An EEMD-based cable fault location detection device

Abstract

This utility model discloses a cable fault location and detection device based on EEMD, relating to the field of underground cable monitoring technology. The EEMD-based cable fault location and detection device includes a Hall effect sensor, a signal amplification circuit, an ADC analog-to-digital converter module, a DSP chip, an EEMD processing unit, a classification model, and a display screen 112. The Hall effect sensor detects magnetic field signals; its output is electrically connected to the input of the signal amplification circuit. The output of the signal amplification circuit is electrically connected to the input of the ADC analog-to-digital converter module, and the output of the ADC module is electrically connected to the input of the DSP chip. The signal amplification circuit and the ADC module amplify and process the magnetic field signal, respectively, and convert the magnetic field signal into a digital signal. The output of the DSP chip is electrically connected to the input of the EEMD processing unit, and the DSP chip is used to preprocess the magnetic field signal. This EEMD-based cable fault location and detection device can determine the fault type and accurately locate the fault point.

Description

Technical Field

[0001] This utility model relates to the field of underground cable monitoring technology, and in particular to a cable fault location and detection device based on EEMD. Background Technology

[0002] Underground cables, as the core carriers of urban power grid transmission, are deployed on a large scale due to their advantages such as strong concealment and resistance to environmental interference. However, their deep underground nature makes it difficult to detect daily defects in a timely manner, and faults are often identified only after they have evolved into serious short circuits or leaks. According to statistics, underground cable faults are diverse: according to their causes, they can be divided into external force damage, chemical corrosion, overload operation, and ground subsidence; according to their manifestations, they can be divided into low-resistance short circuits, open circuits, flashover discharges, and high-resistance leaks; and according to their location, they cover main insulation faults, sheath faults, body faults, and joint faults. Among them, joint faults are prone to occur due to differences in construction processes, while external damage faults are difficult to predict due to third-party construction damage, becoming a major bottleneck in improving the timeliness of fault repair.

[0003] Currently, cable fault location technology is mainly based on active and passive methods, but its practical application still faces multiple constraints. Active methods, represented by the injection method, locate faults by injecting specific signals into the faulty circuit and tracking the reflected signals. However, this method relies on high-precision signal generation and acquisition equipment, and its stability is insufficient in long-distance transmission or complex electromagnetic interference scenarios. Among passive methods, the impedance method estimates the fault distance by calculating the line impedance. Although simple and effective, it is difficult to cope with errors caused by dynamic changes in grounding resistance (such as arc faults). The traveling wave method is based on the principle of electromagnetic wave reflection and locates faults by multiplying the reflection time difference and wave velocity. Although fast, it requires high-sampling-rate waveform recording devices and accurate wavefront identification technology, which limits its practicality in multi-branch distribution network scenarios. In addition, existing technologies are mostly designed for single fault types and cannot simultaneously correlate fault characteristics with location attributes, such as the strong correlation between sheath damage and mechanical damage. This leads to a disconnect between fault type identification and spatial location, and maintenance still relies on manual experience for segmented inspection, which seriously restricts the efficiency of emergency repairs. Utility Model Content

[0004] The technical problem to be solved by this utility model is to provide a cable fault location and detection device based on EEMD, which can determine the fault type and accurately locate the fault point.

[0005] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0006] A cable fault location and detection device based on EEMD includes a Hall effect sensor, a signal amplification circuit, an ADC analog-to-digital converter module, a DSP chip, an EEMD processing unit, a classification model, and a display screen. The Hall effect sensor detects magnetic field signals, and its output is electrically connected to the input of the signal amplification circuit. The output of the signal amplification circuit is electrically connected to the input of the ADC analog-to-digital converter module, and the output of the ADC module is electrically connected to the input of the DSP chip. The signal amplification circuit and the ADC module amplify and process the magnetic field signals and convert them into digital signals, respectively. The output of the DSP chip is electrically connected to the input of the EEMD processing unit, which performs preprocessing on the magnetic field signals. The output of the EEMD processing unit is electrically connected to the input of the classification model, which analyzes the magnetic field signals and extracts fault features. The input of the display screen is electrically connected to the output of the classification model, which classifies the fault features and generates fault information. The display screen displays the fault information.

[0007] Compared with existing technologies, this invention achieves at least the following beneficial effects: This invention acquires cable magnetic field signals using a non-contact Hall sensor, and combines this with bandpass filtering and signal calibration technology from a DSP chip to effectively filter out power frequency interference and high-frequency noise, solving the stability problem of active methods in long-distance or strong electromagnetic environments. Simultaneously, based on EEMD decomposition and multiple white noise averaging processes, it dynamically extracts the energy, frequency, and amplitude characteristics of the IMF component, overcoming the limitations of traditional impedance methods in misjudging dynamic resistance such as arc faults, and achieving accurate classification of various fault types such as low-resistance short circuits and high-resistance leakage. Furthermore, by fusing the triaxial sensor signal strength attenuation gradient with cable topology parameters such as length and branches through a positioning algorithm, it synchronously correlates fault types with spatial locations, reducing the scope of manual inspection from kilometers to meters, effectively improving repair efficiency. Attached Figure Description

[0008] One or more embodiments of the present invention will now be described by way of example only with reference to the accompanying drawings, in which:

[0009] Figure 1 This is a schematic diagram of the cable fault location and detection device based on EEMD according to this utility model.

[0010] Figure 2 This is a circuit block diagram of the cable fault location and detection device based on EEMD according to this utility model.

[0011] The numbers in the diagram are as follows: 1. Housing; 11. Display unit; 111. Plastic housing; 112. Display screen; 12. Shielding unit; 121. Metal housing; 13. Detection unit; 131. Plastic conduit; 2. Switch button; 3. Power module; 4. Charging port. Detailed Implementation

[0012] The present invention will now be described in detail with reference to exemplary embodiments shown in the accompanying drawings. However, it should be understood that the present invention may be implemented in many different forms and should not be construed as limited to the embodiments set forth herein. These embodiments are provided herein to make the disclosure of the present invention more complete and to fully convey the concept of the present invention to those skilled in the art.

[0013] In the description of this utility model, it should be understood that the terms "center", "lateral", "longitudinal", "front", "rear", "left", "right", "upper", "lower", "vertical", "horizontal", "top", "bottom", "inner", and "outer" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this utility model and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting the scope of protection of this utility model.

[0014] like Figures 1 to 2 As shown, the cable fault location and detection device based on EEMD of this utility model includes a Hall effect sensor, a signal amplification circuit, an ADC analog-to-digital converter module, a DSP chip, an EEMD processing unit, a classification model, and a display screen 112. The Hall effect sensor is used to detect magnetic field signals, and its output is electrically connected to the input of the signal amplification circuit. The output of the signal amplification circuit is electrically connected to the input of the ADC analog-to-digital converter module, and the output of the ADC is electrically connected to the input of the DSP chip. The signal amplification circuit and the ADC are used to amplify and process the magnetic field signal and convert it into a digital signal, respectively. The output of the DSP chip is electrically connected to the input of the EEMD processing unit, which is used to preprocess the magnetic field signal. The output of the EEMD processing unit is electrically connected to the input of the classification model, which is used to analyze the magnetic field signal and extract fault features. The input of the display screen 112 is electrically connected to the output of the classification model, which is used to classify the fault features and generate fault information. The display screen 112 is used to display the fault information.

[0015] The Hall effect sensor is used to detect the magnetic field signal around the cable, capturing magnetic field fluctuations caused by abnormal current. It directly reflects the cable's operating status, providing raw data for fault detection. It features high sensitivity and non-contact measurement, avoiding interference with the cable itself. The signal amplification circuit amplifies the weak electrical signal output from the Hall sensor, with a magnification factor of 1000 to 5000 times. This increases signal strength while adapting to the input range of the subsequent ADC module, ensuring effective processing of weak signals. The ADC module converts analog signals to digital signals, digitizing the signal and facilitating algorithmic processing by the DSP chip, such as filtering and calibration. The DSP chip preprocesses the signal, including bandpass filtering (0.1Hz-1kHz), zero-point offset correction, gain compensation, and normalization. This filters out power frequency interference and high-frequency noise, eliminates hardware errors, improves signal quality, and provides clean input for EEMD decomposition. The EEMD processing unit performs ensemble empirical mode decomposition, such as adding white noise, multiple EMD decompositions, and IMF component averaging. It decomposes the signal into IMF components of different frequencies, reducing noise interference and accurately extracting fault characteristics such as energy, frequency, and amplitude. The classification model identifies fault types based on IMF feature vectors (energy, frequency, amplitude). Through pattern matching and threshold determination, it quickly distinguishes fault types such as low-resistance short circuits and high-resistance leakage, improving diagnostic accuracy. Display screen 112 shows the fault type, location information, and system status; it provides an intuitive human-machine interface and supports on-site operation and remote monitoring linkage.

[0016] Specifically, in this embodiment, the DSP chip incorporates an FIR bandpass filter with a filtering frequency band of 0.1Hz to 1kHz. It performs zero-point offset correction and gain compensation to normalize the signal amplitude to the range of -1V to +1V. The bandpass filter removes power frequency interference such as 50Hz and high-frequency noise, preserving valid fault frequency band signals. Simultaneously, zero-point offset correction eliminates sensor hardware errors, gain compensation adapts to subsequent processing requirements, and normalization ensures signal consistency across different scenarios, improving the signal-to-noise ratio and providing high-quality input data for EEMD decomposition.

[0017] Furthermore, the EEMD processing unit is configured such that the white noise intensity ε is 0.2 times the standard deviation of the preprocessed signal. 100 independent EMD decompositions are performed, and the IMF components of the same order are arithmetically averaged to eliminate random noise. This configuration ensures the accuracy and repeatability of the IMF components, providing a reliable foundation for feature extraction.

[0018] Furthermore, the classification model employs a radial basis function kernel support vector machine, using Pearson correlation coefficients to select feature parameters with a correlation greater than 0.8 with fault samples as input. This correlation-based selection eliminates redundant features, reducing computational load and improving classification efficiency.

[0019] Furthermore, the Hall effect sensor includes a three-axis magnetic field detection array for multi-dimensional acquisition of magnetic field signals. The positioning algorithm module analyzes the IMF energy attenuation gradient of the three-axis magnetic field detection array, combines it with cable length and branch structure parameters, establishes a signal strength attenuation model, and uses the least squares method to iteratively solve for the fault point coordinates. By setting up the three-axis array, differences in the spatial distribution of the magnetic field can be captured, further improving positioning accuracy and making the positioning error less than 0.5 meters, suitable for complex power distribution network scenarios.

[0020] The cable fault location and detection device based on EEMD also includes a housing 1. The signal amplification circuit, ADC analog-to-digital conversion module, DSP chip, EEMD processing unit and classification model are set inside the housing 1, the Hall effect sensor is set outside the housing 1, and the display screen 112 is set on the surface of the housing 1.

[0021] Specifically, in this embodiment, the housing 1 includes a display unit 11, a shielding unit 12, and a detection unit 13. The display unit 11 includes a plastic housing 111, and the display screen 112 is mounted on the front surface of the plastic housing 111. The shielding unit 12 includes a metal housing 121, which is connected to the right side of the plastic housing 111. The signal amplification circuit, ADC analog-to-digital conversion module, DSP chip, EEMD processing unit, and classification model are all installed inside the metal housing 121. This arrangement can achieve the effect of local electromagnetic shielding, protecting the core circuit from interference.

[0022] The detection unit 13 includes a plastic conduit 131 vertically disposed on the left side of the plastic housing 111. The other end of the plastic conduit 131 is used to install a Hall effect sensor. In this embodiment, the Hall effect sensor is connected to the signal amplification circuit by a wire, and the plastic conduit 131 is sleeved on the wire.

[0023] The housing 1 also houses a power module 3 for providing power supply voltage, and the surface of the housing 1 is provided with a switch button 2 and a charging port 4. Specifically, in this embodiment, the power module 3 is used to supply power, and the charging port 4 is used to charge the power module 3. The switch button 2 controls the power supply of the device, and a long press will power off the device, which simplifies the operation process, avoids accidental touches, and enhances the safety of the equipment.

[0024] The working principle of this EEMD-based cable fault location and detection device is as follows: A triaxial Hall sensor non-contactly detects the magnetic field signal around the cable, capturing magnetic field fluctuations caused by abnormal current. The analog signal is then digitized by a signal amplification circuit (amplification factor 1000–5000 times) and an ADC analog-to-digital converter module. A DSP chip preprocesses the signal, including bandpass filtering (0.1Hz–1kHz), zero-point offset correction, and normalization (amplitude range -1V–+1V), filtering out power frequency interference and noise to improve the signal-to-noise ratio. The preprocessed signal is input to the EEMD processing unit, where white noise (intensity 0.2 times the signal standard deviation) is added, and 100 independent EMD decompositions are performed. The averaged IMF component is extracted, and its energy, frequency, and amplitude characteristics are calculated. Feature parameters with a correlation > 0.8 are selected. A classification model (radial basis vector SVM) automatically identifies fault types such as low-resistance short circuits and high-resistance leakage based on multi-dimensional feature vectors. Meanwhile, the triaxial sensor array, combined with the RSSI positioning algorithm, analyzes the IMF energy attenuation gradient and cable topology parameters, and uses the least squares method to iteratively solve for the fault point coordinates, achieving precise positioning within ±0.5 meters. The fault type, location, and confidence level are displayed on the screen in real time. The metal casing shields the core circuitry from interference, and the built-in power module supports field operations.

[0025] It should be understood that all the above embodiments are exemplary and not restrictive. Various modifications or variations made by those skilled in the art to the specific embodiments described above under the concept of this utility model should be within the protection scope of this utility model.

Claims

1. An EEMD-based cable fault location detection device, characterized in that: The system includes a Hall effect sensor, a signal amplification circuit, an ADC (Analog-to-Digital Converter) module, a DSP chip, an EEMD (Electronic Energy Management Device) processing unit, a classification model, and a display screen. The Hall effect sensor detects magnetic field signals, and its output is electrically connected to the input of the signal amplification circuit. The output of the signal amplification circuit is electrically connected to the input of the ADC module, and the output of the ADC module is electrically connected to the input of the DSP chip. The signal amplification circuit and the ADC module amplify and convert the magnetic field signals into digital signals, respectively. The output of the DSP chip is electrically connected to the input of the EEMD processing unit, which performs preprocessing on the magnetic field signals. The output of the EEMD processing unit is electrically connected to the input of the classification model, which analyzes the magnetic field signals and extracts fault features. The input of the display screen is electrically connected to the output of the classification model, which classifies the fault features and generates fault information. The display screen displays the fault information.

2. The EEMD-based cable fault location detection device of claim 1, wherein: It also includes a housing, in which the signal amplification circuit, ADC analog-to-digital converter module, DSP chip, EEMD processing unit, and classification model are housed, while the Hall effect sensor is housed outside the housing, and the display screen is housed on the surface of the housing.

3. The EEMD-based cable fault location detection device of claim 2, wherein: The housing also contains a power module for providing power supply voltage, and the surface of the housing is provided with a switch button and a charging port.

4. The EEMD-based cable fault location detection device of claim 2, wherein: The Hall effect sensor is connected to the signal amplification circuit by a wire, and the wire is covered with a plastic conduit.

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