TDR power cable global fault positioning method based on multi-frequency cooperative excitation and environment adaptive correction

Abstract

The invention belongs to the technical field of power cable fault positioning. The invention discloses a TDR power cable global fault positioning method based on multi-frequency cooperative excitation and environment adaptive correction. The method comprises the following steps: 1) multi-frequency pulse cooperative injection and signal acquisition; 2) environment parameter dynamic sensing and speed correction: embedding a miniature temperature and humidity sensor and a cable insulation state monitoring unit along the cable at an interval of 500m, and collecting laying environment temperature, humidity and insulation material dielectric constant change data in real time; constructing a dynamic speed correction formula based on a preset environment-dielectric constant correlation model; according to the method, the complex fault positioning precision is improved, the environment and scene adaptability is enhanced, and the troubleshooting efficiency is optimized.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of power cable fault location, and particularly relates to a TDR (time domain reflectometry) power cable global fault location method based on multi-frequency collaborative excitation and environment adaptive correction, which is suitable for precise ranging in high-resistance faults, long-distance cables and complex environments. BACKGROUND

[0002] With the acceleration of global electrification, power cables, as the core infrastructure of power transmission and distribution systems, have been widely used in complex scenarios such as urban power transmission, industrial park power distribution, underground tunnels and submarine cables. Compared with overhead lines, power cables have the advantages of high safety, strong reliability, and small environmental impact, but due to their special laying environment, once a fault occurs, fault point location and repair are extremely difficult, which may lead to long-term power outages, equipment damage and other serious consequences, causing huge economic losses and safety risks. Therefore, fast and accurate power cable fault location technology has become the key to ensuring the stable operation of the power system.

[0003] Currently, the traditional methods of power cable fault location mainly include bridge method, traveling wave method, etc., but these methods have significant limitations: the bridge method relies on manual adjustment of balance, which is tedious to operate and the accuracy is greatly affected by the cable length, with obvious errors in long-distance cables; the traveling wave method has fast response speed, but it is easily disturbed by reflected waves in multi-branch cable networks, and has weak recognition ability for high-resistance faults, making it difficult to meet the needs of complex scenarios.

[0004] Time domain reflectometry (TDR) is a fault location technology based on electromagnetic wave reflection principle, which injects a pulse signal into the cable and uses the reflection characteristics of the signal at the fault point to achieve ranging. Due to its simple operation and high positioning accuracy, TDR has become an important means of power cable fault location. Its core principle is that when a pulse signal propagates in a cable, if it encounters an open circuit, a short circuit or a high-resistance fault point, it will be partially or completely reflected. By measuring the time difference (ΔT) between the transmitted and reflected signals, combined with the signal propagation speed (v), the fault point distance can be calculated by the formula: where the signal propagation speed is determined by the relative permittivity (ε) of the cable insulation material, i.e. (c is the speed of light) However, existing TDR technology still has many bottlenecks in practical application, which are as follows: ​High resistance fault recognition difficulty: the reflection signal amplitude of high resistance fault is weak and easy to be submerged by noise, which leads to the difficulty of traditional TDR in accurately capturing the reflection characteristics and the significant increase of ranging error; poor environmental adaptability: the signal propagation speed is significantly affected by the cable laying environment and material characteristics, and the traditional TDR uses fixed propagation speed calculation, which ignores the dynamic environmental interference, further reducing the positioning accuracy; long distance cable limitation: for super long cable, the pulse signal is seriously attenuated in the propagation process, and the signal-to-noise ratio of the reflected signal is extremely low, so the traditional TDR cannot effectively identify the reflected wave, resulting in ranging failure.

[0005] The existing research and invention documents on the improvement of TDR technology are mainly concentrated in a single dimension: for example, some inventions improve the short distance positioning accuracy by optimizing the pulse signal frequency, but do not solve the problem of weak signal recognition of high resistance fault; another research introduces a simulation tool to verify the effectiveness of TDR in open circuit and short circuit faults, but does not involve dynamic correction or long distance signal enhancement scheme in complex environment. In addition, in the existing technology, the combination of TDR and other methods is mainly simple superposition, and there is no synergistic optimization mechanism, which is difficult to deal with complex fault scenes such as high resistance, super long distance and multiple interference.

[0006] In summary, in the existing technology for power cable fault positioning, there are still problems such as low recognition accuracy of high resistance fault, poor environmental interference adaptability, and ranging failure of long distance cable. Therefore, it is urgent to develop an improved TDR technology that can break through the above bottlenecks, realize accurate and rapid positioning of power cable faults in complex conditions by innovating signal processing mechanism, dynamic parameter correction and multi-scene adaptation algorithm, and provide protection for the safe and stable operation of power system. SUMMARY

[0007] The purpose of the present application is to at least solve one of the above technical defects, and provide a TDR power cable global fault positioning method based on multi-frequency cooperative excitation and environment adaptive correction, which aims to break through the limitations of traditional TDR technology in high resistance fault recognition, environmental interference adaptation and long distance cable ranging, improve the accuracy, efficiency and scene adaptability of power cable fault positioning, and realize accurate and rapid positioning of cable faults in complex conditions.

[0008] To achieve the above purpose, the technical scheme adopted by the present application is a TDR power cable global fault positioning method based on multi-frequency cooperative excitation and environment adaptive correction, characterized by the following steps: 1) Multi-frequency pulse collaborative injection and signal acquisition: A multi-frequency pulse signal generator is constructed to simultaneously inject high-frequency narrow pulses (1-5 MHz) and low-frequency wide pulses (50-100 kHz) into the cable; the high-frequency pulse is used to improve the resolution of short-distance faults, and the low-frequency pulse enhances the energy penetration and reflection signal strength of high-resistance fault points; the time-domain waveforms of the transmitted and reflected pulses are recorded synchronously by a high-speed data acquisition module to capture the characteristics of weak reflection signals; 2) Dynamic perception of environmental parameters and speed correction: Micro temperature and humidity sensors and cable insulation state monitoring units are embedded at intervals of 500m along the cable to collect real-time data on the laying environment temperature, humidity, and dielectric constant changes of the insulation material; based on a pre-set environmental-dielectric constant correlation model, a dynamic speed correction formula is constructed: Where k(T,H) is the temperature and humidity correction coefficient (generally 0.98-1.02), which replaces the traditional fixed speed calculation method to calibrate the signal propagation speed in real time; Where ε is the relative dielectric constant (generally 2.0-3.7), and c is the speed of light; 3) Intelligent analysis of reflection signals and long-distance reconstruction: Wavelet threshold denoising algorithm is used to preprocess the collected reflection signals to eliminate environmental noise interference; a deep learning model based on CNN (Convolutional Neural Network) is introduced to fuse and analyze the multi-frequency pulse reflection characteristics, and to distinguish the reflection waveform differences of different fault types such as high-resistance faults, open circuits, and short circuits; for long-distance cables (>50km), energy compensation is performed on the attenuated signals by pre-set passive relay nodes (10-15km apart), and a "time-distance matrix" is constructed based on the reflection time difference of each node to realize the segmented reconstruction of the reflection waveform and the global fault location; 4) System adaptive decision and result output: According to the cable length, laying environment, and historical fault data, the frequency ratio and transmission power of the multi-frequency pulse are adaptively adjusted; the fault distance, fault type, and confidence parameter after intelligent analysis are integrated, and the positioning results are output through a visual interface to provide accurate guidance for fault repair.

[0009] The invention point of the present application is: 1. Multi-frequency pulse collaborative injection The traditional TDR pulse signal has few high-frequency components, making it difficult to detect weak defects and having a test blind area; the FDR frequency domain reflection signal is easily distorted by interference, making it difficult to identify and evaluate defects. The present application uses high-frequency narrow pulses and low-frequency wide pulses for collaborative injection, high-frequency pulses to improve short-distance resolution, and low-frequency pulses to enhance the reflection signal strength of high-resistance fault points, solving the problem of weak defect detection and breaking through the limitations of traditional single pulse, which can more accurately detect small faults compared to traditional methods.

[0010] 2. Dynamic correction of environmental parameters Traditional methods do not consider the influence of the environment on signal propagation speed, resulting in large positioning errors. The present application deploys micro temperature and humidity sensors and insulation state monitoring units along the cable line, collects environmental parameters in real time, constructs a dynamic speed correction formula, and calibrates the signal propagation speed based on real-time data, greatly reducing the ranging error caused by environmental factors and improving positioning accuracy. The advantage is significant in complex environments.

[0011] 3. Intelligent analysis and long-distance reconstruction innovation Existing technologies have deficiencies in high-resistance fault identification and long-distance cable detection. The present application uses wavelet threshold denoising algorithm and CNN deep learning model to intelligently analyze the reflected signal, accurately identify different types of faults such as high-resistance faults, and improve identification accuracy. Through the setting of passive relay nodes, energy compensation is carried out for long-distance cable signals, and based on the time difference of each node reflection, a "time-distance matrix" is constructed to realize waveform reconstruction, breaking through the limitation of long-distance cable detection and realizing global fault positioning.

[0012] 4. Adaptive decision-making and result output optimization Other technologies usually lack adaptive adjustment mechanism and comprehensive and intuitive result output. The present application adaptively adjusts multi-frequency pulse parameters and algorithm parameters based on multi-dimensional data such as cable state, environmental parameters and fault characteristics, realizes dynamic optimization of the system, provides result output covering fault core information and auxiliary decision-making data, and through visual interactive interface and various communication interfaces, facilitates operation and data transmission sharing of operation and maintenance personnel, improves fault troubleshooting efficiency and power system stability.

[0013] The beneficial effects of the present application are: 1. Improve complex fault positioning accuracy: Through multi-frequency pulse collaborative excitation and deep learning feature recognition, the identification accuracy of high-resistance faults is improved to more than 95%, and the positioning error is controlled within ±0.5%, which is more than 40% higher than the accuracy of traditional TDR technology.

[0014] 2. Enhance environmental and scene adaptability: The dynamic speed correction mechanism effectively offsets the influence of temperature, humidity and material aging on signal propagation, so that the system still maintains stable accuracy in complex environments with temperature range of-20℃ to 60℃ and humidity range of 30%-90%; the long-distance waveform reconstruction technology extends the effective ranging length to more than 150km, breaking through the short-distance limitation of traditional TDR.

[0015] 3. Optimize fault troubleshooting efficiency: Multi-frequency signal parallel processing and intelligent algorithm analysis shorten the fault response time to within 0.1s, combined with adaptive decision-making mechanism, greatly reducing manual intervention, reducing the time cost and labor intensity of fault troubleshooting, and providing guarantee for the rapid recovery of power supply of power system. BRIEF DESCRIPTION OF DRAWINGS

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart illustrating a specific embodiment of the present invention.

[0018] Figure 2 This is a pulse voltage waveform diagram of the present invention.

[0019] Figure 3 This is a block diagram of the hardware system of the present invention.

[0020] Figure 4(a) shows the relationship between conductor radius and cutoff frequency when the insulation thickness of the cable of the present invention is 5 mm. Figure 4(b) shows the relationship between insulation thickness and cutoff frequency when the conductor radius is 10 mm. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] like Figure 1 As shown, a TDR power cable global fault location method based on multi-frequency cooperative excitation and environmental adaptive correction includes the following specific steps: 1. Step S101: Multi-frequency pulse co-injection and signal acquisition.

[0023] This step mainly includes three modules: parameter design and coordination mechanism of multi-frequency pulse signals, hardware implementation scheme, and signal acquisition and anti-interference design.

[0024] (1) In the parameter design and coordination mechanism of multi-frequency pulse signals, the frequency and pulse parameter selection adopts a coordinated combination of high-frequency narrow pulses and low-frequency wide pulses: High-frequency pulse: frequency range 1-5MHz, pulse width 0.1-1μs, rise and fall time ≤50ns. The short wavelength of high-frequency signals can improve the spatial resolution of short-distance (<10km) faults, and is especially suitable for identifying minute impedance abrupt changes such as cable joints and local insulation damage.

[0025] Low frequency pulse: frequency range 50-100 kHz, pulse width 10-50 μs, amplitude 5-10 V. Low frequency signal energy decay slowly, strong penetration, can enhance the reflection signal strength of high resistance fault and long distance (> 50 km) cable, solve the problem of traditional TDR weak signal flooding.

[0026] Cooperative injection logic: two pulses are injected into the cable by time domain superposition, not time division multiplexing: High frequency pulse as "fine detection signal", used to capture the details of the near distance fault; low frequency pulse as "energy carrier", to ensure that the signal can still produce detectable reflection in high resistance fault point and long distance transmission; pulse amplitude and duty cycle can be adjusted by single chip microcomputer (such as AT89S52): when the signal to noise ratio of the reflected signal is less than 10 dB, automatically increase the power of low frequency pulse and reduce the bandwidth of high frequency pulse, otherwise enhance the high frequency component to improve the resolution.

[0027] (2) In the hardware implementation scheme, about the design of multi-frequency pulse generator, "single chip microcomputer + CPLD" architecture is adopted: Control core: AT89S52 single chip microcomputer receives host computer instructions, sets pulse frequency, pulse width and other parameters, and realizes manual adjustment through 4 × 4 key module; Signal generation: CPLD generates synchronous trigger signal of high frequency and low frequency pulse based on 1 MHz active crystal, which is output to the cable test end through power amplifier circuit; State indication: through red, green and yellow three color LEDs, high frequency pulse, low frequency pulse and synchronous injection state are indicated respectively, to ensure the consistency of double frequency signal timing (error < 10 ns).

[0028] Impedance matching network: to reduce the reflection loss of signal at the injection point, single short stub matching circuit based on Smith chart optimization is adopted: A length of 0.301λ (λ is the wavelength of high frequency signal) transmission line is connected in series, which converts the characteristic impedance of the cable to 50Ω; Parallel short stub (length 0.074λ) compensates the capacitive load, so that the pulse signal injection efficiency is improved to more than 90%, avoiding signal attenuation and waveform distortion caused by impedance mismatch.

[0029] (3) In the design of signal acquisition and anti-interference, high-speed acquisition module: Sampling rate: 100 MSps 16 bit ADC chip is adopted to ensure that the reflection waveform of high frequency pulse (5 MHz) is oversampled at least 20 times, meeting the Nyquist criterion; Synchronization accuracy: through GPS second pulse or rubidium clock synchronous emission and acquisition clock, the time measurement error is less than 1 ns, ensuring the accuracy of ΔT calculation; Dual-channel acquisition: Band-pass filters are designed for high-frequency and low-frequency signals respectively (high-frequency channel 1-5 MHz, low-frequency channel 50-100 kHz) to avoid spectral aliasing.

[0030] Anti-interference measures are as follows: Hardware anti-interference: Shielded twisted pair is used for acquisition lines, with single-point grounding of the shielding layer to suppress electromagnetic coupling interference; magnetic beads are connected in series and TVS tubes are connected in parallel at the signal input end, and IEC61000-4-4 ±4kV level EFT interference test is performed; Software preprocessing: The collected data is first denoised by wavelet thresholding (soft thresholding function, threshold value adaptive calculation) to eliminate the influence of environmental noise on the reflected signal.

[0031] 2. Step S102: Dynamic perception of environmental parameters and speed correction.

[0032] In this step, the core objects of environmental parameter perception and the acquisition method are based on the characteristics of signal propagation speed affected by environmental factors in TDR technology. This part focuses on perceiving two types of parameters: Key environmental parameters: including temperature (-20℃~60℃) and relative humidity (30%~90%) of the cable laying environment, which are collected in real time by deploying micro temperature and humidity sensors every 500m along the cable. The sensors are waterproof packaged to adapt to humid environments such as underground and submarine.

[0033] Cable material characteristic parameters: Focus on monitoring the relative permittivity of the cable insulation layer (εr) ) changes, indirectly obtain the offset caused by material aging and temperature changes through the embedded distributed optical fiber sensor in the cable insulation layer.

[0034] The collected data is aggregated to the data processing unit through a low-power wireless transmission module, with a sampling frequency of 1Hz to ensure the real-time nature of parameter changes.

[0035] The construction and implementation of the speed correction model compared to the fixed calculation method of signal propagation speed: (c is the speed of light) has limitations, a dynamic correction mechanism is introduced: Establishment of correction coefficient: Through experimental data fitting, the correlation between environmental parameters and permittivity is generated to generate the temperature and humidity correction coefficient k(T, H), whose expression is: ​Wherein, T0, H0 are the reference temperature (25℃) and the reference humidity (50%), a, b are the temperature (a is 0.001℃-0.0025℃) and humidity sensitive coefficient (b is 0.0005%-0.002%) determined by experiment. T is the real-time temperature of the cable laying environment, and H is the real-time relative humidity of the cable laying environment.

[0036] Dynamic speed calculation formula: combined with the perceived real-time and environmental parameters, the corrected signal propagation speed is: The formula realizes the dynamic adjustment of the speed in different environments. c is the speed of light, k(T, H) is the temperature and humidity correction coefficient (generally takes the value of 0.98-1.02), is the relative dielectric constant (generally takes the value of 2.0-3.7).

[0037] In the correction process and application scenarios, the real-time correction process includes: The sensor collects temperature, humidity and data in real time and transmits them to the processing unit; The processing unit calculates the correction coefficient according to the preset k(T, H) model and substitutes it into the dynamic speed formula to obtain v 校正 ; v 校正 is applied to fault distance calculation, and the corrected fault distance is output.

[0038] Targeted scene optimization: For underground cables: focus on compensating for the unevenness of εᵣ caused by soil temperature stratification; For submarine cables: strengthen the correction of the influence of salt mist humidity on the dielectric constant of insulating materials to ensure the ranging accuracy in deep-sea high-pressure environments.

[0039] 3. Step S103: intelligent analysis of reflected signals and long-distance reconstruction.

[0040] In this step, the purpose is to solve the problems of traditional TDR technology in high-resistance fault identification and long-distance cable detection. The specific content is as follows: (1) Intelligent analysis of reflected signals Denoising preprocessing: wavelet threshold denoising algorithm is used to preprocess the collected reflected signals. According to the different characteristics of signals and noise under wavelet transform, by setting appropriate threshold, the interference of environmental noise on reflected signals is removed, and effective signal characteristics are preserved, laying a foundation for subsequent accurate analysis.

[0041] Deep learning model construction and training: Introduce a deep learning model based on Convolutional Neural Network (CNN). CNN has strong feature extraction ability and can automatically learn the complex features of reflection signals. A large amount of reflection signal data containing different fault types, different cable parameters and environmental conditions is used to train the model. During the training process, the parameters of the model are adjusted so that the model can accurately distinguish the differences in reflection waveforms of different fault types. For example, through learning, the model can identify the weak features of high resistance fault reflection signals and distinguish them from normal signal fluctuations, thereby improving the identification accuracy of high resistance faults.

[0042] Fault type identification and location: The denoised reflection signal is input into the trained CNN model, which analyzes and judges the signal features and outputs the fault type. At the same time, combined with the time information and signal propagation speed of the reflection signal, the location of the fault point is calculated to achieve accurate fault location.

[0043] (2) Long distance reconstruction Passive relay node setting: In long distance cables (> 50 km), a passive relay node is set every 10-15 km. These nodes are equipped with impedance matching circuits and signal amplification modules. The impedance matching circuit can reduce signal reflection during transmission and improve signal transmission efficiency; the signal amplification module compensates for the energy of the passing pulse signal, enhancing the signal strength and ensuring that the signal will not be undetectable due to attenuation during long distance transmission, solving the problem of single test end unable to capture weak signals at the far end.

[0044] Time-distance matrix construction and waveform reconstruction: Based on the reflection time difference of each relay node, a "time-distance matrix" is constructed. Through this matrix, the reflection time information of the signal at different positions is recorded. Using this information, the reflection waveform is segmented and reconstructed, and the reflection signals received by each relay node are integrated and processed to restore the complete reflection waveform. For example, according to the time-distance matrix, the reflection signal fragments received by different relay nodes at different times are spliced according to the time sequence and distance relationship to form a complete reflection waveform, thereby realizing the positioning of the whole domain fault of long distance cable.

[0045] Interference elimination: Combined with the pre-stored database of cable topology structure, the interference in the reconstruction process is eliminated. Since the branches and joint positions of the cable may also produce reflection signals, these signals may interfere with the judgment of the fault point. Through the pre-stored database, the reflection signal interference generated by non-fault points is identified and excluded, improving the accuracy of fault location.

[0046] 4. Step S104: System adaptive decision and result output.

[0047] This step realizes dynamic parameter optimization and intuitive result presentation by integrating real-time data of multi-frequency pulse injection, environmental parameter correction, and reflected signal analysis modules. The specific content is as follows: The core mechanism of system adaptive decision-making is based on multi-dimensional data of cable state, environmental parameters, and fault characteristics, to build an adaptive decision-making logic of "perception-analysis-adjustment", and to realize dynamic optimization of system parameters. Specifically, it includes: Multi-frequency pulse parameter adaptive adjustment, decision basis: real-time reception of "signal-to-noise ratio (SNR)", "fault type confidence", and cable length data output by the reflected signal analysis module.

[0048] Adjustment logic: if a high resistance fault (confidence > 80%) or a long distance cable (> 50km) is detected, automatically increase the low frequency pulse power, and at the same time reduce the high frequency pulse proportion, to enhance the signal penetration; If the cable is short and the signal-to-noise ratio is > 20dB, increase the high frequency pulse frequency to improve the spatial resolution; If the environmental parameters fluctuate sharply, increase the sampling frequency of the pulse signal simultaneously to ensure the timeliness of the reflected signal capture.

[0049] Implementation: run the preset decision tree algorithm through the embedded processor, and update the control instructions of the multi-frequency pulse generator in real time based on the above rules.

[0050] Dynamic optimization of algorithm parameters for the CNN model of the reflected signal analysis module: if the fault recognition confidence is < 70% for 3 consecutive times, automatically trigger the online fine-tuning of the model, update the convolution layer weights of the model by introducing the labeled data of the last 3 groups of similar faults, and improve the recognition accuracy in specific scenarios; For the "time-distance matrix" of long distance reconstruction: if the attenuation rate of the relay node reflected signal is > 30%, automatically adjust the time window width of the matrix to avoid waveform splicing errors caused by signal delay.

[0051] Fault emergency decision-making: when multiple fault points coexist, the system prioritizes locating the fault point closer to the test end (error < 0.5km), and marks the approximate range of the far-end fault to reduce the search range of the repair personnel; if the positioning error is > 1km for 5 consecutive times, automatically start the backup positioning mode: switch to the "single frequency pulse + traveling wave method fusion" algorithm, and reduce the risk of misjudgment through multi-method cross-validation.

[0052] The specific content and form of the result output aims to be "accurate, intuitive, and interactive", and the output content covers fault core information, auxiliary decision-making data, and interface support to meet the practical needs of power operation and maintenance scenarios: The basic parameters of the core positioning result output include fault point distance, accurate to 0.01 km, dynamic correction error range, fault type (open circuit, short circuit, high resistance, with confidence percentage); Characteristic parameters: amplitude (mV level) of reflected signal, phase shift (°) and pulse propagation time difference (ΔT, accurate to 1 ns), providing professional personnel with fault mechanism analysis basis.

[0053] The hardware carrier of the visualized and interactive interface design adopts a 10.1-inch industrial-grade touch screen integrated in a portable test terminal; The interface presents real-time waveform graphs, including synchronous display of original reflected waveform, denoised waveform and "feature extraction area" identified by the CNN model; Cable topology map: based on the pre-stored cable laying path, the fault point position and the surrounding relay node distribution are marked on the map, supporting zooming and distance measurement; Parameter adjustment area: allows operation and maintenance personnel to manually intervene in adaptive decision-making and records operation logs.

[0054] Data interface and expansion functions include communication interface: supports 4G / 5G wireless transmission, can be uploaded to the power system operation and maintenance platform in real time, and realizes remote monitoring; Historical data comparison: automatically stores nearly 100 positioning results, supports filtering by "fault type", "time" and "cable section", and generates trend analysis report; Fault warning: if the same area has "low confidence fault" for three consecutive times, the warning prompt is triggered to avoid potential faults in advance.

[0055] For hardware and software implementation, including the following aspects, decision core: adopts dual-core processor (ARM Cortex-A72 + Cortex-M4), A72 core runs adaptive decision algorithm, M4 core is responsible for real-time control of pulse generator and sensor, ensuring response delay < 10 ms; storage module: configures 128 GB industrial-grade SSD to store reflected waveform raw data, environmental parameter logs and fault positioning results, supports data encryption; power management: adaptively adjusts power consumption to adapt to portable power supply for field operation.

[0056] Through the synergistic effect of the four steps S101-S104, clear conclusions can be drawn and multi-dimensional technical effects can be achieved, as follows: The core conclusions include breaking through the inherent limitations of traditional TDR technology, realizing the deep integration of "hardware-algorithm-decision", adapting to the universality of diversified power cable scenes, and realizing the technical effects of: significantly improving positioning accuracy, greatly expanding applicable scenarios, optimizing fault troubleshooting efficiency and economy, and enhancing power system stability.

[0057] In one embodiment, as shown in Figure 2 An open circuit fault is set at 5km of the cable, and a pulse signal is applied at the input end as a test signal. Signal type: pulse signal; pulse amplitude: 10V; pulse width: 1us; duration: 0.5us; rise and fall time: 0.1us; signal injection position: start of the cable. The simulation time is set to 1s to ensure that the fault reflection signal can be captured. By monitoring the voltage waveform at the start of the cable, the transmitted pulse signal and the reflection wave generated at the fault point can be observed. By analyzing the time of the pulse signal reaching the fault point and the time of the reflection wave returning, the position of the fault point can be calculated. The time difference between the transmission time and the reflection time is ΔT, that is, the time of the pulse signal from the start of the cable to the fault point and back: wherein, is the time when the reflection wave is received; is the time when the pulse is injected by the signal source.

[0058] According to the calculation of the simulation results, the pulse signal is transmitted at 0.45s and the reflection wave is received at 0.497s. The pulse voltage waveform is shown in Figure 2 The time difference ΔT is calculated to be 0.0497s. The dielectric constant of the cable = 2.2, the signal propagation speed v = 2.02x10 8 m / s, and the fault distance is calculated by the formula: According to the analysis and calculation results, it can be concluded that the fault position calculated by the method of the present application is basically consistent with the actual set position of 5km, thereby verifying the effectiveness of the method of the present application.

[0059] Each embodiment in the specification is described in a related manner, and the same or similar parts between each embodiment can be referred to each other. Each embodiment focuses on the difference from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the related parts can be referred to the part of the method embodiment.

[0060] The above only describes the preferred embodiments of the present application, and is not used to limit the protection scope of the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application is included in the protection scope of the present application.

Claims

1. A TDR power cable global fault location method based on multi-frequency cooperative excitation and environment adaptive correction, characterized in that Comprising the following steps: 1) Multi-frequency pulse injection and signal acquisition: Construct a multi-frequency pulse signal generator to inject high-frequency narrow pulses and low-frequency wide pulses into the cable simultaneously; high-frequency pulses are used to improve the resolution of short-distance faults, and low-frequency pulses enhance the energy penetration and reflected signal strength of high-resistance fault points; Synchronously record the time-domain waveforms of the transmitted and reflected pulses through a high-speed data acquisition module to capture the characteristics of weak reflected signals; 2) Dynamic perception of environmental parameters and speed correction: Embed micro temperature and humidity sensors and cable insulation state monitoring units at intervals of 500m along the cable to collect real-time data on the laying environment temperature, humidity, and dielectric constant changes of the insulation material; based on a pre-set environmental-dielectric constant correlation model, construct a dynamic speed correction formula: Wherein k(T, H) is the temperature and humidity correction coefficient, k(T, H) is generally taken as 0.98-1.02, instead of the traditional fixed speed calculation mode, real-time calibration signal propagation speed; denotes the relative dielectric constant, Generally taken as 2.0-3.7, c is the speed of light; 3) Intelligent analysis of reflected signals and long-distance reconstruction: Use the wavelet threshold denoising algorithm to preprocess the collected reflected signals to eliminate environmental noise interference; introduce a deep learning model based on CNN (Convolutional Neural Network) to fuse and analyze the multi-frequency pulse reflection characteristics, distinguish the reflection waveform differences of different fault types such as high-resistance faults, open circuits, and short circuits; for long-distance cables, use pre-set passive relay nodes to compensate for the energy of attenuated signals, and construct a "time-distance matrix" based on the reflection time differences of each node to achieve segmented reconstruction and global fault location of the reflected waveform; 4) System adaptive decision-making and result output: According to the cable length, laying environment, and historical fault data, adaptively adjust the frequency ratio and transmission power of the multi-frequency pulses; integrate the fault distance, fault type, and confidence parameters obtained through intelligent analysis, and output the positioning results through a visual interface to provide accurate guidance for fault repair.

2. The TDR power cable global fault location method based on multi-frequency cooperative excitation and environment self-adaptive correction according to claim 1, characterized in that In step 1), the high-frequency narrow pulse is 1-5MHz, and the low-frequency wide pulse is 50-100kHz.

3. The TDR power cable global fault location method based on multi-frequency cooperative excitation and environment adaptive correction according to claim 1, characterized in that In step 3), the long-distance cable is >50km.

4. The TDR power cable global fault location method based on multi-frequency cooperative excitation and environment self-adaptive correction according to claim 1, characterized in that Comprising the following specific steps: Step 1: Multi-frequency pulse injection and signal acquisition; In this step, there are mainly three modules: parameter design and coordination mechanism of multi-frequency pulse signal, hardware implementation scheme, and signal acquisition and anti-interference design; (1) In the parameter design and coordination mechanism of multi-frequency pulse signal, the frequency and pulse parameters are selected in a coordinated combination of high-frequency narrow pulses and low-frequency wide pulses: High-frequency pulse: frequency range 1-5MHz, pulse width 0.1-1μs, rise and fall time ≤50ns; high-frequency signal has a short wavelength, which can improve the spatial resolution of short-distance faults, short distance <10km, and is especially suitable for identifying small impedance mutation points such as cable joints and local insulation damage; Low-frequency pulse: frequency range 50-100kHz, pulse width 10-50μs, amplitude 5-10V; low-frequency signal has slow energy attenuation and strong penetration, which can enhance the reflected signal strength of high-resistance faults and long-distance cables, long distance >50km, solving the problem of weak signal flooding in traditional TDR; Coordination injection logic: the two pulses are injected into the cable through time-domain superposition, rather than time division multiplexing: High-frequency pulse as "fine detection signal" is used to capture the details of near-distance faults; Low-frequency pulse as "energy carrier", ensure that the signal in the high resistance fault point and long distance transmission can still produce detectable reflection; Pulse amplitude and duty cycle can be adjusted by single chip microcomputer: when the signal-to-noise ratio of the reflected signal is less than 10 dB, the low-frequency pulse power is automatically increased and the high-frequency pulse bandwidth is reduced, otherwise the high-frequency component is enhanced to improve the resolution; (2) in the hardware implementation scheme, about the design of multi-frequency pulse generator, adopt "single chip microcomputer + CPLD" architecture: Control core: AT89S52 single chip microcomputer receives host computer instructions, sets pulse frequency, pulse width and other parameters, and realizes manual adjustment through 4 × 4 key module; Signal generation: CPLD generates synchronous trigger signal of high and low frequency pulse based on 1 MHz active crystal, which is output to cable test end through power amplifier circuit; State indication: through red, green and yellow three color LED, high frequency pulse, low frequency pulse and synchronous injection state are indicated respectively, to ensure the consistency of double frequency signal timing, error < 10 ns; Impedance matching network: to reduce the reflection loss of signal at the injection point, single short stub matching circuit based on Smith chart optimization is adopted: In series with a length of 0.301λ transmission line, λ is the wavelength of high frequency signal, which converts the cable characteristic impedance to 50Ω; Parallel short circuit stub compensates capacitive load, the length of short circuit stub is 0.074λ, which makes the pulse signal injection efficiency increase to more than 90%, avoiding signal attenuation and waveform distortion caused by impedance mismatch; (3) in the signal acquisition and anti-interference design, high-speed acquisition module: Sampling rate: 100MSps 16-bit ADC chip is adopted to ensure at least 20 times oversampling of high-frequency pulse reflection waveform, which meets the Nyquist criterion; Synchronization accuracy: through GPS second pulse or rubidium clock, synchronous emission and acquisition clock is realized, time measurement error < 1 ns, which ensures the accuracy of ΔT calculation; Dual-channel acquisition: band-pass filter is designed for high and low frequency signals respectively, high frequency channel 1-5 MHz, low frequency channel 50-100 kHz, which avoids frequency spectrum aliasing; Anti-interference measures are as follows: Hardware anti-interference: shielded twisted pair is used for acquisition circuit, the shielding layer is single-point grounded, which suppresses electromagnetic coupling interference; magnetic beads are connected in series and TVS tubes are connected in parallel at signal input end, which passes IEC61000-4-4 ±4kV level EFT interference test; Software preprocessing: acquisition data is first denoised by wavelet threshold, which eliminates the influence of environmental noise on reflected signal; Step 2: dynamic perception of environmental parameters and speed correction; In this step, the key objects of environmental parameter perception and acquisition method are based on the characteristics that signal propagation speed is affected by environmental factors in TDR technology. This part focuses on the perception of two types of parameters: Key environmental parameters: including temperature -20℃~60℃ and relative humidity 30%~90% of cable laying environment, which are realized by real-time acquisition through micro temperature and humidity sensors deployed every 500m along the cable. The sensors are waterproof packaged to adapt to humid environments such as underground and submarine; Cable material characteristic parameters: focus on monitoring the relative permittivity of cable insulation layer Changes caused by material aging and temperature changes are indirectly obtained through the distributed optical fiber sensor embedded in the cable insulation layer Offset The collected data are aggregated to the data processing unit through low-power wireless transmission module, and the sampling frequency is set to 1 Hz to ensure the real-time of parameter change; The construction and implementation of the speed correction model compared to the signal propagation speed: c is the fixed calculation method of the speed of light. The limitations exist, and a dynamic correction mechanism is introduced: The establishment of correction coefficient: through the experimental data fitting the correlation between environmental parameters and dielectric constant, the temperature and humidity correction coefficient k(T, H) is generated, and its expression is: Where, T0, H0 are the reference temperature 25℃ and the reference humidity 50%, α, β are the temperature and humidity sensitive coefficients determined by experiment, α is 0.001℃-0.0025℃, β is 0.0005%-0.002%; T is the real-time temperature of cable laying environment, H is the real-time relative humidity of cable laying environment; Dynamic speed calculation formula: combined with the real-time εᵣ and environmental parameters, the corrected signal propagation speed is: The formula realizes dynamic adjustment of the speed under different environments; c is the speed of light, k(T, H) is a temperature and humidity correction coefficient, k(T, H) generally takes a value of 0.98-1.02, is a relative dielectric constant, Generally takes a value of 2.0-3.7; In the correction process and application scene, the real-time correction process includes: The sensor collects temperature, humidity and data in real time and transmits them to the processing unit. The processing unit calculates the correction coefficient according to a preset k(T, H) model, and substitutes the correction coefficient into a dynamic speed formula to obtain v 校正 ; and applies v 校正 to fault distance calculation to output a corrected fault distance. Targeted scene optimization: For underground cables: emphasis on compensating for the non-uniformity caused by the temperature stratification of the soil; of the soil; For submarine cable: strengthen the correction of the influence of salt spray humidity on the dielectric constant of insulating materials, and ensure the ranging accuracy in deep sea high pressure environment; Step 3: intelligent analysis of reflected signal and long distance reconstruction; In this step, the purpose is to solve the problem of traditional TDR technology in high resistance fault identification and long distance cable detection, the specific content is as follows: (1) intelligent analysis of reflected signal Denoising preprocessing: wavelet threshold denoising algorithm is used to preprocess the collected reflected signal; according to the different characteristics of signal and noise under wavelet transform, by setting appropriate threshold, the interference of environmental noise on reflected signal is removed, and the effective signal characteristics are reserved, which lays a foundation for subsequent accurate analysis; Deep learning model construction and training: introduce deep learning model based on convolutional neural network CNN; CNN has strong feature extraction ability, which can automatically learn the complex characteristics of reflected signal; a large number of reflected signal data containing different fault types, different cable parameters and environmental conditions are used to train the model; in the training process, the parameters of the model are adjusted, so that the model can accurately distinguish the difference of reflected waveform of different fault types; for example, through learning, the model can identify the weak characteristics of high resistance fault reflected signal and its difference with normal signal fluctuation, so as to improve the identification accuracy of high resistance fault; Fault type identification and positioning: the denoised reflected signal is input into the trained CNN model, the model analyzes and judges the signal characteristics, and outputs the fault type; at the same time, combined with the time information and signal propagation speed of reflected signal, the position of fault point is calculated, and the accurate positioning of fault is realized; (2) long distance reconstruction Passive relay node setting: in long distance cable, long distance cable > 50km, set a passive relay node every 10-15km; these nodes are equipped with impedance matching circuit and signal amplification module; the impedance matching circuit can reduce the reflection in the process of signal transmission and improve the signal transmission efficiency; the signal amplification module compensates the energy of the passed pulse signal and enhances the signal strength, so as to ensure that the signal will not be attenuated in the process of long distance transmission and cannot be detected, and solve the problem that single test end cannot capture the weak signal at the far end. Time-distance matrix construction and waveform reconstruction: Based on the reflection time difference of each relay node, a "time-distance matrix" is constructed. Through this matrix, the reflection time information of the signal at different positions is recorded. Using this information, the reflected waveform is reconstructed in segments, and the reflected signals received by each relay node are integrated and processed to restore the complete reflected waveform. For example, according to the time-distance matrix, the reflected signal fragments received by different relay nodes at different times are spliced according to the time sequence and distance relationship to form a complete reflected waveform, thereby realizing the positioning of the global fault of long-distance cables; Interference elimination: Combined with the pre-stored database of cable topology structure, the interference in the reconstruction process is eliminated. Due to the branches and joint positions of the cable, reflection signals may also be generated, which may interfere with the judgment of the fault point. Through the pre-stored database, the reflection signal interference generated by non-fault points is identified and excluded, improving the accuracy of fault positioning; Step 4: System adaptive decision and result output; This step integrates real-time data from multiple frequency pulse injection, environmental parameter correction, and reflected signal analysis modules to achieve dynamic parameter optimization and intuitive result presentation. The specific content is as follows: The core mechanism of system adaptive decision is based on multi-dimensional data of cable state, environmental parameters, and fault characteristics to construct an adaptive decision logic of "perception-analysis-adjustment" and realize dynamic optimization of system parameters. Specifically, it includes: Multi-frequency pulse parameter adaptive adjustment: decision basis: "signal-to-noise ratio (SNR)" and "fault type confidence" output by the reflected signal analysis module, and cable length data; Adjustment logic: if a high resistance fault or a long distance cable is detected, automatically increase the low frequency pulse power and reduce the high frequency pulse proportion to enhance signal penetration; If the cable is short and the signal-to-noise ratio is greater than 20 dB, increase the high frequency pulse frequency to improve spatial resolution; If the environmental parameters fluctuate sharply, increase the sampling frequency of the pulse signal simultaneously to ensure the timeliness of reflected signal capture; Implementation: run the pre-set decision tree algorithm through the embedded processor to update the control instructions of the multi-frequency pulse generator in real time based on the above rules; Algorithm parameter dynamic optimization for the CNN model of the reflected signal analysis module: if the fault recognition confidence is less than 70% for three consecutive times, automatically trigger the online fine-tuning of the model, update the convolution layer weights of the model by introducing the labeled data of the last three groups of similar faults, and improve the recognition accuracy in specific scenarios; For the "time-distance matrix" of long distance reconstruction: if the reflection signal attenuation rate of the relay node is greater than 30%, automatically adjust the time window width of the matrix to avoid waveform splicing errors caused by signal delay; Fault emergency decision: when multiple fault points coexist, the system prioritizes positioning the fault point closer to the test end and marks the approximate range of the far-end fault to reduce the search range of the repair personnel; if the positioning error is greater than 1 km for five consecutive times, automatically start the backup positioning mode: switch to the "single frequency pulse + traveling wave method fusion" algorithm to reduce the risk of misjudgment through multi-method cross-validation; The specific content and form of the result output are targeted at "precision, intuition, and interaction", and the output content covers core fault information, auxiliary decision-making data, and interface support, meeting the practical needs of power operation and maintenance scenarios: The basic parameters of the core positioning result output include the fault point distance, which is accurate to 0.01 km, and the error range after dynamic correction, as well as the fault type; Characteristic parameters: amplitude, phase shift, and pulse propagation time difference of reflected signals, providing professional personnel with fault mechanism analysis basis; The hardware carrier for visual and interactive interface design is a 10.1-inch industrial-grade touch screen integrated into a portable test terminal; The interface presentation includes real-time waveform graphs: synchronous display of original reflected waveforms, denoised waveforms, and "feature extraction areas" identified by CNN models; Cable topology map: based on the pre-stored cable laying path, the fault point location and surrounding relay node distribution are marked on the map, supporting zooming and distance measurement; Parameter adjustment area: allows operation and maintenance personnel to manually intervene in adaptive decision-making and records operation logs; Data interface and expansion functions include communication interface: supports 4G / 5G wireless transmission, can upload to the power system operation and maintenance platform in real time, and realizes remote monitoring; Historical data comparison: automatically stores nearly 100 positioning results, supports filtering by "fault type", "time", and "cable section", and generates trend analysis reports; Fault warning: if the same area has "low confidence fault" for three consecutive times, an early warning prompt is triggered to avoid potential faults in advance; For hardware and software implementation, including the following aspects: Decision core: uses dual-core processor ARM Cortex-A72 + Cortex-M4, A72 core runs adaptive decision-making algorithm, M4 core is responsible for real-time control of pulse generator and sensor, ensures response delay < 10ms; Storage module: configures 128GB industrial-grade SSD to store reflected waveform raw data, environmental parameter logs, and fault positioning results, supports data encryption; Power management: adaptively adjusts power consumption to adapt to portable power supply for field operation and maintenance.

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