PMU configuration method and system based on composite evaluation factor

Abstract

The invention discloses a PMU configuration method and system based on a composite evaluation factor, and belongs to the technical field of PWU configuration. The method comprises the following steps: determining a composite evaluation factor of the integrated energy system, and establishing a target function according to the composite evaluation factor; according to the state variable of the integrated energy system, constructing a global observability constraint condition of the integrated energy system; and under the global observability constraint condition, based on a hedera helix algorithm, solving the target function, and configuring a synchronous phasor measurement unit (PMU) of the industrial park integrated energy system according to a solving result. According to the method, the PMU configuration scheme of the target can be determined by constructing the target function and the constraint condition, and the PMU optimal configuration problem can be effectively solved.

Description

Technical Field

[0001] The present invention relates to the technical field of PWU configuration, and more particularly, to a PMU configuration method and system based on a composite evaluation factor. Background Art

[0002] The data of the integrated energy system in the complex industrial park of "source-grid-load-storage integration" is characterized by high dimensionality and high real-time performance. In order to obtain complete and accurate energy efficiency metering data, it is necessary to optimize the configuration of the PMU in the park. The existing PMU optimization configuration method has the following problems:

[0003] Cost issue: The cost of installing and maintaining PMUs is high, and existing methods still have significant deficiencies in cost control. Too few PMUs will limit the scope of grid monitoring and fail to fully cover the key nodes and lines of the entire grid, thus affecting the comprehensive and timely monitoring of grid operation. On the contrary, too many PMUs will significantly increase installation and maintenance costs, while causing data redundancy and duplication, and increasing the burden of data processing and storage. Therefore, how to optimize cost control to find a solution that meets technical requirements without exceeding the budget has become a challenge that needs to be solved.

[0004] State estimation error accuracy problem. In recent years, there are many nodes and complex branches in the power system, and the number of node state variables that need to be monitored is increasing day by day. However, due to the constraints of economic and technical factors, PMU cannot be configured on a large scale, which leads to a significant increase in the error of system state variable estimation. Most existing methods only focus on the single goal of minimizing the number of PMUs. Although this approach can greatly simplify the configuration difficulty of PMUs, the accuracy of state estimation cannot be effectively guaranteed. At present, multi-objective optimization is an important challenge in power system planning and management. Taking into account both the number of PMU configurations and the accuracy of state estimation is also crucial to the overall performance and economic benefits of the entire power system.

[0005] It is easy to fall into the local optimal solution. The algorithms for dealing with the optimal PMU placement (OPP) problem are mainly divided into heuristic algorithms and numerical algorithms. OPP problems usually involve multiple constraints and high dimensions. When solving multi-objective and high-dimensional models, heuristic optimization methods such as genetic algorithms and simulated annealing algorithms have certain advantages. These methods can effectively explore the search space and find solutions close to the global optimal solution by simulating natural evolution or physical annealing processes. However, the convergence speed of heuristic optimization methods is unstable. The convergence speed is affected by the complexity of the problem and the initial conditions of the population. It may take a long time to find a satisfactory solution, which increases the computational cost. Although numerical algorithms are relatively mature and have the characteristics of fast computing speed and strong convergence ability in small power systems, they have problems such as weak global search ability and poor universality in large systems. Summary of the invention

[0006] In view of the above problems, the present invention proposes a PMU configuration method based on a composite evaluation factor, comprising:

[0007] Determining a composite evaluation factor of the integrated energy system, and establishing an objective function based on the composite evaluation factor;

[0008] According to the state variables of the integrated energy system, constructing global observability constraints of the integrated energy system;

[0009] Under the global observability constraint, the objective function is solved based on the Ivy algorithm, and the synchronous phasor measurement unit (PMU) of the industrial park integrated energy system is configured based on the solution.

[0010] Optionally, a composite evaluation factor, including: minimizing cost and minimizing state estimation error.

[0011] Optionally, objective functions include: economic objective function and state estimation error objective function;

[0012] The economic objective function takes minimizing cost as the optimization goal;

[0013] The state estimation error objective function takes minimizing the state estimation error as an optimization goal.

[0014] Optional, economic objective function, as follows:

[0015]

[0016] Among them, F1 is the economic objective function, ω is the price of a single PMU, f1(i) is the number of PMUs installed in the N-node power system, and i n is a binary number;

[0017] in:

[0018]

[0019] Optional, state estimation error objective function, including:

[0020] F2=αe mag +βe ang

[0021] Among them, F2 is the state estimation error objective function, e mag is the average node voltage amplitude estimation error, e ang is the average node voltage phase angle estimation error, α and β are the amplitude error weight and phase angle error weight, respectively.

[0022] Optionally, based on the ivy algorithm, when solving the objective function, an initial ivy plant population is generated using Bernoulli chaotic mapping, and a Tanh conversion function is added to convert the position of the ivy plant population into a 0-1 variable.

[0023] On the other hand, the present invention also proposes a PMU configuration system based on a composite evaluation factor, comprising:

[0024] Constructing an objective function unit for determining a composite evaluation factor of the integrated energy system and establishing an objective function according to the composite evaluation factor;

[0025] A constraint condition construction unit is used to construct a global observability constraint condition of the integrated energy system according to the state variables of the integrated energy system;

[0026] A solution configuration unit is used to solve the objective function based on the Ivy algorithm under the global observability constraint condition and configure the synchronous phasor measurement unit PMU of the industrial park comprehensive energy system with the solution result.

[0027] Optionally, a composite evaluation factor, including: minimizing cost and minimizing state estimation error.

[0028] Optionally, objective functions include: economic objective function and state estimation error objective function;

[0029] The economic objective function takes minimizing cost as the optimization goal;

[0030] The state estimation error objective function takes minimizing the state estimation error as an optimization goal.

[0031] Optional, economic objective function, as follows:

[0032]

[0033]

[0034] Among them, F1 is the economic objective function, ω is the price of a single PMU, f1(i) is the number of PMUs installed in the N-node power system, and i n is a binary number;

[0035] in:

[0036]

[0037] Optional, state estimation error objective function, including:

[0038] F2=αe mag +βr ang

[0039] Among them, F2 is the state estimation error objective function, e mag is the average node voltage amplitude estimation error, e ang is the average node voltage phase angle estimation error, α and β are the amplitude error weight and phase angle error weight, respectively.

[0040] Optionally, based on the ivy algorithm, when solving the objective function, an initial ivy plant population is generated using Bernoulli chaotic mapping, and a Tanh conversion function is added to convert the position of the ivy plant population into a 0-1 variable.

[0041] In yet another aspect, the present invention further provides a computing device, comprising: one or more processors;

[0042] a processor for executing one or more programs;

[0043] When the one or more programs are executed by the one or more processors, the above-described method is implemented.

[0044] In yet another aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed, the method described above is implemented.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] The present invention provides a PMU configuration method based on a composite evaluation factor, including: determining a composite evaluation factor of an integrated energy system, establishing an objective function according to the composite evaluation factor; constructing a global observability constraint condition of the integrated energy system according to the state variables of the integrated energy system; solving the objective function based on the Ivy algorithm under the global observability constraint condition, and configuring the synchronous phasor measurement unit PMU of the integrated energy system of the industrial park with the solution result. The present invention can determine the target PMU configuration scheme by constructing the objective function and the constraint condition, and can effectively deal with the PMU optimization configuration problem. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 is a flow chart of the method of the present invention;

[0048] Figure 2 is a flow chart of an embodiment of the method of the present invention;

[0049] Figure 3 It is a structural diagram of the system of the present invention. DETAILED DESCRIPTION

[0050] Now, exemplary embodiments of the present invention are described with reference to the accompanying drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. These embodiments are provided to disclose the present invention in detail and completely and to fully convey the scope of the present invention to those skilled in the art. The terms used in the exemplary embodiments shown in the accompanying drawings are not intended to limit the present invention. In the accompanying drawings, the same units / elements are marked with the same reference numerals.

[0051] Unless otherwise specified, the terms (including technical terms) used herein have the commonly understood meanings to those skilled in the art. In addition, it is understood that the terms defined in commonly used dictionaries should be understood to have the same meanings as those in the context of the relevant fields, and should not be understood as idealized or overly formal meanings.

[0052] Embodiment 1:

[0053] The present invention proposes a PMU configuration method based on a composite evaluation factor, such as Figure 1 As shown, including:

[0054] Step 1: Determine a composite evaluation factor of the integrated energy system, and establish an objective function according to the composite evaluation factor;

[0055] Step 2: construct global observability constraints of the integrated energy system according to the state variables of the integrated energy system;

[0056] Step 3: Under the global observability constraint, based on the Ivy algorithm, solve the objective function and configure the synchronous phasor measurement unit (PMU) of the industrial park integrated energy system with the solution.

[0057] Among them, the composite evaluation factors include: minimizing cost and minimizing state estimation error.

[0058] Among them, the objective function includes: economic objective function and state estimation error objective function;

[0059] The economic objective function takes minimizing cost as the optimization goal;

[0060] The state estimation error objective function takes minimizing the state estimation error as an optimization goal.

[0061] Among them, the economic objective function is as follows:

[0062]

[0063] Among them, F1 is the economic objective function, ω is the price of a single PMU, f1(i) is the number of PMUs installed in the N-node power system, and i n is a binary number;

[0064] in:

[0065]

[0066] Among them, the state estimation error objective function includes:

[0067] F2=αe mag +βe ang

[0068] Among them, F2 is the state estimation error objective function, e mag is the average node voltage amplitude estimation error, e ang is the average node voltage phase angle estimation error, α and β are the amplitude error weight and phase angle error weight, respectively.

[0069] Among them, based on the ivy algorithm, when solving the objective function, the initial ivy plant population is generated by using Bernoulli chaotic mapping, and the Tanh conversion function is added to convert the position of the ivy plant population into a 0-1 variable.

[0070] The present invention is further described below with reference to specific cases:

[0071] Based on the global observability of the industrial park as a constraint condition, the present invention comprehensively considers two composite evaluation factors, namely minimizing cost and minimizing state estimation error, and sets them as the objective function. Through the improved ivy optimization algorithm, the optimal configuration scheme of the intelligent sensing device is solved to meet the needs of different business scenarios in the industrial park. This method uses Bernoulli chaotic mapping to generate the initial population to maintain the randomness and diversity of the population. The Tanh conversion function is added to enhance the ability of the ivy algorithm to handle 0 / 1 optimization problems. The elite selection strategy and non-dominated sorting method are introduced to transform the traditional ivy algorithm into a multi-objective optimization algorithm, improve the global search capability of the algorithm and enhance its performance in dealing with complex multi-objective optimization problems. Based on this, the present invention is implemented, and the specific implementation process is as follows: Figure 2 As shown, including:

[0072] 1. Establish economic performance and state estimation error objective functions:

[0073] 1.1 Economic objective function:

[0074] Under the premise that the power system nodes are globally observable, the fewer the number of PMUs configured, the better the economic efficiency of the configuration scheme. The economic objective function of the power system with N nodes is defined as follows:

[0075]

[0076] Where F1 is the economic objective function, ω is the price of a single PMU, which is usually 1. f1(i) represents the number of PMUs installed in the N-node power system; i n is a binary number, whose value is 0 or 1. If node n is equipped with a PMU, then i n The value is 1, otherwise i n The value is 0.

[0077] 1.2 State estimation error objective function:

[0078] The state estimation of the power system is conducive to evaluating the operating state of the system and establishing the state estimation error objective function. The calculation formula is as follows:

[0079] F2=αe mag +βe ang (3)

[0080] Where F2 is the state estimation error objective function: mag is the average node voltage amplitude estimation error; e ang is the average node voltage phase angle estimation error; α and β are the amplitude error weight and phase angle error weight, respectively, both of which are constant 1.

[0081] 2. Establish global observability constraints:

[0082] When the state variables of all nodes in the power system (such as voltage amplitude, phase angle, frequency, etc.) can be accurately measured and calculated through the deployed PMU, the system is said to be fully observable. Taking the global observability of the system as a constraint, the calculation formula is as follows:

[0083] AI…b (4)

[0084]

[0085] Where, I=(i1,i2,...,i k ) represents the PMU configuration of the corresponding node; where b is an n-dimensional column vector of [1,1,...,1] under normal circumstances; the matrix A represents the connection of the node. If node m is connected to node n or nodes m and n are the same node, then A mn =1, otherwise A mn =0.

[0086] 3. Use the improved Ivy algorithm to solve the PMU optimization configuration solution:

[0087] 1) Initialize algorithm parameters. Establish objective functions F1, F2; set global observability constraints according to equations (4) and (5). Set the parameter population size Npop, the number of problem decision variables D, and the maximum number of iterations Iter max And the current number of iterations Iter = 1.

[0088] 2) Use Bernoulli chaos map to initialize the position of ivy plant population. At the same time, calculate the ΔGv of the current population i .

[0089] 3) Use the Tanh transformation function to convert the ivy plant population position into a 0-1 variable Y.

[0090] 4) Combine the constraints of equations (4) and (5) to solve the objective function. Calculate the fitness of the ivy population and perform fast non-dominated sorting with an elite strategy.

[0091] 5) Obtain member I ii . Obtain the multiple parameter β and the ΔGv of the current population i .

[0092] 6) If f(I i )<β·f(I Best ), and obtain Otherwise, obtain

[0093] 7) Calculate the population The adaptability and Update the population and population growth rate, and i=i+1.

[0094] 8) If i>Npop, use Tanh transformation function and calculate the combined population Fitness, and perform fast non-dominated sorting with elite strategy. Otherwise, repeat steps 5) to 7).

[0095] 9) Determine whether the maximum number of iterations Iter has been reached max If the maximum number of iterations is reached, output the optimal solution and the optimal configuration plan. Otherwise, repeat steps 5) to 8)

[0096] The application of the present invention can:

[0097] Reduce costs: In actual power systems, ZIBs are ubiquitous. The present invention utilizes the properties of computing nodes in power systems with zero injection nodes to increase the number of observable nodes and reduce the number of PMU configurations, thereby significantly reducing configuration costs.

[0098] Improve global optimization capability: The present invention introduces Bernoulli chaotic mapping to initialize the initial ivy population, which can effectively maintain population diversity and avoid falling into a local optimal solution.

[0099] Improvement of state estimation accuracy: A multi-objective optimization configuration model for PMUs is established that takes into account the lowest cost and the smallest state estimation error. Fast non-dominated sorting with elite strategy is introduced to improve IVYA, transforming the traditional Ivy algorithm into a multi-objective optimization algorithm, improving the algorithm's global search capability and enhancing its performance in dealing with complex multi-objective optimization problems.

[0100] Extended application scenario: The present invention adopts the Tanh function in IVYA to convert the original IVYA into binary IVYA, which can effectively handle the PMU optimization configuration problem.

[0101] Embodiment 2:

[0102] The present invention also proposes a PMU configuration system 200 based on a composite evaluation factor, such as Figure 3 As shown, including:

[0103] A target function construction unit 201 is used to determine a composite evaluation factor of the integrated energy system and to establish a target function according to the composite evaluation factor;

[0104] A constraint condition construction unit 202 is used to construct a global observability constraint condition of the integrated energy system according to the state variables of the integrated energy system;

[0105] The solution configuration unit 203 is used to solve the objective function based on the Ivy algorithm under the global observability constraint condition and configure the synchronous phasor measurement unit PMU of the industrial park comprehensive energy system with the solution result.

[0106] Among them, the composite evaluation factors include: minimizing cost and minimizing state estimation error.

[0107] Among them, the objective function includes: economic objective function and state estimation error objective function;

[0108] The economic objective function takes minimizing cost as the optimization goal;

[0109] The state estimation error objective function takes minimizing the state estimation error as an optimization goal.

[0110] Among them, the economic objective function is as follows:

[0111]

[0112] Among them, F1 is the economic objective function, ω is the price of a single PMU, f1(i) is the number of PMUs installed in the N-node power system, and i n is a binary number;

[0113] in:

[0114]

[0115] Among them, the state estimation error objective function includes:

[0116] F2=αe mag +βe ang

[0117] Among them, F2 is the state estimation error objective function, e mag is the average node voltage amplitude estimation error, e ang is the average node voltage phase angle estimation error, α and β are the amplitude error weight and phase angle error weight, respectively.

[0118] Among them, based on the ivy algorithm, when solving the objective function, the initial ivy plant population is generated by using Bernoulli chaotic mapping, and the Tanh conversion function is added to convert the position of the ivy plant population into a 0-1 variable.

[0119] The present invention can determine the target PMU configuration scheme by constructing the objective function and constraint conditions, and can effectively handle the PMU optimization configuration problem.

[0120] Embodiment 3:

[0121] Based on the same inventive concept, the present invention also provides a computer device, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding functions, so as to implement the steps of the method in the above embodiment.

[0122] Embodiment 4:

[0123] Based on the same inventive concept, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It is understandable that the computer-readable storage medium here can include both a built-in storage medium in a computer device and an extended storage medium supported by the computer device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the steps of the method in the above embodiment.

[0124] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. The schemes in the embodiments of the present invention may be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0125] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0126] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0127] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0128] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0129] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A PMU configuration method based on a composite evaluation factor, characterized in that: include: Determining a composite evaluation factor of the integrated energy system, and establishing an objective function based on the composite evaluation factor; According to the state variables of the integrated energy system, constructing global observability constraints of the integrated energy system; Under the global observability constraint, the objective function is solved based on the Ivy algorithm, and the synchronous phasor measurement unit (PMU) of the industrial park integrated energy system is configured based on the solution.

2. The PMU configuration method according to claim 1, characterized in that: The composite evaluation factor includes: minimizing cost and minimizing state estimation error.

3. The PMU configuration method according to claim 1, characterized in that: The objective function includes: an economic objective function and a state estimation error objective function; The economic objective function takes minimizing cost as the optimization goal; The state estimation error objective function takes minimizing the state estimation error as an optimization goal.

4. The PMU configuration method according to claim 3, characterized in that: The economic objective function is as follows: Among them, F1 is the economic objective function, ω is the price of a single PMU, f1(i) is the number of PMUs installed in the N-node power system, and i n is a binary number; in:

5. The PMU configuration method according to claim 3, characterized in that: The state estimation error objective function includes: F2=αe mag +βe ang Among them, F2 is the state estimation error objective function, e mag is the average node voltage amplitude estimation error, e ang is the average node voltage phase angle estimation error, α and β are the amplitude error weight and phase angle error weight, respectively.

6. The method according to claim 1, characterized in that Based on the ivy algorithm, when solving the objective function, the initial ivy plant population is generated by using Bernoulli chaotic mapping, and the Tanh conversion function is added to convert the position of the ivy plant population into a 0-1 variable.

7. A PMU configuration system based on composite evaluation factors, characterized in that: include: Constructing an objective function unit for determining a composite evaluation factor of the integrated energy system and establishing an objective function according to the composite evaluation factor; A constraint condition construction unit is used to construct a global observability constraint condition of the integrated energy system according to the state variables of the integrated energy system; A solution configuration unit is used to solve the objective function based on the Ivy algorithm under the global observability constraint condition and configure the synchronous phasor measurement unit PMU of the industrial park comprehensive energy system with the solution result.

8. The PMU configuration system according to claim 7, characterized in that: The composite evaluation factor includes: minimizing cost and minimizing state estimation error.

9. The PMU configuration system according to claim 7, characterized in that: The objective function includes: an economic objective function and a state estimation error objective function; The economic objective function takes minimizing cost as the optimization goal; The state estimation error objective function takes minimizing the state estimation error as an optimization goal.

10. The PMU configuration system according to claim 9, characterized in that: The economic objective function is as follows: Among them, F1 is the economic objective function, ω is the price of a single PMU, f1(i) is the number of PMUs installed in the N-node power system, and i n is a binary number; in:

11. The PMU configuration system according to claim 9, characterized in that: The state estimation error objective function includes: F2=αe mag +βe ang Among them, F2 is the state estimation error objective function, e mag is the average node voltage amplitude estimation error, e ang is the average node voltage phase angle estimation error, α and β are the amplitude error weight and phase angle error weight, respectively.

12. The system according to claim 7, characterized in that Based on the ivy algorithm, when solving the objective function, the initial ivy plant population is generated by using Bernoulli chaotic mapping, and the Tanh conversion function is added to convert the position of the ivy plant population into a 0-1 variable.

13. A computer device, characterized in that: include: one or more processors; a processor for executing one or more programs; When the one or more programs are executed by the one or more processors, the method according to any one of claims 1 to 6 is implemented.

14. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed, the method according to any one of claims 1 to 6 is implemented.

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