​of the power quality monitoring data.

[0037] Specifically, the statistical characteristic values ​​of power quality monitoring data can be calculated through minute-level monitoring data of power quality voltage indicators, and then the sample size estimation principle and the statistical characteristic values ​​are used to calculate the statistical accuracy corresponding to different monitoring point scales to obtain a trend analysis chart, from which the optimal number of monitoring points is determined.

[0038] Step S103: Based on the hierarchical modeling method, an optimization target model is constructed according to the optimal number of monitoring points, grid node attributes, and preset power quality control requirements.

[0039] Specifically, when constructing the optimization target model, a hierarchical modeling method can be used to construct an optimization target component function based on the optimal number of monitoring points and the preset power quality control requirements; then, the constraints of the optimization target model are determined based on the attributes of the power grid nodes; and the optimization target model is constructed using the target component function and the constraints.

[0040] Step S104: Based on the optimization target model, an improved genetic algorithm is used to perform global optimization and solve the problem to obtain a Pareto solution set.

[0041] Specifically, the improved genetic algorithm can be an improved NSGA-Ⅱ genetic algorithm. When using this genetic algorithm, non-dominated sorting is performed by optimizing the objective function and constraints in the target model, and then based on the three basic operations of selection, crossover and mutation of the genetic algorithm, the Pareto solution set is finally obtained through iterative solution.

[0042] Step S105: Determine the optimal monitoring points according to the Pareto solution set and the preset scenario bias weight matrix.

[0043] When determining the optimal monitoring point layout, the preset scenario bias weight matrix and the Pareto solution set can be used for point multiplication calculation to obtain the Pareto solution set scores for various scenarios, and the optimal monitoring point layout plan can be determined based on the scoring results.

[0044] In addition, after determining the optimal number of monitoring points, the above process can be repeated at preset intervals to obtain monitoring data again and update the optimal number of monitoring points.

[0045] The power quality monitoring point optimization method provided by the embodiment of the present invention is based on the existing monitoring data to drive the update of the monitoring optimization point. First, the fundamentals and fluctuation levels of the power quality indicators are mastered through the existing monitoring system data information, and the optimal number of monitoring points that can cover the fundamentals and fluctuation probability characteristics is calculated based on the sample size estimation theory, so as to realize the incremental demand for updating the monitoring points driven by the monitoring data, and avoid the complex grid parameter acquisition and evaluation process compared with the traditional method; secondly, the method comprehensively considers the power quality control needs, clarifies the various optimization goals and constraints of the monitoring points, establishes the optimization target model, introduces the bias target weight matrix, realizes the centralized setting of the bias parameters of various scenarios, and can dynamically adapt to the control needs of various typical operation scenarios, effectively improving the scenario applicability and flexibility of the power quality monitoring point optimization; in addition, the improved genetic algorithm is adopted to reduce the solution sample space while meeting the monitoring point optimization architecture, and improve the optimization calculation efficiency.

[0046] As an optional implementation of the embodiment of the present invention, Figure 3 and Figure 4 As shown, based on the sample size estimation principle, the optimal number of monitoring points is determined according to the statistical characteristic values ​​of the power quality monitoring data, including the following steps:

[0047] Step S201: Calculate the statistical characteristic value of the monitoring data according to the power quality monitoring data. Specifically, the statistical characteristic value includes a mean value and a standard deviation, which can be calculated by minute-level monitoring data of power quality voltage indicators. In addition, the calculation can also be performed by second-level monitoring data, which is not limited in the embodiment of the present invention.

[0048] Step S202: Based on the sample size estimation principle, the corresponding statistical accuracy under different monitoring point scales is calculated according to the statistical characteristic values.

[0049] Specifically, the sample size estimation principle can be used to abstract the monitoring point distribution plan into a non-repetitive random sampling problem. The sample size estimation principle can be expressed by the following formula:

[0050]

[0051] In the formula, r represents the random sampling accuracy; μ and σ represent the mean and standard deviation of the variable respectively; Z represents the standard normal variable, corresponding to the 95% normal interval, Z=1.96; n represents the sample size; N represents the population size.

[0052] When the sample size estimation principle is adopted, μ and σ in the formula can be determined based on the mean and standard deviation of the statistical characteristic values ​​calculated above. As for the sample capacity and the overall capacity, since the optimization is the monitoring points, the number of different monitoring points can be used as the sample capacity, and the overall capacity adopts the number of buses of the voltage level of the regional power grid PMS substation obtained in step S101.

[0053] Therefore, by substituting the above parameters into the calculation formula of the sample estimate, the statistical accuracy corresponding to different monitoring point scales can be calculated.

[0054] Step S203: Draw a trend chart according to the corresponding statistical accuracy under different monitoring point scales. After calculating the number of different monitoring points and their corresponding statistical accuracy, a trend analysis chart can be drawn with the number of monitoring points as the horizontal axis and the statistical accuracy as the vertical axis.

[0055] Step S204: Determine the theoretical optimal number of monitoring points according to the trend graph and the statistical accuracy threshold. In order to determine the optimal number of monitoring points, the statistical accuracy threshold can be determined according to the actual situation, and then in the trend graph, a statistical accuracy threshold line is determined according to the statistical accuracy threshold, and the intersection point of the statistical threshold line and the statistical accuracy is found. The horizontal axis value corresponding to the intersection point is the theoretical optimal number of monitoring points, or the statistically optimal number of monitoring points.

[0056] Step S205: Determine the optimal number of monitoring points according to the theoretical optimal number of monitoring points, the number of existing monitoring points, and the number of monitoring points that meet the installation conditions.

[0057] Specifically, after determining the statistically optimal number of monitoring points, it is also necessary to combine the actual situation, that is, comprehensively consider the on-site deployment conditions and the deployment of existing monitoring points to obtain the actual monitoring point increment demand. Therefore, it can be calculated by the following formula:

[0058] N p =min(N1, N2)-N0

[0059] Where N p represents the optimal number of monitoring points, i.e., the constraint condition; N1 represents the statistically optimal number of monitoring points; N2 represents the number of bus nodes on site that are suitable for installing monitoring terminals; N0 represents the number of existing monitoring points in the power quality monitoring system.

[0060] The optimal number of monitoring points can be calculated by the above formula. Since the optimal number of monitoring points is determined by the difference between the theoretical optimal number of monitoring points and the existing number, the optimal number of monitoring points can be called the incremental demand for monitoring points.

[0061] Step S206: Confirm the rationality of the optimal number of monitoring points. Specifically, since the optimal number of monitoring points is determined by the difference between the optimal number of monitoring points and the existing number, it may be greater than zero or less than or equal to zero. p When it is less than or equal to zero, it indicates that no new monitoring points are needed for the voltage level of the regional power grid. At this time, a new area to be evaluated can be selected to calculate the optimal number of monitoring points. p When it is greater than zero, it indicates that the voltage level of the regional power grid needs to increase the number of monitoring points.

[0062] In order to eliminate the situation where accidental disturbances cause large fluctuations, it is necessary to formulate a test plan for the voltage level in an orderly manner, further expand the scope of general testing and delay the time of temporary testing, and confirm the rationality of the optimal number of monitoring points or incremental demand.

[0063] After the data processing of steps S201-206, the optimal number of monitoring points is input as a fixed value, which effectively reduces the complexity of subsequent calculations, greatly improves the efficiency of optimizing the point layout scheme, and has high engineering practical value.

[0064] As an optional implementation of the embodiment of the present invention, Figure 5 As shown, based on the hierarchical modeling method, an optimization target model is constructed according to the optimal number of monitoring points, grid node attributes and preset power quality control requirements, including:

[0065] Step S301: Mark the grid node attributes according to whether the installation conditions are met and whether the monitoring terminal has been installed. Specifically, for all grid nodes in the area to be evaluated, they are divided into four categories according to whether the on-site demonstration has installation conditions and whether the monitoring nodes have been installed, including monitored nodes, nodes that do not meet monitoring conditions, nodes that do not need to be monitored, and nodes that need to be monitored. Then mark the corresponding grid nodes according to the categories to obtain grid node attributes. A node attribute vector is constructed based on the grid node attributes and is expressed using the following formula:

[0066] X=[x1,x2…x n ] T

[0067] Among them, X is the monitoring point layout plan, which consists of a set of x i i is the number of the regional power grid node, x is i The value is the node monitoring status attribute value.

[0068] Step S302: Determine optimization targets according to power quality control requirements; the optimization targets include: importance, weak area coverage, and monitoring range coverage.

[0069] Specifically, in response to the needs of power quality control, the monitoring nodes mainly consider the access management of interference sources, the power quality operation and maintenance management, and the user marketing services. The monitoring nodes should mainly consider obtaining the power quality status of as many nodes as possible. Therefore, the various needs of the above monitoring points are comprehensively considered. The optimization objectives of the optimization target model are set as: importance, weak area coverage, and monitoring range coverage.

[0070] Among them, the importance can be represented by weighted method based on static data such as ledger data and social experience data, by quantifying interference coverage, coverage rate of important sensitive users and total short-circuit capacity. The higher the importance, the more reasonable the monitoring point layout plan. Therefore, the importance is calculated by the following expression:

[0071]

[0072] In this formula, ω1 is the weight vector of each component, and the weight vector determination method includes but is not limited to the hierarchical analysis method, the fuzzy clustering method, etc.; DT is the interference source coverage of the scheme, IS is the coverage of important sensitive users of the scheme, and CP is the total short-circuit capacity.

[0073] The weak area coverage is characterized by weighted method based on dynamic data such as marketing data and operation scheduling data by quantifying the average line loss rate of covered nodes and lines, the proportion of total power outage events in covered nodes, the proportion of total complaints in covered nodes, and the proportion of equipment damage in covered nodes. The higher the weak area coverage, the more conducive it is to weak point detection or complaint management. The weak area coverage is calculated by the following expression:

[0074]

[0075] Among them, ω2 is the weight vector of each component, and the methods for determining the weight vector include but are not limited to the hierarchical analysis method, the fuzzy clustering method, etc.; LL represents the average line loss rate of the nodes and lines covered by the layout plan; PC represents the proportion of the total number of power outage events in the nodes covered by the plan; CL represents the proportion of the total complaints in the nodes covered by the plan, and EQ represents the proportion of the number of equipment damages in the nodes covered by the plan.

[0076] Considering that the state of nodes without monitoring devices installed can be estimated based on the law of power quality propagation, the monitoring range coverage of the point distribution scheme is evaluated based on the state estimation. That is, the monitoring range coverage can be determined based on the number of observable nodes and the number of grid nodes in the area to be evaluated. The monitoring range coverage is specifically expressed by the following formula:

[0077] f3(X)=A m / N

[0078] Among them, Am is the number of observable nodes, and N is the total number of nodes in the regional power grid.

[0079] Step S303: Taking the optimal number of monitoring points and the optimization target as the objective function and the grid node attributes as the constraint conditions to construct an optimization target model.

[0080] Specifically, taking the optimal number of monitoring points and the optimization target as the objective function and the grid node attributes as the constraint conditions, the optimization of power quality monitoring points can be described as the following mathematical model (optimization target model):

[0081]

[0082] Where X = [x1, x2, …, x n ] T is the optimization variable set; f(X) is the objective function, f1 is the importance, f2 is the coverage of weak areas, f3 is the coverage of monitoring range, f4 is the number of monitoring points, i.e. the optimal number of monitoring points calculated in step S102; U is the basic space, and R is a subset of U. The solution X that satisfies the constraint conditions is called a feasible solution, and the set R represents the set composed of all solutions that satisfy the constraint conditions, which is called the feasible solution set.

[0083] As an optional implementation of an embodiment of the present invention, based on the optimization target model, an improved genetic algorithm is used to perform global optimization solution to obtain a Pareto solution set, including: using an improved genetic algorithm crossover operator and a mutation operator, based on the objective function and constraints, to perform global optimization solution to obtain a Pareto solution set; the crossover operator is determined by randomly selecting two crossover points and satisfying that the algebraic sum of the selected points of the two chromosomes is equal; the mutation operator is determined by the mutation points appearing in pairs and satisfying that the algebraic sum of the mutation points is unchanged before and after.

[0084] Specifically, Figure 6 As shown in the figure, the genetic algorithm solution process is as follows:

[0085] First, an initial population of size N is randomly generated, and a non-dominated sorting is performed based on the optimization target model. The first generation of offspring population is obtained through the three basic operations of selection, crossover, and mutation of the genetic algorithm. The crossover operator is determined by the rule that two points are randomly selected for crossover, and the algebraic sum of the selected points of the two chromosomes is equal. The mutation operator is determined by the rule that the mutation points appear in pairs, and the algebraic sum of the mutation points is constant.

[0086] Secondly, starting from the second generation, the parent population and the child population are merged to perform fast non-dominated sorting. At the same time, the crowding degree of individuals in each non-dominated layer is calculated, and appropriate individuals are selected to form a new parent population based on the non-dominated relationship and the crowding degree of the individuals.

[0087] A new offspring population is generated through the above three basic operations of the genetic algorithm: and so on, until the termination condition that the number of program iterations is greater than the preset value is met.

[0088] As an optional implementation manner of an embodiment of the present invention, the optimal monitoring point distribution is determined according to the Pareto solution set and the preset scenario bias weight matrix, including: determining the scenario bias weight matrix according to the importance, weak area coverage, monitoring range coverage and preset scenario; calculating the Pareto solution set score according to the scenario bias weight matrix and the Pareto solution set; and determining the optimal monitoring point distribution for the preset scenario according to the Pareto solution set score.

[0089] The preset scenarios include but are not limited to grid planning, operation, and marketing. The scenario bias weight matrix can be determined based on the hierarchical analysis method, and the matrix structure can be expressed as:

[0090]

[0091] In this matrix structure, rows represent different scenarios, such as grid planning, operation, and marketing, and columns represent target components, namely, importance (criticality), weak area coverage, and monitoring range coverage. After determining the scenario bias weight matrix, it can be multiplied by the Pareto solution set to obtain the Pareto solution set score E for each scenario, and the solution with the highest formula score is determined as the optimal monitoring point layout solution for the scenario.

[0092] As an optional implementation method of the embodiment of the present invention, an analysis is conducted on a certain S-region power grid area, and the power quality monitoring point optimization method is used to determine the optimal number of monitoring points. In this area, the voltage levels cover 110kV and 220kV, with a total of 230 substation nodes, and the load access types include important hub substations, electrified railways, other nonlinear loads, sensitive and important loads, wind farms, photovoltaic power stations, and other types.

[0093] (1) Data-driven incremental monitoring deployment requirements

[0094] Take 110kV as an example. Currently, the number of monitoring points in the 110kV network is 8. The trend chart of statistical accuracy-distribution scale of the 110kV network is as follows: Figure 7 As shown in the figure, the accuracy threshold is set to 0.05. From the figure, we can see that the optimal number of monitoring points is 17, so the number of additional monitoring points required is 9.

[0095] (2) Establishing an optimization target model

[0096] According to the node attribute vector definition formula, the vector consists of a set of x i i is the number of the regional power grid node, x is iis the state attribute value of the ith node, and its value range is defined as {-2, -1, 0, 1}, where -2 means no monitoring conditions are met, -1 means it has been monitored, 0 means no monitoring is required, and 1 requires monitoring.

[0097] Optimize the weight parameters w1 and w2 of the objective components f1, f2, and f3 in the multi-objective model. Based on the analytic hierarchy process, the values ​​are set to [0.4, 0.4, 0.2], [0.2, 0.3, 0.3, 0.2], and [0.2, 0.3, 0.3, 0.2] respectively.

[0098] (3) Solving the Pareto set for optimal distribution

[0099] The parameter values ​​of the non-dominated sorting genetic algorithm are as follows: population size 100, population generation threshold 200, crossover probability 0.5, and mutation probability 0.1.

[0100] (4) Multi-scenario Optimization Site Plan

[0101] For power quality control scenarios 1, 2, and 3, the weight bias matrix is ​​set as follows:

[0102]

[0103] Draw schematic diagrams of three scene layout schemes as follows Figure 8 As shown, the vertical and horizontal coordinates are the relative geographical locations of the substation, and the light-colored nodes are the locations of the newly added monitoring nodes.

[0104] The power quality monitoring point optimization method provided by the embodiment of the present invention proposes a data-driven power quality monitoring point optimization business process, the business process is executed regularly, the incremental demand for monitoring points is updated in a rolling manner, and the power quality monitoring point management plan is supported. A method for calculating the incremental demand for monitoring points is proposed, which comprehensively considers the fundamentals of the power quality level and the fluctuation probability characteristics, realizes the update of the incremental demand for monitoring points based on the monitoring data drive, and supports the formulation of the power quality test plan. Compared with the traditional method, it avoids the complex grid parameter acquisition and evaluation process; NSGA-Ⅱ genetic algorithm crossover and mutation improvement rules are proposed. According to the incremental demand for monitoring points proposed in this patent, the improved rules meet the monitoring point optimization architecture of this patent. At the same time, due to the reduction of the solution sample space, the optimization calculation efficiency is improved; a bias weight matrix is ​​proposed to realize the centralized setting of bias parameters for various scenarios, which can dynamically adapt to the management and control requirements of various typical operation scenarios, and effectively improve the scenario applicability and flexibility of power quality monitoring point optimization.

[0105] The embodiment of the present invention also provides a device for optimizing the deployment of power quality monitoring points, such as Fig. 9 As shown, including:

[0106] The data acquisition module is used to obtain the power quality monitoring data of the power grid nodes in the area to be evaluated; the specific content refers to the corresponding part of the above method embodiment, which will not be repeated here.

[0107] The quantity calculation module is used to determine the optimal number of monitoring points based on the statistical characteristic values ​​of the power quality monitoring data based on the sample size estimation principle; the specific content refers to the corresponding part of the above method embodiment, which will not be repeated here.

[0108] The model building module is used to build an optimization target model based on a hierarchical modeling method according to the optimal number of monitoring points, grid node attributes and preset power quality control requirements; the specific content refers to the corresponding part of the above method embodiment, which will not be repeated here.

[0109] The genetic algorithm solution module is used to perform global optimization solution based on the optimization target model using an improved genetic algorithm to obtain a Pareto solution set; the specific content can be found in the corresponding part of the above method embodiment, which will not be repeated here.

[0110] The monitoring point determination module is used to determine the optimal monitoring point according to the Pareto solution set and the preset scene bias weight matrix. For details, please refer to the corresponding part of the above method embodiment, which will not be repeated here.

[0111] The power quality monitoring point optimization device provided by the embodiment of the present invention drives the updating of monitoring optimization points based on the existing monitoring data. First, the fundamentals and fluctuation levels of power quality indicators are mastered through the existing monitoring system data information, and the optimal number of monitoring points that can cover the fundamentals and fluctuation probability characteristics is calculated based on the sample size estimation theory, so as to realize the incremental demand for updating the monitoring points driven by the monitoring data, and avoid the complex grid parameter acquisition and evaluation process compared with the traditional method; secondly, the device comprehensively considers the power quality control needs, clarifies the various optimization goals and constraints of the monitoring points, establishes the optimization target model, introduces the bias target weight matrix, realizes the centralized setting of bias parameters for various scenarios, and can dynamically adapt to the control needs of various typical operation scenarios, effectively improving the scenario applicability and flexibility of the optimization of power quality monitoring points; in addition, the improved genetic algorithm is adopted to reduce the solution sample space while meeting the monitoring point optimization architecture, and improve the optimization calculation efficiency.

[0112] For a detailed description of the functions of the power quality monitoring point optimization device provided in an embodiment of the present invention, please refer to the description of the power quality monitoring point optimization method in the above embodiment.

[0113] The embodiment of the present invention also provides a storage medium, such as Fig.10As shown, a computer program 601 is stored thereon, and when the instruction is executed by the processor, the steps of the method for optimizing the deployment of power quality monitoring points in the above-mentioned embodiment are implemented. The storage medium also stores audio and video stream data, feature frame data, interaction request signaling, encrypted data, and preset data size, etc. Among them, the storage medium can be a disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory (Flash Memory), a hard disk (Hard Disk Drive, abbreviated as: HDD) or a solid-state drive (SSD), etc.; the storage medium can also include a combination of the above-mentioned types of memory.

[0114] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the storage medium can be a disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory, a hard disk drive (HDD) or a solid-state drive (SSD), etc.; the storage medium can also include a combination of the above-mentioned types of memory.

[0115] The embodiment of the present invention further provides an electronic device, such as Fig.11 As shown, the electronic device may include a processor 51 and a memory 52, wherein the processor 51 and the memory 52 may be connected via a bus or other means. Fig.11 The example of connecting through bus is taken in the following.

[0116] The processor 51 may be a central processing unit (CPU). The processor 51 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or a combination of the above chips.

[0117] The memory 52 is a non-transient computer-readable storage medium that can be used to store non-transient software programs, non-transient computer executable programs and modules, such as the corresponding program instructions / modules in the embodiment of the present invention. The processor 51 executes various functional applications and data processing of the processor by running the non-transient software programs, instructions and modules stored in the memory 52, that is, to implement the power quality monitoring point optimization method in the above method embodiment.

[0118] The memory 52 may include a program storage area and a data storage area, wherein the program storage area may store an application required for operating the device and at least one function; the data storage area may store data created by the processor 51, etc. In addition, the memory 52 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory 52 may optionally include a memory remotely arranged relative to the processor 51, and these remote memories may be connected to the processor 51 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0119] The one or more modules are stored in the memory 52 and when executed by the processor 51, perform the following steps: Figure 1-8 The power quality monitoring point optimization method in the illustrated embodiment.

[0120] For details of the above electronic equipment, please refer to Figures 1 to 8 The corresponding related descriptions and effects in the illustrated embodiments can be understood and will not be repeated here.

[0121] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A method for optimizing the placement of power quality monitoring points, characterized in that: include: Obtain power quality monitoring data of the power grid nodes in the area to be evaluated; Based on the sample size estimation principle, the optimal number of monitoring points is determined according to the statistical characteristic values ​​of the power quality monitoring data; Based on the hierarchical modeling method, an optimization target model is constructed according to the optimal number of monitoring points, grid node attributes and preset power quality control requirements; Based on the optimization target model, an improved genetic algorithm is used to perform global optimization and solve the problem, thereby obtaining a Pareto solution set; Determine the optimal monitoring points according to the Pareto solution set and the preset scenario bias weight matrix; Based on the hierarchical modeling method, an optimization target model is constructed according to the optimal number of monitoring points, grid node attributes and preset power quality control requirements, including: Mark the grid node attributes according to whether the installation conditions are met and whether the monitoring terminal has been installed; Determine optimization targets based on power quality control requirements; Based on the hierarchical modeling method, the optimal number of monitoring points and the optimization target are used as the objective function, and the grid node attributes are used as constraints to build an optimization target model; The optimization objectives include: importance, coverage of weak areas and coverage of monitoring range; The importance is determined according to the interference coverage, the coverage rate of important sensitive users and the total short-circuit capacity; The weak area coverage is determined based on the average line loss rate of covered nodes and lines, the proportion of total power outage events in covered nodes, the proportion of total complaints in covered nodes, and the proportion of equipment damage in covered nodes; The monitoring range coverage is determined according to the number of observable nodes and the number of power grid nodes in the area to be evaluated; Determining the optimal monitoring points according to the Pareto solution set and the preset scenario bias weight matrix includes: Determine the scenario bias weight matrix based on importance, weak area coverage, monitoring range coverage, and preset scenarios; Calculating a Pareto solution set score based on the scenario bias weight matrix and the Pareto solution set; The optimal monitoring points for a preset scenario are determined based on the Pareto solution set score.

2. The method for optimizing the layout of power quality monitoring points according to claim 1, characterized in that: Based on the sample size estimation principle, the optimal number of monitoring points is determined according to the statistical characteristic values ​​of the power quality monitoring data, including: Calculating statistical characteristic values ​​of monitoring data according to the power quality monitoring data; Based on the sample size estimation principle, the corresponding statistical accuracy under different monitoring point scales is calculated according to the statistical characteristic values; Draw trend graphs based on the corresponding statistical accuracy under different monitoring point scales; The theoretically optimal number of monitoring points is first determined based on the trend graph and statistical accuracy threshold.

3. The method for optimizing the power quality monitoring points according to claim 2, characterized in that: Based on the sample size estimation principle, the optimal number of monitoring points is determined according to the statistical characteristic values ​​of the power quality monitoring data, and further includes: Determine the optimal number of monitoring points based on the theoretical optimal number of monitoring points, the number of existing monitoring points, and the number of monitoring points that have installation conditions; The rationality is confirmed based on the optimal number of monitoring points.

4. The method for optimizing the power quality monitoring points according to claim 1, characterized in that: Based on the optimization target model, an improved genetic algorithm is used to perform global optimization and obtain a Pareto solution set, including: The improved genetic algorithm crossover operator and mutation operator are used to perform global optimization based on the objective function and constraints to obtain a Pareto solution set; the crossover operator is determined by randomly selecting two crossover points and satisfying the equality of the algebraic sum of the selected points of the two chromosomes; the mutation operator is determined by the fact that the mutation points appear in pairs and the algebraic sum of the mutation points remains unchanged.

5. A device for optimizing the distribution of power quality monitoring points, characterized in that: include: A data acquisition module is used to acquire power quality monitoring data of power grid nodes in the area to be evaluated; A quantity calculation module, used to determine the optimal number of monitoring points according to the statistical characteristic values ​​of the power quality monitoring data based on the sample size estimation principle; A model building module, for building an optimization target model based on a hierarchical modeling method according to the optimal number of monitoring points, grid node attributes, and preset power quality control requirements; A genetic algorithm solution module is used to perform global optimization solution based on the optimization target model using an improved genetic algorithm to obtain a Pareto solution set; A monitoring point determination module, used to determine the optimal monitoring point according to the Pareto solution set and a preset scenario bias weight matrix; Based on the hierarchical modeling method, an optimization target model is constructed according to the optimal number of monitoring points, grid node attributes and preset power quality control requirements, including: Mark the grid node attributes according to whether the installation conditions are met and whether the monitoring terminal has been installed; Determine optimization targets based on power quality control requirements; Based on the hierarchical modeling method, the optimal number of monitoring points and the optimization target are used as the objective function, and the grid node attributes are used as constraints to build an optimization target model; The optimization objectives include: importance, coverage of weak areas and coverage of monitoring range; The importance is determined according to the interference coverage, the coverage rate of important sensitive users and the total short-circuit capacity; The weak area coverage is determined based on the average line loss rate of covered nodes and lines, the proportion of total power outage events in covered nodes, the proportion of total complaints in covered nodes, and the proportion of equipment damage in covered nodes; The monitoring range coverage is determined according to the number of observable nodes and the number of power grid nodes in the area to be evaluated; Determining the optimal monitoring points according to the Pareto solution set and the preset scenario bias weight matrix includes: Determine the scenario bias weight matrix based on importance, weak area coverage, monitoring range coverage, and preset scenarios; Calculating a Pareto solution set score based on the scenario bias weight matrix and the Pareto solution set; The optimal monitoring points for a preset scenario are determined based on the Pareto solution set score.

6. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the power quality monitoring point optimization method as described in any one of claims 1 to 4.

7. An electronic device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the power quality monitoring point optimization method as described in any one of claims 1 to 4 by executing the computer instructions.

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