A high impedance arc fault detection method for a renewable energy containing power distribution network
By performing transient extraction and transformation of fault current signals from distribution networks containing renewable energy sources and selecting optimal parameters, and by optimizing the Gaussian function and detection indicators, accurate detection of high-impedance arc faults is achieved, thereby improving the reliability and safety of distribution network protection.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- STATE GRID FUJIAN ELECTRIC POWER CO LTD
- Filing Date
- 2023-11-29
- Publication Date
- 2026-07-24
AI Technical Summary
In distribution networks containing renewable energy, high-impedance arc faults are difficult to predict and their characteristics are difficult to identify, resulting in insufficient safety and accuracy of protection device operation. In particular, noise and harmonic effects are significant when power electronic equipment is connected.
By performing transient extraction and transformation processing on the fault current signal, an optimal parameter selection objective function is constructed, the standard deviation parameter of the Gaussian function is optimized, the drift parameter in the fault detection index is selected, and the energy detection index is calculated using short-time Fourier transform and transient extraction transform to determine high-impedance arc faults.
It effectively distinguishes between high-impedance arc faults and non-high-impedance arc faults, improves the reliability and safety of distribution network protection, and solves the problem of identifying high-impedance grounding faults under the condition of power electronic equipment access.
Smart Images
Figure CN117706266B_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of power system relay protection technology, specifically relating to a method for detecting high-impedance arc faults in renewable energy distribution networks that considers the optimal selection of key parameters. Background Technology
[0002] Because sand, grass, concrete, asphalt, and other materials exhibit nonlinear high impedance, the fault current is very low when a live conductor comes into contact with them. Contact between a live conductor and these components constitutes a high-impedance arc fault, primarily occurring in power distribution networks. Currently, most detection methods for high-impedance arc faults rely on signal processing techniques. High-frequency component extraction, multiple feature identification, and harmonic analysis have been used to classify and characterize fault events occurring in power distribution systems. In addition, statistical methods based on voltage and current distribution characteristics are also used to detect fault events. During detection, besides utilizing the advantages of signal processing techniques, it is also necessary to carefully study the input and output signals, identify appropriate features / high-frequency components, and correctly select the threshold for high-impedance arc faults to effectively distinguish between faults and non-faults.
[0003] In renewable power distribution systems containing power electronic devices, high-impedance arc faults are difficult to predict and their characteristics are hard to identify. When switching occurs near protection devices, such as transformers, capacitor banks, nonlinear loads, or induction motors, the monitored parameters are similar to those during a fault, making it impossible to guarantee the safety and accuracy of the protection device's operation. Furthermore, due to the integration of renewable energy into the distribution network, the impact of noise and harmonics is significant, leaving gaps in previous related technologies. Summary of the Invention
[0004] The purpose of this invention is to provide a method for detecting high-impedance arc faults in distribution networks containing renewable energy, which is beneficial to improving the reliability and security of distribution network protection.
[0005] To achieve the above objectives, the technical solution adopted by the present invention is: a method for detecting high-impedance arc faults in a renewable energy distribution network, comprising:
[0006] Step S1: Perform transient extraction and transformation processing on the fault current signal to extract time-frequency features;
[0007] Step S2: Construct the optimal parameter selection objective function and optimize the standard deviation parameter of the Gaussian function used in the transient extraction transformation;
[0008] Step S3: Based on the energy values of the fault and non-fault datasets, select the drift parameter in the fault detection index;
[0009] Step S4: Perform high-impedance arc fault detection in the distribution network containing renewable energy to determine whether a high-impedance arc fault has occurred in the distribution network.
[0010] Furthermore, in step S1, the transient extraction transform is implemented as follows:
[0011] Since the basic goal of Transient Extraction Transform (TET) is to extract the time-frequency characteristics of fault signals when they occur in short-time transients, consider the following Short-Time Fourier Transform (STFT):
[0012] (1)
[0013] In the formula, For moving the window, Gaussian function, For non-stationary time-varying signal functions; transform basis functions It is a key parameter for identifying time-varying changes in a signal due to modulation, and the Dirac function with amplitude A and time shift of 0 is selected as the [parameter]. The basis functions and signal functions are as follows:
[0014] (2)
[0015] (3)
[0016] Substituting equations (2) and (3) into equation (1), accurate frequency information can be obtained using STFT; to enhance the STFT results, transient extraction transform is adopted, the mathematical expression of which is:
[0017] (4)
[0018] In the formula, For the transient extraction operator, such that Energy distribution ratio To further improve detection performance, the absolute value of the TET energy coefficient over the entire signal length is calculated.
[0019] (5).
[0020] Furthermore, in step S2, the Gaussian function used in the transient extraction transform is as follows:
[0021] (6)
[0022] In the formula, f is the frequency, t is the time scale parameter controlling the window position, and the standard deviation is... Control the width of the Gaussian window;
[0023] The optimal selection model for key parameters is as follows: First, construct the objective function based on the available datasets; assuming there are N available datasets, of which P are fault datasets based on different operating parameters and Q are non-fault datasets, the objective function of the optimal parameter selection model is:
[0024] (7)
[0025] Based on the above formula, generate P units. The optimal value is determined, the maximum optimal value is extracted, and this maximum optimal value is selected as the global optimal value for detecting all possible high-impedance arc faults. :
[0026] (8)
[0027] Optimal parameters are obtained from the non-fault dataset. :
[0028] (9)
[0029] according to , The final optimal value is generated from the faulty and non-faulty datasets. The specific selection formula is as follows:
[0030] < < (10).
[0031] Furthermore,
[0032] In step S3, the residual current signal is transformed by transient extraction after standard deviation optimization to calculate the energy E. When the energy E exceeds 0, the fault detection task is completed. The accumulated sum of TET energy is used as the final fault detection indicator to trigger tripping.
[0033] (11)
[0034] In the formula, G tet Here, v is the fault detection metric, and v is the drift parameter. Ideally, v=0, but this is not safe, so it is corrected based on the energy values of the entire dataset. The minimum value is extracted from the energy values related to the fault. Extract the maximum value from the energy value under non-fault conditions. Then the drift parameter v is selected from the following set:
[0035] (12).
[0036] Furthermore, in step S4, the detection process for high-impedance arc faults in distribution networks containing renewable energy is as follows:
[0037] 1) Acquire three-phase current signals;
[0038] 2) Calculate the residual current;
[0039] 3) Calculate the energy components of the transient extraction transformation using formula (5);
[0040] 4) Calculate the final detection index G using formula (11). tet If G tet If the value is greater than 0, it is determined that a high-impedance arc fault has occurred.
[0041] Compared with existing technologies, this invention has the following advantages: This invention focuses on the optimal parameter selection problem in fault detection methods. Addressing the unpredictable and difficult-to-identify characteristics of HIAF faults, it proposes a high-impedance arc fault detection method for distribution networks with renewable energy, considering the optimal selection of key parameters. It optimizes the standard deviation of the Gaussian window function used in signal analysis and the drift parameter in the fault detection index. It also proposes a high-resistance arc fault detection process for distribution networks with renewable energy, effectively distinguishing between various HIAF and non-HIAF conditions. This solves the problem of difficulty in identifying high-resistance grounding faults under power electronic equipment access conditions, improving the reliability and safety of distribution network protection actions. Attached Figure Description
[0042] Figure 1 This is a flowchart illustrating the method implementation of an embodiment of the present invention;
[0043] Figure 2 This is a diagram of the improved IEEE 30-node system architecture in an embodiment of the present invention;
[0044] Figure 3 This is a HIAF simulation result diagram for the fault path resistance of 0.03~0.55 in this embodiment of the invention. Detailed Implementation
[0045] The present invention will be further described below with reference to the accompanying drawings and embodiments.
[0046] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of this application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.
[0047] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.
[0048] like Figure 1 As shown in the figure, this embodiment provides a method for detecting high-impedance arc faults in distribution networks containing renewable energy, including the following steps:
[0049] Step S1: Perform transient extracting transform (TET) processing on the fault current signal to extract time-frequency features.
[0050] Step S2: Construct the objective function for optimal parameter selection and optimize the standard deviation parameter of the Gaussian function used in the transient extraction transformation.
[0051] Step S3: Based on the energy values of the fault and non-fault datasets, select the drift parameter in the fault detection index.
[0052] Step S4: Perform high impedance arcing fault (HIAF) detection in the distribution network containing renewable energy to determine whether a high impedance arcing fault has occurred in the distribution network.
[0053] In step S1, the transient extraction transform is implemented as follows:
[0054] Since the fundamental goal of TET is to extract the time-frequency characteristics of fault signals when they occur in short-time transients, consider the following Short-Term Fourier Transform (STFT):
[0055] (1)
[0056] In the formula, For moving the window, Gaussian function, It is a non-stationary time-varying signal function.
[0057] Transformation basis functions It is a key parameter for identifying time-varying changes in a signal due to modulation, and the Dirac function with amplitude A and time shift of 0 is selected as the [parameter]. The basis functions and signal functions are as follows:
[0058] (2)
[0059] (3)
[0060] Substituting equations (2) and (3) into equation (1), accurate frequency information can be obtained using STFT. To enhance the STFT results, transient extraction transform (TET) is employed, and its mathematical expression is:
[0061] (4)
[0062] In the formula, For the transient extraction operator, such that Energy distribution ratio To further improve detection performance, the absolute value of the TET energy coefficient over the entire signal length is calculated:
[0063] (5)
[0064] In step S2, the Gaussian function used in the transient extraction transform is as follows:
[0065] (6)
[0066] In the formula, f is the frequency, t is the time scale parameter controlling the window position, and the standard deviation is... Control the width of the Gaussian window. For non-stationary signals, the position parameter is of little importance, but the standard deviation... The choice of [option name] will affect the response result of TET, thereby affecting the accuracy of fault time detection.
[0067] The optimal selection model for the key parameters is as follows:
[0068] First, construct the objective function based on the available datasets. Assuming there are N available datasets, where P are fault datasets based on different operating parameters and Q are non-fault datasets, the objective function of the optimal parameter selection model is:
[0069] (7)
[0070] Based on the above formula, generate P units. The optimal value is determined, the maximum optimal value is extracted, and this maximum optimal value is selected as the global optimal value for detecting all possible high-impedance arc faults. :
[0071] (8)
[0072] Similarly, the optimal parameters are obtained from the non-faulty dataset. :
[0073] (9)
[0074] according to , The final optimal value is generated from the faulty and non-faulty datasets. The specific selection formula is as follows:
[0075] < < (10)
[0076] For large datasets, population-based search algorithms, such as particle swarm optimization, can be used to identify optimal parameters.
[0077] In step S3, the residual current signal undergoes transient extraction transformation optimized by standard deviation to calculate the energy E. The fault detection task is completed when the energy E exceeds 0. The accumulated sum of TET energy is used as the final fault detection indicator to trigger tripping.
[0078] (11)
[0079] In the formula, G tet Here, v is the fault detection metric, and v is the drift parameter. Ideally, v=0, but this is not safe; therefore, it is adjusted based on the energy values of the entire dataset. The minimum value is extracted from the energy values related to the fault. Extract the maximum value from the energy value under non-fault conditions. Then the drift parameter v is selected from the following set:
[0080] (12)
[0081] In step S4, the detection process for high-impedance arc faults in distribution networks containing renewable energy is as follows:
[0082] 1) Acquire three-phase current signals.
[0083] 2) Calculate the residual current.
[0084] 3) Calculate the energy components of the transient extraction transformation using formula (5).
[0085] 4) Calculate the final detection index G using formula (11). tet If G tet If the value is greater than 0, it is determined that a high-impedance arc fault has occurred.
[0086] The improved IEEE 30-node system was used as the test system, and the system diagram is as follows. Figure 2As shown. In this test system, bus 22 is connected to a 1MW diesel generator, bus 29 is connected to a 6MW wind turbine, and bus 25 is connected to a 3MW photovoltaic power generation system. All local generators are connected via star-grounded transformers. A protection device was installed near bus 27, and the fault occurred on the line with nodes 27 and 30. The proposed method was used to analyze the current and voltage signals at this location. In addition to the fault event, non-fault events that might cause protection malfunctions in the vicinity, including capacitor and nonlinear load switching, and transformer switching events, were also created to verify the effectiveness of the proposed method.
[0087] Because this example requires a large dataset to identify the optimal parameters, the standard deviation optimization process employs a particle swarm optimization algorithm. Through extensive fault and non-fault simulations in the test system, the final standard deviation can be obtained. and If the values are 0.262 and 1.576 respectively, then the optimal values for fault detection are... .
[0088] The upper and lower limits of the energy values corresponding to this optimal Gaussian function parameter set are 0.840 and 0.552, respectively. Therefore, the range of the drift parameter is 0.552 to 0.840.
[0089] The improved IEEE 30-bus system was modeled in PSCARD to test the proposed fault detection method. The sampling frequency of current and voltage signals was 1.2 kHz, and a fault was set to occur at 1.1 s. Since the fault signal is related to the physical conditions of the contact surface when the conductor breaks, the detection conditions of the protection were simulated during the test with different resistance values: 1) 0.03~0.05 ;2) 1-1.15 ;3) 2-2.5 The test results show that, under the above three conditions, the fault detection index is met at 1.1s. The condition is >0, where the simulation results for the first case are as follows: Figure 3 As shown.
[0090] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0091] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0092] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0093] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0094] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.
Claims
1. A method for detecting high-impedance arc faults in a distribution network containing renewable energy, characterized in that, include: Step S1: Perform transient extraction and transformation processing on the fault current signal to extract time-frequency features; Step S2: Construct the optimal parameter selection objective function and optimize the standard deviation parameter of the Gaussian function used in the transient extraction transformation; Step S3: Based on the energy values of the fault and non-fault datasets, select the drift parameter in the fault detection index; Step S4: Perform high-impedance arc fault detection in the distribution network containing renewable energy to determine whether a high-impedance arc fault has occurred in the distribution network; In step S1, the transient extraction transform is implemented as follows: Since the basic goal of Transient Extraction Transform (TET) is to extract the time-frequency characteristics of fault signals when they occur in short-time transients, consider the following Short-Time Fourier Transform (STFT): （1） In the formula, For moving the window, Gaussian function, For non-stationary time-varying signal functions; transform basis functions It is a key parameter for identifying time-varying changes in a signal due to modulation, and the Dirac function with amplitude A and time shift of 0 is selected as the [parameter]. The basis functions and signal functions are as follows: （2） （3） Substituting equations (2) and (3) into equation (1), accurate frequency information can be obtained using STFT; to enhance the STFT results, transient extraction transform is adopted, the mathematical expression of which is: （4） In the formula, For the transient extraction operator, such that Energy distribution ratio To further improve detection performance, the absolute value of the TET energy coefficient over the entire signal length is calculated. （5） In step S2, the Gaussian function used in the transient extraction transform is as follows: （6） In the formula, f is the frequency, t is the time scale parameter controlling the window position, and the standard deviation is... Control the width of the Gaussian window; The optimal selection model for key parameters is as follows: First, construct the objective function based on the available datasets; assuming there are N available datasets, of which P are fault datasets based on different operating parameters and Q are non-fault datasets, the objective function of the optimal parameter selection model is: （7） Based on the above formula, generate P units. The optimal value is determined, the maximum optimal value is extracted, and this maximum optimal value is selected as the global optimal value for detecting all possible high-impedance arc faults. : （8） Optimal parameters are obtained from the non-fault dataset. : （9） according to , The final optimal value is generated from the faulty and non-faulty datasets. The specific selection formula is as follows: ＜ ＜ （10） In step S3, the residual current signal is transformed by transient extraction after standard deviation optimization to calculate the energy E. When the energy E exceeds 0, the fault detection task is completed. The accumulated sum of TET energy is used as the fault detection indicator to trigger tripping. （11） In the formula, G tet Here, v is the fault detection metric, and v is the drift parameter. Ideally, v=0, but this is not safe, so it is corrected based on the energy values of the entire dataset. The minimum value is extracted from the energy values related to the fault. Extract the maximum value from the energy value under non-fault conditions. Then the drift parameter v is selected from the following set: （12）。 2. The method for detecting high-impedance arc faults in a distribution network containing renewable energy as described in claim 1, characterized in that, In step S4, the detection process for high-impedance arc faults in distribution networks containing renewable energy is as follows: 1) Acquire three-phase current signals; 2) Calculate the residual current; 3) Calculate the energy components of the transient extraction transformation using formula (5); 4) Calculate the fault detection index G using formula (11) tet If G tet If the value is greater than 0, it is determined that a high-impedance arc fault has occurred.