Adaptive fault reflection method based on adjustable excitation frequency

Abstract

The invention discloses a self-adaptive fault reflection method and device based on adjustable excitation frequency, and belongs to the technical field of cable fault detection and positioning. The method comprises the following steps: injecting an excitation signal which can be adjusted in a range of 1kHz-10MHz through an adjustable impedance matching network; collecting a reflected signal and calculating an SNR (f) curve; optimizing by adopting a particle swarm optimization algorithm and taking maximization of a signal-to-noise ratio as a target, and determining an optimal excitation frequency; and finally, calculating the fault position and type based on the optimal frequency. The particle swarm optimization algorithm is adopted to perform adaptive optimization on the excitation frequency, and the adjustable impedance matching network is combined to form a closed-loop adaptive detection framework of excitation-acquisition-analysis-optimization-re-excitation, so that the excitation frequency can be automatically adjusted according to a real-time detection result, and the detection accuracy is improved. And the fault detection sensitivity, the positioning precision and the energy efficiency are obviously improved.

Description

Technical Field

[0001] This invention relates to the field of power system condition monitoring and fault diagnosis technology, specifically to cable fault detection and location methods, and particularly to an adaptive reflection detection method that can automatically adjust the detection signal frequency according to cable characteristics and operating environment. Background Technology

[0002] With the continuous expansion of power systems and the increasing complexity of distribution networks, the operational safety and reliability of cables, as key carriers of electrical energy transmission, are becoming increasingly important. However, during long-term operation, cables are susceptible to multiple factors such as mechanical stress, environmental humidity, insulation aging, heat loss, and chemical corrosion, leading to various types of faults, including conductor breakage, insulation breakdown, and shielding damage. Currently commonly used cable fault detection methods include time-domain reflection, frequency-domain reflection, and traveling wave methods. These methods locate fault points by injecting excitation signals into the cable and analyzing the reflected waveforms, offering advantages such as clear implementation principles and low implementation costs. However, traditional methods generally suffer from problems such as fixed excitation frequencies, limited signal bandwidth, low signal-to-noise ratios, and poor adaptability to complex cable structures. When the cable length is long, impedance changes significantly, or environmental noise is substantial, the effective components of the reflected signal are easily submerged by noise, leading to decreased detection accuracy or even misjudgment. Furthermore, detection methods at fixed frequencies are difficult to optimize for different cable parameters and load characteristics, and cannot adaptively select the optimal frequency band to improve resolution and anti-interference performance. Therefore, there is an urgent need for an adaptive reflection detection method that can dynamically adjust the excitation frequency according to cable characteristics and noise environment, so as to achieve high-precision and robust cable fault identification and location in different application scenarios. Summary of the Invention

[0003] This invention provides an adaptive fault reflection method and device based on adjustable excitation frequency, which can solve the technical problems of fixed excitation frequency, low signal-to-noise ratio, poor matching efficiency and low degree of automation in the prior art, which makes it difficult to ensure the best detection signal-to-noise ratio in complex noise environment and affect positioning accuracy.

[0004] This invention provides an adaptive fault reflection method based on adjustable excitation frequency, comprising: acquiring the physical parameters (including conductor characteristics and dielectric characteristics) of the cable under test, and modeling and calculating the signal propagation speed based on the physical parameters; generating an excitation signal using digital direct synthesis (DDS) technology, wherein the frequency of the excitation signal can be continuously adjusted within the range of 1kHz to 10MHz, and supports three waveforms: swept sine wave, linear frequency modulated signal (Chirp), and narrowband pulse signal; injecting the excitation signal into the port of the cable under test through an adjustable impedance matching network, wherein the adjustable impedance matching network can dynamically adjust the matching parameters according to the excitation frequency; acquiring the cable reflection signal using a high-speed analog-to-digital converter (ADC); preprocessing the reflection signal while ensuring synchronization of the excitation and sampling time bases; performing discrete Fourier transform on the preprocessed reflection signal to calculate the signal-to-noise ratio (SNR) at each frequency point, obtaining the SNR(f) curve; and using a particle swarm optimization algorithm to optimize the SNR(f) curve, determining the optimal excitation frequency f with the goal of maximizing the SNR. * Based on the optimal excitation frequency f * The system acquires the enhanced reflection waveform to calculate the fault location and identifies the fault type by combining the polarity, amplitude, and spectral characteristics of the reflected signal; it outputs a detection report and issues an alarm when the detection result exceeds a preset threshold.

[0005] Furthermore, the step of optimizing the SNR(f) curve using a particle swarm optimization algorithm further includes: initializing the particle swarm, setting the particle swarm size to N, and defining each particle as having two attributes: velocity and position; and using an iterative formula... and Update the particle state, where is the inertia weight, c1 and c2 are the individual and group learning factors respectively, and r1 and r2 are random numbers in the range [0,1]. For the optimal position in the history of an individual particle, g k The optimal global position is determined by the number of iterations. Iteration stops when the maximum number of iterations is reached, or when the signal-to-noise ratio improvement of the global optimal frequency is less than a threshold (ΔSNR < 0.1dB), and the optimal excitation frequency f is output. * .

[0006] Furthermore, the adjustable impedance matching network serves several purposes: optimizing signal transmission efficiency and reducing reflection loss; dynamically adjusting impedance matching parameters to maintain a low VSWR and prevent signal reflection from interfering with the detection results; the applicable scenarios for the three excitation waveforms include: a swept-frequency sine wave for wide-frequency scanning; a chirp signal for signal extension within the target frequency band; and a narrowband pulse signal for high-resolution fault detection; and the method of identifying fault types by combining the polarity, amplitude, and spectral characteristics of the reflected signal includes: obtaining the arrival time of the reflected signal through peak detection in the time domain; and performing spectral analysis on the reflected signal in the frequency domain to distinguish fault types such as open circuit, short circuit, insulation degradation, and partial discharge.

[0007] Furthermore, the method forms a closed-loop adaptive detection architecture of "excitation-acquisition-analysis-optimization-re-excitation". This architecture can automatically adjust the excitation frequency according to the real-time detection results and maintain the best detection state without manual intervention.

[0008] This invention also provides an adaptive fault reflection device based on an adjustable excitation frequency, comprising: an excitation signal source module for generating an excitation signal with an adjustable frequency; an adjustable impedance matching network module for achieving impedance matching between the excitation signal and the cable under test; a reflection signal acquisition and preprocessing module for acquiring the reflection signal and performing synchronous preprocessing; a signal analysis module for extracting signal spectral features and evaluating the signal-to-noise ratio; an adaptive frequency optimization module for optimizing the excitation frequency using a particle swarm optimization algorithm; a fault identification and location module for identifying the fault type and calculating the location based on the optimal frequency signal; and a result output and alarm module for outputting the detection results and issuing a fault alarm.

[0009] Furthermore, the adaptive frequency optimization module is based on the particle swarm optimization algorithm, automatically searches for the optimal excitation frequency and feeds it back to the excitation signal source in real time.

[0010] Furthermore, the signal analysis module includes a spectrum analysis module and a time-domain envelope analysis module, which combine the two to determine the fault type and locate the position of the reflected signal.

[0011] The beneficial effects of this invention are as follows:

[0012] This invention employs a particle swarm optimization algorithm to adaptively optimize the excitation frequency, aiming to maximize the signal-to-noise ratio to determine the optimal excitation frequency, thus forming a closed-loop adaptive detection architecture of "excitation-acquisition-analysis-optimization-re-excitation". This architecture can automatically adjust the excitation frequency based on real-time detection results, maintaining the best detection state without manual intervention. It ensures the best quality of the detection signal under the current cable and noise environment, significantly improving the sensitivity and location accuracy of fault detection, and has strong adaptability to different types and lengths of cables and changing operating conditions.

[0013] Furthermore, by setting up an adjustable impedance matching network, the present invention can dynamically adjust the matching parameters according to the excitation frequency, ensuring that the excitation signal is transmitted to the cable to the maximum extent, reducing reflection loss and interference, and improving the energy efficiency of the detection system. Attached Figure Description

[0014] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 This is a flowchart illustrating an adaptive fault reflection method based on adjustable excitation frequency provided in an embodiment of the present invention.

[0016] Figure 2 This is a schematic diagram of the structure of an adaptive reflection device based on an adjustable excitation frequency provided in an embodiment of the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this application clearer, the relevant technical solutions will be described in detail below with reference to the accompanying drawings. It should be noted that the embodiments described herein are only a part of this application and not all embodiments. All other embodiments that can be obtained by those skilled in the art based on the embodiments in this application without creative effort should be included within the protection scope of this application.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit this application.

[0019] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0020] In the description of the embodiments of this application, the term "and / or" is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone.

[0021] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0022] In the description of the embodiments of this application, unless otherwise expressly specified and limited, the technical terms such as "installation", "connection", "linking", and "fixing" should be interpreted broadly.

[0023] See Figure 1 To address the shortcomings of existing technologies, such as fixed excitation frequency, low signal-to-noise ratio, poor matching efficiency, and low automation, which make it difficult to ensure the optimal detection signal-to-noise ratio in complex noisy environments and affect positioning accuracy, an embodiment of the present invention provides an adaptive fault reflection method based on an adjustable excitation frequency, comprising the following steps:

[0024] S1. Collect the physical parameters (including conductor characteristics and dielectric characteristics) of the cable under test, and calculate the signal propagation speed based on the physical parameters. In this embodiment of the invention, the signal propagation speed v can be combined with the cable unit length parameters (such as L and C) according to... Calculation. The excitation signal is generated using direct digital synthesis (DDS) technology. The frequency of the excitation signal can be continuously adjusted within the range of 1kHz to 10MHz, and it supports three waveforms: swept sine wave, chirp linear frequency modulation signal, and narrowband pulse signal.

[0025] S2, the excitation signal is injected into the port of the cable under test through an adjustable impedance matching network. This network, composed of a variable inductor, capacitor array, or programmable active circuit, dynamically adjusts the matching parameters according to changes in the excitation frequency to maximize the energy transfer efficiency of the excitation signal. A high-speed analog-to-digital converter (ADC) is used to acquire the cable reflection signal, with the ADC sampling rate set to at least five times the highest excitation frequency. The reflected signal is preprocessed, including anti-aliasing low-pass filtering, DC bias removal, and window function slicing. If a pulse or chirp signal is used, additional matching filtering is applied, ensuring synchronization of the excitation and sampling time bases.

[0026] S3, perform a Discrete Fourier Transform on the preprocessed reflected signal to extract frequency domain features and calculate the power spectral density at each frequency point, then apply the formula:

[0027]

[0028] Calculate the signal-to-noise ratio (SNR) at each frequency point to obtain the SNR(f) curve. Where P... signal (f) represents the signal power, P noise (f) represents the noise power.

[0029] S4. The Particle Swarm Optimization (PSO) algorithm is used to optimize the SNR(f) curve, and the optimal excitation frequency f is determined with the goal of maximizing the signal-to-noise ratio. * Through iterative formulas

[0030]

[0031] Update the particle state, where ω is the inertia weight, c1 and c2 are the individual and group learning factors, respectively, and r1 and r2 are random numbers in the range [0,1]. For the optimal position in the history of an individual particle, g k The optimal position for the entire group;

[0032] S5, based on the optimal excitation frequency f * The enhanced reflection waveform is obtained, and the arrival delay t of the main reflection is determined by detecting the time-domain envelope peak. This is then combined with the signal propagation speed v, based on... Calculate the fault location and identify the fault type by combining the polarity, amplitude, and spectral characteristics of the reflected signal.

[0033] S6, Output the optimal excitation frequency f * SNR(f) curve, reflected waveform, fault location L f The system identifies the fault type and generates a structured detection report. An alarm is issued when the detection result exceeds a preset threshold.

[0034] In one embodiment, the technical features in S2 and S5 are further defined as follows: the adjustable impedance matching network functions to optimize signal transmission efficiency, ensuring that the excitation signal is transmitted to the cable to the maximum extent at different frequencies, reducing reflection loss; dynamically adjusting impedance matching parameters to maintain a low standing wave ratio (VSWR) and avoid signal reflection interfering with the detection results. The applicable scenarios for the three excitation waveforms include: a swept-frequency sine wave for wide-range frequency scanning, covering the entire detection frequency band; a linear frequency modulated signal (Chirp) for signal extension within the target frequency band, ensuring good spectral distribution; and a narrowband pulse signal for high-resolution fault detection, adapting to rapidly changing fault location requirements. Furthermore, the method of identifying fault types by combining the polarity, amplitude, and spectral characteristics of the reflected signal includes: obtaining the arrival time of the reflected signal through peak detection in the time domain to assist in determining the characteristics associated with the fault location; and performing spectral analysis on the reflected signal in the frequency domain to distinguish fault types such as open circuit, short circuit, insulation degradation, and partial discharge based on differences in spectral characteristics.

[0035] In one embodiment, the method forms a closed-loop adaptive detection architecture of "excitation-acquisition-analysis-optimization-re-excitation". The architecture consists of an excitation signal source, an adjustable impedance matching network, a reflected signal acquisition module, a signal analysis module, a frequency optimization module, and a result output module. It can automatically adjust the excitation frequency according to the real-time detection results and maintain the best detection state without manual intervention.

[0036] like Figure 2 As shown, based on the above method embodiments, corresponding apparatus embodiments are provided;

[0037] Excitation signal source module 10 is used to generate an adjustable frequency excitation signal;

[0038] Adjustable impedance matching network module 20 is used to achieve impedance matching between the excitation signal and the cable under test;

[0039] The reflected signal acquisition and preprocessing module 30 is used to acquire reflected signals and perform synchronous preprocessing.

[0040] Signal analysis module 40 is used to extract signal spectral features and evaluate signal-to-noise ratio;

[0041] The adaptive frequency optimization module 50 optimizes the excitation frequency using a particle swarm optimization algorithm.

[0042] The fault identification and location module 60 is used to identify the fault type and calculate the location based on the optimal frequency signal;

[0043] The result output and alarm module 70 is used to output the detection results and issue fault alarms.

[0044] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0045] Finally, it should be noted that the above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. An adaptive fault reflection method based on adjustable excitation frequency, characterized in that, The method includes: Collect the physical parameters (including conductor characteristics and dielectric characteristics) of the cable under test, and calculate the signal propagation speed based on the physical parameters; The excitation signal is generated using digital direct synthesis (DDS) technology. The frequency of the excitation signal can be continuously adjusted within the range of 1kHz to 10MHz, and it supports three waveforms: swept sine wave, linear frequency modulated signal (Chirp), and narrowband pulse signal. The excitation signal is injected into the port of the cable under test through an adjustable impedance matching network. The adjustable impedance matching network is composed of a variable inductor, a capacitor array, or a programmable active circuit, and can dynamically adjust the matching parameters according to the change of excitation frequency to maximize the energy transmission efficiency of the excitation signal. The cable reflection signal is acquired using a high-speed analog-to-digital converter (ADC), with the ADC sampling rate set to more than 5 times the highest excitation frequency; The reflected signal is preprocessed, including anti-aliasing low-pass filtering, DC bias removal, and window function slicing. If a pulse or chirp signal is used, matched filtering is added, and the excitation and sampling time base are synchronized. Perform a Discrete Fourier Transform on the preprocessed reflected signal to extract frequency domain features and calculate the power spectral density P(f) at each frequency point, then apply the formula: Where P signal (f) represents the signal power, P noise (f) represents the noise power. The signal-to-noise ratio (SNR) at each frequency point is calculated to obtain the SNR(f) curve. The SNR(f) curve is optimized using a particle swarm optimization algorithm to determine the optimal excitation frequency f with the goal of maximizing the signal-to-noise ratio. * ; Based on the optimal excitation frequency f * The enhanced reflection waveform is obtained, and the arrival delay t of the main reflection is determined by the time-domain envelope peak detection. This is combined with the signal propagation speed v (determined by the cable unit length parameters L′ and C′). (Calculation), based on Calculate the fault location and identify the fault type by combining the polarity, amplitude, and spectral characteristics of the reflected signal; Output optimal excitation frequency f * SNR curve, reflected waveform, fault location L f The system identifies the fault type and generates a structured detection report. An alarm is issued when the detection result exceeds a preset threshold.

2. The adaptive fault reflection method based on adjustable excitation frequency as described in claim 1, characterized in that, The step of using particle swarm optimization to optimize the SNR(f) curve further includes: Initialize the particle swarm, setting the swarm size to N, with each particle corresponding to a candidate frequency f. i And each particle has a velocity v i With position f i Two attributes; Through iterative formula and Update the particle state, where ω is the inertia weight, c1 and c2 are the individual and group learning factors, respectively, and r1 and r2 are random numbers in the range [0,1]. For the optimal position in the history of an individual particle, g k The optimal position for the entire group; When the maximum number of iterations is reached, or the signal-to-noise ratio improvement of the globally optimal frequency is less than the threshold (ΔSNR < 0.1dB), the iteration stops, and the optimal excitation frequency f is output. * .

3. The method according to claim 1, characterized in that, The adjustable impedance matching network serves several purposes: optimizing signal transmission efficiency to ensure maximum transmission of excitation signals to the cable at different frequencies, reducing reflection loss; dynamically adjusting impedance matching parameters to maintain a low VSWR and prevent signal reflection from interfering with detection results; the three excitation waveforms are applicable to various scenarios: swept sine waves for wide-range frequency scanning, covering the entire detection frequency band; chirped linear frequency signals for signal extension within the target frequency band, ensuring good spectral distribution; and narrowband pulse signals for high-resolution fault detection, adapting to rapidly changing fault location requirements; furthermore, the fault type identification based on the polarity, amplitude, and spectral characteristics of the reflected signal includes: obtaining the arrival time of the reflected signal through peak detection in the time domain to assist in determining the characteristics associated with the fault location; and performing spectral analysis on the reflected signal in the frequency domain to distinguish fault types such as open circuits, short circuits, insulation degradation, and partial discharge based on differences in spectral characteristics.

4. The method according to claim 1, characterized in that, The method forms a closed-loop adaptive detection architecture of "excitation-acquisition-analysis-optimization-re-excitation". The architecture consists of an excitation signal source, an adjustable impedance matching network, a reflection signal acquisition module, a signal analysis module, a frequency optimization module, and a result output module. It can automatically adjust the excitation frequency according to the real-time detection results and maintain the best detection state without manual intervention.

5. An adaptive fault reflection device based on an adjustable excitation frequency, characterized in that, include: Excitation signal source module, used to generate an adjustable frequency excitation signal; An adjustable impedance matching network module is used to achieve impedance matching between the excitation signal and the cable under test; The reflected signal acquisition and preprocessing module is used to acquire reflected signals and perform synchronous preprocessing. The signal analysis module is used to extract signal spectral features and evaluate the signal-to-noise ratio; The adaptive frequency optimization module optimizes the excitation frequency using a particle swarm optimization algorithm. The fault identification and location module is used to identify the fault type and calculate the location based on the optimal frequency signal. The results output and alarm module is used to output the detection results and issue fault alarms.

6. The adaptive fault reflection detection device according to claim 5, characterized in that, The adaptive frequency optimization module is based on the particle swarm optimization algorithm, which automatically searches for the optimal excitation frequency and feeds it back to the excitation signal source in real time.

7. The adaptive fault reflection detection device according to claim 5, characterized in that, The signal analysis module includes a spectrum analysis module and a time-domain envelope analysis module, which combine the two to determine the fault type and location of the reflected signal.

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