Fault traveling wave detection method and device and storage medium

Abstract

The invention relates to the technical field of power distribution network fault detection, and discloses a fault traveling wave detection method and device and a storage medium. The method comprises the following steps: collecting traveling wave signals on a distribution line, and preprocessing the collected traveling wave signals; carrying out the first screening of fault wave heads on the collected traveling wave signals through a threshold value discrimination method; extracting the characteristics of the high-frequency part of the screened traveling wave signal through a wavelet transform method, and carrying out the second screening of the fault wave head of the screened traveling wave signal according to the high-frequency characteristics; establishing a fault traveling wave detection model according to historical data of over-high frequency features extracted through wavelet transform, inputting the high frequency features of the traveling wave signals into the fault traveling wave detection model, and performing third screening on fault wave heads; and in combination with the position of the fault wave head screened for the third time, determining the final position and time information of the fault wave head. According to the method, the accuracy, robustness and real-time performance of fault wave head detection can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power distribution network fault detection, and in particular to a fault traveling wave detection method, device and storage medium. Background Art

[0002] Fault detection in distribution networks is a crucial component of power system automation, especially given the increasing demands for power supply stability and fault handling efficiency. As a key component of distribution networks, feeder automation units (FTUs) play a crucial role in fault diagnosis, location, and isolation. By monitoring traveling wave signals, FTUs can respond to faults in the distribution network in real time and quickly locate them, thereby isolating the fault and restoring power. However, current traveling wave detection methods used in FTUs typically rely on threshold discrimination, wavelet transform, and modulus maximum methods. The threshold discrimination method is highly sensitive to noise and has low accuracy for slowly changing signals or complex waveforms. The wavelet transform method has high computational complexity and poor real-time performance. While the modulus maximum method offers rapid detection, it is also highly sensitive to noise and has difficulty processing complex signals. Due to the limitations of these methods, the accuracy, robustness, and real-time performance of fault traveling wave detection remain to be improved. Summary of the Invention

[0003] In order to overcome the deficiencies of the prior art, the present invention aims to provide a method for detecting fault traveling waves, which can improve the accuracy, robustness and real-time performance of fault traveling wave detection with lower computing resource requirements.

[0004] To solve the above problems, the technical solution adopted by the present invention is as follows: a fault traveling wave detection method, comprising the following steps:

[0005] Collect traveling wave signals on the distribution line and pre-process the collected traveling wave signals;

[0006] The collected traveling wave signal is screened for the first fault wave head by using the threshold discrimination method;

[0007] The characteristics of the high-frequency part of the filtered traveling wave signal are extracted by wavelet transform method, and the fault wave head of the filtered traveling wave signal is screened for the second time according to the high-frequency characteristics;

[0008] A fault traveling wave detection model is established based on historical data with high-frequency features extracted through wavelet transform. The high-frequency features of the traveling wave signal are input into the fault traveling wave detection model to perform a third screening of the fault wave head.

[0009] Combined with the position of the fault wave head from the third screening, the final position and time information of the fault wave head are determined.

[0010] Compared with the existing technology, the beneficial effect of the present invention is that by combining the traditional threshold discrimination method and wavelet transform method with the AI ​​model trained by historical data, the wave head position of the fault traveling wave can be accurately and quickly detected in a complex distribution network environment, thereby improving the accuracy and detection efficiency of fault detection in the distribution network, so that the fault position can be quickly located and the fault can be handled in time, thereby improving the stability of the distribution network.

[0011] In the above-mentioned fault traveling wave detection method, the high-frequency features of the filtered traveling wave signal are extracted by the wavelet transform method, and the fault wave head of the filtered traveling wave signal is screened for the second time according to the high-frequency features. The db4 wavelet is selected as the mother wavelet for wavelet decomposition, and only single-scale wavelet decomposition is performed.

[0012] The above-mentioned fault traveling wave detection method, wherein the steps of establishing a fault traveling wave detection model based on historical data from which high-frequency features have been extracted through wavelet transform, inputting the high-frequency features of the traveling wave signal into the fault traveling wave detection model, and performing a third screening of the fault wave head include:

[0013] Build a distribution network fault simulation environment;

[0014] Use the fault simulation environment to simulate various faults, add noise interference to the fault simulation environment, and collect the traveling wave signal of the fault simulation environment;

[0015] Preprocess the traveling wave signal of the fault simulation environment;

[0016] The fault wave head is first screened on the traveling wave signal of the fault simulation environment by using the threshold discrimination method;

[0017] The high-frequency features of the filtered traveling wave signal are extracted through wavelet transform to form historical data for model training;

[0018] Divide the historical data for model training into a training set and a test set, establish a fault traveling wave detection model, and use the training set and the test set to train and optimize the fault traveling wave detection model;

[0019] The trained and optimized fault traveling wave detection model is deployed on the FTU equipment, and the high-frequency characteristics of the traveling wave signal on the actual distribution line are input into the fault traveling wave detection model to perform a third screening of the fault wave head.

[0020] The above-mentioned fault traveling wave detection method, the architecture of the fault traveling wave detection model includes an input layer, a convolutional neural network layer, a long short-term memory network layer, a fully connected layer and an output layer connected in sequence.

[0021] The above-mentioned fault traveling wave detection method deploys the trained and optimized fault traveling wave detection model on the FTU device, inputs the high-frequency characteristics of the traveling wave signal on the actual distribution line into the fault traveling wave detection model, and performs a third screening of the fault wave head. In the step, the trained fault identification model is deployed on the FTU device through the TensorFlow Lite framework.

[0022] In the above-mentioned fault traveling wave detection method, in the step of collecting the traveling wave signal on the distribution line and preprocessing the collected traveling wave signal, the traveling wave signal on the distribution line is sampled at a high speed at a sampling rate of more than 100 MHz, and the collected traveling wave signal is filtered and preprocessed through a bandpass filter.

[0023] In the above-mentioned fault traveling wave detection method, in the step of performing the first screening of the fault wave head on the collected traveling wave signal through the threshold discrimination method, the threshold of the threshold discrimination method is a dynamic threshold, and the dynamic threshold is set according to the noise situation in the actual traveling wave signal by a dynamic threshold model established based on the historical data of the fault traveling wave. The dynamic threshold may be set according to the statistical characteristics of the noise in the real-time traveling wave signal.

[0024] A computer-readable storage medium stores a computer program, which implements the above-mentioned fault traveling wave detection method when called and executed by a processor.

[0025] A fault traveling wave detection device includes a processor and a memory, wherein the memory is electrically connected to the processor, and the processor can implement the above-mentioned fault traveling wave detection method by calling and executing a computer program in the memory.

[0026] A fault traveling wave detection system includes an FTU device, multiple voltage transformers and multiple current transformers. The multiple voltage transformers and multiple current transformers are distributed on the distribution line. The multiple voltage transformers and multiple current transformers are electrically connected to the FTU device. The FTU device is embedded with the above-mentioned fault traveling wave detection device.

[0027] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 Flowchart of a fault traveling wave detection method according to an embodiment of the present invention;

[0029] Figure 2 Flowchart of a fault traveling wave detection method according to an embodiment of the present invention;

[0030] Figure 3 2 is an architecture diagram of a fault traveling wave detection model according to an embodiment of the present invention;

[0031] Figure 4 This is a principle block diagram of a fault traveling wave system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0032] The embodiments of the present invention are described in detail below. Figure 1 , an embodiment of the present invention provides a fault traveling wave detection method, comprising the following steps:

[0033] Collect traveling wave signals on the distribution line and pre-process the collected traveling wave signals;

[0034] The collected traveling wave signal is screened for the first fault wave head by using the threshold discrimination method;

[0035] The characteristics of the high-frequency part of the filtered traveling wave signal are extracted by wavelet transform method, and the fault wave head of the filtered traveling wave signal is screened for the second time according to the high-frequency characteristics;

[0036] A fault traveling wave detection model is established based on historical data with high-frequency features extracted through wavelet transform. The high-frequency features of the traveling wave signal are input into the fault traveling wave detection model to perform a third screening of the fault wave head.

[0037] Combined with the position of the fault wave head from the third screening, the final position and time information of the fault wave head are determined.

[0038] This fault traveling wave detection method combines the traditional threshold discrimination method and wavelet transform method with an AI model, combining the fault head wave judgment results of the three methods. This method combines the advantages of the threshold discrimination method's low computational complexity, the wavelet transform method's applicability to non-stationary signals, and the artificial intelligence technology's adaptability to complex signal recognition, thereby improving the accuracy and robustness of fault head wave recognition in traveling wave signals. Furthermore, because the AI ​​model identifies the characteristics of the high-frequency portion of the traveling wave signal after processing using the threshold discrimination method and wavelet transform method, noise interference and false head waves in the traveling wave signal are partially filtered out, reducing the AI ​​model's computational complexity. This allows accurate fault head wave identification with fewer computing resources, allowing deployment in embedded devices with average computing performance within FTU equipment without compromising real-time recognition.

[0039] In this embodiment, in order to further reduce the demand for computing resources during the identification process, reduce the performance requirements of the processor during the identification process, and ensure the real-time performance of the identification process, a simplified wavelet transform method is used to extract the high-frequency features of the traveling wave signal after the first screening. In this embodiment, only single-scale wavelet decomposition is performed during the wavelet transform process, and the Daubechies4 (db4) wavelet is selected as the mother wavelet. The simplified wavelet transform method not only maintains a high level of fault head wave location accuracy, but also reduces the consumption of computing resources, making it suitable for real-time operation of embedded systems.

[0040] In this embodiment, to further improve the recognition accuracy of the threshold discrimination method, a dynamic threshold is used as the threshold of the threshold discrimination method. The dynamic threshold can be set based on the noise level in the actual traveling wave signal using a dynamic threshold model trained based on historical data. The dynamic threshold can also be set based on the statistical characteristics of the noise in the real-time traveling wave signal. For example, the dynamic threshold can be set as T = μ + kσ, where k is an empirical coefficient, μ is the mean of the background noise, and σ is the standard deviation of the background noise.

[0041] It is understandable that the fault traveling wave detection model can be trained by a neural network model such as a convolutional neural network model. Figure 3 The fault traveling wave detection model adopts a convolutional neural network (CNN) + long short-term memory network (LSTM) architecture, which includes an input layer, a convolutional neural network layer, a long short-term memory network layer, a fully connected layer, and an output layer in sequence. After being trained with the features of historical data extracted through wavelet transform, the convolutional neural network layer can identify fault signals in complex environments and fuse them with the results of the threshold discrimination method. It classifies the signals according to the local features of the input signals, effectively filters out noise and false head waves, and improves the accuracy of fault location. The long short-term memory network layer can effectively process the time series characteristics of the signal, and further optimize the positioning of the fault head wave based on the dynamic changes of the historical signal. It can maintain a high detection accuracy in environments with high interference or slow signal changes, and improve the robustness of the recognition process.

[0042] Reference Figure 2In this embodiment, to ensure sufficient historical data for training the fault traveling wave detection model, historical data is obtained by performing fault simulations in a distribution network fault simulation environment. The distribution network fault simulation environment is constructed using PSCAD software, and various fault types, such as single-phase grounding, phase-to-phase short circuit, and switch operation, are simulated within the simulation environment. Current and voltage signals are collected during various fault simulations to obtain traveling wave signals under different fault conditions. Using the same recognition methods used in actual identification, the traveling wave signals from the simulation environment are preprocessed, using a threshold discrimination method and a wavelet transform method to extract the high-frequency features of the traveling wave signals, forming historical data for model training. The historical data is then divided into a training set and a test set in an 8:2 ratio, and the fault traveling wave detection model is trained and optimized. During training, the cross-entropy loss function is used, and optimization is performed using the Adam optimizer. After optimization, the trained model is deployed in an experimental environment for testing. The recognition performance of the model is evaluated by comparing the detection and recognition results with the actual fault location. In some embodiments, in order to continuously optimize the fault traveling wave detection model, the results of each identification and the position of the actual fault wave head can be recorded, and the fault traveling wave detection model can be continuously learned from the recorded results to continuously optimize the applicability of the fault traveling wave detection model to the deployed distribution network.

[0043] In this embodiment, to further reduce the computing resource requirements of the AI ​​model so that it can be deployed on low-power devices for inference, the trained fault traveling wave detection model is deployed in the embedded device of the FTU device using the TensorFlow Lite framework. The fault traveling wave detection model is lightweight and optimized using the TensorFlow Lite framework.

[0044] In this embodiment, in order to further improve the recognition accuracy of the fault head wave, it is necessary to adopt high-speed sampling technology, that is, a voltage transformer or current transformer with a sampling frequency of more than 10MHz is used to sample the current and voltage signals on the distribution line to ensure high-precision capture of the traveling wave signal. Since the collected traveling wave signal usually contains a large amount of high-frequency noise and low-frequency interference, it is necessary to filter the collected traveling wave signal through a bandpass filter for pre-processing to filter out the power frequency component (50Hz and 60Hz) and high-frequency interference in the non-fault signal to retain the effective information of the traveling wave signal.

[0045] It is understandable that the results of the threshold discrimination method, wavelet transform, and fault traveling wave detection model can ultimately be fused by weighted averaging or other suitable fusion methods to further eliminate the interference of false wave heads, obtain the final fault wave head position and time information, and transmit the results in real time to the distribution network monitoring center through the communication module of the FTU device for subsequent fault location and processing. In some embodiments, the total recognition accuracy of the threshold discrimination method, wavelet transform, and fault traveling wave detection model after each detection can be statistically analyzed, and the confidence of each algorithm can be calculated based on the recognition accuracy. The confidence is used as a weight, and the results of the three algorithms are input into the decision tree. Each fault head wave is voted on to obtain the final recognition result.

[0046] Referring to the table below, the fault traveling wave identification method according to an embodiment of the present invention was deployed in an experimental environment. Testing showed that for common single-phase grounding faults, interphase short-circuit faults, two-phase grounding faults, and three-phase short-circuit faults, the method achieved wave head positioning errors below 35m and detection delays below 10ns, demonstrating high detection accuracy and real-time performance. Furthermore, in environments with varying signal-to-noise ratios, combined with AI algorithms, the accuracy rate was improved, remaining stable above 90%, demonstrating strong noise suppression capabilities.

[0047] Table 1 Experimental results

[0048]

[0049]

[0050]

[0051] Based on the same inventive concept, an embodiment of the present invention further provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, the above-mentioned fault traveling wave detection method can be implemented.

[0052] In some possible embodiments, various aspects of the fault traveling wave detection method provided by the present invention can also be implemented in the form of a program product, which includes program code. When the program product is run on the device, the program code is used to enable the control device to execute the steps of the fault traveling wave detection method according to various exemplary embodiments of the present application described above in this specification.

[0053] Based on the same inventive concept, an embodiment of the present invention also provides an identification device for implementing the above-mentioned fault traveling wave detection method, including a processor and a memory, the memory is electrically connected to the processor, and the processor is used to implement the above-mentioned fault traveling wave detection method when executing the computer program stored in the memory.

[0054] In one possible design, the processor may include one or more processing units, and the processor and memory may be implemented on the same chip or separately on separate chips. The processor may be a general-purpose processor, such as a central processing unit (CPU), a digital signal processor, an application-specific integrated circuit, a field programmable gate array or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component, which may implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present application. The general-purpose processor may be a microprocessor or any conventional processor, etc. The steps of the fault traveling wave detection method disclosed in conjunction with the embodiments of the present application may be directly embodied as being executed by a hardware processor, or may be executed by a combination of hardware and software modules in the processor.

[0055] As a non-volatile computer-readable storage medium, memory can be used to store non-volatile software programs, non-volatile computer executable programs and modules.Memory can include at least one type of storage medium, for example, can include flash memory, hard disk, multimedia card, card-type memory, random access memory (Random Access Memory, RAM), static random access memory (Static Random Access Memory, SRAM), programmable read-only memory (Programmable Read Only Memory, PROM), read-only memory (Read Only Memory, ROM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, EEPROM), magnetic storage, disk, optical disk, etc. Memory is any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory in the embodiment of the present application can also be a circuit or other arbitrarily capable of implementing a storage function, for storing program instructions and / or data.

[0056] By designing and programming a processor, the code corresponding to the fault traveling wave detection method described in the aforementioned embodiment can be embedded in the chip, thereby enabling the chip to execute the steps of the fault traveling wave detection method described in the embodiment of the present invention when running. How to design and program a processor is well known to those skilled in the art and will not be further described here.

[0057] Reference Figure 4In some possible real-time examples, the fault traveling wave detection device includes a threshold discrimination module, a wavelet transform module, a fault traveling wave detection model and a detection result fusion module. The threshold discrimination module is used to perform a first screening of the fault wave head on the input pre-processed traveling wave signal by a threshold discrimination method. The wavelet transform module is used to extract the characteristics of the high-frequency part of the traveling wave signal after the first magnetic screening by using a simplified wavelet transform method, and perform a second screening of the fault wave head based on the characteristics of the high-frequency part. The fault traveling wave detection model is used to perform a third screening of the fault wave head based on the characteristics of the high-frequency part of the traveling wave signal obtained by wavelet transform and infer the position of the fault wave head according to historical data. The detection result fusion module is used to fuse the three screening results to obtain the final result of the fault wave head.

[0058] Reference Figure 4 Based on the same inventive concept, an embodiment of the present invention also provides a fault traveling wave detection system, including an FTU device, multiple voltage transformers and multiple current transformers, the multiple voltage transformers and multiple current transformers are distributed on the distribution line, the multiple voltage transformers and multiple current transformers are electrically connected to the FTU device, and the FTU device is embedded with the above-mentioned fault traveling wave detection device. In order to improve the accuracy of fault head wave recognition, the sampling frequency of the current transformer and the voltage transformer is above 10MHz. A bandpass filter and a communication module are also provided in the FTU device. The bandpass filter is used to filter and pre-process the traveling wave signals collected by the voltage transformer and the current transformer, and the communication module is used for the FTU device to communicate with the distribution network monitoring center.

[0059] It should be noted that in the description of the present invention, "several" means one or more, "more" means two or more, "greater than," "less than," and "exceed" are understood to exclude the number itself, while "above," "below," and "within" are understood to include the number itself. If there are descriptions of "first," "second," and so on, these are used solely to distinguish technical features and are not to be construed as indicating or implying relative importance, implicitly specifying the number of the indicated technical features, or implicitly specifying the order of the indicated technical features.

[0060] The present application is described with reference to the flowcharts and / or block diagrams of the methods, apparatus (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1A device that provides the functions specified in a block or multiple blocks.

[0061] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0062] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0063] In the description of the present invention, unless otherwise clearly defined, terms such as setting, installing, and connecting should be understood in a broad sense, and technicians in the relevant technical field can reasonably determine the specific meanings of the above terms in the present invention based on the specific content of the technical solution.

[0064] The above embodiments are only preferred embodiments of the present invention and cannot be used to limit the scope of protection of the present invention. Any non-substantial changes and replacements made by technicians in this field on the basis of the present invention fall within the scope of protection required by the present invention.

Claims

1. A fault traveling wave detection method, characterized in that: The steps include: Collect traveling wave signals on the distribution line and pre-process the collected traveling wave signals; The collected traveling wave signal is screened for the first fault wave head by using the threshold discrimination method; The characteristics of the high-frequency part of the filtered traveling wave signal are extracted by wavelet transform method, and the fault wave head of the filtered traveling wave signal is screened for the second time according to the high-frequency characteristics; A fault traveling wave detection model is established based on historical data with high-frequency features extracted through wavelet transform. The high-frequency features of the traveling wave signal are input into the fault traveling wave detection model to perform a third screening of the fault wave head. Combined with the position of the fault wave head from the third screening, the final position and time information of the fault wave head are determined.

2. The fault traveling wave detection method according to claim 1, characterized in that: In the step of extracting the high-frequency features of the filtered traveling wave signal by wavelet transform and performing a second screening of the fault wave head of the filtered traveling wave signal according to the high-frequency features, db4 wavelet is selected as the mother wavelet for wavelet decomposition, and only single-scale wavelet decomposition is performed.

3. The fault traveling wave detection method according to claim 1, characterized in that: The steps of establishing a fault traveling wave detection model based on historical data with high-frequency features extracted through wavelet transform, inputting the high-frequency features of the traveling wave signal into the fault traveling wave detection model, and performing a third screening of the fault wave head include: Build a distribution network fault simulation environment; Use the fault simulation environment to simulate various faults, add noise interference to the fault simulation environment, and collect the traveling wave signal of the fault simulation environment; Preprocess the traveling wave signal of the fault simulation environment; The fault wave head is first screened on the traveling wave signal of the fault simulation environment by using the threshold discrimination method; The high-frequency features of the filtered traveling wave signal are extracted through wavelet transform to form historical data for model training; Divide the historical data for model training into a training set and a test set, establish a fault traveling wave detection model, and use the training set and the test set to train and optimize the fault traveling wave detection model; The trained and optimized fault traveling wave detection model is deployed on the FTU equipment, and the high-frequency characteristics of the traveling wave signal on the actual distribution line are input into the fault traveling wave detection model to perform a third screening of the fault wave head.

4. The fault traveling wave detection method according to any one of claims 1 or 3, characterized in that: The architecture of the fault traveling wave detection model includes an input layer, a convolutional neural network layer, a long short-term memory network layer, a fully connected layer and an output layer connected in sequence.

5. The fault traveling wave detection method according to claim 3, characterized in that: The trained and optimized fault traveling wave detection model is deployed on the FTU device, the high-frequency characteristics of the traveling wave signal on the actual distribution line are input into the fault traveling wave detection model, and the fault wave head is screened for the third time. Then, the trained fault identification model is deployed on the FTU device through the TensorFlow Lite framework.

6. The fault traveling wave detection method according to claim 1, characterized in that: In the step of collecting the traveling wave signal on the distribution line and preprocessing the collected traveling wave signal, the traveling wave signal on the distribution line is sampled at a high speed at a sampling rate of more than 10 MHz, and the collected traveling wave signal is filtered and preprocessed through a bandpass filter.

7. The fault traveling wave detection method according to claim 1, characterized in that: In the step of performing the first screening of the fault wave head on the collected traveling wave signal by the threshold discrimination method, the threshold of the threshold discrimination method is a dynamic threshold, which is set according to the noise situation in the actual traveling wave signal by a dynamic threshold model established based on the historical data of the fault traveling wave. The dynamic threshold may be set according to the statistical characteristics of the noise in the real-time traveling wave signal.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is called and executed by a processor, the fault traveling wave detection method according to any one of claims 1 to 7 is implemented.

9. A fault traveling wave detection device, characterized in that: The system comprises a processor and a memory, wherein the memory is electrically connected to the processor, and the processor can implement the fault traveling wave detection method according to any one of claims 1 to 7 by calling and executing a computer program in the memory.

10. A fault traveling wave detection system, characterized in that: It includes an FTU device, multiple voltage transformers and multiple current transformers. The multiple voltage transformers and multiple current transformers are distributed on the distribution line. The multiple voltage transformers and multiple current transformers are electrically connected to the FTU device. The FTU device is embedded with the fault traveling wave detection device according to claim 9.

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