Equipment and method for measuring moisture content in main insulating material of cable

Abstract

The invention discloses equipment and a method for measuring the moisture content in a cable main insulating material, and belongs to the field of measurement. Comprising a near-infrared laser emitter used for modulating and emitting near-infrared light; the input optical fiber is communicated with the near-infrared laser transmitter through the coaxial adapter and is used for guiding the near-infrared light to the optical fiber probe; the optical fiber probe is used for performing transmission or diffuse reflection measurement on the main insulating material of the target cable according to a preset optical path mode to form detection light and guiding the detection light to the output optical fiber; the data preprocessing module is in communication connection with the output optical fiber and is used for preprocessing the detection light to form a digital signal; and the data analysis module is used for obtaining the moisture content of the main insulating material of the target cable according to the digital signal. By using the near-infrared laser transmitter and the moisture content estimation model, the moisture content information of the main insulation material of the cable can be quickly and accurately obtained under the condition that the cable to be measured is not contacted, and the method is particularly suitable for measuring the moisture content of the superficial layer of the main insulation of the XLPE cable in the field.

Description

Technical Field

[0001] This invention belongs to the field of cable insulation material testing, and in particular relates to a device and method for measuring the moisture content in the main insulation material of cables. Background Technology

[0002] With the rapid development of my country's economy, people have higher and higher requirements for power supply reliability, which has also brought about new changes and requirements for the daily operation and maintenance of operation and maintenance units.

[0003] Cross-linked polyethylene (XLPE) is widely used as the main insulation material for high-voltage and ultra-high-voltage cables (hereinafter referred to as cables) due to its excellent electrical properties and chemical stability.

[0004] Cables are typically rope-like structures made up of several or groups of conductors (at least two conductors per group), with each group of conductors insulated from each other and often twisted around a central conductor, and the entire cable is covered with a highly insulating outer layer.

[0005] Cables are characterized by being energized internally and insulated externally.

[0006] Currently, many cities have a large number of aging power distribution cables. After years of operation, these cables, especially XLPE cables, are susceptible to moisture and water intrusion due to the complex operating environment. This leads to an increase in the moisture content of the main insulation material, which in turn affects the cable's electrical performance and service life. Moisture intrusion is particularly pronounced in high humidity and high temperature environments, increasing the risk of cable failure.

[0007] Because the actual operating cable lines are generally quite long, and due to limitations in manufacturing technology and laying length, intermediate joints must be used to reliably connect two sections of cable for longer cable lines.

[0008] Currently, the cable coverage rate of urban power distribution networks is increasing, leading to the widespread use of cable joints.

[0009] A cable joint is a connector located in the middle of a cable line, used to connect two cable segments. It differs from a cable termination joint (often called a cable end joint in the industry) in function, purpose, and application: cable termination joints are commonly used to connect cables to equipment, transmitting signals and current, while ensuring the quality and stability of the connection through insulation and waterproofing designs; while cable joints are used to connect two ends (or two segments) of cable, continuing the transmission of power and signals, and ensuring the insulation level at the cable joint for safe and reliable operation. The differences between cable termination joints and cable joints lie in their application scenarios, installation and connection methods, and waterproofing performance.

[0010] Cable joints can generally be divided into two main categories based on their connection process, material properties, and applicable scenarios: cold shrink cable joints and hot melt cable joints. (Hot melt joints achieve conductor molecular-level fusion through high-temperature welding, while cold shrink joints rely on the natural shrinkage of elastomer materials to complete the seal.)

[0011] A cable cold-shrink joint is a device used to connect cables. It achieves rapid installation through a cold-shrink process, requiring no heating or welding, and is primarily used to ensure the sealing and electrical performance of cable connections. It typically consists of an insulating sleeve, a conductive metal tube, and a mesh sealing element, which is fitted over both ends of the cables to be connected. The shrinkage properties of an elastic material (such as silicone rubber) create a sealing and insulating structure. Some models use epoxy resin insulating tubing to further enhance pressure resistance.

[0012] In practical construction and use, cable cold-shrink joints are widely used in power distribution networks due to their convenient installation, good insulation performance, and resistance to high temperatures and acids and alkalis. However, the problem of cable joint failures is also on the rise, seriously threatening the safe operation of the power grid and even causing feeder tripping.

[0013] During cable operation, moisture intrusion into the cable insulation layer may cause problems such as electrical breakdown, electrical treeing, and space charge, seriously affecting the long-term reliability and stability of the cable.

[0014] Moisture content is one of the key factors affecting the insulation performance of XLPE.

[0015] Traditional moisture detection methods typically require complex sample preparation and lengthy analysis time, which cannot meet the needs of real-time, online monitoring at construction sites or during operation.

[0016] The invention patent CN 109490133 B, with an authorization announcement date of January 8, 2021, discloses "a cable moisture content detection device." The device includes a cabinet with a workbench at its upper end. An upper mounting plate is fixed to the upper end of the workbench via a support rod. Wire rollers are installed at the lower end of the upper mounting plate and the upper end of the workbench. Cables are rotatably connected to the outer walls of the wire rollers. A fixing frame is welded to the upper end of the workbench, and an electronic scale is mounted on the upper end of the fixing frame. A cable-collecting wheel is installed on the inner wall of the support rod. The cable is rotatably connected to the outer wall of the cable-collecting wheel through the input end face of the electronic scale. A motor is installed on the inner wall of the support rod. This technical solution, by incorporating a cabinet, wire rollers, an upper mounting plate, a protective box, an electronic scale, a cable-collecting wheel, an operation panel, an alarm, a control console, a hot air blower, a drying oven, wire holes, and a processor, solves the problems of low accuracy and low working efficiency in detection equipment. However, it focuses on detecting the moisture content of cables during the manufacturing process (or on the production line), without addressing the detection of the moisture content of the main insulation surface of cables that have already been put into operation. Therefore, it cannot meet the requirement of detecting or monitoring the "moisture intrusion into the cable insulation layer" that cables already in operation need.

[0017] The invention patent application with publication date of March 21, 2025, and publication number CN 119666783 A, discloses "a method and apparatus for detecting the moisture content of insulating materials," which includes acquiring a transmission terahertz time-domain signal to be tested; the transmission terahertz time-domain signal to be tested is the time-domain signal after the terahertz wave is transmitted through the insulating material to be tested; converting the transmission terahertz time-domain signal to be tested into a frequency-domain signal to obtain a transmission terahertz frequency-domain signal to be tested; extracting features from the transmission terahertz time-domain signal and the transmission terahertz frequency-domain signal to obtain the terahertz spectral characteristics of the insulating material to be tested; the terahertz spectral characteristics include: the peak difference of the time-domain signal and the integral value of the absorption coefficient; and determining the moisture content of the insulating material to be tested based on the terahertz spectral characteristics of the insulating material to be tested and the quantitative curve of the terahertz spectral characteristics versus moisture. Because this technical solution relies on spectral analysis, it requires a terahertz emitter, cuvette, and terahertz receiver, resulting in a high cost for the entire moisture content detection device. Furthermore, the device (actually a chromatograph) has poor portability and is not easy to carry. It is not suitable for on-site detection of the moisture content of the main insulation surface of cables already in operation.

[0018] Traditional methods for detecting the moisture content of XLPE cables mainly rely on point-to-point transmission measurements. This method has certain limitations and randomness, and cannot achieve comprehensive detection of the moisture content of the cable's main insulation material. Therefore, how to efficiently and accurately determine the moisture content of the shallow surface layer of the main insulation of XLPE cables and effectively carry out corresponding dehydration treatment has become an urgent problem to be solved in the field of power engineering. Summary of the Invention

[0019] The purpose of this invention is to provide a device and method for measuring the moisture content in the main insulation material of cables. By using a near-infrared laser emitter and a moisture content estimation model, it can quickly and accurately obtain information on the moisture content of the main insulation material of cables without contacting the cable under test. This is particularly suitable for determining the moisture content of the shallow surface layer of the main insulation of XLPE cables in the field.

[0020] The technical solution of the present invention is: to provide a device for measuring the moisture content in the main insulation material of a cable, characterized in that it includes:

[0021] Near-infrared laser emitters are used to modulate and emit near-infrared light;

[0022] An input optical fiber is connected to the near-infrared laser emitter via a coaxial adapter to guide the near-infrared light to the Y-shaped fiber optic probe.

[0023] A Y-shaped fiber optic probe is used to measure the transmission or diffuse reflection of the main insulation material of the target cable according to a preset optical path mode to form detection light, and to guide the detection light to the output fiber optic cable.

[0024] The data preprocessing module is connected to the output optical fiber and is used to preprocess the detection light to form a digital signal.

[0025] The data analysis module is used to obtain the moisture content of the main insulation material of the target cable based on digital signals.

[0026] Specifically, the device for measuring the moisture content in the main insulation material of cables, by using a near-infrared laser emitter and a moisture content estimation model, can quickly and accurately obtain information on the moisture content of the main insulation material of cables without contacting the cable under test. It can also monitor the operating status of the cable in real time, provide timely early warning information, improve the operational reliability and service life of the cable, and provide solid technical support for the stable operation of the power system. It is particularly suitable for determining the moisture content of the shallow surface layer of the main insulation of XLPE cables in the field.

[0027] Specifically, the near-infrared laser emitter is a narrow-spectrum LD laser; the input optical fiber is a near-infrared Y-type multimode optical fiber.

[0028] Furthermore, the device for measuring the moisture content in the main insulation material of the cable, after obtaining the moisture content of the target cable main insulation material, uses a data transmission module to securely send the moisture content of the target cable main insulation material to a data center or remote monitoring system, and displays the measurement results and device status on the user interface.

[0029] The technical solution of the present invention also provides a method for measuring the moisture content in the main insulation material of a cable, characterized by comprising the following steps:

[0030] Step 1) Use a near-infrared laser emitter to perform transmission or diffuse reflection measurements on the main insulation material of cables with different moisture contents to form calibration light;

[0031] Step 2) Input the calibration light into the data preprocessing module to obtain the digital signals corresponding to the main insulation materials of the cable with different moisture contents;

[0032] Step 3) Perform denoising processing on the digital signal to obtain the denoised digital signal;

[0033] Step 4) Input the denoised digital signal as a sample into the neural network for training to obtain the moisture content estimation model;

[0034] Step 5) Use the moisture content estimation model to detect the moisture content of the main insulation material of the target cable.

[0035] Specifically, in step 3), the digital signal is denoised to obtain a denoised digital signal, which includes the following steps:

[0036] Step 3.1) Decompose the digital signal to obtain wavelet coefficients at different scales;

[0037] Step 3.2) Construct a denoising threshold based on wavelet coefficients at different scales;

[0038] Step 3.3) Construct a wavelet coefficient selection function using the denoising threshold;

[0039] Step 3.4) Process each wavelet coefficient using the wavelet coefficient filtering function to obtain the processed wavelet coefficients;

[0040] Step 3.5) Reconstruct the processed wavelet coefficients to obtain the denoised digital signal.

[0041] Furthermore, the denoising threshold is:

[0042]

[0043] Where, λ j σ represents the noise reduction threshold. j N represents the standard deviation of the wavelet coefficients at the j-th decomposition scale. j This represents the length of the digital signal at the j-th decomposition scale.

[0044] Specifically, in step 3.3), the wavelet coefficient selection function is constructed using the denoising threshold, including:

[0045] Formula used:

[0046]

[0047] Construct a wavelet coefficient selection function;

[0048] Among them, W j,k This represents the k-th wavelet coefficient at the j-th decomposition scale. denoted as the processed wavelet coefficients, sgn(·) is the sign function, α represents the first empirical coefficient, and β represents the second empirical coefficient.

[0049] Specifically, in step 4), the denoised digital signal is used as a sample input into the neural network for training to obtain a moisture content estimation model, including:

[0050] A moisture content estimation model is obtained by optimizing and training a neural network using a loss function; wherein the loss function is:

[0051]

[0052] Where n represents the number of samples, C represents the number of labels with different moisture contents, and y i,c This represents the actual moisture content label for the i-th sample. This represents the probability that the i-th sample, predicted by the neural network, belongs to the c-th water content label.

[0053] Furthermore, in step 4), the loss function is:

[0054]

[0055] Where n represents the number of samples, C represents the number of labels with different moisture contents, and y i,c This represents the actual moisture content label for the i-th sample. This represents the probability that the i-th sample, predicted by the neural network, belongs to the c-th water content label.

[0056] Compared with the prior art, the advantages of the present invention are:

[0057] 1. The technical solution of the present invention, by using a near-infrared laser emitter and a moisture content estimation model, can quickly and accurately obtain the moisture content information of the main insulation material of a cable without contacting a cable sample.

[0058] 2. The technical solution of the present invention, by using a near-infrared laser emitter and a moisture content estimation model, can quickly and accurately obtain the moisture content information of the main insulation material of the cable without contacting the cable under test. It is particularly suitable for determining the moisture content of the shallow surface layer of the main insulation of XLPE cables in the field.

[0059] 3. The technical solution of the present invention uses a near-infrared laser emitter and a data analysis module. The entire detection device is portable and easy to move, and is particularly suitable for quickly and accurately obtaining information on the moisture content of the main insulation material of cables in the field. Attached Figure Description

[0060] Figure 1 This is a schematic diagram of the modular structure of the device for measuring the moisture content in the main insulation material of cables as described in this invention;

[0061] Figure 2(a) is a schematic diagram of the near-infrared spectra of water and XLPE;

[0062] Figure 2(b) is a schematic diagram of the second-order differential of water and XLPE;

[0063] Figure 3(a) is a schematic diagram of the near-infrared standard deviation of XLPE samples with different moisture concentrations according to the present invention;

[0064] Figure 3(b) is a schematic diagram of the first-order differential of the 1450nm spectral bands of XLPE samples with different moisture concentrations in this invention;

[0065] Figure 4 This is a schematic diagram of the standard curve of XLPE moisture concentration according to the present invention;

[0066] Figure 5 This is the near-infrared spectrum of the main insulation of the cable of this invention;

[0067] Figure 6 This is the near-infrared spectrum of the present invention after processing by the airPLS algorithm. Detailed Implementation

[0068] To make the technical problems, technical solutions, and beneficial effects of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0069] Please see Figure 1 The present invention provides a device for measuring the moisture content in the main insulation material of a cable, comprising:

[0070] Near-infrared laser emitters are used to modulate and emit near-infrared light;

[0071] An input optical fiber is connected to the near-infrared laser emitter via a coaxial adapter to guide the near-infrared light to the Y-shaped fiber optic probe.

[0072] A Y-shaped fiber optic probe is used to measure the transmission or diffuse reflection of the main insulation material of the target cable according to a preset optical path mode to form detection light, and to guide the detection light to the output fiber optic cable.

[0073] The data preprocessing module is connected to the output optical fiber and is used to preprocess the detection light to form a digital signal.

[0074] The data analysis module is used to obtain the moisture content of the main insulation material of the target cable based on digital signals.

[0075] It should be noted that after obtaining the moisture content of the main insulation material of the target cable, the present invention needs to use a data transmission module to securely send the moisture content of the main insulation material of the target cable to a data center or remote monitoring system, and display the measurement results and equipment status on the user interface.

[0076] Furthermore, the data preprocessing module includes:

[0077] The detector, connected to the output optical fiber, is used to receive the detection light transmitted through the output optical fiber and convert it into an electrical signal;

[0078] The signal amplification and filtering module is used to filter and amplify the electrical signal to obtain a preprocessed electrical signal;

[0079] An AD conversion module is used to convert preprocessed electrical signals into digital signals.

[0080] The technical solution of this invention involves the design and implementation of an infrared spectrometer based on a near-infrared spectroscopy physical model. First, suitable sensors and light sources are selected, and an efficient optical path system is designed to ensure high measurement accuracy and sensitivity. Then, an infrared spectral data acquisition and processing system is developed to ensure accurate transmission and rapid analysis of real-time data. The size and structural design of the equipment are optimized to meet the needs of actual cable joint water measurement operations, taking into account field application requirements. Simultaneously, functional testing and performance optimization are conducted to ensure the stability and reliability of the equipment under various environmental conditions. Ultimately, a complete infrared spectroscopy water measurement system is formed, enabling efficient and accurate detection of the shallow surface water content of the main insulation of cables, and providing timely early warning information by monitoring the cable's operating status in real time.

[0081] During the implementation of this technical solution, functional testing and performance optimization of the equipment ensured its stability and reliability under various environmental conditions. This ultimately resulted in a complete infrared spectroscopy water content measurement system, enabling efficient and accurate detection of the shallow surface moisture content of the cable's main insulation, and providing real-time monitoring of the cable's operational status and timely early warning information. This not only improves the cable's operational reliability and service life but also provides solid technical support for the stable operation of the power system.

[0082] Molecular spectroscopy can reflect the composition and structure of substances at the molecular level. Combined with chemometric methods, it can achieve rapid analysis of the composition and properties of substances. This technology has been widely used in industry and agriculture. However, due to the special structure of cable insulation layers and the complex molecular spectral fingerprint of XLPE materials, existing molecular spectrometers cannot directly acquire signals that meet the analytical requirements. This makes the molecular spectral detection of moisture concentration in cable joint insulation layers a technically challenging problem, requiring the use of scientific algorithms to analyze and process the spectral results. Currently, this field is still in a research gap.

[0083] In response, the present invention also provides a method for measuring the moisture content in the main insulation material of a cable, comprising:

[0084] Step 1: Use a near-infrared laser emitter to perform transmission or diffuse reflection measurements on the main insulation material of cables with different moisture contents to form calibration light;

[0085] Step 2: Input the calibration light into the data preprocessing module to obtain the digital signals corresponding to the main insulation materials of the cable with different moisture contents;

[0086] Step 3: Perform denoising processing on the digital signal to obtain the denoised digital signal;

[0087] This invention performs noise reduction processing on the digital signal before acquisition, filtering out noise signals from the environment, making the acquired digital signal more accurate and reliable. The process is as follows:

[0088] Furthermore, step 3 includes:

[0089] Step 3.1: Decompose the digital signal to obtain wavelet coefficients at different scales;

[0090] Step 3.2: Construct a denoising threshold based on wavelet coefficients at different scales;

[0091] The noise reduction threshold is:

[0092]

[0093] Where, λ j σ represents the noise reduction threshold. j N represents the standard deviation of the wavelet coefficients at the j-th decomposition scale. j This represents the length of the digital signal at the j-th decomposition scale.

[0094] Step 3.3: Construct a wavelet coefficient selection function using the denoising threshold;

[0095] In step 3.3, the present invention may employ the following formula:

[0096]

[0097] Construct a wavelet coefficient selection function; where W j,k This represents the k-th wavelet coefficient at the j-th decomposition scale. denoted as the processed wavelet coefficients, sgn(·) is the sign function, α represents the first empirical coefficient, and β represents the second empirical coefficient.

[0098] Step 3.4: Process each wavelet coefficient using the wavelet coefficient filtering function to obtain the processed wavelet coefficients;

[0099] Step 3.5: Reconstruct the processed wavelet coefficients to obtain the denoised digital signal.

[0100] Commonly used wavelet denoising threshold functions include hard threshold functions and soft threshold functions. Soft threshold functions usually smooth the wavelet coefficients to reduce noise. However, excessive smoothing can lead to the loss of signal detail information, resulting in over-denoising.

[0101] Furthermore, there is always a constant deviation between the actual wavelet coefficients and the processed wavelet coefficients, which reduces the quality of the reconstructed signal.

[0102] The hard threshold function sets wavelet coefficients smaller than the threshold to zero, which completely eliminates small-amplitude signals in the signal, resulting in the loss of effective information.

[0103] Furthermore, since the hard threshold function directly truncates the wavelet coefficients, oscillations will occur during signal reconstruction, further generating the pseudo-Gibbs phenomenon.

[0104] The threshold function proposed in this invention can be easily controlled by adjusting empirical coefficients. While eliminating the discontinuity of the threshold function, it makes the function approach the desired position more quickly and also ensures the continuity of the threshold function, thereby avoiding the occurrence of pseudo-Gibbs phenomenon and enabling the denoised signal to maintain the smoothness of the original signal.

[0105] Step 4: Input the denoised digital signal as a sample into the neural network for training to obtain the moisture content estimation model;

[0106] It should be noted that the present invention can use a loss function to optimize and train a neural network to obtain a moisture content estimation model; wherein, the loss function is:

[0107]

[0108] Where n represents the number of samples, C represents the number of labels with different moisture contents, and y i,cThis represents the actual moisture content label for the i-th sample. This represents the probability that the i-th sample, predicted by the neural network, belongs to the c-th water content label.

[0109] Step 5: Use the moisture content estimation model to detect the moisture content of the main insulation material of the target cable.

[0110] Furthermore, in the technical solution of this invention, the specific algorithm and verification process are as follows:

[0111] (1) Basic spectral acquisition:

[0112] exist Figure 5 In this context, "dry" represents a new cable main insulation sample with extremely low internal moisture content, while "wet" represents a cable main insulation sample with a service life of 20 years, containing a certain amount of moisture.

[0113] (2) Spectral baseline correction algorithm:

[0114] The airPLS (adaptive iteratively reweighted penalized least squares) algorithm is an adaptive iterative weighted penalized least squares algorithm used to remove background signals from data, and is particularly suitable for baseline correction of spectral data.

[0115] This algorithm is widely used in fields such as spectral analysis and mass spectrometry. Its purpose is to extract the actual signal components from the measurement data while removing baseline drift caused by instruments, samples or other factors.

[0116] (3) Algorithm formula and steps:

[0117] The goal of airPLS is to remove the baseline z from the original signal y, such that the remaining signal yz is as close as possible to the actual signal, while z should be a smooth curve. To achieve this, airPLS minimizes the following objective function:

[0118]

[0119] in:

[0120] y i These are the data points of the original signal.

[0121] z i It is a baseline estimate.

[0122] w i These are the weights in the iii-th iteration.

[0123] λ is a smoothing parameter used to control the smoothness of the baseline.

[0124] In each iteration, the weights wi are updated based on the residuals (i.e., the difference between the original data and the baseline estimate);

[0125]

[0126] The second penalty term involves the second difference of the baseline:

[0127]

[0128] This term ensures the smoothness of the baseline z, and the smoothing parameter λ determines the degree of smoothing. A larger λ value makes the baseline smoother, while a smaller λ allows the baseline to better approximate local fluctuations in the data.

[0129] (4) Data Processing: When using the airPLS algorithm to perform baseline correction on spectral data, the original spectral data must first be imported into the MATLAB analysis software. Then, the airPLS algorithm is run. It iteratively adjusts the baseline fit using a weighted approach to eliminate interference from peak regions and focus on baseline correction. The algorithm stops when it reaches the maximum number of iterations or when the baseline change is below a preset threshold. At this point, the corrected results can be evaluated. If the baseline correction effect is unsatisfactory, the parameters can be adjusted appropriately and the algorithm can be run again. Finally, the baseline-corrected spectral data is obtained. Figure 6 .

[0130] Analysis of the absorption spectrum revealed significant absorption peaks at wavelengths of 1310 nm and 1450 nm, which is crucial for testing the moisture content in the main insulation of cross-linked polyethylene (XLPE) cables.

[0131] Example:

[0132] 1) Test sample:

[0133] The XLPE cable main insulation samples containing water and brand-new XLPE samples used in this test were collected at a power company's emergency repair site.

[0134] The new XLPE cable main insulation samples are well-preserved, non-service cables. The water-containing samples include 10kV XLPE three-core cables with service lives ranging from 5 to 20 years that were scrapped due to fault repairs.

[0135] Ten 20cm sections were cut from each of the aforementioned cables. The moisture concentration ranged from 0.5% to 2%, with a mean of 1.27%, a standard deviation of 0.6%, and a coefficient of variation of 47%.

[0136] 2) Test equipment:

[0137] HF-C2100 Fourier Transform Near Infrared Spectrometer (Xite (Beijing) Technology Co., Ltd.), with a spectral range of 1100-1600nm, equipped with a dedicated fiber optic probe accessory; XQ501T Halogen Moisture Analyzer (Shanghai Instrument Co., Ltd.); JQB-III Cross-linked Cable Slicer (Jiaxing Experimental Instrument Co., Ltd.).

[0138] 3) XLPE sample preparation:

[0139] XLPE samples with controllable and uniform thickness were prepared using a JQB-III type cross-linked cable slicing machine. The outer sheath, armor layer, and waterproof layer of the cable were removed using a utility knife and scissors. A single-core cable segment was then removed and placed on the clamp of the cross-linked cable slicing machine. The relative position of the slicing machine blade to the upper surface of the cable was adjusted to remove the black outer semiconducting layer of the single-core cable segment. The height of the slicing machine blade was then adjusted again to obtain a single-layer XLPE sample with a length of 20 mm, a width of 10 mm, and a thickness of 1 mm.

[0140] 4) Determination of moisture concentration in XLPE insulation layer samples:

[0141] The moisture concentration of the XLPE sample was obtained according to GB / T 29249-2012 (Electronic Weighing Drying Method Moisture Analyzer).

[0142] Turn on the halogen moisture analyzer and preheat it to a constant temperature of 80℃. Prepare 10 groups of XLPE samples with different service periods, and cut each group into 3 sample pieces with a length of 6mm.

[0143] Each time, a sample is placed into the measuring chamber. The initial weight of the sample is g1. Under the illumination of the halogen lamp, as the moisture in the sample evaporates, the weight of the sample decreases continuously. After 10 minutes, the weight reaches a constant value, which is g2. The average value of the moisture concentration of the three samples is taken as the moisture concentration value of the sample.

[0144] The formula for calculating the moisture concentration of a single sample is as follows:

[0145]

[0146] The meaning of each parameter in the above formula can be found in the relevant provisions of GB / T 29249-2012 (Electronic Weighing Type Drying Method Moisture Analyzer).

[0147] 5) Spectral acquisition:

[0148] The ambient temperature was room temperature. The spectrometer was preheated for 10 minutes. The prepared XLPE sample was placed in the fiber optic probe holder, the probe was fixed with a bracket, and the sample was collected after standing for 30 seconds.

[0149] The spectral range is set to 1100–1600 nm, the resolution to 0.4 nm, and the number of scans to 25.

[0150] Ten groups of XLPE samples with different service periods were prepared. Three points were collected for each sample, and three spectra were collected at each point, resulting in nine spectra. The average spectrum was taken as the sample spectrum.

[0151] 6) Data processing:

[0152] Data processing was performed using MATLAB R2023b. A spectral matrix X was constructed from the spectra of all samples, and its standard deviation spectrum and first-order differential spectrum were calculated. ORIGIN was used for data processing.

[0153] 7) Spectral analysis:

[0154] The original and second-order differential spectra of the pure water sample and XLPE sample collected in this experiment are shown in Figure 2(a) and Figure 2(b), respectively. Based on the characteristic peaks of the second-order differential spectrum shown in Figure 2(b), the main characteristic groups in the sample were analyzed.

[0155] The characteristic peak of pure water at 1450 nm is attributed to the first harmonic of the stretching vibration of hydroxyl groups; the band of XLPE at 1214 nm is attributed to the second harmonic absorption of the stretching vibration of CH groups, the band at 1391 nm is the combined second harmonic absorption of the stretching and bending vibrations of CH groups, and the band at 1415 nm is the combined second harmonic absorption of the stretching vibration of CH groups and the first harmonic absorption of the bending vibration.

[0156] As shown in Figure 2(a), the spectral bands of the pure water sample in the 1350–1500 nm range overlap with those of the XLPE sample. This indicates that the 1350–1500 nm spectral bands of the XLPE sample severely interfere with the establishment of the water content calibration curve based on the characteristic peak of the water hydroxyl group at 1450 nm. However, the actual measured spectrum of the XLPE sample in the cable connector is a superposition of the XLPE spectrum and the moisture spectrum.

[0157] A spectral matrix was constructed using the spectra of XLPE cable samples with different moisture concentrations, and its standard deviation was calculated, as shown in Figure 3(a).

[0158] It can be seen that the XLPE band disappears in the 1350–1500 nm range, while the water band in the 1350–1600 nm range is basically preserved. This highlights the variation in water concentration in the spectra of different samples, while the variation in XLPE is very small and can be approximated as a fixed signal, and its influence can be eliminated using the first derivative.

[0159] To eliminate the interference of XLPE spectral signal on the characteristic peak of water hydroxyl group, the spectra of XLPE cable samples with different moisture concentrations were processed by first-order differentiation (window 7, fitting number 2).

[0160] Figure 3(b) shows the first-order differential spectrum. It can be seen that the first-order differential value of the 1450 nm characteristic peak increases with increasing moisture concentration. This indicates that using first-order differential spectroscopy can effectively eliminate the interference of XLPE spectroscopy on the moisture spectrum, providing a possibility for establishing a calibration curve for moisture concentration using first-order differential spectroscopy.

[0161] 8) Calibration and Verification:

[0162] 8.1) Calibration:

[0163] Four samples were taken from 10 water-containing cable samples to establish a standard curve, and the remaining six samples were used for verification.

[0164] The near-infrared spectrum was processed using the first derivative (window 7, degree 2). The first derivative value at 1450 nm was then used to perform a univariate linear regression on the water concentration, and a standard curve was established as follows: Figure 4 As shown, the fitting equation is:

[0165] y = 0.26·x + 0.92 (2)

[0166] In the formula: x represents the mass fraction of water concentration (w%), and y represents the first derivative value at 1450 nm.

[0167] The R² of the fitted curve in equation (2) is 0.90958, and the person coefficient is 0.95372.

[0168] 8.2) Verification:

[0169] Substituting the first-order differential value at 1450 nm of the validation set samples into equation (2), the predicted moisture concentration values ​​are shown in Table 1. It can be seen that the relative deviation between the predicted and reference values ​​for all samples is less than 4%, meeting the accuracy requirements for rapid on-site detection of moisture in the XLPE main insulation layer of cable joints.

[0170] The above verification results further confirm the hypothesis that "the XLPE spectra of different samples have small variations and their spectral signals can be regarded as a constant signal". The influence of XLPE on the 1450nm characteristic peak of moisture can be basically eliminated by first-order differential processing, the interference of XLPE component spectra can be eliminated, and a unit calibration curve can be established even in the case of spectral overlap.

[0171] Table 1. Comparison of predicted and actual moisture concentration values ​​of the verification samples.

[0172]

[0173] 9) Verification of test data from a portable near-infrared spectrometer:

[0174] The accuracy of the model is verified by comparing and analyzing the moisture content measured by the drying method (actual value) with the moisture content predicted by the model (model predicted value).

[0175] The model predictions and actual values ​​of the main insulation of 23 XLPE cables are shown in Table 2.

[0176] As shown in the table, the relative deviation between the predicted and actual values ​​is 0.5% to 7%, with an average relative deviation of 2.87%. The model has a small prediction deviation and high accuracy, indicating that the established moisture model has reliable prediction results.

[0177] The specific values ​​of the predicted and actual values ​​for the 23 validation samples are shown in Table 2.

[0178] Table 2 Verification values ​​of on-site sampling data

[0179] serial number Actual value, % Forecast value, % Relative deviation, % 1 0.206 0.209 1.13 2 1.144 1.159 1.270 3 0.964 1.009 4.660 4 0.872 0.884 1.430 5 0.678 0.723 6.640 6 1.033 1.074 3.890 7 0.700 0.723 3.000 8 1.427 1.459 2.220 9 0.582 0.588 1.12 10 0.614 0.635 3.460 11 1.267 1.320 4.190 12 0.846 0.851 0.620 13 1.045 1.069 2.34 14 1.481 1.547 4.480 15 0.254 0.261 2.57 16 0.139 0.149 6.630 17 0.746 0.764 2.42 18 1.332 1.370 2.840 19 1.245 1.287 3.340 20 0.258 0.26 0.75 21 1.566 1.580 0.890 22 0.391 0.396 1.27 23 0.738 0.774 4.860

[0180] In summary, the innovations of this invention mainly include the following aspects:

[0181] (1) High sensitivity and accuracy of spectral measurement: Near-infrared spectroscopy can accurately detect the moisture content of the shallow surface layer of the main insulation of XLPE cables, achieving high sensitivity measurement of minute moisture changes. This method overcomes the limitations of traditional detection methods and provides more accurate data support.

[0182] (2) Application of the dual-path algorithm: A dual-path algorithm was introduced to process and analyze different spectral data paths separately, thereby improving the accuracy and reliability of the measurement. This algorithm effectively separates and processes different components in the spectral data, improves the accuracy of the measurement results, and reduces the influence of external interference.

[0183] (3) Practical Application Verification and Optimization: The technical solution has been verified in various field application scenarios. The measurement process has been optimized through practice to ensure the practicality and reliability of the technology. This optimization based on feedback from practical applications makes the technology closer to actual engineering needs and enhances its application value.

[0184] This invention can be widely used in the fields of detecting the moisture content of the shallow surface layer of the main insulation of cables in operation and the design and manufacture of related testing equipment.

Claims

1. A device for measuring the moisture content in the main insulation material of a cable, characterized in that, include: Near-infrared laser emitters are used to modulate and emit near-infrared light; An input optical fiber is connected to the near-infrared laser emitter via a coaxial adapter to guide the near-infrared light to the Y-shaped fiber optic probe. A Y-shaped fiber optic probe is used to measure the transmission or diffuse reflection of the main insulation material of the target cable according to a preset optical path mode to form detection light, and to guide the detection light to the output fiber optic cable. The data preprocessing module is connected to the output optical fiber and is used to preprocess the detection light to form a digital signal. The data analysis module is used to obtain the moisture content of the main insulation material of the target cable based on digital signals.

2. The device for measuring the moisture content in the main insulation material of a cable according to claim 1, characterized in that: The device described above, used to measure the moisture content in the main insulation material of cables, uses a near-infrared laser emitter and a moisture content estimation model to quickly and accurately obtain information on the moisture content of the main insulation material of cables without contacting the cable under test. It can also monitor the operating status of the cable in real time, provide timely early warning information, improve the operational reliability and service life of the cable, and provide solid technical support for the stable operation of the power system. It is particularly suitable for determining the moisture content of the shallow surface layer of the main insulation of XLPE cables in the field.

3. The apparatus for measuring the moisture content in the main insulation material of a cable according to claim 1, characterized in that: The near-infrared laser emitter is a narrow-spectrum LD laser; the input optical fiber is a near-infrared Y-type multimode optical fiber.

4. The apparatus for measuring the moisture content in the main insulation material of a cable according to claim 1, characterized in that: The device for measuring the moisture content in the main insulation material of a cable, after obtaining the moisture content of the target cable's main insulation material, uses a data transmission module to securely transmit the moisture content of the target cable's main insulation material to a data center or remote monitoring system, and displays the measurement results and device status on the user interface.

5. A method for measuring the moisture content in the main insulation material of a cable, characterized in that, Includes the following steps: Step 1) Use a near-infrared laser emitter to perform transmission or diffuse reflection measurements on the main insulation material of cables with different moisture contents to form calibration light; Step 2) Input the calibration light into the data preprocessing module to obtain the digital signals corresponding to the main insulation materials of the cable with different moisture contents; Step 3) Perform denoising processing on the digital signal to obtain the denoised digital signal; Step 4) Input the denoised digital signal as a sample into the neural network for training to obtain the moisture content estimation model; Step 5) Use the moisture content estimation model to detect the moisture content of the main insulation material of the target cable.

6. The method for measuring the moisture content in the main insulation material of a cable according to claim 5, characterized in that: Step 3) involves denoising the digital signal to obtain a denoised digital signal, which includes the following steps: Step 3.1) Decompose the digital signal to obtain wavelet coefficients at different scales; Step 3.2) Construct a denoising threshold based on wavelet coefficients at different scales; Step 3.3) Construct a wavelet coefficient selection function using the denoising threshold; Step 3.4) Process each wavelet coefficient using the wavelet coefficient filtering function to obtain the processed wavelet coefficients; Step 3.5) Reconstruct the processed wavelet coefficients to obtain the denoised digital signal.

7. The method for measuring the moisture content in the main insulation material of a cable according to claim 6, characterized in that: The noise reduction threshold is: Where, λ j σ represents the noise reduction threshold. j N represents the standard deviation of the wavelet coefficients at the j-th decomposition scale. j This represents the length of the digital signal at the j-th decomposition scale.

8. The method for measuring the moisture content in the main insulation material of a cable according to claim 6, characterized in that... In step 3.3), the wavelet coefficient selection function is constructed using the denoising threshold, including: Formula used: Construct a wavelet coefficient selection function; Among them, W j,k This represents the k-th wavelet coefficient at the j-th decomposition scale. denoted as the processed wavelet coefficients, sgn(·) is the sign function, α represents the first empirical coefficient, and β represents the second empirical coefficient.

9. The method for measuring the moisture content in the main insulation material of a cable according to claim 5, characterized in that... In step 4), the denoised digital signal is used as a sample input into the neural network for training to obtain a moisture content estimation model, including: A moisture content estimation model is obtained by optimizing and training a neural network using a loss function; wherein the loss function is: Where n represents the number of samples, C represents the number of labels with different moisture contents, and y i,c This represents the actual moisture content label for the i-th sample. This represents the probability that the i-th sample, predicted by the neural network, belongs to the c-th water content label.

10. The method for measuring the moisture content in the main insulation material of a cable according to claim 9, characterized in that... In step 4), the loss function is: Where n represents the number of samples, C represents the number of labels with different moisture contents, and y i,c This represents the actual moisture content label for the i-th sample. This represents the probability that the i-th sample, predicted by the neural network, belongs to the c-th water content label.

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