Power quality responsibility subject positioning and model building method and system

Abstract

The invention discloses an electric energy quality responsibility subject positioning and model building method and system. The method comprises the steps of obtaining electric energy quality detection data, decomposing and reconstructing current and voltage in the electric energy quality detection data through discrete wavelet Mallat transformation, extracting components of high-frequency disturbance, and judging the disturbance direction based on positive and negative high-frequency disturbance energy; an improved quantum genetic algorithm is adopted, quantum crossover and quantum mutation operation is introduced, a disturbance weight factor is set according to high-frequency disturbance energy, and a disturbance source is accurately positioned; and establishing a corresponding mathematical model based on a responsibility subject positioning result, and evaluating and improving the electric energy quality through the mathematical model. According to the method, the local features of the signals can be analyzed in time and frequency, the source direction of power quality disturbance can be accurately judged, excellent individuals can be more effectively reserved, and the diversity of populations can be more effectively improved.

Description

Technical Field

[0001] The present invention belongs to the technical fields of distribution networks and new energy, and particularly relates to a method and system for positioning the responsible entity of power quality and constructing a model. Background Art

[0002] In recent years, distributed power sources have developed rapidly. These new power sources have made great contributions to alleviating power shortages and improving the energy structure. However, at the same time, their grid connection has brought new problems and challenges to the optimization and control of traditional power systems. When a distribution network is connected to various distributed power sources, the grid structure and operation mode have changed greatly. The grid will change from a single-source power supply mode to a multi-source power supply mode, and the location, capacity, and operation mode of the distributed power sources have a great impact on the line power flow, node voltage, and network loss of the grid. The accurate positioning of the power quality disturbance source in the distribution network helps to quickly solve power quality problems, reduce economic losses and the responsibility for disturbance events, and is of great significance.

[0003] Regarding the discrimination of the disturbance direction, there are methods for disturbing positioning based on disturbance energy and disturbance power, which regard the disturbance source as an "energy pool" that absorbs energy from the grid, but lack theoretical basis; there are methods that apply the superposition theorem to analyze the network before and after the disturbance, and use the disturbance components in the three-phase voltage and current to obtain the disturbance power, with a relatively high positioning accuracy; there are applications based on the particle swarm optimization algorithm in the distribution network, which has the advantages of easy implementation, high accuracy, and fast convergence speed, but there are still some deficiencies in the interference intensity and interference direction. Summary of the Invention

[0004] To solve the above technical problems, the present invention provides a method and system for positioning the responsible entity of power quality and constructing a model. The discrete wavelet Mallat transform is used to decompose and reconstruct the monitored current and voltage, and the direction of the power quality disturbance is discriminated. The improved quantum genetic algorithm is used to accurately locate the disturbance source. Based on the result of the responsible entity positioning, a corresponding mathematical model is established.

[0005] Among them, a method for positioning the responsible entity of power quality and constructing a model includes:

[0006] Obtain power quality detection data, decompose and reconstruct the current and voltage in the power quality detection data through the discrete wavelet Mallat transform, extract the components of high-frequency disturbances, and discriminate the disturbance direction based on the positive and negative of the high-frequency disturbance energy;

[0007] Adopt an improved quantum genetic algorithm, introduce quantum crossover and quantum mutation operations, set a disturbance weight factor according to the high-frequency disturbance energy, and accurately locate the disturbance source;

[0008] Based on the results of the responsible entity positioning, a corresponding mathematical model is established, and the power quality is evaluated and improved through the mathematical model.

[0009] Preferably, the power quality detection data includes current waveform data and voltage waveform data.

[0010] Preferably, the process of decomposing and reconstructing the current and voltage in the power quality detection data, extracting the components of high-frequency disturbances, and discriminating the disturbance direction based on the positive and negative of the high-frequency disturbance energy includes:

[0011] Perform discrete wavelet Mallat transform on the current waveform data and voltage waveform data, select Db45 wavelet for 6-layer wavelet decomposition and reconstruct the signal to extract the components of high-frequency disturbances;

[0012] Calculate the high-frequency disturbance components of the voltage and current to obtain the corresponding voltage power and current power;

[0013] Integrate the voltage power and current power, and use the positive and negative of the obtained high-frequency disturbance energy to discriminate the disturbance direction.

[0014] Preferably, the process of integrating the voltage power and current power and using the positive and negative of the obtained high-frequency disturbance energy to discriminate the disturbance direction includes: collecting voltage signals and current signals and performing preprocessing, calculating the instantaneous power, then decomposing the voltage signals and current signals through wavelet transform and extracting the high-frequency disturbance power signals, integrating the high-frequency disturbance power signals to obtain the high-frequency disturbance energy, and discriminating the direction of the power quality disturbance source according to the positive and negative of the high-frequency disturbance energy;

[0015] Among them, the process of discriminating the direction of the power quality disturbance source according to the positive and negative of the high-frequency disturbance energy includes: identifying the energy concentration situation within a specific frequency range, and taking the direction with concentrated energy distribution as the source direction of the power quality disturbance.

[0016] Preferably, the process of adopting the improved quantum genetic algorithm and introducing quantum crossover and quantum mutation operations includes:

[0017] Initialize the population and each parameter, and perform quantum coding;

[0018] According to the factors of positioning accuracy and stability, define the fitness function, evaluate the fitness of each individual, and obtain the fitness evaluation result;

[0019] According to the fitness evaluation result, perform selection operation on the individuals, and retain the individuals with fitness greater than the preset threshold;

[0020] Perform crossover operation on the selected individuals, and obtain new individuals through chromosome exchange and recombination;

[0021] Perform mutation operations on the individuals obtained after crossover, and update the population after evaluating the fitness of the new individuals;

[0022] Determine whether the algorithm reaches the convergence condition that an individual with an adaptation value appears in the population, or the algorithm reaches the maximum update upper limit. After reaching the convergence condition, output the optimal solution obtained after convergence, which is the positioning result of the power quality disturbance source.

[0023] Preferably, based on the result of the responsible entity positioning, establish a corresponding mathematical model. The process of evaluating and improving power quality through the mathematical model includes:

[0024] Based on the improved quantum genetic algorithm, set the model parameters as quantum bits and gene coding, evaluate the position of the responsible entity through the fitness function, and use crossover and mutation operations for evolutionary optimization; at the same time, introduce an adaptive parameter adjustment strategy and a local search mechanism to construct a power quality responsible entity positioning model;

[0025] Evaluate and improve power quality according to the power quality responsible entity positioning model.

[0026] The present invention also provides a power quality responsible entity positioning and model construction system, including:

[0027] A data acquisition and processing module, used to obtain power quality detection data, decompose and reconstruct the current and voltage in the power quality detection data through discrete wavelet Mallat transform, extract the components of high-frequency disturbances, and judge the disturbance direction based on the positive and negative of the high-frequency disturbance energy;

[0028] A disturbance source positioning module, used to adopt an improved quantum genetic algorithm, introduce quantum crossover and quantum mutation operations, set a disturbance weight factor according to the high-frequency disturbance energy, and accurately locate the paired disturbance sources;

[0029] A model evaluation module, used to establish a corresponding mathematical model based on the result of the responsible entity positioning, and evaluate and improve power quality through the mathematical model.

[0030] Compared with the prior art, the present invention has the following advantages and technical effects:

[0031] The present invention uses discrete wavelet Mallat transform to decompose and reconstruct the monitored current and voltage, which can decompose the signal into sub-signals of different frequencies, provides multi-scale analysis capabilities, can capture disturbances in different frequency ranges, has time-frequency locality, and can analyze the local characteristics of the signal in time and frequency, which is beneficial to accurately determining the source direction of power quality disturbances.

[0032] The present invention adopts an improved quantum genetic algorithm, uses a qubit encoding method, and performs global search through the Monte Carlo random strategy, which can effectively avoid falling into local optimal solutions, improve the search ability, and the qubit encoding method can quickly converge to the global optimal solution, reducing the number of algorithm iterations and running time. The algorithm uses improved selection and mutation operations, which can more effectively retain excellent individuals and increase the diversity of the population. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The drawings forming a part of this application are used to provide a further understanding of this application. The schematic embodiments and descriptions thereof of this application are used to explain this application and do not constitute an improper limitation to this application. In the drawings:

[0034] Figure 1 It is a schematic flowchart of the method according to the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0035] It should be noted that, without conflict, the embodiments in this application and the features in the embodiments can be combined with each other. The following will refer to the drawings and combine with the embodiments to detail this application.

[0036] It should be noted that the steps shown in the flowchart of the drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0037] Embodiment 1

[0038] As Figure 1 shown, in this embodiment, a method for positioning the main body of power quality responsibility and constructing a model is provided, including:

[0039] Obtain power quality detection data, decompose and reconstruct the current and voltage in the power quality detection data through discrete wavelet Mallat transform, extract the components of high-frequency disturbances, and judge the disturbance direction based on the positive and negative of the high-frequency disturbance energy;

[0040] Adopt an improved quantum genetic algorithm, introduce quantum crossover and quantum mutation operations, set a disturbance weight factor according to the high-frequency disturbance energy, and accurately locate the disturbance source;

[0041] Establish a corresponding mathematical model based on the result of the main body of responsibility positioning, and evaluate and improve the power quality through the mathematical model.

[0042] Further, it specifically includes the following steps:

[0043] S1: Use the discrete wavelet Mallat transform to decompose and reconstruct the monitored current and voltage, extract the components of high-frequency disturbances, and determine the disturbance direction based on the positive and negative of the high-frequency disturbance energy.

[0044] Collect power quality data, including the waveform data of current and voltage. Perform the discrete wavelet Mallat transform on the collected current and voltage waveform data. Select the Db45 wavelet with a higher wavelet order for 6-layer wavelet decomposition and reconstruct the signal. Extract the components of high-frequency disturbances, calculate the high-frequency disturbance components of voltage and current, obtain the corresponding power, integrate it, and determine the disturbance direction based on the positive and negative of the obtained high-frequency disturbance energy. The source direction of power quality disturbances can be identified as the energy concentration within a specific frequency range. By analyzing the energy distribution, the source direction of power quality disturbances can be determined.

[0045] Specifically, the process of integrating the voltage power and current power and determining the disturbance direction based on the positive and negative of the obtained high-frequency disturbance energy includes:

[0046] Collect the voltage signal and current signal and perform preprocessing, calculate the instantaneous power, then decompose the voltage signal and current signal through wavelet transform and extract the high-frequency disturbance power signal. Integrate the high-frequency disturbance power signal to obtain the high-frequency disturbance energy, and determine the source direction of power quality disturbances based on the positive and negative of the high-frequency disturbance energy.

[0047] Among them, the process of determining the source direction of power quality disturbances based on the positive and negative of the high-frequency disturbance energy includes: identifying the energy concentration within a specific frequency range, and taking the direction with concentrated energy distribution as the source direction of power quality disturbances.

[0048] S2: Use the improved quantum genetic algorithm, introduce quantum crossover and quantum mutation operations, set the disturbance weight factor according to the high-frequency disturbance energy, highlight the role of strong signals in the positioning process, and complete the accurate positioning of the disturbance source.

[0049] (1) Initialize the population and each parameter, and perform quantum coding.

[0050] (2) Define the fitness function, evaluate the fitness of each individual, and the design of the fitness function should consider factors such as the accuracy and stability of positioning.

[0051] (3) According to the evaluation results of the fitness function, perform selection operations on the individuals to retain the individuals with higher fitness.

[0052] (4) Perform crossover operations on the selected individuals, and generate new individuals through partial exchange and recombination of chromosomes.

[0053] (5) Perform mutation operations on the individuals obtained after crossover to increase the diversity of the population and the coverage of the search space;

[0054] (6) Evaluate the fitness of the newly generated individuals and update the population;

[0055] (7) Determine whether the algorithm reaches the convergence condition, that is, individuals with fitness values appear in the population, or complete the experimental steps when the algorithm reaches the maximum update limit;

[0056] (8) Output the optimal solution obtained after convergence, that is, the location result of the power quality disturbance source.

[0057] S3: Based on the result of the responsible entity location, establish a corresponding mathematical model for evaluating and improving power quality.

[0058] Based on the improved quantum genetic algorithm, construct a power quality responsible entity location model. The parameters in the model are set as qubit and gene encoding. Evaluate the position of the responsible entity through the fitness function, and use operations such as crossover and mutation for evolutionary optimization. At the same time, to improve the convergence speed and global search ability of the algorithm, introduce an adaptive parameter adjustment strategy and a local search mechanism to complete the model construction of the power quality responsible entity, evaluate and improve power quality.

[0059] The present invention proposes a method and system for power quality responsible entity location and model construction based on an improved quantum genetic algorithm. It uses discrete wavelet Mallat transform to decompose and reconstruct the monitored current and voltage. By decomposing the power quality signal into different frequency components, it accurately extracts features, effectively processes non-stationary signals, and improves the location accuracy and model construction accuracy. It uses an improved quantum genetic algorithm, introduces quantum crossover and quantum mutation operations, sets the perturbation weight factor according to the high-frequency perturbation energy, highlights the role of strong signals in the location process, completes the accurate location of the disturbance source, adopts the encoding method of qubits, can quickly converge to the global optimal solution, reduces the iteration times and running time of the algorithm, and the algorithm uses improved selection operations and mutation operations, which can more effectively retain excellent individuals and increase the diversity of the population.

[0060] Embodiment 2

[0061] Based on the same inventive concept, this embodiment also provides a power quality responsible entity location and model construction system, including:

[0062] A data acquisition and processing module for obtaining power quality detection data, decomposing and reconstructing the current and voltage in the power quality detection data through discrete wavelet Mallat transform, extracting the components of high-frequency perturbations, and judging the perturbation direction based on the positive and negative of the high-frequency perturbation energy;

[0063] The disturbance source location module is used to accurately locate paired disturbance sources by adopting an improved quantum genetic algorithm, introducing quantum crossover and quantum mutation operations, and setting a disturbance weight factor according to the high-frequency disturbance energy.

[0064] The model evaluation module is used to establish a corresponding mathematical model based on the result of the responsible entity location and evaluate and improve the power quality through the mathematical model.

[0065] The power quality responsible entity location and model construction system provided in this embodiment has all the advantages of the power quality responsible entity location and model construction method provided in Embodiment 1.

[0066] Embodiment 3

[0067] This embodiment also discloses a computer device, including a memory, a processor, and a computer program stored on the memory. The processor executes the computer program to implement the steps of the method described in Embodiment 1.

[0068] Embodiment 4

[0069] This embodiment also discloses a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the steps of the method described in Embodiment 1.

[0070] Embodiment 5

[0071] This embodiment also discloses a computer program product, including a computer program. When the computer program is executed by a processor, it implements the steps of the method described in Embodiment 1.

[0072] The above is only the preferred specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed in the present application should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for locating the responsible party for power quality and building a model, characterized in that: include: Acquire power quality detection data, decompose and reconstruct the current and voltage in the power quality detection data through discrete wavelet Mallat transform, extract the high-frequency disturbance component, and determine the disturbance direction based on the positive and negative of the high-frequency disturbance energy; An improved quantum genetic algorithm is used, quantum crossover and quantum mutation operations are introduced, and the disturbance weight factor is set according to the high-frequency disturbance energy to accurately locate the disturbance source; A corresponding mathematical model is established based on the result of the responsible party positioning, and the power quality is evaluated and improved through the mathematical model.

2. The method according to claim 1, characterized in that The power quality detection data includes current waveform data and voltage waveform data.

3. The method according to claim 1, characterized in that The process of decomposing and reconstructing the current and voltage in the power quality detection data, extracting the high-frequency disturbance component, and determining the disturbance direction based on the positive and negative of the high-frequency disturbance energy includes: Perform discrete wavelet Mallat transform on current waveform data and voltage waveform data, select Db45 wavelet for 6-layer wavelet decomposition and reconstruct the signal to extract high-frequency disturbance components; Calculate the high-frequency disturbance components of voltage and current to obtain the corresponding voltage power and current power; The voltage power and the current power are integrated, and the disturbance direction is determined by using the positive or negative value of the obtained high-frequency disturbance energy.

4. The method according to claim 1, characterized in that: The process of integrating the voltage power and the current power and using the positive and negative values ​​of the obtained high-frequency disturbance energy to determine the disturbance direction includes: The voltage signal and the current signal are collected and preprocessed, the instantaneous power is calculated, and then the voltage signal and the current signal are decomposed by wavelet transform to extract the high-frequency disturbance power signal, the high-frequency disturbance power signal is integrated to obtain the high-frequency disturbance energy, and the direction of the power quality disturbance source is determined according to the positive or negative energy of the high-frequency disturbance energy; The process of determining the direction of the power quality disturbance source according to the positive or negative energy of the high-frequency disturbance energy includes: identifying the energy concentration within a specific frequency range, and taking the direction of energy distribution concentration as the source direction of the power quality disturbance.

5. The method according to claim 1, characterized in that The process of using the improved quantum genetic algorithm and introducing quantum crossover and quantum mutation operations includes: Initialize the population and various parameters, and perform quantum encoding; According to the accuracy and stability factors of positioning, the fitness function is defined, the fitness of each individual is evaluated, and the fitness evaluation result is obtained; According to the fitness evaluation result, individuals are selected by a selection operation, and individuals whose fitness is greater than a preset threshold are retained; Perform crossover operation on the selected individuals to obtain new individuals through chromosome exchange and recombination; Perform mutation operation on the individuals obtained after crossover, evaluate the fitness of the new individuals and then update the population; It is judged whether the algorithm reaches the convergence condition that individuals with fitness values ​​appear in the group, or the algorithm reaches the maximum update upper limit. After reaching the convergence condition, the optimal solution obtained after convergence is output as the positioning result of the power quality disturbance source.

6. The method according to claim 1, characterized in that Based on the result of the responsible party location, a corresponding mathematical model is established. The process of evaluating and improving the power quality through the mathematical model includes: Based on the improved quantum genetic algorithm, the model parameters are set as quantum bits and gene encoding, the location of the responsible party is evaluated through the fitness function, and evolutionary optimization is performed using crossover and mutation operations. At the same time, an adaptive parameter adjustment strategy and a local search mechanism are introduced to construct a model for locating the responsible party for power quality. The power quality is evaluated and improved according to the power quality responsible subject positioning model.

7. A system for locating and modeling responsible entities for power quality, characterized in that: include: A data acquisition and processing module is used to obtain power quality detection data, decompose and reconstruct the current and voltage in the power quality detection data through discrete wavelet Mallat transformation, extract the high-frequency disturbance component, and determine the disturbance direction based on the positive and negative of the high-frequency disturbance energy; The disturbance source positioning module is used to use an improved quantum genetic algorithm, introduce quantum crossover and quantum mutation operations, set the disturbance weight factor according to the high-frequency disturbance energy, and accurately locate the disturbance source in pairs; The model evaluation module is used to establish a corresponding mathematical model based on the result of the responsible entity positioning, and evaluate and improve the power quality through the mathematical model.

8. A computer device comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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