Distribution network complex fault waveform-oriented feeder automation test method and system

Abstract

The invention discloses a distribution network complex fault waveform-oriented feeder automation test method and system, and relates to the technical field of distribution automation, and the method comprises the steps: building a corresponding distribution network topology model according to the grounding mode of a target region distribution network; configuring the distribution network topology model in a preset simulation environment to simulate different fault scenes so as to establish a complex fault waveform library; sequentially carrying out expansion and feature extraction on each waveform in the complex fault waveform library; carrying out sequence arrangement on the extracted fault feature data and carrying out reverse mapping to obtain simulation adjustable parameters, and updating a simulation environment by using the simulation adjustable parameters; and connecting the updated simulation environment to a feeder automation test system, and carrying out an iterative test on the fault of the power distribution network in the target area, so that the response accuracy and reliability of the feeder automation test can be realized.

Description

Technical Field

[0001] This invention relates to the field of power distribution automation technology, and in particular to a feeder automation testing method and system for complex fault waveforms in power distribution networks. Background Technology

[0002] With the rapid development of distribution automation systems, feeder automation (FA), as a core technology for improving the operating efficiency of distribution networks, has the core function of achieving rapid fault isolation and recovery. Therefore, to ensure timely and accurate fault isolation during distribution network operation, it is necessary to improve the response accuracy of feeder automation testing.

[0003] However, since new distribution networks often incorporate a large number of distributed power sources, traditional testing methods cannot fully cover complex operating conditions, and often suffer from slow speed and poor real-time performance during simulation, making it difficult to meet the needs of actual distribution network fault testing.

[0004] Therefore, how to improve the FA testing process and enhance the reliability of distribution network fault testing has become a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] This invention provides a feeder automation testing method and system for complex fault waveforms in distribution networks, aiming to solve the problem of how to generate fault characteristic waveforms and associate them with the testing and verification process of feeder automation, thereby improving the response accuracy of the feeder automation system to complex fault waveforms.

[0006] To address the aforementioned technical problems, embodiments of the present invention provide an automated feeder testing method for complex fault waveforms in distribution networks, comprising:

[0007] Based on the grounding method of the target area's distribution network, construct the corresponding distribution network topology model;

[0008] The distribution network topology model is configured in a preset simulation environment to simulate different fault scenarios in order to establish a complex fault waveform library;

[0009] Each waveform in the complex fault waveform library is sequentially expanded and its features are extracted.

[0010] The extracted fault feature data is sequenced and back-mapped to simulation adjustable parameters, and the simulation environment is updated with the simulation adjustable parameters.

[0011] The updated simulation environment was integrated into the feeder automation testing system to conduct iterative testing of distribution network faults in the target area.

[0012] Furthermore, configuring the distribution network topology model in a preset simulation environment to simulate different fault scenarios in order to establish a complex fault waveform library includes:

[0013] The midpoint of the main trunk line, the beginning and end of the branch line load nodes of the distribution network topology model are respectively set as simulation fault points;

[0014] Inject corresponding fault parameters into the simulated fault points to establish various grounding fault scenarios and trigger simulation operation;

[0015] Voltage and current waveform data are collected at the simulated fault point at a preset sampling frequency;

[0016] The voltage and current waveform data are structured and stored to obtain the complex fault waveform library.

[0017] Furthermore, the step of sequentially expanding and extracting features from each waveform in the complex fault waveform library includes:

[0018] Based on the historical fault data of the target area distribution network, a multi-dimensional hierarchical disturbance mechanism is constructed, which includes parameter disturbance propagation strategy, noise injection propagation strategy and time-series propagation strategy; the multi-dimensional hierarchical disturbance mechanism divides the propagation strategy into level gradients according to the disturbance intensity.

[0019] The multi-dimensional hierarchical perturbation mechanism is used to expand each waveform in the complex fault waveform library to generate extended fault waveforms.

[0020] Time-frequency analysis is performed on the extended fault waveform to extract multidimensional features.

[0021] Furthermore, the expansion of each waveform in the complex fault waveform library using the multi-dimensional hierarchical perturbation mechanism includes:

[0022] Based on the response intensity of each waveform in the complex fault waveform library at different stages, each waveform is divided and labeled according to the transient stage, steady-state stage, and recovery stage.

[0023] Based on the division and labeling results, the corresponding extended strategy in the multi-dimensional hierarchical perturbation mechanism is applied to each waveform.

[0024] Furthermore, the step of applying the corresponding extended strategy in the multi-dimensional perturbation mechanism to each waveform also includes:

[0025] During the transient phase, the higher-order extension strategy in the multi-dimensional perturbation mechanism is executed on each waveform;

[0026] During the steady-state phase, the low-order extension strategy in the multi-dimensional perturbation mechanism is executed on each waveform;

[0027] During the recovery phase, the intermediate-order extension strategy in the multi-dimensional perturbation mechanism is applied to each waveform.

[0028] Furthermore, the step of sequentially arranging the extracted fault feature data and back-mapping it into adjustable simulation parameters, and updating the simulation environment with these adjustable parameters, includes:

[0029] The fault feature data is time-series arranged to generate an arrangement feature vector;

[0030] The arrangement feature vector is mapped to the simulation adjustable parameters, and the simulation adjustable parameters are configured in the simulation environment for simulation.

[0031] Furthermore, the updated simulation environment is then integrated with the feeder automation testing system to perform iterative testing of distribution network faults in the target area, including:

[0032] The feeder automation test system is integrated with the updated simulation environment, and the feeder automation test scenario is determined based on the control action logic of the feeder automation system.

[0033] In the aforementioned automated feeder testing scenario, the target fault waveform output by the simulation is invoked for testing;

[0034] Based on the test results, determine the response time of the feeder automation test system;

[0035] When the response time exceeds a preset response threshold, feedback is sent to the simulation environment to trigger the correction operation of the adjustable simulation parameters;

[0036] The simulation and iterative tests were then performed again using the revised adjustable simulation parameters.

[0037] Another embodiment of the present invention provides an automated feeder testing system for complex fault waveforms in distribution networks, comprising:

[0038] The topology model building module is used to build the corresponding distribution network topology model based on the grounding method of the distribution network in the target area.

[0039] The waveform library creation module is used to configure the distribution network topology model in a preset simulation environment to simulate different fault scenarios in order to create a complex fault waveform library.

[0040] The waveform expansion module is used to sequentially expand and extract features from each waveform in the complex fault waveform library;

[0041] The simulation update module is used to sequence and back-map the extracted fault feature data into adjustable simulation parameters, and update the simulation environment with the adjustable simulation parameters.

[0042] The iterative testing module is used to connect the updated simulation environment to the feeder automation testing system to perform iterative testing of distribution network faults in the target area.

[0043] Another embodiment of the present invention provides a computer device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the feeder automated testing method for complex fault waveforms in distribution networks as described above.

[0044] In another embodiment of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing a computer program, wherein when the device containing the computer-readable storage medium executes the computer program, it implements the feeder automated testing method for complex fault waveforms in distribution networks as described above.

[0045] Compared with the prior art, the beneficial effects of the embodiments of the present invention are at least one of the following:

[0046] This invention generates a structured fault waveform library through typical distribution network modeling and waveform library construction. It then expands each waveform in the fault waveform library using a multi-dimensional hierarchical perturbation mechanism composed of multiple expansion strategies, achieving comprehensive coverage and efficient simulation of complex distribution network fault conditions. By mapping and arranging features between the expanded waveforms and the simulation environment, adjustable parameters for updating the simulation can be generated, resolving model inaccuracies, improving simulation fidelity, and providing a rich data source for feeder automation system testing. Furthermore, by interfacing with the feeder automation system and simultaneously correcting and optimizing the adjustable simulation parameters for response time, the accuracy and reliability of feeder automation testing are improved. Attached Figure Description

[0047] Figure 1 This is a schematic diagram of the feeder automated testing method for complex fault waveforms in distribution networks, according to one embodiment of the present invention.

[0048] Figure 2 This is a schematic diagram of the structure of an automated feeder testing system for complex fault waveforms in a distribution network, according to one embodiment of the present invention.

[0049] Figure 3 This is a structural block diagram of a preferred embodiment of a computer device provided by the present invention. Detailed Implementation

[0050] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0051] In the description of this application, the terms "first," "second," "third," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined with "first," "second," "third," etc., may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0052] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections or electrical connections; they can refer to direct connections or indirect connections through an intermediate medium; and they can refer to the internal communication between two components. The terms "vertical," "horizontal," "left," "right," "upper," "lower," and similar expressions used herein are for illustrative purposes only and do not indicate or imply that the device or component referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as limiting the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0053] In the description of this application, it should be noted that, unless otherwise defined, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this specification is for the purpose of describing specific embodiments only and is not intended to limit the invention. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0054] One embodiment of the present invention provides an automated testing method for feeders accommodating complex fault waveforms in distribution networks. For details, please refer to [link to relevant documentation]. Figure 1 , Figure 1 The diagram shown is a flowchart of an automated feeder testing method for complex fault waveforms in a distribution network, according to one embodiment of the present invention, which includes the following steps:

[0055] S1. Based on the grounding method of the target area's distribution network, build the corresponding distribution network topology model.

[0056] It should be understood that common types of distribution networks include neutral-point ungrounded systems and systems grounded via arc suppression coils. Based on this, this embodiment selects to build a distribution network topology model that includes overhead lines, cables, and mixed lines for these two grounding methods.

[0057] During the setup process, for example, if a neutral-point ungrounded topology model is selected, the following configuration can be executed: 110kV busbar, 5 feeders (1 pure overhead + 2 mixed lines + 1 pure cable + 1 branch type), and configuration of key parameters including main transformer capacity of 30-50MVA, capacitor current of 5-100A, and setting the end to have a balanced load. For an arc suppression coil connected topology model, a Z-type grounding transformer can be added to form a neutral point, and an automatically tuned arc suppression coil can be connected, setting the topology's capacitor current to 65A / 150A and the arc suppression coil compensation degree to 5%.

[0058] S2. Configure the distribution network topology model in a preset simulation environment to simulate different fault scenarios in order to establish a complex fault waveform library.

[0059] Configure the established distribution network topology model in suitable simulation software to generate a simulation environment, such as power system simulation tools like DIgSILENT.

[0060] Specifically, in this embodiment, the midpoint of the main line, the beginning and end load nodes of the branch lines in the distribution network topology model are set as simulated fault points, and corresponding fault parameters are injected into the simulated fault points to establish various grounding fault scenarios and trigger simulation operation. Among them, the midpoint of the main line can be selected at 50% of its length.

[0061] The fault scenarios to be simulated can be single-phase ground faults or arcing ground faults. For example, for a single-phase ground fault, the fault parameters injected at the midpoint of the main line can be: phase A metallic ground (0Ω), phase C high-resistance ground (6000Ω), with the initial phase angle of the fault set to 90° and the fault duration to 300s (preferably between 10s and 5min). The simulation of arcing ground faults can be modeled using the Mayr arc dynamic equation.

[0062] Furthermore, voltage and current waveform data are collected at the simulated fault point using a preset sampling frequency. For example, voltage and current waveforms are recorded from 100ms before the fault to 500ms after the fault (sampling frequency ≥ 10kHz), with a focus on capturing the transient characteristics of the first cycle (20ms) after the fault. After sampling, time-domain feature extraction is performed on the acquired voltage and current waveforms. The extracted content includes changes in voltage / current amplitude before and after the fault, waveform distortion, and zero-potential point offset data, as well as frequency-domain feature extraction via FFT, including the proportion of fundamental and harmonic components, with a focus on the 3rd and 5th harmonics.

[0063] The voltage and current waveform data obtained from the above feature analysis are structured and stored to obtain a complex fault waveform library. Specifically, the voltage and current waveform data can be categorized according to the neutral point grounding method or stored in a structured manner according to a single file naming rule. For example, the waveform data can be stored in the format of: grounding method_fault type_location_initial phase angle_resistance.

[0064] S3. Expand and extract features from each waveform in the complex fault waveform library in sequence.

[0065] In this embodiment, the constructed extension strategy is a multi-dimensional hierarchical perturbation mechanism, including a parameter perturbation extension strategy, a noise injection extension strategy, and a timing extension strategy, which extend the waveform from the perspectives of parameter perturbation, noise injection, and timing extension, respectively.

[0066] For the construction process of this multi-dimensional hierarchical disturbance mechanism, this embodiment first obtains relevant historical fault data of the target area distribution network, including: collecting the value of the transition resistance from the fault recorder report, collecting the Gaussian and impulse noise generated when the fault occurs from the electromagnetic compatibility test record, and the duration of the fault.

[0067] Based on this fault data, specific values ​​for each expansion strategy in the multi-dimensional hierarchical disturbance mechanism can be set as a reference. For example, for the parameter disturbance expansion strategy, the parameter values ​​of the fault location, transition resistance, and fault initial phase angle can be randomly adjusted; for the noise injection expansion strategy, Gaussian white noise or impulse noise of a corresponding percentage can be superimposed; for the timing expansion strategy, the fault cycle can be shortened / delayed.

[0068] For these strategies, different disturbance intensities, i.e., adjustment intensities, have different effects on waveform expansion. For example, in the process of parameter disturbance, a strategy with a transition resistance adjustment range of 0.5 to 2.0Ω, a fault initial phase angle offset of ±10°, and a fault location randomized by ±1 segment is considered a low-order adjustment strategy; a medium-order adjustment strategy can be set as: a transition resistance adjustment range of 0.2 to 5.0Ω, a fault initial phase angle offset of ±30°, and a fault location randomized by ±2 segments; a high-order adjustment strategy can be set as: a transition resistance adjustment range of 0.1 to 10.0Ω, a fault initial phase angle offset of ±60°, and randomized position adjustment without segment limitation.

[0069] For noise injection propagation strategies, a noise superposition ratio ≤ 5% is considered low-order adjustment strength, while operations with a superposition ratio between 5% and 10% are considered medium-order adjustment strength. High-order adjustment strength is defined as a noise superposition ratio ≥ 10%. Similarly, for timing propagation strategies, a shortening / delaying fault cycle ratio ≤ 10% is considered low-order adjustment strength, operations between 10% and 30% are considered medium-order adjustment strength, and ≥ 30% is considered high-order adjustment strength.

[0070] By classifying and categorizing the above-mentioned extension strategies, a multi-dimensional hierarchical perturbation mechanism is constructed. Furthermore, this multi-dimensional hierarchical perturbation mechanism is used to extend each waveform in the complex fault waveform library to generate extended fault waveforms.

[0071] Specifically, in the waveform expansion process, this embodiment further uses each response stage of the waveform as an indicator, aiming to implement different expansion strategies according to the evolution of the waveform. That is, for each waveform in the complex fault waveform library, according to its response intensity, it is divided and labeled as transient stage, steady-state stage, and recovery stage from large to small. It should be understood that the waveform in the transient stage is in a stage with strong fault suddenness, violent voltage / current fluctuations, and high waveform gradient, so it is considered that the transient stage reflects a large response intensity of the waveform; while the waveform in the steady-state stage often shows the characteristics of low fluctuation, periodicity, and stable amplitude, so it is considered that it reflects a relatively moderate response intensity of the waveform; finally, the recovery stage represents the waveform gradually recovering from the abnormal state to normal, which often shows the characteristics of damped oscillation and asymptotic convergence, so it is considered that it reflects a low response intensity of the waveform.

[0072] Based on this, in this embodiment, the aforementioned indicators such as peak value, energy, frequency change rate, and current / voltage amplitude can be used to label the intensity of waveforms at different stages, i.e., stage labeling. For example, fault fluctuation data cases can be queried from the database of the target power system, and then labeled as high response intensity if the peak change rate exceeds 5000kA / s or the harmonic energy ratio exceeds 30%, while it can be labeled as low response intensity if the peak change rate is less than 10kA / s, the harmonic energy ratio is less than 5%, or the current amplitude is less than 0.8.

[0073] Based on the characteristics of each waveform stage, this embodiment further applies the corresponding extended strategy in the multi-dimensional hierarchical perturbation mechanism to each waveform according to the division and labeling results.

[0074] For example, during the transient phase, this embodiment will apply a higher-order extension strategy in the multi-dimensional perturbation mechanism to each waveform, that is, an extension strategy with a higher perturbation intensity in the multi-dimensional perturbation mechanism.

[0075] During the steady-state phase, a low-order extension strategy in the multi-dimensional perturbation mechanism is applied to each waveform, which is an extension strategy with low perturbation intensity in the multi-dimensional perturbation mechanism.

[0076] During the recovery phase, an intermediate-order extension strategy in the multi-dimensional perturbation mechanism is applied to each waveform, which is an extension strategy with intermediate perturbation intensity in the multi-dimensional perturbation mechanism.

[0077] For example, in this embodiment, when the data displayed by each waveform in the constructed complex fault waveform library shows that the rate of change of voltage or current exceeds a threshold, such as a voltage change rate > 10% of the rated voltage / ms, it will be identified as the response intensity / characteristic of the transient phase, triggering the execution of higher-order extended strategies, such as adjusting the transition resistance from 0.1Ω to 10Ω, injecting pulse noise with an amplitude up to 3 times the normal voltage, and shortening the fault duration by 50%, from the measured 200ms to 100ms. Similarly, when the display shows characteristics such as a stable zero-sequence voltage, it is considered to be in a steady-state phase; similarly, when the display shows a voltage recovery to 90% of the rated value, it is considered to be in a recovery phase, and each will execute its corresponding extended strategy.

[0078] In some embodiments of the present invention, the extension strategy may also be a strategy of combining waveform features of different fault types, that is, linearly superimposing each waveform.

[0079] At this point, after performing targeted expansion operations on each waveform in the complex fault waveform library, time-frequency analysis is performed on the generated expanded fault waveforms to extract multi-dimensional features in order to obtain fault feature data for subsequent simulations.

[0080] Specifically, this embodiment extracts harmonic components and dominant frequency components from the extended fault waveform using Fourier transform, and analyzes the time-varying frequency characteristics of the extended fault waveform using wavelet transform and short-time Fourier transform. For example, the features extracted through time-domain analysis include peak voltage / current, rise time, and decay time.

[0081] S4-S5: The extracted fault feature data is sequenced and back-mapped to simulation adjustable parameters. The simulation environment is updated with the simulation adjustable parameters. The updated simulation environment is then connected to the feeder automation test system to perform iterative testing of distribution network faults in the target area.

[0082] First, the orchestration feature vector is mapped to adjustable simulation parameters, that is, the extracted physical features are converted into adjustable parameters required by the simulation model. In some embodiments of the present invention, the orchestration feature vector is generated by performing a time-series orchestration operation on the acquired fault feature data, i.e., time-series feature orchestration. For example, if the fault feature data shows: attenuation time constant τ = 0.8ms, it can be mapped back to: line inductance parameter L = τ × R, where R is a known parameter; another example: if the fault feature data shows: fault waveform peak current Imax = 18kA, main frequency f = 50Hz, attenuation time constant τ = 0.8ms, it can be deduced that the fault type is a three-phase short circuit (maximum peak current). Specifically, some mapping relationships are shown in the table below:

[0083] Correspondence table between waveform features and simulation models

[0084] Waveform characteristics Correlated simulation parameters Parameter range Peak current Imax Fault type, transition resistance 5kA~20kA main frequency f System impedance and grounding method 45Hz~55Hz decay time constant τ Line inductance and capacitance parameters 0.1ms~2ms

[0085] This embodiment aims to convert fault feature data into standardized "feature vectors", establish a mapping relationship between the expanded waveform features and the simulation topology model, and realize waveform reproduction from feature input to simulation output.

[0086] The simulation environment is updated by configuring the adjustable simulation parameters and performing the simulation again. In some embodiments of the present invention, the simulation model can be continuously updated according to the adjustable simulation parameters. In the feature extraction stage, edge cases of the power grid can be added, such as fault characteristics under extreme weather conditions. For insulator flashover faults under thunderstorm weather, waveform features containing impulsive high-frequency components are extracted, thereby mapping the adjustable simulation parameters that can reflect the response characteristics of extreme weather.

[0087] During this period, the updated simulation environment is connected to the feeder automation test system. In some embodiments of the present invention, the fault data generated by the simulation is input into the feeder automation test system through digital and analog interfaces respectively. The simulation software is controlled to output three-phase current and voltage waveforms to the power amplifier and send switch position signals to the feeder automation test system, thereby realizing the connection and direct call of the simulation output waveform.

[0088] The feeder automation test scenario is determined based on the control action logic of the feeder automation system. Under this scenario, the target fault waveform output from the simulation is used for testing. Specifically, the control action logic includes reclosing type, voltage-time type, and local type control logic, which generates corresponding test requirements. For example, the reclosing success rate test requires a reclosing success rate ≥95% under transient faults; the voltage fluctuation resistance test requires no malfunction under voltage dips or automatic recovery capability, requiring automatic reclosing after a 1-minute delay after the transient fault disappears.

[0089] Based on the test results, the response time of the feeder automation test system is determined. When the response time exceeds a preset response threshold, feedback is sent to the simulation environment to trigger a correction operation of the adjustable simulation parameters. For example, if the response time of the feeder automation test system in a high-resistance grounding scenario is detected to be 41ms, while the threshold is 40ms, the response effect of the feeder automation test system is considered "unacceptable." In this case, this embodiment will correct the adjustable simulation parameters injected into the simulation environment, and re-perform simulation and iterative testing with the corrected adjustable simulation parameters until the response time meets the preset conditions and converges. The threshold can be set according to power system testing standards, the maximum number of iterations is 5, and the convergence criterion is: the difference between two consecutive response times < 1ms.

[0090] For example, the sensitivity of the transition resistance can be adjusted, with the adjustable range limited to ±30% based on the transformer's measurement accuracy of ±1%, to prevent over-range correction.

[0091] In summary, the embodiments of the present invention can solve the problem of incomplete waveform coverage by generating and expanding different types of complex fault libraries and implementing different levels of disturbance expansion strategies considering different response stages of waveforms. By arranging feature sequences for extended complex waveforms and defining standardized test scenarios and iterative testing and response verification of faults, the invention can overcome the response accuracy problems caused by incomplete coverage of complex fault waveforms, weak anti-interference ability, and poor scenario adaptability in traditional feeder automation testing technology. Ultimately, the invention significantly improves the testing effect and response speed of feeder automation during iterative testing.

[0092] One embodiment of the present invention provides an automated feeder testing system for complex fault waveforms in distribution networks. For details, please refer to [link to documentation]. Figure 2 , Figure 2 The diagram shown is a schematic of an automated feeder testing system for complex fault waveforms in a distribution network, according to one embodiment of the present invention, comprising:

[0093] The topology model building module M1 is used to build the corresponding distribution network topology model based on the grounding method of the distribution network in the target area.

[0094] The waveform library creation module M2 is used to configure the distribution network topology model in a preset simulation environment to simulate different fault scenarios in order to create a complex fault waveform library.

[0095] The waveform extension module M3 is used to sequentially extend and extract features from each waveform in the complex fault waveform library;

[0096] The simulation update module M4 is used to sequence and back-map the extracted fault feature data into adjustable simulation parameters, and update the simulation environment with the adjustable simulation parameters.

[0097] The Iterative Test Module M5 is used to connect the updated simulation environment to the feeder automation test system to perform iterative testing of distribution network faults in the target area.

[0098] like Figure 3 As shown, this embodiment of the invention also provides a computer device. Figure 3 This is a structural block diagram of a preferred embodiment of a computer device provided by the present invention. The computer device includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the method described above.

[0099] Preferably, the computer program can be divided into one or more modules / units (such as computer program 1, computer program 2, ...), and the one or more modules / units are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units can be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program in the computer device.

[0100] The processor can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor, or the processor can be any conventional processor. The processor is the control center of the terminal device, connecting various parts of the terminal device through various interfaces and lines.

[0101] The memory mainly includes a program storage area and a data storage area. The program storage area can store the operating system, applications required for at least one function, etc., while the data storage area can store related data, etc. Furthermore, the memory can be a high-speed random access memory, or a non-volatile memory, such as a plug-in hard drive, a SmartMedia Card (SMC), a Secure Digital (SD) card, and a Flash Card, or other volatile solid-state storage devices.

[0102] It should be noted that the aforementioned terminal devices may include, but are not limited to, processors and memory, as will be understood by those skilled in the art. Figure 3 The structural block diagram is merely an example of a terminal device and does not constitute a limitation on the terminal device. It may include more or fewer components than shown, or combine certain components, or use different components. Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium may be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0103] Accordingly, embodiments of the present invention provide a computer-readable storage medium, the computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the steps in the method of the above embodiments, for example... Figure 1 Steps S1 to S5 as described above.

[0104] The technical features and effects of the feeder automation testing system for complex fault waveforms in distribution networks proposed in this embodiment of the invention are the same as those of the feeder automation testing method for complex fault waveforms in distribution networks proposed in this embodiment of the invention, and will not be repeated here.

[0105] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. An automated testing method for feeders accommodating complex fault waveforms in distribution networks, characterized in that, include: Based on the grounding method of the target area's distribution network, construct the corresponding distribution network topology model; The distribution network topology model is configured in a preset simulation environment to simulate different fault scenarios in order to establish a complex fault waveform library; Each waveform in the complex fault waveform library is sequentially expanded and its features are extracted. The extracted fault feature data is sequenced and back-mapped to simulation adjustable parameters, and the simulation environment is updated with the simulation adjustable parameters. The updated simulation environment was integrated into the feeder automation testing system to conduct iterative testing of distribution network faults in the target area.

2. The automated feeder testing method for complex fault waveforms in distribution networks as described in claim 1, characterized in that, The step of configuring the distribution network topology model in a preset simulation environment to simulate different fault scenarios in order to establish a complex fault waveform library includes: The midpoint of the main trunk line, the beginning and end of the branch line load nodes of the distribution network topology model are respectively set as simulation fault points; Inject corresponding fault parameters into the simulated fault points to establish various grounding fault scenarios and trigger simulation operation; Voltage and current waveform data are collected at the simulated fault point at a preset sampling frequency; The voltage and current waveform data are structured and stored to obtain the complex fault waveform library.

3. The automated feeder testing method for complex fault waveforms in distribution networks as described in claim 1, characterized in that, The step of sequentially expanding and extracting features from each waveform in the complex fault waveform library includes: Based on the historical fault data of the target area distribution network, a multi-dimensional hierarchical disturbance mechanism is constructed, which includes parameter disturbance propagation strategy, noise injection propagation strategy and time-series propagation strategy; the multi-dimensional hierarchical disturbance mechanism divides the propagation strategy into level gradients according to the disturbance intensity. The multi-dimensional hierarchical perturbation mechanism is used to expand each waveform in the complex fault waveform library to generate extended fault waveforms. Time-frequency analysis is performed on the extended fault waveform to extract multidimensional features.

4. The automated feeder testing method for complex fault waveforms in distribution networks as described in claim 3, characterized in that, The expansion of each waveform in the complex fault waveform library using the multi-dimensional hierarchical perturbation mechanism includes: Based on the response intensity of each waveform in the complex fault waveform library at different stages, each waveform is divided and labeled according to the transient stage, steady-state stage, and recovery stage. Based on the division and labeling results, the corresponding extended strategy in the multi-dimensional hierarchical perturbation mechanism is applied to each waveform.

5. The automated feeder testing method for complex fault waveforms in distribution networks as described in claim 4, characterized in that, The step of applying the corresponding extended strategy in the multi-dimensional perturbation mechanism to each waveform also includes: During the transient phase, the higher-order extension strategy in the multi-dimensional perturbation mechanism is executed on each waveform; During the steady-state phase, the low-order extension strategy in the multi-dimensional perturbation mechanism is executed on each waveform; During the recovery phase, the intermediate-order extension strategy in the multi-dimensional perturbation mechanism is applied to each waveform.

6. The automated feeder testing method for complex fault waveforms in distribution networks as described in claim 1, characterized in that, The step of sequentially arranging the extracted fault feature data and back-mapping it into adjustable simulation parameters, and updating the simulation environment with the adjustable simulation parameters, includes: The fault feature data is time-series arranged to generate an arrangement feature vector; The arrangement feature vector is mapped to the simulation adjustable parameters, and the simulation adjustable parameters are configured in the simulation environment for simulation.

7. The automated feeder testing method for complex fault waveforms in distribution networks as described in claim 6, characterized in that, The step of connecting the updated simulation environment to the feeder automation testing system to perform iterative testing of distribution network faults in the target area includes: The feeder automation test system is integrated with the updated simulation environment, and the feeder automation test scenario is determined based on the control action logic of the feeder automation system. In the aforementioned automated feeder testing scenario, the target fault waveform output by the simulation is invoked for testing; Based on the test results, determine the response time of the feeder automation test system; When the response time exceeds a preset response threshold, feedback is sent to the simulation environment to trigger the correction operation of the adjustable simulation parameters; The simulation and iterative tests were then performed again using the revised adjustable simulation parameters.

8. An automated feeder testing system for complex fault waveforms in distribution networks, characterized in that, include: The topology model building module is used to build the corresponding distribution network topology model based on the grounding method of the distribution network in the target area. The waveform library creation module is used to configure the distribution network topology model in a preset simulation environment to simulate different fault scenarios in order to create a complex fault waveform library. The waveform expansion module is used to sequentially expand and extract features from each waveform in the complex fault waveform library; The simulation update module is used to sequence and back-map the extracted fault feature data into adjustable simulation parameters, and update the simulation environment with the adjustable simulation parameters. The iterative testing module is used to connect the updated simulation environment to the feeder automation testing system to perform iterative testing of distribution network faults in the target area.

9. A computer device, characterized in that, The method includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the feeder automation testing method for complex fault waveforms in distribution networks as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein when the device containing the computer-readable storage medium executes the computer program, it implements the feeder automated testing method for complex fault waveforms in distribution networks as described in any one of claims 1 to 7.

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