High-voltage mining module power distribution automation switch configuration method, device and equipment

Abstract

The invention relates to a high-voltage mining module power distribution automation switch configuration method, device and equipment, and the method comprises the steps: obtaining power supply data of a power supply branch of a power distribution network, building a simulation model of the power supply branch of the power distribution network for simulation, and obtaining a fault signal collected by a high-voltage mining module; evaluating a waveform acquisition effect and a synchronization time change rule to obtain a simulation analysis result definition fitness function; randomly generating a topological structure of a power distribution network power supply branch by using a random topology generation algorithm based on the number and the position of the power distribution automation switch, and calculating the power distribution network power supply branch by using an adaptive optimization algorithm based on the topological structure and the fitness function of the power distribution network power supply branch. And obtaining an optimal configuration scheme of the number and the positions of the distribution automation switches of the power supply branches of the distribution network. The waveform acquisition effect and the synchronization time change rule of the fault signal can be accurately captured and analyzed, and the distribution automation switch of the high-voltage acquisition module can be dynamically configured in real time.

Description

Technical Field

[0001] The present invention relates to the technical field of power distribution automation, and in particular to a method, device and equipment for configuring a high-power distribution automation switch. Background Art

[0002] In recent years, the technology of configuring automatic switches for high-speed modules in distribution networks has been widely used, especially in smart grids and distribution automation systems. Existing technologies mainly rely on advanced sensors and data acquisition technologies. In practical applications, the configuration of automatic switches is usually based on empirical rules or simple rules to determine the number and location of distribution automation switches. These configuration schemes can improve the reliability and safety of distribution networks to a certain extent.

[0003] Although the existing technology has achieved certain results in the configuration of automatic switches for high-speed modules in distribution networks, there are still some obvious defects. First, the existing configuration methods usually lack systematic and comprehensive considerations, and often only focus on certain specific factors while ignoring other important variables, which may lead to insufficient optimization of the configuration scheme and the inability to fully realize the potential of automatic switches. Secondly, the existing technology lacks real-time and dynamic adaptability. Since the state and load of the distribution network are constantly changing, the fixed configuration scheme may not be able to respond to changes in the network state in a timely manner, thereby reducing the reliability and efficiency of the system. In addition, the existing technology also has shortcomings in fault signal analysis. The waveform acquisition effect and synchronization time change law of the fault signal are crucial to the evaluation of system performance and reliability, but the existing technology is often unable to accurately capture and analyze the waveform acquisition effect and synchronization time change law of the fault signal, thereby limiting the application and development of the existing technology in the configuration of automatic switches for high-speed modules in distribution networks. Summary of the invention

[0004] In view of this, the purpose of the present invention is to provide a high-collection module distribution automation switch configuration method, device and equipment to solve the problem that the prior art cannot accurately capture and analyze the waveform acquisition effect and synchronization time change law of the fault signal, resulting in an insufficiently optimized configuration scheme.

[0005] According to a first aspect of an embodiment of the present invention, a method for configuring a high-power distribution automation switch is provided, comprising:

[0006] Acquire power supply data of a power supply branch of a power distribution network, and establish a simulation model of the power supply branch of the power distribution network according to the power supply data, wherein the power supply data includes load data, equipment parameters and network structure;

[0007] Using the parallel simulation technology of cloud computing, the simulation model of the power supply branch of the distribution network is simulated to obtain the fault signal collected by the high-speed sampling module in the simulation model;

[0008] According to the waveform characteristics of the fault signal, a quantified value of the waveform acquisition effect and a quantified value of the synchronization time change law are obtained, and the quantified value of the waveform acquisition effect and the quantified value of the synchronization time change law are integrated into a simulation analysis result;

[0009] Defining a fitness function according to the simulation analysis results;

[0010] According to the preset range of number and position of distribution automation switches, a random topology generation algorithm is used to randomly generate the topology structures of several power supply branches of the distribution network;

[0011] The topological structures of the power supply branches of the distribution network are input into the simulation model in turn to obtain the simulation analysis results corresponding to each topological structure. The fitness value of each simulation analysis result is calculated using the fitness function, and the configuration scheme corresponding to the optimal topological structure is obtained according to the fitness value.

[0012] Preferably, the high-power distribution automation switch configuration method, after obtaining the fault signal collected by the high-power distribution module in the simulation model, further comprises:

[0013] The fault signal is preprocessed using continuous wavelet transform to extract waveform features; the waveform features include amplitude, frequency, phase and attenuation characteristics.

[0014] Preferably, the fault signal is preprocessed using continuous wavelet transform to extract waveform features, including:

[0015] Continuous wavelet transform is performed using the following formula:

[0016]

[0017] Wherein, W(a,b) represents the wavelet coefficient obtained by continuous wavelet transform of fault signal, a represents the scale parameter, which is used to control the frequency of wavelet, and b represents the translation parameter, which is used to control the time of wavelet.

[0018] Represents the fault signal x(t) at time t and the mother wavelet function after scale transformation and translation transformation Conjugation The integral over the entire time domain [-∞,∞].

[0019] Preferably, according to the waveform characteristics of the fault signal, a waveform acquisition effect quantification value is obtained, including:

[0020] Standardize the waveform characteristics of all fault signals collected by the high-speed sampling module in the simulation model to obtain all standardized waveform characteristics;

[0021] Based on the correlation coefficients, a correlation coefficient matrix between all the standardized waveform features is calculated;

[0022] The differences and similarities of waveform characteristics are analyzed according to the correlation coefficient matrix to obtain the quantified values ​​of waveform acquisition effects of all fault signals.

[0023] Preferably, according to the waveform characteristics of the fault signal, a quantized value of the synchronization time variation law is obtained, including:

[0024] Use the time synchronization algorithm to perform time synchronization processing on all fault signals collected by the high-speed sampling module in the simulation model to obtain all processed fault signals;

[0025] Calculate the mean and standard deviation of the synchronization time of all processed fault signals using statistical analysis methods;

[0026] According to the mean and standard deviation of the synchronization time of all fault signals, the variation law of the synchronization time of all fault signals is analyzed;

[0027] The quantized values ​​of the synchronization time variation law of all fault signals are obtained according to the synchronization time variation law.

[0028] Preferably, defining a fitness function according to the simulation analysis result includes:

[0029] Define a fitness function according to the quantized value of the waveform acquisition effect and the quantized value of the synchronization time variation law in the simulation analysis result;

[0030] The defined fitness function is used to quantify the advantages and disadvantages of the distribution automation switch configuration scheme in the topological structure of the power supply branch of the distribution network.

[0031] Preferably, the defined fitness function is expressed as the following formula:

[0032]

[0033] Among them, F represents the fitness value, w E The weight coefficient representing the quantized value E of the waveform acquisition effect of the fault signal, E min Indicates the minimum value among the quantized values ​​of the waveform acquisition effect of all fault signals, E max Indicates the maximum value of the waveform acquisition effect quantization value of all fault signals, w T The weight coefficient of the quantized value T representing the change law of the synchronization time of the fault signal, T min Represents the minimum value of the quantized value of the synchronous time variation law of all fault signals, T max Indicates the maximum value among the quantized values ​​of the synchronous time variation law of all fault signals.

[0034] Preferably, after randomly generating the topological structures of several power distribution network power supply branches by using a random topology generation algorithm, the method further includes:

[0035] The topological structure of the power supply branch of the distribution network is input into the adaptive genetic algorithm; the number of distribution automation switches represents the total number of nodes of the topological structure, and the position of the distribution automation switch represents the node state of the topological structure;

[0036] S71, taking the number and position of distribution automation switches in the topological structure of the power supply branch of the distribution network as the parent individuals, and calculating the fitness value of the parent individuals;

[0037] S72, randomly changing the number and position of distribution automation switches to generate first-generation individuals, and calculating the fitness value of the first-generation individuals;

[0038] S73, randomly combining the number and position of the distribution automation switches of the parent generation individual and the first generation individual to generate a second generation individual, and calculating the fitness value of the second generation individual;

[0039] S74, taking the number and position of a group of distribution automation switches with the largest fitness value among the parent generation individuals, the first child generation individuals, and the second child generation individuals as the optimal solution for the current iteration;

[0040] S75, taking the optimal solution of the current iteration as the latest parent individual, and repeating steps S71 to S75 until the number of iterations reaches the maximum number of iterations;

[0041] S76. Select a group of distribution automation switch quantities and positions corresponding to the largest fitness value among all fitness values ​​as the optimal configuration solution.

[0042] According to a second aspect of an embodiment of the present invention, a high-power distribution automation switch configuration device is provided, comprising:

[0043] A simulation model building module, used to obtain power supply data of a power supply branch of a power distribution network, and to build a simulation model of the power supply branch of the power distribution network according to the power supply data, wherein the power supply data includes load data, equipment parameters and network structure;

[0044] A simulation analysis module is used to simulate the simulation model of the power supply branch of the distribution network by using the parallel simulation technology of cloud computing to obtain the fault signal collected by the high-speed sampling module in the simulation model; according to the waveform characteristics of the fault signal, the waveform collection effect quantization value and the synchronization time change law quantization value are obtained, and the waveform collection effect quantization value and the synchronization time change law quantization value are integrated into the simulation analysis result;

[0045] A fitness function definition module, used to define a fitness function according to the simulation analysis result;

[0046] A topology structure generation module is used to randomly generate the topology structures of several power distribution network power supply branches using a random topology generation algorithm according to a preset number range and position range of distribution automation switches;

[0047] The configuration scheme screening module is used to input the topological structures of the power supply branches of the distribution network into the simulation model in sequence, obtain the simulation analysis results corresponding to each topological structure, calculate the fitness value of each simulation analysis result using the fitness function, and obtain the configuration scheme corresponding to the optimal topological structure according to the fitness value.

[0048] According to a third aspect of an embodiment of the present invention, a high-capacity modular power distribution automation switch configuration device is provided, comprising:

[0049] A main controller, and a memory connected to the main controller;

[0050] The memory stores program instructions;

[0051] The main controller is used to execute program instructions stored in the memory to perform any of the above methods.

[0052] The technical solution provided by the embodiments of the present invention may have the following beneficial effects:

[0053] It can be understood that the technical solution shown in the present invention establishes a high-precision simulation model by acquiring the power supply data of the power supply branches of the distribution network, and at the same time, with the help of the parallel simulation technology of cloud computing, efficiently simulates the fault signal acquisition process in the distribution network, and significantly improves the accuracy of the evaluation of the fault signal waveform characteristics and the synchronization time change law, so that the simulation analysis results are closer to the actual operating conditions.

[0054] By using random topology generation algorithm and adaptive optimization algorithm, based on the topological structure of the power supply branch of the distribution network and the fitness function defined according to the simulation analysis results, the optimal number and location configuration scheme of the distribution automation switch can be automatically and intelligently searched. It not only greatly reduces the cost of manual intervention and trial and error, but also optimizes the configuration of the distribution automation switch to more effectively isolate the fault area, reduce the scope of power outage, and improve the fault recovery speed and power supply quality, bringing users a more reliable and stable power supply. It can accurately capture and analyze the waveform acquisition effect of the fault signal and the law of synchronization time change, and configure the distribution automation switch of the high-collection module in real time and dynamically.

[0055] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0057] Figure 1 It is a schematic diagram of the steps of a method for configuring a high-power distribution automation switch according to an exemplary embodiment;

[0058] Figure 2 The figure is a basic flow chart of determining an optimal configuration scheme according to an exemplary embodiment. DETAILED DESCRIPTION

[0059] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Instead, they are merely examples of devices and methods consistent with some aspects of the present invention as detailed in the appended claims.

[0060] Embodiment 1

[0061] Figure 1 is a schematic diagram of a method for configuring a high-power distribution automation switch according to an exemplary embodiment, see Figure 1 , a high-power module distribution automation switch configuration method is provided, comprising:

[0062] Step S11, acquiring power supply data of a power supply branch of a distribution network, and establishing a simulation model of the power supply branch of the distribution network according to the power supply data, wherein the power supply data includes load data, equipment parameters and network structure.

[0063] The simulation model can simulate the operating status of the distribution network, including normal conditions and fault conditions. This embodiment collects load data, equipment parameters and network structure of the power supply branch of the distribution network, and uses simulation technology to construct a simulation model of the power supply branch of the distribution network to reflect the actual operating conditions of the power supply branch of the distribution network.

[0064] Step S12: simulating the simulation model of the power supply branch of the power distribution network by using the parallel simulation technology of cloud computing to obtain the fault signal collected by the high-speed sampling module in the simulation model.

[0065] This embodiment utilizes the powerful computing power and parallel simulation technology of the cloud computing platform to perform efficient and rapid simulation calculations on the simulation model of the power supply branch of the distribution network, simulates the signal acquisition process of the high-speed data acquisition module under fault conditions, and can efficiently and accurately obtain the fault signals collected by the high-speed data acquisition module in the simulation model.

[0066] Step S13: according to the waveform characteristics of the fault signal, quantified values ​​of waveform acquisition effect and quantified values ​​of synchronization time variation law are obtained, and the quantified values ​​of waveform acquisition effect and quantified values ​​of synchronization time variation law are integrated into simulation analysis results.

[0067] This embodiment analyzes the waveform characteristics of the fault signal, such as amplitude, frequency, phase, and attenuation characteristics, evaluates the accuracy of waveform acquisition and the stability of synchronization time, and can accurately quantify the waveform acquisition effect and the variation law of synchronization time, thereby obtaining simulation analysis results and providing key indicators for optimizing the configuration of distribution automation switches.

[0068] Step S14: defining a fitness function according to the simulation analysis results.

[0069] This embodiment defines a fitness function based on the quantified values ​​of the key indicator waveform acquisition effect and the quantified values ​​of the synchronization time variation law in the simulation analysis results, which is used to quantify the advantages and disadvantages of the distribution automation switch configuration scheme. The fitness function is a key part of the adaptive optimization algorithm of this embodiment, which can accurately reflect the advantages and disadvantages of the distribution automation switch configuration scheme and is used to guide the adaptive optimization algorithm to search for the optimal configuration scheme of the number and location of the distribution automation switches of the power supply branch of the distribution network.

[0070] Step S15: Based on the preset number range and position range of the distribution automation switches, a random topology generation algorithm is used to randomly generate the topology structures of several power supply branches of the distribution network.

[0071] This embodiment uses a random topology generation algorithm to randomly generate a variety of possible topological structures of the power supply branches of the distribution network, and provides the adaptive optimization algorithm with a variety of possible configuration schemes for the number and position of distribution automation switches of the initial power supply branches of the distribution network, thereby increasing the possibility of searching for the optimal configuration scheme for the number and position of distribution automation switches of the power supply branches of the distribution network.

[0072] Step S16: input the topological structures of the power supply branches of the distribution network into the simulation model in sequence, obtain the simulation analysis results corresponding to each topological structure, calculate the fitness value of each simulation analysis result using the fitness function, and obtain the configuration scheme corresponding to the optimal topological structure according to the fitness value.

[0073] This embodiment utilizes an adaptive optimization algorithm such as a genetic algorithm to iteratively search for the optimal configuration scheme for the number and position of distribution automation switches that meet the conditions according to the topological structure and fitness function of the power supply branches of the distribution network. This realizes real-time and dynamic search for the optimal number and position configuration scheme of distribution automation switches, thereby improving the operating efficiency and reliability of the distribution network.

[0074] It can be understood that the high-collection module distribution automation switch configuration method introduced in this embodiment can accurately evaluate the fault signal acquisition effect and synchronization time variation law of the power supply branch of the distribution network through the synergistic effect of multiple steps, and optimize the configuration scheme of the distribution automation switch based on the fault signal acquisition effect and synchronization time variation law, thereby improving the operating efficiency and reliability of the distribution network. It solves the problem that the existing technology cannot accurately capture and analyze the waveform acquisition effect of the fault signal and the synchronization time variation law, and configure the distribution automation switch of the high-collection module in real time and dynamically.

[0075] Embodiment 2

[0076] The invention concept is the same as that of the first embodiment. This embodiment shows a method for configuring a high-power distribution automation switch of a high-power module, including the following steps:

[0077] Step S11, acquiring power supply data of a power supply branch of a distribution network, and establishing a simulation model of the power supply branch of the distribution network according to the power supply data, wherein the power supply data includes load data, equipment parameters and network structure.

[0078] It should be noted that, in this embodiment, the power supply branches of the power distribution network include T-type branches and macromolecular structure branches, and are also applicable to other types of power supply branches of the power distribution network.

[0079] This embodiment has wide applicability and can meet the simulation and optimization requirements of power supply branches of distribution networks with different structures and scales. By collecting power supply data of power supply branches of distribution networks with different structures and scales and building corresponding simulation models, a more in-depth analysis and optimization of the distribution network can be performed, thereby improving the operating efficiency and reliability of the distribution network.

[0080] Step S12: simulating the simulation model of the power supply branch of the power distribution network by using the parallel simulation technology of cloud computing to obtain the fault signal collected by the high-speed sampling module in the simulation model.

[0081] Step S12 of this embodiment is to simulate the operation state of the power distribution network under various fault conditions and obtain the fault signal collected by the high-speed mining module in the simulation model. The parallel simulation technology of cloud computing can give full play to its powerful computing power to ensure the accuracy and efficiency of the simulation.

[0082] It should be noted that after obtaining the fault signal collected by the high-speed sampling module in the simulation model, it also includes: preprocessing the fault signal using continuous wavelet transform to extract waveform features; the waveform features include amplitude, frequency, phase and attenuation characteristics.

[0083] Continuous wavelet transform is a powerful signal processing tool that can accurately extract the waveform features of fault signals. The waveform features that need to be extracted and analyzed in this embodiment include amplitude, frequency, phase and attenuation characteristics.

[0084] Through the preprocessing of continuous wavelet transform, clearer and more accurate fault signal waveform characteristics can be obtained, which provides strong support for the definition of fitness function and the application of adaptive optimization algorithm in the subsequent steps. At the same time, the preprocessing of fault signals by continuous wavelet transform also ensures the accuracy and reliability of subsequent analysis, and provides a solid data foundation for the optimal configuration of power supply branches in the distribution network.

[0085] It should be noted that the continuous wavelet transform is performed by the following formula:

[0086]

[0087] Wherein, W(a,b) represents the wavelet coefficients obtained by continuous wavelet transform of fault signal, a represents the scale parameter, which is used to control the frequency of wavelet, and b represents the translation parameter, which is used to control the time of wavelet.

[0088] Represents the fault signal x(t) at time t and the mother wavelet function after scale transformation and translation transformation Conjugation The integral over the entire time domain [-∞,∞].

[0089] It can be understood that this embodiment uses continuous wavelet transform to pre-process the fault signal collected by the high-speed sampling module in the simulation model, which significantly improves the accuracy of waveform feature extraction. And through the time synchronization algorithm based on correlation coefficient and statistical analysis method, the waveform acquisition effect and the law of synchronization time change are quantitatively evaluated, so as to define the fitness function more scientifically, which is used to quantify the advantages and disadvantages of the distribution automation switch configuration scheme in the topological structure of the power supply branch of the distribution network, and effectively improve the operation efficiency and reliability of the distribution network.

[0090] Step S13: according to the waveform characteristics of the fault signal, quantified values ​​of waveform acquisition effect and quantified values ​​of synchronization time variation law are obtained, and the quantified values ​​of waveform acquisition effect and quantified values ​​of synchronization time variation law are integrated into simulation analysis results.

[0091] In this embodiment, based on the waveform characteristics of the fault signal, the waveform acquisition effect and the synchronization time variation law are deeply evaluated, and detailed simulation analysis results are obtained. This process mainly includes evaluating the waveform acquisition effect and evaluating the synchronization time variation law.

[0092] In the first aspect, according to the waveform characteristics of the fault signal, a quantitative value of the waveform acquisition effect is obtained, including: standardizing the waveform characteristics of all fault signals collected by the high-speed sampling module in the simulation model to obtain all standardized waveform characteristics; based on the correlation coefficient, calculating the correlation coefficient matrix between all standardized waveform characteristics; according to the correlation coefficient matrix, analyzing the differences and similarities of the waveform characteristics to obtain the quantitative value of the waveform acquisition effect of all fault signals.

[0093] In specific practice, in order to quantify the waveform acquisition effect, this embodiment first uses a correlation coefficient-based method to standardize the waveform features of all fault signals collected by the high-speed acquisition module in the simulation model, such as amplitude, frequency, phase, and attenuation characteristics, to ensure comparability between different features. The standardized waveform features will form a feature matrix, in which each row represents the waveform feature of a fault signal.

[0094] Next, this embodiment calculates the correlation coefficient matrix between all the standardized waveform features in the waveform features of the fault signal. Each element in the correlation coefficient matrix represents the degree of correlation between two waveform features, and its value is between -1 and 1. Through the correlation coefficient matrix, the differences and similarities of the waveform features can be analyzed, thereby judging the accuracy and completeness of the waveform acquisition.

[0095] Finally, according to the analysis results of the correlation coefficient matrix, the quantified values ​​of the waveform acquisition effects of all fault signals are obtained. The quantified values ​​of the waveform acquisition effects comprehensively reflect the accuracy and completeness of waveform acquisition, providing an important basis for subsequent optimization configuration work.

[0096] In the second aspect, according to the waveform characteristics of the fault signal, a quantitative value of the synchronization time variation law is obtained, including: using a time synchronization algorithm to perform time synchronization processing on all fault signals collected by the high-speed sampling module in the simulation model to obtain all processed fault signals; using a statistical analysis method to calculate the mean and standard deviation of the synchronization time of all processed fault signals; based on the mean and standard deviation of the synchronization time of all fault signals, analyzing and obtaining the synchronization time variation law of all fault signals; and obtaining the quantitative value of the synchronization time variation law of all fault signals based on the synchronization time variation law.

[0097] In practice, in order to quantify the change law of synchronization time, this embodiment combines the time synchronization algorithm and the statistical analysis method. First, the time synchronization algorithm is used to perform time synchronization processing on all fault signals collected by the high-speed sampling module in the simulation model to ensure the consistency of all fault signals in time. The processed fault signals will form a new signal set.

[0098] Then, the mean and standard deviation of the synchronization time of all fault signals in the new signal set are calculated using a statistical analysis method. The mean reflects the average level of the synchronization time, while the standard deviation reflects the degree of fluctuation of the synchronization time. Through these two statistics, this embodiment can analyze the variation law of the synchronization time of all fault signals, thereby determining the accuracy and stability of time synchronization.

[0099] Finally, according to the results of statistical analysis, this embodiment can obtain the quantified value of the synchronization time variation law of all fault signals. The quantified value of the synchronization time variation law comprehensively reflects the accuracy and stability of time synchronization, providing strong support for subsequent optimization configuration work.

[0100] Combining the evaluation results of the above two aspects, this embodiment can obtain the final result of the simulation analysis, which not only reflects the actual situation of the waveform acquisition effect and the synchronization time change law, but also provides important data support for subsequent optimization configuration work. By continuously optimizing the waveform acquisition effect and time synchronization mechanism, this embodiment can further improve the operating efficiency and reliability of the distribution network.

[0101] Step S14: defining a fitness function according to the simulation analysis results.

[0102] It should be noted that a fitness function is defined based on the quantified value of the waveform acquisition effect and the quantified value of the synchronization time change law in the simulation analysis results; the defined fitness function is used to quantify the advantages and disadvantages of the distribution automation switch configuration scheme in the topological structure of the power supply branch of the distribution network.

[0103] First, this embodiment extracts quantitative indicators reflecting the above characteristics by analyzing the waveform acquisition effect of the fault signal, including the accuracy, completeness and stability of the waveform characteristics, and the change law of the synchronization time. The quantitative indicators can directly reflect the response speed and accuracy of the distribution automation switch in the case of a fault, as well as the time synchronization performance of the entire distribution network.

[0104] Then, this embodiment uses these quantitative indicators to construct a fitness function. The value of the fitness function will directly reflect the quality of the distribution automation switch configuration scheme. Specifically, an excellent configuration scheme should be able to ensure that the distribution automation switch can respond quickly and accurately when a fault occurs, and the time synchronization performance of the entire distribution network remains good, so that the value of the fitness function is high.

[0105] On the contrary, if there are defects in the configuration scheme of the distribution automation switch, such as slow response speed, poor accuracy or unstable time synchronization performance, then these defects will be directly reflected in the waveform acquisition effect of the fault signal and the change law of the synchronization time, which will lead to a lower value of the fitness function.

[0106] Therefore, by defining the fitness function based on the simulation analysis results, the configuration scheme of the distribution automation switch in the topological structure of the power supply branch of the distribution network can be quantitatively evaluated, so as to find the optimal configuration scheme. The process of finding the optimal configuration scheme not only improves the operating efficiency and reliability of the distribution network, but also provides strong support for the subsequent optimization configuration work.

[0107] It should be noted that the defined fitness function is expressed as the following formula:

[0108]

[0109] Among them, F represents the fitness value, w E The weight coefficient representing the quantized value E of the waveform acquisition effect of the fault signal, E min Indicates the minimum value among the quantized values ​​of the waveform acquisition effect of all fault signals, E max Indicates the maximum value of the waveform acquisition effect quantization value of all fault signals, w T The weight coefficient of the quantized value T representing the change law of the synchronization time of the fault signal, T min Represents the minimum value of the quantized value of the synchronous time variation law of all fault signals, T max Indicates the maximum value among the quantized values ​​of the synchronous time variation law of all fault signals.

[0110] Step S15: Based on the preset number range and position range of the distribution automation switches, a random topology generation algorithm is used to randomly generate the topology structures of several power supply branches of the distribution network.

[0111] In order to explore the impact of different numbers and positions of distribution automation switches on the performance of power supply branches of the distribution network, this embodiment uses a random topology generation algorithm to randomly generate the topology structure of the power supply branches of the distribution network.

[0112] First, this embodiment sets the quantity range and location range of distribution automation switches according to the actual requirements and constraints of the power supply branches of the distribution network. The constraints include power supply reliability requirements, fault recovery time requirements, equipment cost restrictions, etc.

[0113] Then, this embodiment uses a random topology generation algorithm to randomly generate multiple topological structures of power supply branches of the distribution network within the scope of the constraints. The topological structures of the power supply branches of the distribution network are different in the number and position of distribution automation switches, thereby forming multiple possible initial configuration schemes.

[0114] Step S16: input the topological structures of the power supply branches of the distribution network into the simulation model in sequence, obtain the simulation analysis results corresponding to each topological structure, calculate the fitness value of each simulation analysis result using the fitness function, and obtain the configuration scheme corresponding to the optimal topological structure according to the fitness value.

[0115] In specific practice, the topological structures of multiple power distribution network power supply branches are input into the simulation model in turn to collect the waveform of the fault signal and evaluate the change law of synchronization time. By calculating the fitness function, the performance of the topological structure of each power distribution network power supply branch is quantified, and the topological structure of the power distribution network power supply branch with the best performance is screened out. The optimal configuration scheme of the number and location of the distribution automation switches of the power distribution network power supply branch is obtained, which improves the operation efficiency and reliability of the distribution network.

[0116] It should be noted that although the random topology generation algorithm can generate multiple possible configuration schemes, it does not mean that all generated schemes are feasible or optimal. Therefore, in practical applications, it is necessary to further screen and optimize the generated schemes in combination with actual conditions and constraints.

[0117] In this embodiment, the topological structure of several power distribution network power supply branches can also be used to obtain an optimized configuration scheme by using an adaptive optimization algorithm and an adaptive genetic algorithm, and the optimal configuration scheme is obtained from the optimized configuration scheme. The process is shown in Figure 2 .

[0118] After randomly generating the topological structures of several power supply branches of the distribution network using a random topology generation algorithm, the topological structures of the power supply branches of the distribution network are input into an adaptive genetic algorithm; the number of the distribution automation switches represents the total number of nodes of the topological structure, and the position of the distribution automation switch represents the node status of the topological structure.

[0119] The adaptive genetic algorithm performs the following steps:

[0120] S71, taking the number and position of distribution automation switches in the topological structure of the power supply branch of the distribution network as the parent individuals, and calculating the fitness value of the parent individuals;

[0121] S72, randomly changing the number and position of distribution automation switches to generate first-generation individuals, and calculating the fitness value of the first-generation individuals;

[0122] S73, randomly combining the number and position of the distribution automation switches of the parent generation individual and the first generation individual to generate a second generation individual, and calculating the fitness value of the second generation individual;

[0123] S74, taking the number and position of a group of distribution automation switches with the largest fitness value among the parent generation individuals, the first child generation individuals, and the second child generation individuals as the optimal solution for the current iteration;

[0124] S75, taking the optimal solution of the current iteration as the latest parent individual, and repeating steps S71 to S75 until the number of iterations reaches the maximum number of iterations;

[0125] S76. Select a group of distribution automation switch quantities and positions corresponding to the largest fitness value among all fitness values ​​as the optimal configuration solution.

[0126] It is understandable that this embodiment also supports simulation analysis of different power supply branch structures such as T-type branches and macromolecular structure branches, and defines a fitness function based on the simulation analysis results. Through the intelligent search of the adaptive optimization algorithm, the optimal configuration scheme for the number and position of distribution automation switches in the power supply branches of the distribution network can be found. This embodiment not only has a powerful global search capability, but also can be dynamically adjusted according to the actual situation of the distribution network, so as to obtain a configuration scheme that better meets actual needs. At the same time, considering the diversity of power supply branch structures, the configuration scheme is more flexible and applicable, which not only greatly improves the configuration efficiency, but also reduces the cost of manual intervention, and provides strong technical support for the intelligent management and optimization of the distribution network.

[0127] Embodiment 3

[0128] Based on the same inventive concept as other embodiments, a high-power distribution automation switch configuration device is provided, comprising:

[0129] A simulation model building module, used to obtain power supply data of a power supply branch of a power distribution network, and to build a simulation model of the power supply branch of the power distribution network according to the power supply data, wherein the power supply data includes load data, equipment parameters and network structure;

[0130] A simulation analysis module is used to simulate the simulation model of the power supply branch of the distribution network by using the parallel simulation technology of cloud computing to obtain the fault signal collected by the high-speed sampling module in the simulation model; according to the waveform characteristics of the fault signal, the waveform collection effect quantization value and the synchronization time change law quantization value are obtained, and the waveform collection effect quantization value and the synchronization time change law quantization value are integrated into the simulation analysis result;

[0131] A fitness function definition module, used to define a fitness function according to the simulation analysis result;

[0132] A topology structure generation module is used to randomly generate the topology structures of several power distribution network power supply branches using a random topology generation algorithm according to a preset number range and position range of distribution automation switches;

[0133] The configuration scheme screening module is used to input the topological structures of the power supply branches of the distribution network into the simulation model in sequence, obtain the simulation analysis results corresponding to each topological structure, calculate the fitness value of each simulation analysis result using the fitness function, and obtain the configuration scheme corresponding to the optimal topological structure according to the fitness value.

[0134] Embodiment 4

[0135] Based on the same inventive concept as other embodiments, a high-power module distribution automation switch configuration device is provided, comprising:

[0136] A main controller, and a memory connected to the main controller;

[0137] The memory stores program instructions;

[0138] The main controller is used to execute the program instructions stored in the memory and perform the steps of the method described in the above embodiment one or two.

[0139] Embodiment 5

[0140] Based on the same inventive concept as other embodiments, this embodiment introduces a computer-readable storage medium on which computer instructions are stored, characterized in that when the computer instructions are executed by a processor, the steps of the method described in the above-mentioned embodiment one or two are implemented.

[0141] Embodiment 6

[0142] Based on the same inventive concept as other embodiments, this embodiment introduces a computer program product, including computer instructions, characterized in that when the computer instructions are executed by a processor, the steps of the method described in the above-mentioned embodiment one or two are implemented.

[0143] It can be understood that the same or similar parts of the above embodiments can be referenced to each other, and the contents not described in detail in some embodiments can refer to the same or similar contents in other embodiments.

[0144] It should be noted that, in the description of the present invention, the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In addition, in the description of the present invention, unless otherwise specified, the meaning of "plurality" refers to at least two.

[0145] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention belong.

[0146] 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.

[0147] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0148] In addition, each functional unit in each embodiment of the present invention may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0149] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0150] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0151] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A method for configuring a high-power distribution automation switch, characterized in that: include: Acquire power supply data of a power supply branch of a power distribution network, and establish a simulation model of the power supply branch of the power distribution network according to the power supply data, wherein the power supply data includes load data, equipment parameters and network structure; Using the parallel simulation technology of cloud computing, the simulation model of the power supply branch of the distribution network is simulated to obtain the fault signal collected by the high-speed sampling module in the simulation model; According to the waveform characteristics of the fault signal, a quantified value of the waveform acquisition effect and a quantified value of the synchronization time change law are obtained, and the quantified value of the waveform acquisition effect and the quantified value of the synchronization time change law are integrated into a simulation analysis result; Defining a fitness function according to the simulation analysis results; According to the preset number range and position range of the distribution automation switches, the topology structures of several power supply branches of the distribution network are randomly generated by using the random topology generation algorithm; The topological structures of the power supply branches of the distribution network are input into the simulation model in turn to obtain the simulation analysis results corresponding to each topological structure. The fitness value of each simulation analysis result is calculated using the fitness function, and the configuration scheme corresponding to the optimal topological structure is obtained according to the fitness value.

2. The high-power distribution automation switch configuration method according to claim 1 is characterized in that: After obtaining the fault signal collected by the high-speed sampling module in the simulation model, the method further includes: The fault signal is preprocessed using continuous wavelet transform to extract waveform features; the waveform features include amplitude, frequency, phase and attenuation characteristics.

3. The high-power distribution automation switch configuration method according to claim 2 is characterized in that: The fault signal is preprocessed using continuous wavelet transform to extract waveform features, including: Continuous wavelet transform is performed using the following formula: Wherein, W(a,b) represents the wavelet coefficients obtained by continuous wavelet transform of fault signal, a represents the scale parameter, which is used to control the frequency of wavelet, and b represents the translation parameter, which is used to control the time of wavelet. Represents the fault signal x(t) at time t and the mother wavelet function after scale transformation and translation transformation Conjugation The integral over the entire time domain [-∞,∞].

4. The high-power distribution automation switch configuration method according to claim 1 is characterized in that: According to the waveform characteristics of the fault signal, a quantified value of the waveform acquisition effect is obtained, including: Standardize the waveform characteristics of all fault signals collected by the high-speed sampling module in the simulation model to obtain all standardized waveform characteristics; Based on the correlation coefficients, a correlation coefficient matrix between all the standardized waveform features is calculated; The differences and similarities of waveform characteristics are analyzed according to the correlation coefficient matrix to obtain the quantified values ​​of waveform acquisition effects of all fault signals.

5. The high-power distribution automation switch configuration method according to claim 1 is characterized in that: According to the waveform characteristics of the fault signal, a quantified value of the synchronization time variation law is obtained, including: Use the time synchronization algorithm to perform time synchronization processing on all fault signals collected by the high-speed sampling module in the simulation model to obtain all processed fault signals; Calculate the mean and standard deviation of the synchronization time of all processed fault signals using statistical analysis methods; According to the mean and standard deviation of the synchronization time of all fault signals, the variation law of the synchronization time of all fault signals is analyzed; The quantized values ​​of the synchronization time variation law of all fault signals are obtained according to the synchronization time variation law.

6. The high-power distribution automation switch configuration method according to claim 1 is characterized in that: Defining a fitness function according to the simulation analysis results includes: Define a fitness function according to the quantized value of the waveform acquisition effect and the quantized value of the synchronization time variation law in the simulation analysis result; The defined fitness function is used to quantify the advantages and disadvantages of the distribution automation switch configuration scheme in the topological structure of the power supply branch of the distribution network.

7. The method for configuring high-power distribution automation switches according to claim 6 is characterized in that: The defined fitness function is expressed as the following formula: F＝ w E (SIGN IN min ) - w T (T-T min ) E max -E min T max -T min Among them, F represents the fitness value, w E The weight coefficient representing the quantized value E of the waveform acquisition effect of the fault signal, E min Indicates the minimum value among the quantized values ​​of the waveform acquisition effect of all fault signals, E max Indicates the maximum value of the waveform acquisition effect quantization value of all fault signals, w T The weight coefficient of the quantized value T representing the change law of the synchronization time of the fault signal, T min Represents the minimum value of the quantized value of the synchronous time variation law of all fault signals, T max Indicates the maximum value among the quantized values ​​of the synchronous time variation law of all fault signals.

8. The high-power distribution automation switch configuration method according to claim 1 is characterized in that: After randomly generating the topological structures of several power distribution network power supply branches by using a random topology generation algorithm, the method further includes: The topological structure of the power supply branch of the distribution network is input into the adaptive genetic algorithm; the number of distribution automation switches represents the total number of nodes of the topological structure, and the position of the distribution automation switch represents the node state of the topological structure; S71, taking the number and position of distribution automation switches in the topological structure of the power supply branch of the distribution network as the parent individuals, and calculating the fitness value of the parent individuals; S72, randomly changing the number and position of distribution automation switches to generate first-generation individuals, and calculating the fitness value of the first-generation individuals; S73, randomly combining the number and position of the distribution automation switches of the parent generation individual and the first generation individual to generate a second generation individual, and calculating the fitness value of the second generation individual; S74, taking the number and position of a group of distribution automation switches with the largest fitness value among the parent generation individuals, the first child generation individuals, and the second child generation individuals as the optimal solution for the current iteration; S75, taking the optimal solution of the current iteration as the latest parent individual, and repeating steps S71 to S75 until the number of iterations reaches the maximum number of iterations; S76. Select a group of distribution automation switch quantities and positions corresponding to the largest fitness value among all fitness values ​​as the optimal configuration solution.

9. A high-power module distribution automation switch configuration device, characterized in that: include: A simulation model building module, used to obtain power supply data of a power supply branch of a power distribution network, and to build a simulation model of the power supply branch of the power distribution network according to the power supply data, wherein the power supply data includes load data, equipment parameters and network structure; A simulation analysis module is used to simulate the simulation model of the power supply branch of the distribution network by using the parallel simulation technology of cloud computing to obtain the fault signal collected by the high-speed sampling module in the simulation model; according to the waveform characteristics of the fault signal, the waveform collection effect quantization value and the synchronization time change law quantization value are obtained, and the waveform collection effect quantization value and the synchronization time change law quantization value are integrated into the simulation analysis result; A fitness function definition module, used to define a fitness function according to the simulation analysis result; A topology structure generation module is used to randomly generate the topology structures of several power distribution network power supply branches using a random topology generation algorithm according to a preset number range and position range of distribution automation switches; The configuration scheme screening module is used to input the topological structures of the power supply branches of the distribution network into the simulation model in sequence, obtain the simulation analysis results corresponding to each topological structure, calculate the fitness value of each simulation analysis result using the fitness function, and obtain the configuration scheme corresponding to the optimal topological structure according to the fitness value.

10. A high-power module distribution automation switch configuration device, characterized in that: include: A main controller, and a memory connected to the main controller; The memory stores program instructions; The main controller is used to execute program instructions stored in the memory and perform the method according to any one of claims 1 to 8.

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