NSGA-II-based distribution network line impedance identification method and system

Abstract

The invention relates to the field of power distribution network parameter identification, and discloses a distribution network line impedance identification method and system based on NSGA-II, and the method comprises the steps: obtaining the basic data of a target distribution network, and building an impedance parameter multivariate nonlinear model; establishing a distribution network line impedance parameter multi-objective solving model; and performing iterative calculation based on an NSGA-II algorithm to obtain distribution network line parameters. Through the multi-objective optimization method based on the NSGA-II algorithm, the impedance parameters of the distribution network line can be efficiently and accurately identified, and the defects of a traditional method in precision and calculation efficiency are overcome. Real-time voltage, current, power and other data are obtained in combination with a mu PMU device, and a target function for minimizing voltage and power calculation errors is established, so that accurate impedance parameter calculation is realized.

Description

Technical Field

[0001] The present invention relates to the field of distribution network parameter identification, and in particular to a distribution network line impedance identification method and system based on NSGA-II. Background Art

[0002] As a bridge connecting the main grid and the load, the operating status of the distribution network directly affects the normal power consumption of users, so it is very important to conduct power flow analysis on the distribution network. At present, power flow analysis is mostly focused on theoretical research on simulation models, and the impedance parameters are all known quantities. With the expansion of the scale of new energy grid connection, the problems emerging in the distribution network are also increasing, which puts forward more urgent and complex requirements on the power flow analysis capabilities of the power grid. Accurate line impedance identification is the basis for ensuring the accuracy of power flow calculation, so it is of great significance to study the identification of distribution network parameters.

[0003] At present, the main methods for identifying the impedance parameters of distribution network lines are: 1. Considering factors such as the environment, the parameters are calculated by combining the line length with the line unit impedance, but this method does not conform to the actual situation. On the one hand, it does not consider the aging of the line, and on the other hand, it is difficult to obtain the line length; 2. By obtaining the voltage, active power and reactive power data of each load, the voltage is pushed forward to the head end and finally calculated by the nonlinear solution method, but this method over-pursues the voltage accuracy, but does not consider whether the power meets the accuracy requirements, so the error is large. 3. The parameters are preliminarily identified by the forward-backward method, and accurately identified by the Newton-Raphael method, but this method belongs to the parameter simplification method, ignoring the voltage drop of the load section line, so the calculation accuracy is not high; 4. Connect the μPMU device at the key position to obtain the voltage and phase information of each load node, and solve it by formula derivation with the help of traditional methods such as the steepest descent method, but this calculation method has poor adaptability and is easy to fall into the local optimum, so the calculation ability is poor.

[0004] In order to overcome the defects of the prior art, the present invention discloses a distribution network line impedance identification method based on NSGA-Ⅱ. On the basis of existing research, the method considers the voltage accuracy and power accuracy of the line, and considers factors more comprehensively; and adopts NSGA-Ⅱ algorithm for multi-objective solution. The algorithm has stronger adaptability and better convergence, so that the calculation results are more accurate. Summary of the invention

[0005] In view of the above-mentioned problems, the present invention is proposed.

[0006] Therefore, the technical problem solved by the present invention is that in the existing distribution network line impedance identification, it is difficult to accurately calculate the line impedance parameters due to the insufficient accuracy or high calculation complexity of the traditional method. In order to solve the above technical problem, the present invention provides the following technical solution: a distribution network line impedance identification method based on NSGA-Ⅱ, comprising:

[0007] Obtain the basic data of the target distribution network and establish a multivariate nonlinear model of impedance parameters;

[0008] Establish a multi-objective solution model for distribution network line impedance parameters;

[0009] The distribution network line parameters are obtained by iterative calculation based on the NSGA-Ⅱ algorithm.

[0010] As a preferred solution of the distribution network line impedance identification method based on NSGA-Ⅱ described in the present invention, the acquisition of the basic data of the target distribution network includes obtaining the voltage, current, power, and phase information of the load and the outlet side of the substation by connecting a micro-synchronous phasor measurement unit device to each load and the outlet side of the substation, and obtaining the distribution network topology information.

[0011] As a preferred solution of the distribution network line impedance identification method based on NSGA-Ⅱ described in the present invention, the impedance parameter multivariate nonlinear model includes an impedance parameter multivariate nonlinear model based on voltage estimation and an impedance parameter multivariate nonlinear model based on power estimation.

[0012] As a preferred solution of the distribution network line impedance identification method based on NSGA-Ⅱ described in the present invention, the impedance parameter multivariate nonlinear model based on voltage estimation includes establishing the relationship between the voltage of each line node and the voltage of the load node according to Ohm's law, and the formula is expressed as follows:

[0013]

[0014] Z i-n =R i-n +jX i-n

[0015] in, represents the voltage of load n, represents the node i to which load n is connected, It represents the voltage difference between load n and its connection point i, Z i-n represents the line impedance between load n and node i, n represents the current flowing through the line;

[0016] and To be measurable, Z i -n and As an unknown quantity, the equation of impedance and i-node voltage can be established through orthogonal decomposition:

[0017] U i =f ( R i-n ,X i-n )

[0018] Establish the functional relationship between the voltage at the outlet of the substation and the line impedance parameters:

[0019] U o =f ( R 1 ,R 2 ,...,R l ,X 1 ,X 2 ,...,X l )

[0020] Among them, U o Indicates the voltage at the outlet of the substation, (R 1 ,R 2 ,...,R l ,X 1 ,X 2 ,...,X l ) represents the impedance parameters of each section of the line from the outlet of the substation to the terminal load;

[0021] Starting from different load nodes, the outlet voltage of the substation is derived according to the line and load parameters, and the average value of the derived outlet voltage of the substation is calculated. The formula is expressed as:

[0022]

[0023] Among them, U oi represents the output voltage of the substation derived from the i-th node; U oav It indicates the average value of the voltage at the outlet of the substation calculated from different load nodes.

[0024] As a preferred solution of the distribution network line impedance identification method based on NSGA-Ⅱ described in the present invention, the impedance parameter multivariate nonlinear model based on power estimation includes calculating the total power of the distribution network, including line loss and load power, and the formula is expressed as follows:

[0025]

[0026] S 2 =P 2 +Q 2

[0027] Among them, P represents the total active power of the target distribution network obtained by calculation, Q represents the total reactive power of the target distribution network obtained by calculation, and P i Indicates the active power consumed by each load, Q i Represents the reactive power consumed by each load, P i j represents the active power loss of each line section, Qlj Indicates the reactive power loss of each line section;

[0028] P i , Q i For measurable, P l j, Q l The formula for j is:

[0029]

[0030] Among them, I lj Indicates the current flowing through each line segment. The current from the load side to the line node side is directly measured and obtained through μPMU;

[0031] The current between each node of the line is obtained by superimposing the back-end load current value, and the formula is expressed as:

[0032]

[0033] Among them, I jl It represents the current flowing through the jl segment line, I j Indicates the load current at the rear end of the jl section line; establishes the functional relationship between the output voltage of the substation and the line impedance, and the formula is expressed as follows:

[0034] S o =f ( R 1 ,R 2 ,…,R l ,X 1 ,X 2 ,…,X l )

[0035] Among them, S o Indicates the power at the substation side, i.e. the total power of the distribution network, (R 1 ,R 2 ,…,R l ,X 1 ,X 2 ,…,X l ) represents the impedance parameters of each section of the line from the substation outlet to the terminal load.

[0036] As a preferred solution of the distribution network line impedance identification method based on NSGA-Ⅱ described in the present invention, wherein: the establishment of a multi-objective solution model for distribution network line impedance parameters includes subtracting the voltage and power values ​​at the outlet side of the substation area derived from the formula from the actual measured values ​​to establish a multi-objective optimization objective function with the minimum voltage error and the minimum power error;

[0037] Assume that the voltage measurement value at the outlet of the transformer substation is U m , the power measurement value is S m, the square of the difference is used to represent the error of voltage and power estimation as the objective function, and the calculation errors are expressed as:

[0038] U error = ( U oav -U m) 2

[0039] S error = ( S o -S m) 2

[0040] At this time U error and S error Still with (R 1 ,R 2 ,…,R l ,X 1 ,X 2 ,…,X l ) has a functional relationship, and the objective function equation is established:

[0041] F 1 =f 1( R 1 ,R 2 ,…,R l ,X 1 ,X 2 ,…,X l )

[0042] F 2 =f 2( R 1 ,R 2 ,…,R l ,X 1 ,X 2 ,…,X l )

[0043] Among them, F 1 Indicates voltage error, F 2 represents the power error, f 1 and f 2 Represents impedance parameters and F 1 、F 2 The functional relationship of

[0044] Since the distribution network line is an inductive impedance, the constraint condition formula is expressed as:

[0045]

[0046] Among them, u m Indicates the voltage measurement value at the outlet of the transformer substation, S mIndicates the power measurement value at the outlet of the transformer substation, U error Indicates voltage error, U oav It represents the average value of the estimated output voltage of the substation area, S error Represents the power error, S o Indicates the estimated output power of the substation, F 1 The objective function of voltage error, F 2 The objective function of power error, R 1 ,R 2 ,…,R l Indicates the resistance of each line segment, X 1 ,X 2 ,…,X l It represents the reactance of each line segment, R represents resistance, and X represents reactance.

[0047] As a preferred solution of the distribution network line impedance identification method based on NSGA-Ⅱ described in the present invention, wherein: the distribution network line parameters are obtained by iterative calculation based on the NSGA-Ⅱ algorithm, including setting algorithm parameters, including population size, crossover probability, mutation probability, and maximum number of iterations;

[0048] Initialize the population, perform fast non-dominated sorting, and calculate the congestion degree;

[0049] Perform evolutionary operations on the population, including selection, crossover, and mutation, and merge the parent population and the offspring population to form a larger population;

[0050] Perform non-dominated sorting on the new population and calculate the crowding distance of each individual;

[0051] According to the level of non-dominated sorting and the crowding distance of individuals, a certain number of individuals are selected from the merged population as the new population;

[0052] Until the preset number of iterations is reached or other stopping conditions are met;

[0053] Output results: The algorithm will output a set of Pareto optimal solutions, from which the most appropriate solution can be selected according to actual needs.

[0054] A distribution network line impedance identification system based on NSGA-Ⅱ, wherein:

[0055] Data acquisition module, which obtains the basic data of the target distribution network and establishes a multivariate nonlinear model of impedance parameters;

[0056] Impedance parameter calculation module, establishes a multi-objective solution model for distribution network line impedance parameters;

[0057] The impedance parameter output module obtains the distribution network line parameters through iterative calculation based on the NSGA-Ⅱ algorithm.

[0058] A computer device comprises: a memory and a processor; the memory stores a computer program, wherein the processor implements the steps of any one of the methods of the present invention when executing the computer program.

[0059] A computer-readable storage medium stores a computer program, wherein the computer program, when executed by a processor, implements the steps of any one of the methods of the present invention.

[0060] Beneficial effects of the present invention: The impedance identification method of the distribution network line based on NSGA-Ⅱ provided by the present invention can efficiently and accurately identify the impedance parameters of the distribution network line through a multi-objective optimization method based on the NSGA-Ⅱ algorithm, overcoming the shortcomings of the traditional method in terms of accuracy and computational efficiency. By combining the μPMU device to obtain real-time voltage, current, power and other data, and establishing an objective function that minimizes the voltage and power estimation errors, accurate impedance parameter calculation is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0062] Figure 1 An overall flow chart of a distribution network line impedance identification method based on NSGA-Ⅱ provided in the first embodiment of the present invention;

[0063] Figure 2 A schematic diagram of a distribution line of a distribution network line impedance identification method based on NSGA-Ⅱ provided in the first embodiment of the present invention;

[0064] Figure 3 A distribution network impedance identification flow chart of a distribution network line impedance identification method based on NSGA-Ⅱ provided in the first embodiment of the present invention;

[0065] Figure 4 A schematic diagram of a substation topology simulation of a distribution network line impedance identification method based on NSGA-Ⅱ provided in the first embodiment of the present invention;

[0066] Figure 5 The Pareto optimal frontier simulation operation result of a distribution network line impedance identification method based on NSGA-Ⅱ provided in the first embodiment of the present invention;

[0067] Figure 6The impedance identification result of a distribution network line impedance identification method based on NSGA-Ⅱ provided in the first embodiment of the present invention;

[0068] Figure 7 A system functional module diagram of a distribution network line impedance identification method based on NSGA-Ⅱ provided in the second embodiment of the present invention. DETAILED DESCRIPTION

[0069] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0070] Example 1, reference Figure 1 , which is an embodiment of the present invention, provides a distribution network line impedance identification method based on NSGA-Ⅱ, including:

[0071] S1: Obtain the basic data of the target distribution network and establish a multivariate nonlinear model of impedance parameters.

[0072] To obtain basic distribution network data, it is necessary to connect μPMU (micro-synchronous phasor measurement unit) devices on each load side and the outlet side of the substation to obtain the voltage, current, power, and phase information of the load and the outlet side of the substation, and obtain the distribution network topology information.

[0073] exist Figure 2 In the figure, the line topology is assumed to be the topology of the target distribution network. The hollow points in the figure are the load terminal nodes, and the solid points are the distribution network line connection nodes. The o node in the figure is the node at the outlet side of the substation, 1-16 is the load number, and AH is the line connection point directly connected to the load.

[0074] In order to obtain the electrical quantity data of the load and the outlet side of the substation, including voltage, current, power, and phase, μPMU devices must be added on the load side and the outlet side of the substation for measurement.

[0075] By connecting μPMU (micro-synchronous phasor measurement unit) devices on the load side and the outlet side of the substation, the key electrical quantity data such as voltage, current, power, phase, etc. of the distribution network can be obtained in real time, and combined with the distribution network topology information, high-precision original measurement data can be provided for subsequent impedance parameter identification. This design can ensure the comprehensiveness and accuracy of the data, overcome the limitations of the traditional methods for the collection of distribution network electrical parameters, especially in complex distribution network structures. The μPMU device can provide data with high time resolution and support dynamic monitoring and accurate analysis of the power grid operation status. This provides reliable input data for the multi-objective optimization solution based on the NSGA-Ⅱ algorithm, thereby improving the accuracy and adaptability of the distribution network line impedance identification, which can better reflect the actual operation of the distribution network and has significant practical application value.

[0076] S2: Establish a multi-objective solution model for distribution network line impedance parameters.

[0077] exist Figure 2 In the figure, the loads 1-16 are numbered by letter i, the distribution network line nodes directly connected to them are numbered by letter n, and the nodes on the outlet side of the substation are numbered by letter o. The impedance parameter multivariate nonlinear model includes an impedance parameter multivariate nonlinear model based on voltage estimation and an impedance parameter multivariate nonlinear model based on power estimation.

[0078] The impedance parameter multivariate nonlinear model based on voltage estimation includes establishing the relationship between the voltage of each line node and the voltage of the load node according to Ohm's law. The formula is expressed as follows:

[0079]

[0080] Z i-n =R i-n +jX i-n

[0081] in, represents the voltage of load n, represents the node i to which load n is connected, It represents the voltage difference between load n and its connection point i, Z i-n represents the line impedance between load n and node i, n represents the current flowing through the line.

[0082] and To be measurable, Z i -n and As an unknown quantity, the equation of impedance and i-node voltage can be established through orthogonal decomposition:

[0083] U i =f ( Ri-n ,X i-n )

[0084] Establish the functional relationship between the voltage at the outlet of the substation and the line impedance parameters:

[0085] U o =f ( R 1 ,R 2 ,...,R l ,X 1 ,X 2 ,...,X l )

[0086] Among them, U o Indicates the voltage at the outlet of the substation, (R 1 ,R 2 ,...,R l ,X 1 ,X 2 ,...,X l ) represents the impedance parameters of each section of the line from the substation outlet to the terminal load.

[0087] Starting from different load nodes, the outlet voltage of the substation is derived according to the line and load parameters, and the average value of the derived outlet voltage of the substation is calculated. The formula is expressed as:

[0088]

[0089] Among them, U oi represents the output voltage of the substation derived from the i-th node; U oav It indicates the average value of the voltage at the outlet of the substation calculated from different load nodes.

[0090] exist Figure 2 In the figure, the loads 1-16 are numbered by the letter i, the distribution network line nodes directly connected to them are numbered by the letter n, and the substation export nodes are numbered by the letter o.

[0091] Calculate the total power of the distribution network, including line loss and load power. The formula is:

[0092]

[0093] S 2 =P 2 +Q 2

[0094] Among them, P represents the total active power of the target distribution network obtained by calculation, Q represents the total reactive power of the target distribution network obtained by calculation, and Pi Indicates the active power consumed by each load, Q i Represents the reactive power consumed by each load, P i j represents the active power loss of each line section, Q lj Indicates the reactive power loss of each line section.

[0095] P i , Q i For measurable, P l j, Q l The formula for j is:

[0096]

[0097] Among them, I lj It indicates the current flowing through each line section. The current from the load side to the line node side is directly measured and obtained through μPMU.

[0098] The current between each node of the line is obtained by superimposing the back-end load current value, and the formula is expressed as:

[0099]

[0100] Among them, I jl It represents the current flowing through the jl segment line, I j Indicates the load current at the rear end of the jl section line; establishes the functional relationship between the output voltage of the substation and the line impedance, and the formula is expressed as follows:

[0101] S o =f ( R 1 ,R 2 ,…,R l ,X 1 ,X 2 ,…,X l )

[0102] Among them, S o Indicates the power at the substation side, i.e. the total power of the distribution network, (R 1 ,R 2 ,…,R l ,X 1 ,X 2 ,…,X l ) represents the impedance parameters of each section of the line from the substation outlet to the terminal load.

[0103] The voltage and power values ​​at the outlet of the substation derived from the formula are subtracted from the actual measured values ​​to establish a multi-objective optimization function with the minimum voltage error and the minimum power error.

[0104] Assume that the voltage measurement value at the outlet of the transformer substation is U m, the power measurement value is S m , the square of the difference is used to represent the error of voltage and power estimation as the objective function, and the calculation errors are expressed as:

[0105] U error = ( U oav -U m) 2

[0106] S error = ( S o -S m) 2

[0107] At this time U error and S error Still with (R 1 ,R 2 ,…,R l ,X 1 ,X 2 ,…,X l ) has a functional relationship, and the objective function equation is established:

[0108] F 1 =f 1( R 1 ,R 2 ,…,R l ,X 1 ,X 2 ,…,X l )

[0109] F 2 =f 2( R 1 ,R 2 ,…,R l ,X 1 ,X 2 ,…,X l )

[0110] Among them, F 1 Indicates voltage error, F 2 represents the power error, f 1 and f 2 Represents impedance parameters and F 1 、F 2 The functional relationship of .

[0111] Since the distribution network line is an inductive impedance, the constraint condition formula is expressed as:

[0112]

[0113] in, Indicates the voltage measurement value at the outlet of the transformer substation. Indicates the power measurement value at the outlet of the transformer substation, U error Indicates voltage error, U oav It represents the average value of the estimated output voltage of the substation area, S error Represents the power error, S o Indicates the estimated output power of the substation, F 1 The objective function of voltage error, F 2 The objective function of power error, R 1 ,R 2 ,…,R l Indicates the resistance of each line segment, X 1 ,X 2 ,…,X l It represents the reactance of each line segment, R represents resistance, and X represents reactance.

[0114] By establishing a multivariate nonlinear model of distribution network line impedance parameters based on voltage and power estimation, the relationship between distribution network voltage and power is fully considered, and the impedance parameters of distribution network lines are accurately identified in combination with actual power grid data. First, based on the voltage estimation model, by establishing a nonlinear functional relationship between the load node voltage and the line impedance, the outlet voltage of the platform is estimated, and the average voltage is further calculated through the voltage difference of the load node. This process ensures the high reliability of voltage accuracy. Secondly, based on the power estimation model, by calculating the total active power and reactive power of the distribution network, considering the influence of line loss and load power, the functional relationship between voltage and power is constructed, thereby improving the power accuracy and avoiding the error accumulation that may be caused by relying solely on voltage data.

[0115] By combining these two estimation methods, the identification process of distribution network line impedance parameters can be optimized from multiple dimensions, and a multi-objective optimization solution model can be formed to minimize the voltage and power errors and ensure the accuracy and reliability of the identification results. The key significance of this step is that by comprehensively considering voltage and power data, and using nonlinear modeling and multi-objective optimization algorithms, a more accurate and comprehensive solution framework is provided for distribution network line impedance identification, thereby effectively improving the accuracy of distribution network parameter identification and adapting to complex power systems under different load conditions.

[0116] S3: The distribution network line parameters are obtained by iterative calculation based on the NSGA-Ⅱ algorithm.

[0117] The distribution network line parameters are obtained by iterative calculation based on the NSGA-Ⅱ algorithm. Figure 3 This is a flow chart of impedance identification of distribution network based on NSGA-Ⅱ in the example of the present invention, including the following steps, specifically:

[0118] S51: Set algorithm parameters, including population size, crossover probability, mutation probability, maximum number of iterations, etc.

[0119] S52: Initialize the population, perform fast non-dominated sorting, and calculate the congestion degree.

[0120] S53: Perform evolutionary operations on the population, including selection, crossover, and mutation, and merge the parent population and the offspring population to form a larger population.

[0121] S54: Perform non-dominated sorting on the new population and calculate the crowding distance of each individual.

[0122] S55: According to the level of non-dominated sorting and the crowding distance of individuals, a certain number of individuals are selected from the merged population as the new population.

[0123] S56: Repeat steps S53 to S55 until a preset number of iterations is reached or other stop conditions are met.

[0124] S57: Output result. The algorithm will output a set of Pareto optimal solutions, that is, non-dominated solutions, from which the most appropriate solution can be selected according to actual needs.

[0125] The schematic diagram of the station area topology simulation used in the present invention is as follows Figure 4 shown.

[0126] The effect diagram of the Pareto front of NSGA-Ⅱ used in the present invention is as follows: Figure 5 As shown in the figure, it can be seen that the minimum values ​​of the optimal solutions of the two objective functions tend to 0, which shows that the solution effect of this method is significant.

[0127] The identification results of the impedance of each line by the method of the present invention are as follows: Figure 6 As shown. Figure 6 It can be seen that the impedance identification method has a high degree of recognition, and most of the calculation accuracy meets the requirements.

[0128] By applying the iterative calculation method based on NSGA-Ⅱ (non-dominated sorting genetic algorithm-II), high-precision and low-error distribution network line impedance parameter identification can be achieved. The NSGA-Ⅱ algorithm can effectively avoid the local optimal solution problem that may exist in the traditional method while considering the minimization of voltage and power errors through multi-objective optimization, and has strong adaptability and convergence. In the implementation process of step S3, the population is first initialized and a fast non-dominated sort is performed, and then the population is continuously optimized by evolutionary operations such as crossover and mutation, so as to obtain the line impedance parameters close to the optimal solution. The diversity and optimization direction of the solution process are ensured by non-dominated sorting calculation and congestion evaluation. Finally, the Pareto optimal solution set output by the algorithm provides a strong basis for selecting the most appropriate solution, ensuring the efficiency and accuracy of the distribution network line impedance identification results.

[0129] Example 2, reference Figure 7 , which is an embodiment of the present invention, provides a distribution network line impedance identification system based on NSGA-Ⅱ, including:

[0130] The data acquisition module obtains the basic data of the target distribution network and establishes a multivariate nonlinear model of impedance parameters.

[0131] Impedance parameter calculation module, establishes a multi-objective solution model for distribution network line impedance parameters.

[0132] The impedance parameter output module obtains the distribution network line parameters through iterative calculation based on the NSGA-Ⅱ algorithm.

[0133] Embodiment 3, an embodiment of the present invention, is different from the first two embodiments in that:

[0134] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0135] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0136] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0137] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0138] Example 4, a regional distribution network is selected as the experimental object. The distribution network is mainly composed of 6 substations and several load points, connecting different transformers and distribution lines. In order to accurately obtain the required data, during the experiment, μPMU (micro-synchronous phasor measurement unit) devices are installed in each substation and load node to monitor basic data such as voltage, current, power, and phase in real time. In addition, the topological structure of the distribution network is accurately modeled through a geographic information system (GIS) and processed as input data.

[0139] The voltage, current, power and phase information of each load node and the outlet side of the substation are obtained through the μPMU device, and the relevant node information and line data are recorded in combination with the distribution network topology to form a data set. A total of 1,000 sets of basic distribution network data were collected in the experiment.

[0140] Based on Ohm's law, the impedance parameters of each line section are calculated using the orthogonal decomposition method through the measured voltage and current data. Specifically, starting from each load node, the voltage at the outlet of the platform is calculated, and the impedance parameter model is established based on the voltage and current relationship of each line section.

[0141] The power data (active and reactive power) obtained by measurement is used to further establish the relationship model between power and line impedance. The voltage estimation results and power estimation results are jointly optimized to improve the accuracy of parameter identification. By taking the estimation errors of voltage and power as the objective function, a multi-objective optimization model based on NSGA-Ⅱ is constructed. This model considers both the minimization of voltage error and the minimization of power error, thereby comprehensively optimizing the impedance parameters of the distribution network line.

[0142] The NSGA-Ⅱ algorithm is used for multi-objective optimization calculation. The impedance parameters are optimized through an iterative algorithm to obtain the Pareto optimal solution, and the most suitable solution is selected as the impedance parameter of the distribution network line. The experimental results are shown in Table 1.

[0143] Table 1 Experimental results

[0144]

[0145] According to the experimental data table, it can be seen that by adopting the distribution network line impedance identification method based on NSGA-Ⅱ, from the perspective of voltage and power calculation results, the advantage of the inventive method lies in its lower error range. For example, in the 6 experimental substations, the error between the voltage calculation value and the measured value on the outlet side of the substation can be maintained within 1V, which shows that the high-precision requirements of the method for voltage measurement can be fully met. The power calculation results also performed well. In the 6 experimental substations, the error between the power measurement value and the calculated value on the substation side can be maintained within 2kVA, further indicating that the inventive method has high reliability. Compared with the traditional voltage forward push method and power forward push method, the multi-objective optimization method of the present invention effectively reduces the voltage and power calculation errors and improves the identification accuracy of the line impedance.

[0146] In traditional methods, the calculation of voltage and power is usually optimized as a single objective, and the mutual influence between them is easily ignored, resulting in large errors in the calculation results. However, through the NSGA-Ⅱ algorithm, the present invention can simultaneously optimize the errors of voltage and power calculation, and adopt a multi-objective optimization framework to ensure that the accuracy of voltage and power reaches the optimal level, thereby achieving high-precision impedance parameter identification.

[0147] In terms of algorithm operation effect comparison, the NSGA-Ⅱ algorithm has stronger convergence and global search capabilities than traditional optimization algorithms such as gradient descent and Newton-Ray method. By comparing the operation results of different algorithms, the NSGA-Ⅱ algorithm adopted by the present invention shows good adaptability in the iterative process, is not easy to fall into the local optimal solution, and the calculation efficiency is also improved by about 20% compared with the traditional method.

[0148] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A distribution network line impedance identification method based on NSGA-Ⅱ, characterized in that: include: Obtain the basic data of the target distribution network and establish a multivariate nonlinear model of impedance parameters; Establish a multi-objective solution model for distribution network line impedance parameters; The distribution network line parameters are obtained by iterative calculation based on the NSGA-Ⅱ algorithm.

2. The distribution network line impedance identification method based on NSGA-II as claimed in claim 1, characterized in that: The acquisition of the target distribution network basic data includes acquiring the voltage, current, power, and phase information of the load and the outlet side of the substation by connecting a micro-synchronous phasor measurement unit device to each load and the outlet side of the substation, and acquiring the distribution network topology information.

3. The distribution network line impedance identification method based on NSGA-II as claimed in claim 2, characterized in that: The impedance parameter multivariate nonlinear model includes an impedance parameter multivariate nonlinear model based on voltage estimation and an impedance parameter multivariate nonlinear model based on power estimation.

4. The distribution network line impedance identification method based on NSGA-II as claimed in claim 3, characterized in that: The impedance parameter multivariate nonlinear model based on voltage estimation includes establishing the relationship between the voltage of each line node and the voltage of the load node according to Ohm's law, which is expressed as follows: Z i-n =R i-n +jX i-n in, represents the voltage of load n, represents the node i to which load n is connected, represents the voltage difference between load n and its connection point i, Z i-n represents the line impedance between load n and node i, Indicates the current flowing through the line; and To be measurable, Z i -n and As an unknown quantity, the equation of impedance and i-node voltage can be established through orthogonal decomposition: U i =f ( R i-n ,X i-n ) Establish the functional relationship between the voltage at the outlet of the substation and the line impedance parameters: U o =f ( R1,R2,...,R l ,X1,X2,...,X l ) Among them, U o Indicates the voltage at the outlet of the substation, (R1, R2, ..., R l ,X1,X2,...,X l ) represents the impedance parameters of each section of the line from the outlet of the substation to the terminal load; Starting from different load nodes, the outlet voltage of the substation is derived according to the line and load parameters, and the average value of the derived outlet voltage of the substation is calculated. The formula is expressed as: Among them, U oi represents the output voltage of the substation derived from the i-th node; U oav It indicates the average value of the voltage at the outlet of the substation calculated from different load nodes.

5. The distribution network line impedance identification method based on NSGA-II as claimed in claim 4, characterized in that: The impedance parameter multivariate nonlinear model based on power estimation includes calculating the total power of the distribution network, including line loss and load power, and the formula is expressed as follows: S 2 =P 2 +Q 2 Among them, P represents the total active power of the target distribution network obtained by calculation, Q represents the total reactive power of the target distribution network obtained by calculation, and P i Indicates the active power consumed by each load, Q i Represents the reactive power consumed by each load, P i j represents the active power loss of each line section, Q lj Indicates the reactive power loss of each line section; P i , Q i For measurable, P l j, Q l The formula for j is: Among them, I lj Indicates the current flowing through each line segment. The current from the load side to the line node side is directly measured and obtained through μPMU; The current between each node of the line is obtained by superimposing the back-end load current value, and the formula is expressed as: Among them, I jl It represents the current flowing through the jl segment line, I j Indicates the load current at the rear end of the jl section line; establishes the functional relationship between the output voltage of the substation and the line impedance, and the formula is expressed as follows: S o =f ( R1,R2,…,R l ,X1,X2,…,X l ) Among them, S o Indicates the power at the substation side, i.e. the total power of the distribution network, (R1, R2,…, R l ,X1,X2,…,X l ) represents the impedance parameters of each section of the line from the substation outlet to the terminal load.

6. The distribution network line impedance identification method based on NSGA-II as claimed in claim 5, characterized in that: The establishment of a multi-objective solution model for distribution network line impedance parameters includes subtracting the voltage and power values ​​at the outlet of the substation area derived from the formula from the actual measured values ​​to establish a multi-objective optimization objective function with the minimum voltage error and the minimum power error; Assume that the voltage measurement value at the outlet of the transformer substation is U m , the power measurement value is S m , the square of the difference is used to represent the error of voltage and power estimation as the objective function, and the calculation errors are expressed as: IN error = ( IN oav -IN m) 2 S error = ( S o -S m) 2 At this time U error and S error Still with (R1, R2, …, R l ,X1,X2,…,X l ) has a functional relationship, and the objective function equation is established: F1=f 1( R1,R2,…,R l ,X1,X2,…,X l ) F2=f 2( R1,R2,…,R l ,X1,X2,…,X l ) Among them, F1 represents voltage error, F2 represents power error, f1 and f2 represent the functional relationship between impedance parameter and F1 and F2 respectively; Since the distribution network line is an inductive impedance, the constraint condition formula is expressed as: in, Indicates the voltage measurement value at the outlet of the transformer substation. Indicates the power measurement value at the outlet of the transformer substation, U error Indicates voltage error, U oav It represents the average value of the estimated output voltage of the substation area, S error Represents the power error, S o represents the estimated output power of the substation, F1 represents the target function of voltage error, F2 represents the target function of power error, R1, R2, …, R l Indicates the resistance of each line segment, X1, X2, ..., X l It represents the reactance of each line segment, R represents resistance, and X represents reactance.

7. The distribution network line impedance identification method based on NSGA-II as claimed in claim 6, characterized in that: The distribution network line parameters obtained by iterative calculation based on NSGA-Ⅱ algorithm include setting algorithm parameters, including population size, crossover probability, mutation probability, and maximum number of iterations; Initialize the population, perform fast non-dominated sorting, and calculate the congestion degree; Perform evolutionary operations on the population, including selection, crossover, and mutation, and merge the parent population and the offspring population to form a larger population; Perform non-dominated sorting on the new population and calculate the crowding distance of each individual; According to the level of non-dominated sorting and the crowding distance of individuals, a certain number of individuals are selected from the merged population as the new population; Until the preset number of iterations is reached or other stopping conditions are met; Output results: The algorithm will output a set of Pareto optimal solutions, from which the most appropriate solution can be selected according to actual needs.

8. A distribution network line impedance identification system based on NSGA-II using the method according to any one of claims 1 to 7, characterized in that: Data acquisition module, which obtains the basic data of the target distribution network and establishes a multivariate nonlinear model of impedance parameters; Impedance parameter calculation module, establishes a multi-objective solution model for distribution network line impedance parameters; The impedance parameter output module obtains the distribution network line parameters through iterative calculation based on the NSGA-Ⅱ algorithm.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the distribution network line impedance identification method based on NSGA-II described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the distribution network line impedance identification method based on NSGA-II described in any one of claims 1 to 7 are implemented.

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