Cable joint damp defect diagnosis method and system

Abstract

The invention discloses a cable joint damp defect diagnosis method and system. The method comprises the following steps: determining a to-be-detected cable; testing a to-be-detected cable based on an FMCW method to obtain an incident signal and a reflected signal; acquiring an inversion signal of the incident signal and an inversion signal of the reflected signal; establishing a simulation model of the to-be-detected cable, and injecting the inversion signals of the incident signal and the reflection signal into the simulation model of the to-be-detected cable; obtaining inversion signals at different positions in the simulation model, and constructing an energy curve based on the inversion signals at different positions in the simulation model; performing mean filtering and normalization processing on the energy curve to obtain a cable defect diagnosis spectrogram; and judging whether the to-be-detected cable has a joint damp defect based on the cable defect diagnosis spectrogram. The FMCW technology and the TR technology are combined, the position of the connector and the impedance change property of the connector are directly detected by detecting the focusing conditions of inversion signals at different positions, the wetting condition of the connector is diagnosed, and the diagnosis result is accurate.

Description

Technical Field

[0001] The present invention relates to the field of cable defect diagnosis, and particularly to a method and system for diagnosing the moisture ingress defect of a cable joint. Background Art

[0002] With the continuous development of urbanization, cross-linked polyethylene power cables are widely used in urban power grids. Power cables are usually laid in underground environments such as soil, shallow trenches, and pipe ducts, where there is a large amount of moisture and water. During long-term service, when the outer sheath of the cable body is damaged or the waterproofing of the intermediate joint fails, moisture and water may invade the cable, causing the cable to be affected by moisture, thereby triggering insulation faults of the cable. Therefore, timely and accurate detection of the moisture ingress defect of the cable intermediate joint is of great significance for ensuring the stability of the urban power grid.

[0003] Currently, the detection methods for cable defects mainly include partial discharge detection method, dielectric spectroscopy method, polarization depolarization current method, etc. The partial discharge detection method is mainly used to detect discharge-type defects caused by impurities, air gaps, etc., and has low detection sensitivity to insulation degradation defects such as moisture ingress, and its detection effect is easily affected by the complex electromagnetic environment on site; the dielectric spectroscopy method and the polarization depolarization current method are mainly used to detect the overall insulation aging state of the cable, and it is difficult to diagnose the moisture ingress area of the cable.

[0004] In the prior art, the traveling wave reflection method realizes the insulation diagnosis of cables by detecting the change of the characteristic impedance of the cables, and has great application prospects. Among them, the time domain reflectometry (TDR) locates and diagnoses the defects of the cables through the time domain reflected wave of a single pulse signal. However, due to problems such as strong attenuation of high-frequency signals and weak energy of the reflected signals in TDR, the detection sensitivity of TDR to insulation defects in the cables is low. The frequency domain reflectometry (FDR) analyzes the insulation state of the cables through the frequency domain characteristics of the reflected wave of a swept-frequency signal. With the multi-frequency distribution characteristics of the swept-frequency signal, FDR has better insulation defect detection ability than TDR. As a typical FDR method, the broadband impedance spectroscopy (BIS) is widely used in the field of cable defect diagnosis. In the article named "Location and Moisture Characteristics Analysis of the Defects of the Main Body of the Distribution Cable" published by Li Rong, Zhou Kai, Rao Xianjie, etc., and the article named "Location of Moisture in the Intermediate Joint of 10kV Distribution Cables Based on Frequency Domain Reflectometry" published by Li Rong, Zhou Kai, Wan Hang, etc., it is proposed that the distortion peak in the BIS defect diagnosis and location spectrogram can locate the moisture defects of the cables and identify their moisture paths. However, this method not only requires comparing the results of multiple power-off periodic tests, but also it is difficult to observe the change nature of the characteristic impedance of the moisture defects. In the article named "Moisture Diagnosis of the Cold Shrink Intermediate Joint of the Distribution Network Cable Based on the Time-Frequency Domain Conversion Method" published by Wang Zijian, Zhou Kai, Zhu Guangya, etc., and the article named "Characteristic Time Domain Waveform Recovery Technology Based on Frequency Domain Reflectometry" published by Rao Xianjie, Zhou Kai, Xie Min, etc., the impedance change nature of the joint is observed with the help of the BIS test principle and the time domain recovery technology to diagnose the moisture condition of the joint. However, the missing frequency band data in the BIS test results will affect the time-frequency domain conversion analysis results and interfere with the final defect diagnosis results. Therefore, on the basis of BIS, the cable defect diagnosis method based on the frequency modulated continuous wave (FMCW) has been gradually developed. This method uses the linearly frequency modulated continuous wave to replace the sinusoidal wave of the discrete stepped frequency points in BIS and has stronger anti-noise ability. Although the existing FMCW technology has high defect diagnosis sensitivity, it cannot detect the impedance change nature of the joint, resulting in difficulty in diagnosing the moisture condition of the joint in engineering.

[0005] Based on this, there is an urgent need for a method for diagnosing the moisture defects of cable joints to solve the problem that it is difficult to diagnose the moisture condition of joints in the prior art. Summary of the Invention

[0006] The object of the present invention is to overcome the deficiencies existing in the prior art, and provides a method and system for diagnosing the defect of moisture ingress in a cable joint. When applied, the FMCW technology is combined with the time reversal technology (TR technology), and the position of the joint and the nature of its impedance change can be directly detected by detecting the focusing situation of the time-reversed signals at different positions, so as to diagnose the moisture ingress situation of the joint, and the diagnosis result is accurate.

[0007] The object of the present invention is mainly achieved by the following technical solutions:

[0008] In the first aspect, the present application proposes a method for diagnosing the defect of moisture ingress in a cable joint, including the following steps:

[0009] Determine the cable to be detected;

[0010] Based on the FMCW method, test the cable to be detected to obtain the incident signal s 1 (t) and the reflected signal s 2 (t);

[0011] Obtain the time-reversed signal s 1 (-t) of the incident signal s 1 and the time-reversed signal s 2 (-t) of the reflected signal s 2 (-t);

[0012] Establish a simulation model of the cable to be detected, and inject the time-reversed signals of the incident signal s 1 (t) and the reflected signal s 2 (t) into the simulation model of the cable to be detected;

[0013] Obtain the time-reversed signals at different positions in the simulation model, and construct an energy curve p(x) based on the time-reversed signals at different positions in the simulation model;

[0014] Perform mean filtering and normalization processing on the energy curve p(x) to obtain a cable defect diagnosis spectrogram;

[0015] Based on the cable defect diagnosis spectrogram, judge whether there is a defect of moisture ingress in the joint of the cable to be detected.

[0016] In the prior art, the cable defect diagnosis method based on frequency-modulated continuous wave uses linear frequency-modulated continuous wave to replace the discrete stepped-frequency point sine wave in BIS, and has stronger anti-noise ability. However, although the existing FMCW technology has high defect diagnosis sensitivity, it cannot detect the impedance change property of joints, resulting in difficulty in diagnosing the moisture condition of joints in engineering. In this application, the FMCW technology is combined with the time-reversal technology, and the position of the joint and the impedance change property of the joint are directly detected by detecting the focusing condition of the time-reversed signals at different positions, so as to diagnose the moisture condition of the joint. Specifically, in this application, the cable to be detected is detected by the FMCW method to obtain the incident signal s 1 (t) and the reflected signal s 2 (t). Then, the time-reversed signals of the incident signal s 1 (t) and the reflected signal s 2 (t) are obtained by the time-reversal method, and the two groups of time-reversed signals are injected into the simulation model of the cable to be detected, so as to obtain the time-reversed signals at different positions in the simulation model, and the energy curve p(x) is constructed according to the time-reversed signals at different positions; by performing mean filtering and normalization processing on the energy curve p(x), a cable defect diagnosis spectrogram is obtained, and based on the cable defect diagnosis spectrogram, it is judged whether the cable to be detected has joint moisture defects and the defect positions. It should be noted that under normal conditions, the capacitance per unit length of the intermediate joint is lower than that of the cable body. As the moisture severity of the intermediate joint increases, the capacitance per unit length value of the intermediate joint will gradually increase and finally exceed that of the cable body. Therefore, in the cable defect diagnosis spectrogram, for the intermediate joint under normal conditions, a waveform feature of first positive and then negative will appear, so that it can be judged that the characteristic impedance of the intermediate joint area is greater than that of the cable body, that is, the capacitance per unit length of the intermediate joint is lower than that of the cable body. For a moisture-affected joint, a waveform feature of first negative and then positive will appear, so that it can be judged that the characteristic impedance of the intermediate joint area is less than that of the cable body, that is, the capacitance per unit length of the intermediate joint is higher than that of the cable body.

[0017] Furthermore, testing the cable to be detected based on the FMCW method to obtain the incident signal s 1 (t) and the reflected signal s 2 (t) specifically includes:

[0018] Setting the end of the cable to be detected to an open state;

[0019] Inputting the CLFM signal s(t) at the test end of the cable to be detected, and dividing the CLFM signal s(t) into two identical signals through a power divider, which are respectively recorded as the first incident signal and the reference signal. Among them, the reference signal is transmitted to the signal collector as the incident signal s 1(t), the first incident signal is injected into the cable to be detected through the circulator;

[0020] The signal reflected by the cable to be detected from the first incident signal is used as the reflected signal s 2 (t).

[0021] Furthermore, the CLFM signal s(t) is specifically expressed as:

[0022]

[0023] In the formula, t is the time variable; rectwin(τ) is a rectangular window with a length of τ; τ is the duration of the signal; f 0 is the center frequency of the signal; μ is the frequency change rate of the signal;

[0024] The incident signal s 1 (t) is expressed as:

[0025] s 1 (t) = s(t) * h 1 (t)

[0026] In the formula, * is the convolution symbol; h 1 (t) is the impulse response of the incident signal acquisition system, and h 1 (t) = 1;

[0027] The reflected signal s 2 (t) is expressed as:

[0028] s 2 (t) = s(t) * h 2 (t)

[0029] In the formula, h 2 (t) is the impulse response of the reflected signal acquisition system, and h 2 (t) = IFT(e -2γ(ω)l ), where IFT represents the inverse Fourier transform, γ(ω) is the signal propagation constant of the cable to be detected, ω represents the angular frequency of the signal, and l represents the actual generation position of the reflected signal.

[0030] Furthermore, the inversion signals at different positions are specifically:

[0031] The inversion signal of the reflected signal, which is the inversion signal measured at the position where the reflected signal is generated, is denoted as the second inversion signal y 2 (t):

[0032] y 2 (t) = s 2 (-t) * h 2 (t) = s(-t) * h 2 (-t) * h2 (t)

[0033] In the formula, s(-t) is the inversion signal of the CLFM signal s(t);

[0034] The inversion signal of the incident signal measured at the reflection signal generation position is denoted as the first inversion signal y 1 (t):

[0035] y 1 (t) = s 1 (-t)*h 1 (t) = s(-t)*h 1 (-t)*h 1 (t).

[0036] Furthermore, the energy curve p(x) is specifically:

[0037] p(x) = ∫v 1 (x,t)v 2 (x,t)dt

[0038] In the formula, x is the position of the assumed impedance discontinuity point; v 1 (x,t) is the signal detected at position x after injecting the inversion signal of the incident signal into the simulation model; v 2 (x,t) is the signal detected at position x after injecting the inversion signal of the reflection signal into the simulation model, where,

[0039] v 1 (x,t) = s 1 (-t)*h 1 (t)

[0040]

[0041] Among them, is the impulse response when the reflection signal generation position is x, specifically expressed as:

[0042] Furthermore, based on the cable defect diagnosis spectrogram to judge whether there is a joint moisture defect and the defect position in the cable to be detected is specifically:

[0043] Based on the undulation of the cable defect diagnosis spectrogram to judge whether there is a joint moisture defect and the defect position in the cable to be detected, including: if there is a positioning peak feature of negative first and then positive in the cable defect diagnosis spectrogram, it is judged that there is a cable joint at the positioning peak and the cable joint has a moisture defect; if there is a positioning peak feature of positive first and then negative in the cable defect diagnosis spectrogram, it is judged that there is a cable joint at the positioning peak and the cable joint has no moisture defect; the positioning peak is defined as the position of the impedance discontinuity point in the cable.

[0044] In a second aspect, the present application also proposes a cable joint moisture defect diagnosis system, including a signal generation module, a signal acquisition module, and a signal processing module. Among them,

[0045] The signal generation module is used to generate a first incident signal and a reference signal, send the reference signal to the signal acquisition module, and inject the first incident signal into the cable to be detected;

[0046] The signal acquisition module is used to collect the reference signal and the reflection signal generated after the first incident signal passes through the cable to be detected, and send the reference signal and the reflection signal to the signal processing module;

[0047] The signal processing module is used to receive the reference signal and the reflection signal collected by the signal acquisition module, use the reference signal as the incident signal, perform time reversal processing on the incident signal and the reflection signal to obtain the inverse signals of the incident signal and the reflection signal, construct a simulation model of the cable to be detected and inject the inverse signals of the incident signal and the reflection signal into the simulation model of the cable to be detected; obtain the inverse signals at different positions in the simulation model, construct an energy curve based on the inverse signals at different positions in the simulation model, perform mean filtering and normalization processing on the energy curve to obtain a cable defect diagnosis spectrogram, and judge whether the cable to be detected has a joint moisture defect and the defect position based on the cable defect diagnosis spectrogram.

[0048] Further, the signal generation module includes a signal generator, a power splitter, and a circulator. Among them, the signal generator is used to generate a CLFM signal; the power splitter is used to divide the CLFM signal generated by the signal generator into a reference signal and a first incident signal with the same signal, send the reference signal to the signal acquisition module, and send the first incident signal to the circulator; the circulator is used to inject the first incident signal into the cable to be detected and send the reflection signal to the signal acquisition module.

[0049] Further, the signal acquisition module is a signal collector.

[0050] Further, the signal processing module is a computer.

[0051] In summary, compared with the prior art, the present invention has the following beneficial effects:

[0052] (1) The present invention selects a CLFM signal as the incident signal, which has strong anti-noise ability and can effectively detect the defect situation of the cable to obtain a reflection signal containing rich defect information.

[0053] (2) The present invention performs time reversal on the collected reflected signal and incident signal and injects them into the simulation model, and a defect diagnosis spectrogram of the cable can be obtained. Through the signal focusing characteristics of the TR technology, this spectrogram can accurately locate the cable joint and diagnose the moisture defect.

[0054] (3) In the defect diagnosis spectrogram of the cable of the present invention, the direction of the positioning peak can be used to judge the impedance change property of the impedance discontinuity point in the cable, so as to distinguish the impedance change property difference between the normal joint and the moisture-affected joint, and realize the moisture defect diagnosis of the cable joint.

[0055] (4) Compared with the traditional method, the present invention uses the TR technology to perform energy detection on the inversion signals of the reflected signal and incident signal at each position in the cable. By assuming different position distances and observing the focusing situation of the inversion signals, the resolution of the distance horizontal axis can be effectively improved, ensuring the high-resolution waveform characteristics of the defect positioning peak, making the waveform characteristics of the defect positioning peak clearer and easier to identify. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, form a part of this application, and do not limit the embodiments of the present invention. In the drawings:

[0057] Figure 1 is a schematic diagram of a system for diagnosing moisture defects in cable joints according to an embodiment of the present invention;

[0058] Figure 2 is an equivalent distributed parameter circuit model of a cable according to an embodiment of the present invention;

[0059] Figure 3 is a defect diagnosis spectrogram of cables of simulation models 1# and 2# according to an embodiment of the present invention;

[0060] Figure 4 is a defect diagnosis spectrogram of cables of simulation models 3# and 4# according to an embodiment of the present invention;

[0061] Figure 5 is a flow block diagram according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0062] To make the objectives, technical solutions, and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below in conjunction with the embodiments and the drawings. The illustrative embodiments and descriptions thereof of the present invention are only used to explain the present invention and do not limit the present invention.

[0063] Embodiment:

[0064] As Figure 1 shown, a system for diagnosing moisture defects in cable joints includes a signal generation module, a signal acquisition module, and a signal processing module, wherein,

[0065] The signal generation module is used to generate a first incident signal and a reference signal, send the reference signal to the signal acquisition module, and inject the first incident signal into the cable to be detected;

[0066] The signal acquisition module is used to acquire the reference signal and the reflection signal generated after the first incident signal passes through the cable to be detected, and send the reference signal and the reflection signal to the signal processing module;

[0067] The signal processing module is used to receive the reference signal and the reflection signal acquired by the signal acquisition module, use the reference signal as the incident signal, perform time reversal processing on the incident signal and the reflection signal to obtain the inverse signals of the incident signal and the reflection signal, construct a simulation model of the cable to be detected and inject the inverse signals of the incident signal and the reflection signal into the simulation model of the cable to be detected; obtain the inverse signals at different positions in the simulation model, construct an energy curve based on the inverse signals at different positions in the simulation model, perform mean filtering and normalization processing on the energy curve to obtain a cable defect diagnosis spectrogram, and judge whether there is a joint moisture defect and the defect position of the cable to be detected based on the cable defect diagnosis spectrogram.

[0068] Further, the signal generation module includes a signal generator, a power splitter and a circulator. Among them, the signal generator is used to generate a CLFM signal; the power splitter is used to divide the CLFM signal generated by the signal generator into a reference signal and a first incident signal with the same signal, send the reference signal to the signal acquisition module, and send the first incident signal to the circulator; the circulator is used to inject the first incident signal into the cable to be detected and send the reflection signal to the signal acquisition module. Among them, the signal acquisition module is a signal collector; the signal processing module is a computer. Figure 1 The corresponding names of the reference numerals in the figure are: 3. Signal generator; 4. Power splitter; 5. Circulator; 6. Cable to be detected; 7. Signal collector; 8. Computer; 9. CLFM signal; 901. First incident signal; 902. Reference signal; 903. Reflection signal.

[0069] Based on the above cable joint moisture defect diagnosis system, this embodiment proposes a cable joint moisture defect diagnosis method, as Figure 5 shown, including the following steps: determining the cable to be detected; testing the cable to be detected based on the FMCW method to obtain the incident signal s 1 (t) and the reflection signal s 2 (t); obtaining the inverse signal s 1 (-t) of the incident signal s 1 (t) and the inverse signal s 2 (t) of the reflection signal s 2 (-t); establishing a simulation model of the cable to be detected and injecting the incident signal s1 (t) and the reflected signal s 2 (t) is injected into the simulation model of the cable to be detected; the time-reversed signals at different positions in the simulation model are obtained, and an energy curve p(x) is constructed based on the time-reversed signals at different positions in the simulation model; the energy curve p(x) is subjected to mean filtering and normalization processing to obtain a cable defect diagnosis spectrogram; based on the cable defect diagnosis spectrogram, it is determined whether the cable to be detected has a joint moisture defect.

[0070] In the prior art, the cable defect diagnosis method based on frequency-modulated continuous wave uses linear frequency-modulated continuous wave to replace the discrete stepped-frequency point sine wave in BIS, and has stronger anti-noise ability. However, although the existing FMCW technology has high defect diagnosis sensitivity, it cannot detect the impedance change property of the joint, resulting in difficulty in diagnosing the moisture condition of the joint in engineering. In the embodiments of the present application, the FMCW technology is combined with the time-reversal technology, and the position and impedance change property of the joint are directly detected by detecting the focusing condition of the time-reversed signals at different positions, so as to diagnose the moisture condition of the joint. Specifically, in the embodiments of the present application, the cable to be detected is detected by the FMCW method, so as to obtain the incident signal s 1 (t) and the reflected signal s 2 (t), and then the time-reversed signals of the incident signal s 1 (t) and the reflected signal s 2 (t) are obtained through the time-reversal method, and the two groups of time-reversed signals are injected into the simulation model of the cable to be detected, so as to obtain the time-reversed signals at different positions in the simulation model, and an energy curve p(x) is constructed according to the time-reversed signals at different positions; by performing mean filtering and normalization processing on the energy curve p(x), a cable defect diagnosis spectrogram is obtained, and based on the cable defect diagnosis spectrogram, it is determined whether the cable to be detected has a joint moisture defect and the defect position. It should be noted that under normal conditions, the capacitance per unit length of the intermediate joint is lower than that of the cable body, and as the moisture severity of the intermediate joint increases, the capacitance per unit length value of the intermediate joint will gradually increase and eventually exceed the capacitance per unit length of the cable body. Therefore, in the cable defect diagnosis spectrogram, for the intermediate joint under normal conditions, a waveform feature of first positive and then negative will appear, so that it can be judged that the characteristic impedance of the intermediate joint area is greater than that of the cable body, that is, the capacitance per unit length of the intermediate joint is lower than that of the cable body, while for the moisture-affected joint, a waveform feature of first negative and then positive will appear, so that it can be judged that the characteristic impedance of the intermediate joint area is less than that of the cable body, that is, the capacitance per unit length of the intermediate joint is higher than that of the cable body.

[0071] As Figure 2 shown is the equivalent distributed parameter circuit model of the cable. In Figure 2 R0 、L 0 、G 0 and C 0 are respectively the resistance (Ω / m), inductance (H / m), conductance (S / m), and capacitance (F / m) per unit length in the normal cable body. For abnormal regions such as defects and joints, the electrical parameters per unit length are different from those of the normal cable body, and the corresponding resistance, inductance, conductance, and capacitance are respectively defined as R 1 、L 1 、G 1 and C 1 . Figure 2 The corresponding names of the reference numerals in the figure are: 1, normal body region; 2, abnormal region.

[0072] According to the transmission line theory, the characteristic impedance Z 0 of the normal cable body region is:

[0073]

[0074] where ω is the angular frequency of the signal.

[0075] For high-frequency transmission signals, it can be known that ωL 0 >> R 0 , ωC 0 >> G 0 , and thus it can be obtained that:

[0076]

[0077] Similarly, it can be known that the characteristic impedance Z 1 of the abnormal regions such as defects and joints is:

[0078]

[0079] where R 1 、L 1 、G 1 and C 1 are respectively the resistance (Ω / m), inductance (H / m), conductance (S / m), and capacitance (F / m) per unit length in the abnormal cable region.

[0080] Taking the end of the cable as an example, when the incident signal transmits to the end of the cable, due to the impedance discontinuity characteristic usually existing at the end of the cable, a reflected signal will be formed, and the reflection coefficient ρ 0 of this reflected signal is:

[0081]

[0082] where Z L is the load impedance at the end of the cable.

[0083] Similarly, for abnormal regions such as defects and joints, considering the length distribution of the abnormal region, the incident signal will cause two sets of reflected signals, and the corresponding reflection coefficients ρ 1 and ρ 2 are respectively:

[0084]

[0085] In the above two equations, the reflection coefficients ρ 1 and ρ 2 can characterize the impedance change of abnormal regions such as defects and joints. In the existing FMCW technology, only the amplitudes of ρ 1 and ρ 2 are used to locate the cable insulation defects, and the impedance change nature of the abnormal region cannot be analyzed, so the diagnostic result has great limitations.

[0086] For an intermediate joint, the capacitance per unit length of a joint in the normal state is lower than that of the cable body region. As the moisture content of the joint increases, the capacitance per unit length of the joint region will gradually increase and eventually exceed that of the cable body. From Equation it can be seen that the capacitance value per unit length is strongly correlated with the characteristic impedance value. Therefore, the characteristic impedance of the joint region in the normal state is greater than that of the cable body, and ρ 1 >0 and ρ 2 <0; in the severely moisture-absorbed state, the characteristic impedance of the joint region is less than that of the cable body, and ρ 1 <0 and ρ 2 >0.

[0087] In the embodiment of the present application, by observing the position of the positioning peak in the cable defect diagnosis spectrogram, the accurate position of the cable joint can be accurately judged, and whether the cable joint has a moisture-absorbed defect can be judged by the waveform of the positioning peak. The diagnostic result is accurate and has good engineering application effects.

[0088] Further, based on the FMCW method, the cable to be detected is tested to obtain the incident signal s 1 (t) and the reflected signal s 2 (t) specifically as follows:

[0089] Set the end of the cable to be detected to an open state;

[0090] Input the CLFM signal s(t) at the test end of the cable to be detected, and divide the CLFM signal s(t) into two identical signals through a power splitter, which are respectively recorded as the first incident signal and the reference signal. Among them, the reference signal is transmitted to the signal collector as the incident signal s 1 (t), and the first incident signal is injected into the cable to be detected after passing through the circulator;

[0091] Take the signal after the first incident signal is reflected by the cable to be detected as the reflected signal s 2 (t).

[0092] Furthermore, the CLFM signal s(t) is specifically expressed as:

[0093]

[0094] In the formula, t is the time variable; rectwin(τ) is a rectangular window with a length of τ; τ is the duration of the signal; f 0 is the center frequency of the signal; μ is the frequency change rate of the signal;

[0095] The incident signal s 1 (t) is expressed as:

[0096] s 1 (t) = s(t) * h 1 (t)

[0097] In the formula, * is the convolution symbol; h 1 (t) is the impulse response of the incident signal acquisition system, and h 1 (t) = 1;

[0098] The reflected signal s 2 (t) is expressed as:

[0099] s 2 (t) = s(t) * h 2 (t)

[0100] In the formula, h 2 (t) is the impulse response of the reflected signal acquisition system, and h 2 (t) = IFT(e -2γ(ω)l ), where IFT represents the inverse Fourier transform, γ(ω) is the signal propagation constant of the cable to be detected, ω represents the angular frequency of the signal, and l represents the actual generation position of the reflected signal.

[0101] Furthermore, the inversion signals at different positions are specifically:

[0102] The inversion signal of the reflected signal, the inversion signal measured at the position where the reflected signal is generated, is denoted as the second inversion signal y 2 (t):

[0103] y 2 (t) = s 2 (-t) * h 2 (t) = s(-t) * h 2 (-t) * h 2 (t)

[0104] In the formula, s(-t) is the inversion signal of the CLFM signal s(t);

[0105] The inversion signal of the incident signal measured at the reflection signal generation position is denoted as the first inversion signal y 1 (t):

[0106] y 1 (t) = s 1 (-t) * h 1 (t) = s(-t) * h 1 (-t) * h 1 (t).

[0107] Furthermore, the energy curve p(x) is specifically:

[0108] p(x) = ∫v 1 (x, t)v 2 (x, t)dt

[0109] In the formula, x is the position of the assumed impedance discontinuity point; v 1 (x, t) is the signal detected at position x after injecting the inversion signal of the incident signal into the simulation model; v 2 (x, t) is the signal detected at position x after injecting the inversion signal of the reflection signal into the simulation model, where,

[0110] v 1 (x, t) = s 1 (-t) * h 1 (t)

[0111]

[0112] Among them, is the impulse response when the reflection signal generation position is x, specifically expressed as:

[0113] It should be noted that in the embodiments of the present application, to reduce the analysis difficulty, the simulation model of the cable to be detected is a transmission line model without attenuation effect and dispersion effect. At this time, can be expressed as:

[0114]

[0115] In the formula, v is the high-frequency signal wave velocity of the cable to be detected, which is usually regarded as a constant value.

[0116] Furthermore, based on the cable defect diagnosis spectrogram, determining whether there is a joint moisture defect and the defect position in the cable to be detected is specifically:

[0117] Based on the undulation of the cable defect diagnosis spectrogram, determine whether the cable to be detected has a joint moisture defect and the defect location, including: if a positioning peak feature of negative first and then positive appears in the cable defect diagnosis spectrogram, it is determined that there is a cable joint at the positioning peak and the cable joint has a moisture defect; if a positioning peak feature of positive first and then negative appears in the cable defect diagnosis spectrogram, it is determined that there is a cable joint at the positioning peak and the cable joint has no moisture defect; the positioning peak is defined as the position of the impedance discontinuity point in the cable.

[0118] To facilitate the understanding and elaboration of the method described in the embodiments of the present application, the following will be illustrated through two more specific embodiments.

[0119] According to Figure 2 the equivalent distributed parameter circuit model of the cable shown, establish simulation models 1# and 2# of 10kV cables, with their total lengths being 200m and 300m respectively. Set the terminal load impedance of the 1# cable model to 1.1Z 0 , presenting a state of increasing characteristic impedance; set the terminal load impedance of the 2# cable model to 0.9Z 0 , presenting a state of decreasing characteristic impedance. During simulation, inject a CLFM signal as the incident signal at the test end of the cable, with a frequency bandwidth of 1MHz - 50MHz and a duration of 1ms. When the incident signal transmits to the cable end position, reflection will occur. Collect the incident signal and the reflected signal, and perform TR processing to obtain the cable defect diagnosis spectrogram as Figure 3 shown. As can be seen from Figure 3 , the cable defect diagnosis spectrograms of the simulation models 1# and 2# form positioning peaks at the positions of 199.0m and 299.4m respectively. Define the direction of the peak according to the undulation of the cable defect diagnosis spectrogram. Define the peak protruding in the positive direction as a positive peak, and the peak sunken in the negative direction as a negative peak. From Figure 3In [Model 1], the end positioning peak of the 1# model is a positive peak, and the end positioning peak of the 2# model is a negative peak. Considering the actual end load impedance conditions in the 1# and 2# models, there is an impedance discontinuity point with increasing impedance at the cable end of the 1# model, and an impedance discontinuity point with decreasing impedance at the cable end of the 2# model. This phenomenon indicates that the direction of the positioning peak in the cable defect diagnosis spectrogram can be used to judge the impedance change property of the impedance discontinuity point in the cable. When the positioning peak is a positive peak, it can be judged that there is an increase in characteristic impedance at this position; when the positioning peak is a negative peak, it can be judged that there is a decrease in characteristic impedance at this position. It should be noted that during the simulation test, the entire test process is carried out in a virtual environment. Since the cable to be detected is a simulation model, the CLFM signal can be directly injected into the cable to be detected and directly collected as the incident signal without dividing the CLFM signal into the first incident signal and the reference signal through a power splitter. However, in the actual application process, if the CLFM signal is directly injected into the cable to be detected and used as the incident signal, the incident signal at this time is not convenient to directly measure. Therefore, the CLFM is divided into the first incident signal and the reference signal with the same signal through a power splitter, and the reference signal is collected as the incident signal. In the embodiment of the present application, when performing TR processing on the incident signal and the reflected signal, the incident signal and the reflected signal are subjected to time reversal processing, and the inverted signals of the incident signal and the reflected signal are injected into a new cable model to be detected, thereby obtaining the energy curve p(x) and obtaining the cable defect diagnosis spectrogram.

[0120] As can be seen from the above simulation results, the FMCW-TR method can reflect the impedance change property of the impedance discontinuity point in the cable. Therefore, the FMCW-TR method can be applied to the diagnosis of damp joints. According to Figure 2 the equivalent distributed parameter circuit model of the cable shown, cable models 3# and 4# with intermediate joints are established. The total length of the cables is 300m, the intermediate joints are all set at 200m, the joint length is 1m, and the cable ends are set to the open state. Among them, the intermediate joint of the 3# model is set to the normal state, and the intermediate joint of the 4# model is set to the damp state. Use the FMCW cable local defect diagnosis platform (as shown in Figure 1 ) to collect the incident signals and reflected signals of cable models 3# and 4#. The frequency bandwidth of the CLFM signal is set to 1MHz - 50MHz, and the duration is set to 1ms. Perform TR processing on the collected incident signals and reflected signals to obtain the cable defect diagnosis spectrogram as shown in Figure 4 . As shown in Figure 4It can be seen that the 3# and 4# cable models respectively show a positioning peak that is first positive and then negative and a positioning peak that is first negative and then positive near the 200m position. From the results of this positioning peak, it can be known that the characteristic impedance of the joint area in the 3# cable model is higher than that of the body area, and the capacitance per unit length of the joint area is lower than that of the body area, indicating that the joint of the 3# cable model is in a normal state. In the 4# cable model, the characteristic impedance of the joint area is lower than that of the body area, and the unit capacitance of the joint area is higher than that of the body area, indicating that the joint of the 4# cable model is in a damp state.

[0121] The specific embodiments described above further elaborate on the purpose, technical solutions, and beneficial effects of the present invention. It should be understood that the above description is only for the specific embodiments of the present invention and is not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for diagnosing moisture defects in cable joints, characterized in that: The following steps are involved: Determine the cable to be tested; The cable to be detected is tested based on the FMCW method to obtain the incident signal s1(t) and the reflected signal s2(t); Obtain an inversion signal s1(-t) of the incident signal s1(t) and an inversion signal s2(-t) of the reflected signal s2(t); A simulation model of the cable to be detected is established, and the inversion signals of the incident signal s1(t) and the reflected signal s2(t) are injected into the simulation model of the cable to be detected; Acquire inversion signals at different positions in the simulation model, and construct energy curves p(x) based on the inversion signals at different positions in the simulation model; Perform mean filtering and normalization processing on the energy curve p(x) to obtain the cable defect diagnosis spectrum; Based on the cable defect diagnosis spectrum, determine whether the cable to be tested has a joint moisture defect.

2. A cable joint moisture defect diagnosis method according to claim 1, characterized in that: Based on the FMCW method, the cable to be detected is tested, and the incident signal s1(t) and the reflected signal s2(t) are obtained as follows: Set the end of the cable to be tested to an open circuit state; A CLFM signal s(t) is input at the test end of the cable to be detected, and the CLFM signal s(t) is divided into two identical signals through a power divider, which are respectively recorded as a first incident signal and a reference signal, wherein the reference signal is transmitted to the signal collector as an incident signal s1(t), and the first incident signal is injected into the cable to be detected after passing through a circulator; The signal after the first incident signal is reflected by the cable to be detected is used as the reflected signal s2 (t).

3. A cable joint moisture defect diagnosis method according to claim 2, characterized in that: The CLFM signal s(t) is specifically expressed as: Where t is the time variable; rectwin(τ) is a rectangular window with a length of τ; τ is the duration of the signal; f0 is the center frequency of the signal; μ is the frequency change rate of the signal; The incident signal s1(t) is expressed as: s1(t)=s(t)*h1(t) Where * is the convolution symbol; h1(t) is the impulse response of the incident signal acquisition system, and h1(t) = 1; The reflected signal s2(t) is expressed as: s2(t)=s(t)*h2(t) Where h2(t) is the impulse response of the reflection signal acquisition system, and h2(t) = IFT(e -2γ(ω)l ), where IFT represents inverse Fourier transform, γ(ω) is the signal propagation constant of the cable to be detected, ω represents the angular frequency of the signal, and l represents the actual generation position of the reflected signal.

4. A cable joint moisture defect diagnosis method according to claim 3, characterized in that: The inversion signals at different positions include: The inversion signal of the reflected signal is the inversion signal measured at the reflected signal generation position, which is recorded as the second inversion signal y2(t): y2(t)=s2(-t)*h2(t)=s(-t)*h2(-t)*h2(t) Where s(-t) is the inversion signal of CLFM signal s(t); The inversion signal of the incident signal is measured at the location where the reflected signal is generated, and is recorded as the first inversion signal y1(t): y1(t)=s1(-t)*h1(t)=s(-t)*h1(-t)*h1(t).

5. A cable joint moisture defect diagnosis method according to claim 4, characterized in that: The energy curve p(x) is specifically: p(x)=∫v1(x,t)v2(x,t)dt Where x is the assumed impedance discontinuity position; v1(x, t) is the signal detected at position x after the inversion signal of the incident signal is injected into the simulation model; v2(x, t) is the signal detected at position x after the inversion signal of the reflected signal is injected into the simulation model, where v1(x,t)=s1(-t)*h1(t) in, is the impulse response when the reflected signal is generated at position x, which is specifically expressed as:

6. A cable joint moisture defect diagnosis method according to claim 1, characterized in that: Based on the cable defect diagnosis spectrum, it is determined whether the cable to be tested has a joint moisture defect as follows: Based on the fluctuation of the cable defect diagnosis spectrum, it is judged whether the cable to be tested has a moisture defect in the joint, including: if a positioning peak feature that is first negative and then positive appears in the cable defect diagnosis spectrum, it is judged that the cable joint at the positioning peak has a moisture defect; if a positioning peak feature that is first positive and then negative appears in the cable defect diagnosis spectrum, it is judged that the cable joint at the positioning peak has no moisture defect; the positioning peak is defined as the position of the impedance discontinuity point in the cable.

7. A cable joint moisture defect diagnosis system, characterized in that: It includes a signal generating module, a signal collecting module and a signal processing module, wherein: The signal generating module is used to generate a first incident signal and a reference signal, send the reference signal to the signal acquisition module, and inject the first incident signal into the cable to be detected; The signal acquisition module is used to collect a reference signal and a reflection signal generated by the first incident signal after passing through the cable to be detected, and send the reference signal and the reflection signal to the signal processing module; The signal processing module is used to receive the reference signal and the reflected signal collected by the signal acquisition module, take the reference signal as the incident signal, and perform time inversion processing on the incident signal and the reflected signal to obtain the inversion signals of the incident signal and the reflected signal, build a simulation model of the cable to be detected, and inject the inversion signals of the incident signal and the reflected signal into the simulation model of the cable to be detected; obtain the inversion signals at different positions in the simulation model, and build an energy curve based on the inversion signals at different positions in the simulation model, perform mean filtering and normalization processing on the energy curve, obtain a cable defect diagnosis spectrum, and judge whether the cable to be detected has a joint moisture defect and the defect position based on the cable defect diagnosis spectrum.

8. A cable joint moisture defect diagnosis system according to claim 6, characterized in that: The signal generating module comprises a signal generator, a power divider and a circulator, wherein the signal generator is used to generate a CLFM signal; the power divider is used to divide the CLFM signal generated by the signal generator into a reference signal and a first incident signal with the same signal, and send the reference signal to the signal acquisition module, and send the first incident signal to the circulator; the circulator is used to inject the first incident signal into the cable to be detected, and send the reflected signal to the signal acquisition module.

9. A cable joint moisture defect diagnosis system according to claim 6, characterized in that: The signal acquisition module is a signal collector.

10. A cable joint moisture defect diagnosis system according to claim 6, characterized in that: The signal processing module is a computer.

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