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WO2025189667A1 - Procédé et système d'analyse de caractéristiques de données de défaillance d'un réseau de distribution actif - Google Patents

Procédé et système d'analyse de caractéristiques de données de défaillance d'un réseau de distribution actif

Info

Publication number
WO2025189667A1
WO2025189667A1 PCT/CN2024/110863 CN2024110863W WO2025189667A1 WO 2025189667 A1 WO2025189667 A1 WO 2025189667A1 CN 2024110863 W CN2024110863 W CN 2024110863W WO 2025189667 A1 WO2025189667 A1 WO 2025189667A1
Authority
WO
WIPO (PCT)
Prior art keywords
fault
distribution network
current
voltage
data
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
PCT/CN2024/110863
Other languages
English (en)
Chinese (zh)
Inventor
马明
杨洪灿
张弓帅
张碧华
叶文华
叶小虎
杨隽
陈仕龙
缪应芳
刘旭
李邦源
李少华
陈黎玲
沈航
王明
陈君
白霖
杜秀平
刘斌
徐书杰
唐娜
徐原
胡林志
邓云书
翟笛
杨京晨
高黎明
党军朋
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Yuxi Power Supply Bureau of Yunnan Power Grid Co Ltd
Yunnan Electric Power Test and Research Institute Group Co Ltd
Original Assignee
Yuxi Power Supply Bureau of Yunnan Power Grid Co Ltd
Yunnan Electric Power Test and Research Institute Group Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Yuxi Power Supply Bureau of Yunnan Power Grid Co Ltd, Yunnan Electric Power Test and Research Institute Group Co Ltd filed Critical Yuxi Power Supply Bureau of Yunnan Power Grid Co Ltd
Publication of WO2025189667A1 publication Critical patent/WO2025189667A1/fr
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/08—Locating faults in cables, transmission lines, or networks

Definitions

  • the present invention relates to the field of power distribution technology, and in particular to a method and system for analyzing fault data characteristics of an active power distribution network.
  • Active distribution networks represent a major advancement in power systems. By integrating distributed energy resources such as renewable energy, energy storage systems, and electric vehicles, they enable bidirectional energy flow and intelligent management. These networks not only improve the reliability and flexibility of power supply, but also promote energy efficiency and reduce environmental impact.
  • the high level of automation and intelligence in active distribution networks enables real-time monitoring, analysis, and response to changes in various operating conditions, thereby optimizing grid operations and maintenance.
  • Fault data feature analysis plays a crucial role in active power distribution networks. By collecting and processing operational data such as voltage, current, and frequency, it identifies and predicts potential faults and anomalies. This analysis helps operators quickly locate faults, reduce power outages, and guide grid optimization and improvement measures. With technological advancements, the accuracy and efficiency of fault data analysis are continuously improving, providing strong support for ensuring stable and efficient grid operation.
  • the technical problem solved by the present invention is that the existing fault data analysis method is inefficient and cannot quickly and accurately locate the fault point of the active power distribution network.
  • the present invention provides the following technical solutions: a method for analyzing the fault data characteristics of an active distribution network, comprising: analyzing the voltage changes and single-phase grounding faults of the active distribution network; obtaining distribution network fault data through simulation; establishing a fault location prediction model, analyzing the fault data characteristics and locating the fault.
  • the voltage change includes, for the network before the active distribution network line fault, the voltage at the fault point f is expressed as:
  • each element represents the mutual impedance between the corresponding two nodes.
  • the voltage between nodes fs is expressed as:
  • the voltage change also includes that when a fault occurs in the active distribution network line, each node will have a voltage drop of varying degrees, which can be equivalent to adding a voltage drop of magnitude at the fault point.
  • the injected current is expressed as:
  • the voltage drop of the entire network can be expressed as:
  • the single-phase grounding fault analysis includes: when a single-phase grounding fault occurs, the distribution network undergoes a transient process from a pre-fault steady state to a post-fault steady state, and the current change caused by the fault can be expressed as:
  • I 1A represents the RMS value of the phase A current of line 1 in the steady state before the fault
  • I′ 1A represents the RMS value of the phase A current of line 1 in the steady state after the fault
  • ⁇ I 1A represents the change in the phase A current of line 1
  • N 0 represents the total number of lines
  • the feature ⁇ I related to the fault location is selected to locate the fault. Because different faults will produce different current distributions on different lines, each set of data will correspond to a unique fault location.
  • the simulation includes: in the sample generation process, the fault location distribution is set to be uniformly distributed in all phases of all three-phase lines, and randomly occurs at any position in the line;
  • a random value is selected between 0 and 1400 ⁇ . Due to the complex grid structure of the distribution network, the operating mode often changes. Therefore, the system impedance is re-valued every certain number of samples, and the value is randomly selected between (3+4)j and (7+8)j ⁇ .
  • the connected load is always in a fluctuating state.
  • the load is set to remain unchanged within 1s of each simulation time.
  • each load is randomly selected within the range of 0.8 to 1.2 times to simulate the fluctuation of different loads.
  • the fault location information is extracted by comprehensively considering the change in current before and after the fault and the impedance value of each node, which is expressed as:
  • Vf represents the predicted voltage change value of the fault section
  • N represents the total number of nodes in the distribution network
  • T represents the length of the observation time window
  • ⁇ represents the attenuation coefficient
  • t represents time
  • Iinj (t) represents the injected current function
  • M represents the number of voltage change data points
  • ⁇ Vij represents the voltage change of node i before and after the fault occurs
  • ⁇ represents the regularization parameter
  • ⁇ ( ⁇ Ii , zi ) represents the information filtering function
  • Imax represents the maximum amplitude of the injected current
  • h represents the frequency of the injected current
  • ⁇ represents the attenuation coefficient
  • zi represents the impedance value of the i-th node
  • ⁇ represents the adjustment parameter
  • ⁇ Iavg represents the average current change of all nodes.
  • the fault location prediction model further includes defining a loss function using a square error with distance error attenuation, which is expressed as:
  • L represents the loss function
  • w i represents the weight of the i-th sample
  • yi represents the actual fault location of the i-th sample
  • ⁇ represents the adjustment parameter
  • d i represents the distance between the predicted fault location and the actual fault location
  • ⁇ represents the adjustment parameter
  • the present invention also provides an analysis system for the fault data characteristics of an active distribution network, including an analysis module, which analyzes the voltage changes before and after the active distribution network line fault, and performs equivalentization by injecting current, analyzes single grounding faults, and locates single grounding fault points; a simulation module, which obtains fault sample data through simulation model training, considers fault location distribution, transition resistance setting, operation mode switching, noise interference and load fluctuation factors, so that the simulation results are close to the real data; a positioning module, which establishes a fault location prediction model according to the voltage change law before and after the fault in the distribution network, improves the prediction accuracy through the loss function, and realizes the positioning of the fault point.
  • an analysis module which analyzes the voltage changes before and after the active distribution network line fault, and performs equivalentization by injecting current, analyzes single grounding faults, and locates single grounding fault points
  • a simulation module which obtains fault sample data through simulation model training, considers fault location distribution, transition resistance setting, operation mode switching, noise interference and load fluctuation factors,
  • the present invention further provides a computing device, comprising: a memory and a processor;
  • the memory is used to store computer-executable instructions
  • the processor is used to execute the computer-executable instructions.
  • the steps of the method for analyzing fault data characteristics of the active power distribution network are implemented.
  • the present invention further provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the method for analyzing fault data characteristics of the active power distribution network.
  • the method of the present invention analyzes the voltage changes after an active distribution network fault, equating the voltage changes with injected current, analyzing single-phase grounding faults and locating the fault point.
  • the complex information filtering function deeply analyzes the complex relationship between current changes and node impedance, extracting more detailed fault characteristics, optimizing the level of detail in fault location, enhancing the model's adaptability to different fault scenarios, and improving the flexibility and robustness of the diagnostic process.
  • FIG1 is an overall flow chart of a method for analyzing fault data characteristics of an active power distribution network provided by one embodiment of the present invention
  • FIG2 is a schematic diagram of equivalent current injection into a fault point after a fault according to a method for analyzing fault data characteristics of an active power distribution network provided by one embodiment of the present invention
  • FIG3 is an equivalent schematic diagram of injected currents at nodes on both sides of a faulty line after a fault, according to a method for analyzing fault data characteristics of an active power distribution network provided by an embodiment of the present invention.
  • an embodiment of the present invention provides a method for analyzing fault data characteristics of an active power distribution network, including:
  • S1 Perform voltage change and single-phase grounding fault analysis after active distribution network.
  • the voltage at the fault point can be expressed as:
  • each element represents the mutual impedance between the corresponding two nodes.
  • the voltage between the nodes fs is expressed as:
  • each node will experience a voltage drop of varying degrees, which is equivalent to adding a voltage drop of As shown in Figure 2, the voltage change caused by the injected current can be expressed as:
  • the voltage drop of the entire network can be expressed as:
  • the distribution network goes through a transient process from the steady state before the fault to the new steady state after the fault.
  • different faults occurring at different locations will produce different voltage drops at each node, thus producing different line current distributions.
  • the distribution network topology is known and each line The current effective value of the steady-state current is available. Then, when a fault occurs in the distribution network, the current change caused by the fault can be expressed as:
  • I 1A represents the effective value of the phase A current of line 1 in the steady state before the fault
  • I 1 ′ A represents the effective value of the phase A current of line 1 in the steady state after the fault
  • ⁇ I 1A represents the change in the phase A current of line 1
  • N 0 represents the total number of lines
  • the feature ⁇ I related to the fault location is selected to locate the fault. Because different faults will produce different current distributions on different lines, each set of data will correspond to a unique fault location.
  • Matlab/Simulink was used to build a model and simulate training samples. To ensure that the simulation results closely matched the real data, the sample generation process fully considered various factors, including fault location distribution, transition resistance setting, operating mode switching, noise interference, and load fluctuations, and these factors were reflected in the model simulation process.
  • faults were uniformly distributed across all phases of the three-phase lines, occurring randomly at any location within the lines. Furthermore, by testing the relationship between the number of training samples and training effectiveness, a number of training samples was selected that both ensured coverage of all lines and achieved sufficient training effectiveness.
  • the IEEE 123 node system has 118 lines, 59 of which are three-phase lines. Assuming 4000 sets of fault samples in the training data, at least 57 sets of fault samples were guaranteed for each three-phase line, ensuring relative sample coverage.
  • the transition resistance When setting the fault transition resistance, a random value is selected between 0 and 1400 ⁇ .
  • Research at Texas A&M University shows that when a high-resistance ground fault occurs in a distribution network, the transition resistance is generally below 100 ⁇ . However, this is typically set to 500-1000 ⁇ during algorithm validation.
  • the transition resistance was randomly set to a range of 0-1400 ⁇ .
  • the connected load In the actual distribution network, the connected load is always in a fluctuating state. In order to simulate the change of load, during the model simulation, the load is set to remain unchanged within 1s of each simulation time. Before simulating each group of samples, each load is randomly selected within the range of 0.8 to 1.2 times to simulate the fluctuation of different loads.
  • an attenuation function is introduced to simulate the natural attenuation process of the voltage changing with time after the fault occurs.
  • the current change before and after the fault and the impedance value of each node are comprehensively considered to extract the fault location information.
  • the logarithmic function is used to amplify the impact of small current changes, while the impact of large current changes is relatively reduced.
  • the difference between the current change and the average current change is smoothed to establish an information filtering function, which is expressed as:
  • Vf represents the predicted voltage change value of the fault section
  • N represents the total number of nodes in the distribution network
  • T represents the length of the observation time window
  • ⁇ represents the attenuation coefficient
  • t represents time
  • Iinj (t) represents the injected current function
  • M represents the number of voltage change data points
  • ⁇ Vij represents the voltage change of node i before and after the fault occurs
  • ⁇ represents the regularization parameter
  • ⁇ ( ⁇ Ii , zi ) represents the information filtering function
  • Imax represents the maximum amplitude of the injected current
  • h represents the frequency of the injected current
  • ⁇ represents the attenuation coefficient
  • zi represents the impedance value of the i-th node
  • ⁇ represents the adjustment parameter
  • ⁇ Iavg represents the average current change of all nodes.
  • the exponential decay function e - ⁇ t is introduced to simulate the natural decay process of current or voltage changing with time after a fault occurs, and the decreasing effect of time factors on the fault is taken into account, so that the model can reflect the dynamic changes in the actual physical process.
  • the square error with distance error attenuation is used to define the loss function, which is expressed as:
  • L represents the loss function
  • w i represents the weight of the i-th sample
  • yi represents the actual fault location of the i-th sample
  • ⁇ represents the adjustment parameter
  • d i represents the distance between the predicted fault location and the actual fault location
  • ⁇ represents the adjustment parameter
  • This embodiment also provides an analysis system for fault data characteristics of an active distribution network, including an analysis module, which analyzes voltage changes before and after a fault in an active distribution network line, performs equivalent analysis by injecting current, analyzes single-phase grounding faults, and locates the single-phase grounding fault point; a simulation module, which obtains fault sample data through simulation model training, takes into account fault location distribution, transition resistance setting, operation mode switching, noise interference, and load fluctuation factors, so that the simulation results are close to real data; and a positioning module, which establishes a fault location prediction model based on the voltage change law before and after the fault in the distribution network, improves the prediction accuracy through a loss function, and realizes the positioning of the fault point.
  • an analysis module which analyzes voltage changes before and after a fault in an active distribution network line, performs equivalent analysis by injecting current, analyzes single-phase grounding faults, and locates the single-phase grounding fault point
  • a simulation module which obtains fault sample data through simulation model training, takes into account fault location distribution, transition
  • This embodiment also provides a computing device, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the method for analyzing fault data characteristics of an active power distribution network as proposed in the above embodiment.
  • This embodiment further provides a storage medium having a computer program stored thereon.
  • the program is executed by a processor, the method for analyzing fault data characteristics of an active power distribution network proposed in the above embodiment is implemented.
  • the storage medium proposed in this embodiment and the method for analyzing fault data characteristics of an active distribution network proposed in the above embodiment belong to the same inventive concept.
  • the present invention can be implemented with the help of software and necessary general-purpose hardware, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method.
  • the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer's floppy disk, read-only memory (ROM), random access memory (RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods of various embodiments of the present invention.
  • a computer-readable storage medium such as a computer's floppy disk, read-only memory (ROM), random access memory (RAM), flash memory (FLASH), hard disk or optical disk, etc.
  • the following is an embodiment of the present invention, which provides a method for analyzing fault data characteristics of an active power distribution network.
  • a scientific demonstration is carried out through simulation experiments.
  • the fault location method of the present invention comprehensively utilizes voltage change information before and after the fault, equivalent processing of injected current, and single-phase grounding fault analysis. Through in-depth analysis of these data, a model that can accurately predict the fault location is established. In addition, in order to improve the adaptability and robustness of the model, the present invention takes into account multiple factors such as fault location distribution, transition resistance setting, operation mode switching, noise interference, and load fluctuation during the simulation process. Three experimental data are selected for demonstration, as shown in Table 1.
  • Existing technology A locates the fault based on the impedance calculation between the fault point and the measurement point; existing technology B locates the fault using the traveling wave signal generated by the fault.
  • the calculation of the fault location accuracy takes into account the influence of positioning accuracy.
  • the traveling wave positioning of the existing technology B performs better than the method of the present invention in terms of positioning accuracy.
  • traveling wave positioning is easily affected by line characteristics and environmental factors, and performs poorly in complex active distribution network environments.
  • it compared with the present invention, it takes longer to synchronize high-precision time measurements and requires a higher system configuration.
  • the method of the present invention outperforms existing technologies A and B in key performance indicators such as fault identification time, fault location accuracy, average fault response time, and fault type coverage, demonstrating the significant improvement of the present invention in improving fault location accuracy and system adaptability.

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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

L'invention concerne un procédé et système d'analyse de caractéristiques de données de défaillance d'un réseau de distribution actif. Le procédé consiste à : analyser une variation de tension après un défaut dans un réseau de distribution actif et analyser un défaut de masse monophasé (S1) ; acquérir des données de défaut de réseau de distribution au moyen d'une simulation analogique (S2) ; et établir un modèle de prédiction d'emplacement de défaut pour analyser des caractéristiques de données de défaut et réaliser une localisation de défaut (S3). Dans le procédé, une variation de tension après une défaillance dans un réseau de distribution actif est analysée, la variation de tension est représentée de manière équivalente à l'aide d'un courant injecté, un défaut de masse monophasé est analysé, et la localisation d'un point de défaut est réalisée. En simulant une variation de courant pendant un processus d'injection de défaut réel au moyen d'une fonction de courant injectée, un modèle peut refléter plus étroitement un état de fonctionnement réel, ce qui permet d'améliorer l'authenticité de la simulation de défaut et la précision de prédiction. Une relation complexe entre une variation de courant et une impédance de nœud est analysée en profondeur au moyen d'une fonction de filtrage d'informations complexes, et des caractéristiques de défaut plus affinées sont extraites, ce qui permet d'optimiser la précision de localisation de défaut, et d'améliorer l'adaptabilité du modèle à différents scénarios de défaut.
PCT/CN2024/110863 2024-03-14 2024-08-08 Procédé et système d'analyse de caractéristiques de données de défaillance d'un réseau de distribution actif Pending WO2025189667A1 (fr)

Applications Claiming Priority (2)

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CN202410292429.6 2024-03-14
CN202410292429 2024-03-14

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WO2025189667A1 true WO2025189667A1 (fr) 2025-09-18

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Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP1693679A1 (fr) * 2005-02-21 2006-08-23 Adaptive Regelsysteme GmbH Procédé pour la détermination d'un paramètre dans un réseau d'alimentation électrique
US20080036472A1 (en) * 2006-08-14 2008-02-14 Collins Edward R Jr Impedance-based arc fault determination device (iadd) and method
CN110927519A (zh) * 2019-11-20 2020-03-27 东南大学 基于μPMU量测值的主动配电网故障定位方法
CN112649695A (zh) * 2020-10-05 2021-04-13 华北电力大学 一种基于节点全覆盖的配电网电能质量评估方案
CN114609470A (zh) * 2022-02-17 2022-06-10 国网青海省电力公司果洛供电公司 一种配电网故障定位终端优化配置系统及方法
CN115436748A (zh) * 2022-08-15 2022-12-06 国网山东省电力公司潍坊供电公司 基于零序特征系数的单相接地故障区段定位方法及系统
CN116430174A (zh) * 2023-05-12 2023-07-14 中国矿业大学 一种基于基追踪算法的配电网故障区间定位及测距方法
CN117368636A (zh) * 2023-09-27 2024-01-09 贵州电网有限责任公司 一种智能配电房有源消弧接地故障定位方法及系统

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP1693679A1 (fr) * 2005-02-21 2006-08-23 Adaptive Regelsysteme GmbH Procédé pour la détermination d'un paramètre dans un réseau d'alimentation électrique
US20080036472A1 (en) * 2006-08-14 2008-02-14 Collins Edward R Jr Impedance-based arc fault determination device (iadd) and method
CN110927519A (zh) * 2019-11-20 2020-03-27 东南大学 基于μPMU量测值的主动配电网故障定位方法
CN112649695A (zh) * 2020-10-05 2021-04-13 华北电力大学 一种基于节点全覆盖的配电网电能质量评估方案
CN114609470A (zh) * 2022-02-17 2022-06-10 国网青海省电力公司果洛供电公司 一种配电网故障定位终端优化配置系统及方法
CN115436748A (zh) * 2022-08-15 2022-12-06 国网山东省电力公司潍坊供电公司 基于零序特征系数的单相接地故障区段定位方法及系统
CN116430174A (zh) * 2023-05-12 2023-07-14 中国矿业大学 一种基于基追踪算法的配电网故障区间定位及测距方法
CN117368636A (zh) * 2023-09-27 2024-01-09 贵州电网有限责任公司 一种智能配电房有源消弧接地故障定位方法及系统

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