Power cable safety monitoring system based on Raman temperature measurement system

Abstract

The invention discloses a power cable safety monitoring system based on a Raman temperature measurement system, and belongs to the technical field of safety monitoring. A power cable safety monitoring system based on a Raman temperature measurement system comprises a light source module, a sensing module, a light splitting module, a temperature calibration module, a photoelectric conversion module, a data acquisition module, a power supply module and a computer. The problems that in the prior art, safety risks exist in a complex environment, the complexity of long-distance temperature measurement is high, and a distributed optical fiber temperature measurement system based on Raman scattering is low in output precision are solved, the safety in the complex environment is improved, and the reliability of the system is improved. The safety risk of the electrical temperature sensor in the environments of a strong magnetic field, no open fire and the like is avoided, the complexity of long-distance temperature measurement is reduced, long-distance efficient and safe monitoring of a cable line can be achieved, the output precision of the system is remarkably improved, the reference temperature precision is improved through optimization of the temperature calibration module, and the temperature measurement precision output by the system is improved.

Description

Technical Field

[0001] The invention relates to the technical field of cable line safety monitoring, and in particular to a power cable safety monitoring system based on a Raman temperature measurement system. Background Art

[0002] Traditional cable line safety monitoring mostly uses electrical temperature sensors based on temperature detection principles such as thermocouples, thermal resistors or PN junctions. This type of temperature sensor has the advantages of low cost, mature technology, and simple operation. However, when dealing with strong magnetic fields, open flames and other environments, the use of electrical temperature sensors increases the risk of accidents. In addition, when facing long-distance temperature measurement, the complexity of using this type of temperature detection device increases exponentially with the increase in distance. Some distributed optical fiber temperature measurement systems based on Raman scattering have been applied to cable line safety monitoring. These systems are developed based on the principle of Raman scattering and can stably collect temperature data of the environment in which the optical fiber is located.

[0003] The existing cable line safety monitoring technology has problems, including: safety risks in complex environments, the complexity of long-distance temperature measurement, and the low output accuracy of the distributed optical fiber temperature measurement system based on Raman scattering, such as low reference temperature accuracy and signal noise pollution; therefore, it does not meet the existing needs. In response to this, we proposed a power cable safety monitoring system based on the Raman temperature measurement system. Summary of the invention

[0004] The purpose of the present invention is to provide a power cable safety monitoring system based on a Raman temperature measurement system, which improves the safety in complex environments, avoids the safety risks of electrical temperature sensors in strong magnetic fields, open flames and other environments, reduces the complexity of long-distance temperature measurement, and can achieve efficient and safe long-distance monitoring of cable lines. The output accuracy of the system is significantly improved, the influence of self-heating on the internal temperature field of the temperature calibration module is reduced, the reference temperature accuracy is improved, and the temperature measurement accuracy of the system output is improved, solving the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solution: a power cable safety monitoring system based on a Raman temperature measurement system, comprising:

[0006] The light source module is used to emit a laser signal using a high-speed pulse light source. The pulse modulation circuit adjusts the pulse width according to the size of the laser signal. After the adjustment is completed, the laser signal is transmitted to the sensor module and returned to the spectrometer module through the sensor module;

[0007] The sensor module is used to receive the laser signal emitted by the light source module, generate back Raman scattered light using the laser signal transmitted by the light source module, and transmit the back Raman scattered light back to the light splitting module;

[0008] A light splitting module, used for splitting the back Raman scattered light generated in the sensing module into Raman-Stokes light and anti-Stokes light through a wavelength division multiplexer, and transmitting the Raman-Stokes light and the anti-Stokes light to the photoelectric conversion module;

[0009] A temperature calibration module is used to measure the temperature of the sensor module in real time and calibrate according to the real-time monitored temperature;

[0010] A photoelectric conversion module, used for converting Raman-Stokes light and anti-Stokes light into analog voltage signals, amplifying the analog voltage signals, and transmitting the converted analog voltage signals to a data acquisition module;

[0011] The data acquisition module is used to read the analog voltage signal of the spectrophotometer, convert the analog voltage signal into a digital signal, process the digital signal, and transmit the digital signal to the computer after the processing is completed;

[0012] The power module is used to provide a stable power supply for the light source module, the spectrometer module, the temperature calibration module, the photoelectric conversion module and the data acquisition module;

[0013] The computer is used to process the digital voltage signal to obtain the temperature distribution along the sensing optical fiber of the distributed optical fiber Raman temperature measurement system, analyze the monitoring results of the power cable according to the temperature distribution results, identify abnormal temperature rise points or hot spots, and display them on the display interface.

[0014] Preferably, the temperature calibration module is composed of a DS1624 temperature sensor, a controller, a calibration unit and an optical fiber fixing frame, and the sensor is separated from the controller, and the controller is separated from the calibration unit, wherein the calibration module includes:

[0015] The first constant temperature reference module is used to perform preliminary calibration on the temperature data of the DS1624 temperature sensor to obtain preliminary calibration data of the DS1624 temperature sensor;

[0016] The second constant temperature reference module is used to perform secondary calibration on the DS1624 temperature sensor during the period of measuring the temperature of the sensing module by using the DS1624 temperature sensor to obtain secondary calibration data of the DS1624 temperature sensor;

[0017] The comprehensive calibration module is used to perform comprehensive calibration on the temperature of the sensor module using the preliminary calibration data and the secondary calibration data to obtain the actual temperature data of the sensor module.

[0018] Preferably, the sensing module includes a reference optical fiber, a measuring optical fiber and a sensing optical fiber. The front end of the measuring optical fiber is provided with a reference optical fiber, and the rear end of the measuring optical fiber is provided with a sensing optical fiber. The central wavelength of the light source module is 1550nm, the frequency is 10kHz, and it is equipped with an SMA interface for receiving signals from the data acquisition module, a 5V power supply interface, and a multimode optical fiber FC / APC connector.

[0019] Preferably, the light splitting module comprises:

[0020] A receiving module, used for receiving the backscattered Raman light returned by the sensing module;

[0021] The wavelength division multiplexer is used for dividing the back Raman scattered light into Raman Stokes light and anti-Stokes light by using the wavelength division multiplexer.

[0022] Preferably, the wavelength division multiplexer is equipped with a 1550nm interface adapted to the light source, a COM port connected to the sensing optical fiber, and 1450nm and 1650nm interfaces adapted to the APD module to ensure a good separation effect of the back Raman light.

[0023] Preferably, the photoelectric conversion module is equipped with 1450nm and 1650nm optical path interfaces connected to the wavelength division multiplexer, and two SMA interfaces connected to the data acquisition module, and has ±5V power supply and grounding interfaces.

[0024] Preferably, the data acquisition module comprises:

[0025] A data acquisition module is used to acquire the analog voltage signal converted by the pulse modulation circuit and the photoelectric conversion module using the high-speed data acquisition module;

[0026] The data processing module is used to perform noise reduction processing on the temperature measurement signal output by the temperature calibration module by combining three noise reduction algorithms: cumulative averaging, statistical sorting and wavelet transform;

[0027] The data conversion module is used to convert the analog voltage signal after data processing into a digital signal and transmit the digital signal to the computer.

[0028] Preferably, the data processing module includes:

[0029] The cumulative average method is used to measure each group of temperature measurement signals multiple times and accumulate the average value to reduce random noise;

[0030] The statistical sorting method is used to sort the temperature measurement signals after cumulative averaging, remove abnormal values, and reduce the errors caused by abnormal values;

[0031] Use wavelet transform to decompose the processed temperature measurement signal, decompose the signal into high-frequency and low-frequency components, perform threshold processing on the high-frequency components, and reconstruct the temperature measurement signal after the processing;

[0032] The temperature measurement signal after noise reduction is verified to determine whether the temperature measurement signal meets the requirements. If it does not meet the requirements, the cumulative average, statistical sorting and wavelet transform processing are repeated until the temperature measurement signal meets the requirements.

[0033] Preferably, the data processing module further includes:

[0034] An outlier number extraction module is used to extract the number corresponding to the outlier value;

[0035] A comparison module, used for comparing the number corresponding to the abnormal value with a preset number threshold;

[0036] A signal generation time acquisition module, used for retrieving the signal generation time corresponding to each abnormal value when the number corresponding to the abnormal value exceeds a preset number threshold;

[0037] An abnormal value generation coefficient acquisition module is used to obtain the abnormal value generation coefficient by using the signal generation time corresponding to each abnormal value;

[0038] The outlier generation coefficient is obtained by the following formula:

[0039]

[0040] Where J represents the outlier generation coefficient; n represents the number of outliers; T i+1 represents the time when the i+1th abnormal value is generated; T i represents the time when the ith abnormal value is generated; T c Indicates the preset time interval reference value; Y i represents the abnormal data value corresponding to the i-th abnormal value; Y i+1 represents the abnormal data value corresponding to the i+1th abnormal value; X p Indicates the average value corresponding to the temperature measurement signal; Y max Indicates the maximum value of abnormal data corresponding to n abnormal values; Y min Indicates the minimum value of abnormal data corresponding to n abnormal values;

[0041] a first coefficient comparison module, used for comparing the abnormal value generation coefficient with a preset generation coefficient threshold;

[0042] The abnormality severity determination module is used to determine the abnormality severity of the abnormal value when the abnormal value generation coefficient exceeds a preset generation coefficient threshold.

[0043] Preferably, the abnormal severity determination module includes:

[0044] A temperature measurement signal value extraction module, used to extract signal values ​​corresponding to two valid temperature measurement signals adjacent to each abnormal value generation time before and after as a first temperature measurement signal value and a second temperature measurement signal value;

[0045] An abnormality degree coefficient acquisition module, used to obtain the abnormality degree coefficient by using the first temperature measurement signal value and the second temperature measurement signal value corresponding to each abnormal value;

[0046] The abnormality degree coefficient is obtained by the following formula:

[0047]

[0048] Among them, R represents the abnormality coefficient; n represents the number of abnormal values; Y i represents the abnormal data value corresponding to the i-th abnormal value; X xi and X hi represents the first temperature measurement signal value and the second temperature measurement signal value corresponding to the i-th abnormal value; J represents the abnormal value generation coefficient; J0 represents the preset generation coefficient threshold; X p represents the average value corresponding to the temperature measurement signal; S represents the adjustment coefficient, and the adjustment coefficient is obtained by the following formula:

[0049]

[0050] Among them, S represents the adjustment coefficient; n represents the number of outliers; Y max Indicates the maximum value of abnormal data corresponding to n abnormal values; Y min represents the minimum value of abnormal data corresponding to n abnormal values; X max and X min Indicates the maximum and minimum values ​​corresponding to the temperature measurement signal; Y b represents the standard deviation of abnormal data corresponding to n abnormal values; X b Indicates the standard deviation of the temperature measurement signal; Y i represents the abnormal data value corresponding to the i-th abnormal value; X xi and X hi represents the first temperature measurement signal value and the second temperature measurement signal value corresponding to the i-th abnormal value; X p Indicates the average value corresponding to the temperature measurement signal;

[0051] A second coefficient comparison module, used for comparing the abnormal degree coefficient with a preset degree coefficient threshold;

[0052] The abnormal value serious warning module is used to determine that an abnormal value serious warning is needed when the abnormal degree coefficient exceeds a preset degree coefficient threshold.

[0053] Compared with the prior art, the present invention has the following beneficial effects:

[0054] The present invention improves the safety in complex environments, avoids the safety risks of electrical temperature sensors in strong magnetic fields, open flame-free environments, etc., reduces the complexity of long-distance temperature measurement, can achieve efficient and safe long-distance monitoring of cable lines, and significantly improves the output accuracy of the system. By separating the sensor from the controller, and the controller from the temperature calibration module, the influence of self-heating on the internal temperature field of the temperature calibration module is reduced, the reference temperature accuracy is improved, and the temperature measurement signal output by the temperature calibration module is subjected to noise reduction processing using three noise reduction algorithms, namely, joint cumulative averaging, statistical sorting, and wavelet transform, thereby improving the temperature measurement accuracy of the system output. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is a module schematic diagram of the power cable safety monitoring system based on the Raman temperature measurement system of the present invention;

[0056] Figure 2 It is a schematic diagram of the process of the power cable safety monitoring system based on the Raman temperature measurement system of the present invention. DETAILED DESCRIPTION

[0057] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0058] In order to solve the problems of safety risks in existing cable line safety monitoring technology in complex environments, high complexity of long-distance temperature measurement, and low output accuracy of distributed optical fiber temperature measurement systems based on Raman scattering, please refer to Figure 1-Figure 2 , this embodiment provides the following technical solutions:

[0059] The power cable safety monitoring system based on Raman temperature measurement system includes:

[0060] The light source module is used to emit a laser signal using a high-speed pulse light source with high precision and stability. The pulse modulation circuit adjusts the pulse width according to the size of the laser signal. After the adjustment is completed, the laser signal is transmitted to the sensor module and returned to the spectrometer module through the sensor module;

[0061] The sensor module is used to receive the laser signal emitted by the light source module, generate back Raman scattered light using the laser signal transmitted by the light source module, and transmit the back Raman scattered light back to the light splitting module;

[0062] A light splitting module, used for splitting the back Raman scattered light generated in the sensing module into Raman-Stokes light and anti-Stokes light through a wavelength division multiplexer, and transmitting the Raman-Stokes light and the anti-Stokes light to the photoelectric conversion module;

[0063] A temperature calibration module is used to measure the temperature of the sensor module in real time and calibrate according to the real-time monitored temperature;

[0064] A photoelectric conversion module, used for converting Raman-Stokes light and anti-Stokes light into analog voltage signals, amplifying the analog voltage signals, and transmitting the converted analog voltage signals to a data acquisition module;

[0065] The data acquisition module is used to read the analog voltage signal of the spectrophotometer, convert the analog voltage signal into a digital signal, process the digital signal, and transmit the digital signal to the computer after the processing is completed;

[0066] The power module is used to provide a stable power supply for the light source module, the spectrometer module, the temperature calibration module, the photoelectric conversion module and the data acquisition module;

[0067] The computer is used to process the digital voltage signal to obtain the temperature distribution along the sensing optical fiber of the distributed optical fiber Raman temperature measurement system, analyze the monitoring results of the power cable according to the temperature distribution results, identify abnormal heating points or hot spots, and display them on the display interface. When the temperature exceeds the preset threshold, an early warning signal is issued to promptly notify the operation and maintenance personnel to intervene.

[0068] The temperature calibration module is composed of a DS1624 temperature sensor, a controller, a calibration unit, and an optical fiber fixing frame, and the sensor is separated from the controller, and the controller is separated from the calibration unit. The calibration module includes:

[0069] The first constant temperature reference module is used to perform preliminary calibration on the temperature data of the DS1624 temperature sensor to obtain preliminary calibration data of the DS1624 temperature sensor;

[0070] The second constant temperature reference module is used to perform secondary calibration on the DS1624 temperature sensor during the period of measuring the temperature of the sensing module by using the DS1624 temperature sensor to obtain secondary calibration data of the DS1624 temperature sensor;

[0071] A comprehensive calibration module is used to perform comprehensive calibration on the temperature of the sensor module using the preliminary calibration data and the secondary calibration data to obtain actual temperature data of the sensor module;

[0072] Through the separation design, the sensor is separated from the controller, and the controller is separated from the temperature calibration module, reducing the impact of self-heating on the internal temperature field of the temperature calibration module. At the same time, the high-precision DS1624 is used to replace the original DS18B20, and the control circuit in the module is optimized and upgraded, which can reduce the reference temperature error by 1.5℃.

[0073] The sensing module includes reference optical fiber, measuring optical fiber and sensing optical fiber. The front end of the measuring optical fiber is provided with a reference optical fiber, and the rear end of the measuring optical fiber is provided with a sensing optical fiber. The central wavelength of the light source module is 1550nm and the frequency is 10kHz. It is equipped with an SMA interface for receiving signals from the data acquisition module, a 5V power supply interface and a multi-mode optical fiber FC / APC connector.

[0074] The optical splitter module includes:

[0075] A receiving module, used for receiving the backscattered Raman light returned by the sensing module;

[0076] The wavelength division multiplexer is used to separate the back Raman scattered light into Raman Stokes light and anti-Stokes light by using the wavelength division multiplexer. At the transmitting end, the wavelength division multiplexer combines the Raman Stokes light and the anti-Stokes light into one beam and transmits it through the same optical fiber. At the receiving end, the demultiplexer separates the Raman Stokes light and the anti-Stokes light for further processing and recovery of the original information.

[0077] The wavelength division multiplexer is equipped with a 1550nm interface adapted to the light source, a COM port connected to the sensing optical fiber, and 1450nm and 1650nm interfaces adapted to the APD module to ensure a good separation effect of the back Raman light.

[0078] The main function of the pulse modulation circuit is to adjust the pulse width. By changing the width of the pulse, the pulse modulation circuit can achieve fine control of the output signal. Specifically, the pulse modulation circuit changes the average value or amplitude of the output signal by adjusting the duty cycle of the pulse, that is, the ratio of the pulse width to the period.

[0079] The photoelectric conversion module is equipped with 1450nm and 1650nm optical path interfaces connected to the wavelength division multiplexer, and two SMA interfaces connected to the data acquisition module, and also has ±5V power supply and grounding interfaces.

[0080] Convert light radiation energy into electrical signals, thereby realizing the conversion between optical signals and electrical signals.

[0081] The photoelectric detector module is used to detect optical signals and convert them into electrical signals to achieve high-speed data transmission. It can quickly capture weak optical signals and convert them into electrical signals, support high-speed data transmission, adapt to optical signals of different wavelengths, meet the needs of diverse application scenarios, ensure stable operation of the equipment, and reduce energy consumption.

[0082] Data acquisition module, including:

[0083] A data acquisition module is used to acquire the analog voltage signal converted by the pulse modulation circuit and the photoelectric conversion module using the high-speed data acquisition module;

[0084] The data processing module is used to perform noise reduction processing on the temperature measurement signal output by the temperature calibration module by combining three noise reduction algorithms: cumulative averaging, statistical sorting and wavelet transform;

[0085] The data conversion module is used to convert the analog voltage signal after data processing into a digital signal and transmit the digital signal to the computer.

[0086] The data acquisition module utilizes a high-speed data acquisition card with a high sampling rate, an integrated SMA interface connected to a photodetector and a high-speed pulse light source, and a PCI interface for communicating with a host computer to improve the spatial resolution of the system.

[0087] Data processing module, including:

[0088] The cumulative average method is used to measure each group of temperature measurement signals multiple times and accumulate the average value to reduce random noise;

[0089] The statistical sorting method is used to sort the temperature measurement signals after cumulative averaging, remove abnormal values, and reduce the errors caused by abnormal values;

[0090] Use wavelet transform to decompose the processed temperature measurement signal, decompose the signal into high-frequency and low-frequency components, perform threshold processing on the high-frequency components, and reconstruct the temperature measurement signal after the processing;

[0091] The temperature measurement signal after noise reduction is verified to determine whether the temperature measurement signal meets the requirements. If it does not meet the requirements, the cumulative average, statistical sorting and wavelet transform processing are repeated until the temperature measurement signal meets the requirements.

[0092] The cumulative average method reduces random noise by performing multiple measurements and accumulating the average value. It is suitable for processing random noise because the results of multiple measurements can offset each other, thereby reducing the impact of noise. In the temperature measurement signal, measuring the same temperature value multiple times and accumulating the average value can effectively reduce random noise caused by environmental interference and other factors. The statistical sorting method reduces errors caused by outliers by sorting the measured data and removing outliers. It is suitable for processing signals containing outliers. In the temperature measurement signal, sorting the data of multiple measurements, removing the largest and smallest data points, and then calculating the average value of the remaining data can effectively reduce the noise caused by measurement errors or outliers. The wavelet transform decomposes the signal into sub-bands of different frequencies, performs threshold processing on each sub-band, and then reconstructs the signal. It is suitable for processing signals containing high-frequency noise. In the temperature measurement signal, the wavelet transform is used to decompose the signal into high-frequency and low-frequency components, threshold processing is performed on the high-frequency components to remove noise, and then the signal is reconstructed, which can effectively reduce the impact of high-frequency noise.

[0093] By effectively combining the advantages of three noise reduction algorithms, namely cumulative averaging, statistical sorting and wavelet transform, the temperature measurement signal is subjected to comprehensive noise reduction processing to improve the signal-to-noise ratio and accuracy. Aiming at the problem that the Raman scattering signal is weak and easily contaminated by noise, the temperature measurement signal output by the temperature calibration module is subjected to noise reduction processing by using three noise reduction algorithms, namely joint cumulative averaging, statistical sorting and wavelet transform. Among them, cumulative averaging is used to reduce white noise, statistical sorting filtering is used to process pulse noise, and wavelet transform is used to make up for the shortcomings of the first two algorithms in frequency domain filtering. Experimental verification shows that the joint noise reduction algorithm can improve the temperature measurement accuracy of the system output to ±0.3℃.

[0094] Working principle: When in use, the power cable safety monitoring system based on the Raman temperature measurement system of the present invention is based on Figure 1 and Figure 2 , including the following steps:

[0095] Step 1: Use a high-speed pulse light source with high precision and stability to emit a laser signal. The pulse modulation circuit adjusts the pulse width according to the size of the laser signal. After the adjustment is completed, the laser signal is transmitted to the sensor module and then returned to the spectrometer module through the sensor module;

[0096] Step 2: receiving the laser signal emitted by the light source module, and using the laser signal transmitted by the light source module to generate back Raman scattered light, and transmitting the back Raman scattered light back to the light splitting module;

[0097] Step 3: Splitting the back Raman scattered light generated in the sensor module into Raman-Stokes light and anti-Stokes light through a wavelength division multiplexer, and transmitting the Raman-Stokes light and anti-Stokes light to the photoelectric conversion module;

[0098] Step 4: During the transmission of Raman-Stokes light and anti-Stokes light, the temperature data on the DS1624 temperature sensor is preliminarily calibrated to obtain the preliminarily calibrated data of the DS1624 temperature sensor. During the period of measuring the temperature of the sensor module by using the DS1624 temperature sensor, the DS1624 temperature sensor is recalibrated to obtain the secondary calibration data of the DS1624 temperature sensor. The temperature of the sensor module is comprehensively calibrated by using the preliminarily calibrated data and the secondary calibration data to obtain the actual temperature data of the sensor module.

[0099] Step 5: converting the Raman-Stokes light and the anti-Stokes light into analog voltage signals and amplifying the analog voltage signals, and transmitting the converted analog voltage signals to a data acquisition module;

[0100] Step 6: Read the analog voltage signal of the spectrometer module, convert the analog voltage signal into a digital signal, process the digital signal, and transmit the digital signal to the computer after the processing is completed;

[0101] Step 7: Process the digital voltage signal to obtain the temperature distribution along the sensing optical fiber of the distributed optical fiber Raman temperature measurement system. Analyze the monitoring results of the power cable based on the temperature distribution results, identify abnormal heating points or hot spots, and issue a warning signal when the temperature exceeds the preset threshold to promptly notify the operation and maintenance personnel to intervene.

[0102] In summary, the power cable safety monitoring system based on the Raman temperature measurement system of the present invention reduces the influence of self-heating on the internal temperature field of the temperature calibration module by separating the sensor from the controller and the controller from the temperature calibration module. At the same time, the high-precision DS1624 is selected to replace the original DS18B20, and the control circuit in the module is optimized and upgraded, which can reduce the reference temperature error by 1.5°C. By effectively combining the advantages of the three noise reduction algorithms of cumulative averaging, statistical sorting and wavelet transform, the temperature measurement signal is subjected to comprehensive noise reduction processing to improve the signal-to-noise ratio and accuracy. In view of the problem that the Raman scattering signal is weak and easily contaminated by noise, the temperature measurement signal output by the temperature calibration module is subjected to noise reduction processing by using the three noise reduction algorithms of combined cumulative averaging, statistical sorting and wavelet transform, wherein the cumulative averaging is used to reduce white noise, the statistical sorting filter is used to process pulse noise, and the wavelet transform is used to make up for the deficiencies of the first two algorithms in frequency domain filtering. It has been verified through experiments that the combined noise reduction algorithm can reduce the system output The temperature measurement accuracy is improved to ±0.3℃. The Raman temperature measurement system is used to monitor the safety of power cables. It can measure the temperature changes along the cable in real time and accurately with an accuracy of up to 0.1℃, providing reliable data support for timely discovery of potential overheating faults. It can continuously measure the temperature and display the temperature of each point on the line in real time, as well as the temperature change curve of the entire line, so that the operation and maintenance personnel can grasp the operation status of the cable in time, and realize continuous temperature monitoring of the entire cable line. There is no monitoring blind spot, and the temperature status of the cable can be fully grasped. By introducing optical cables into the cables, the temperature changes of the cables within a range of more than ten kilometers can be dynamically monitored. Using optical fibers as sensors makes the Raman temperature measurement system unaffected by electromagnetic interference and can work stably in strong electromagnetic field environments. Optical fiber sensors are electrically insulating and can resist the impact of high voltage and high current. At the same time, optical fiber materials are fire-resistant, water-resistant, non-corrosive, and have a long service life. The Raman temperature measurement system supports remote monitoring and can achieve remote monitoring of dozens of kilometers without relays.

[0103] Specifically, the data processing module further includes:

[0104] An outlier number extraction module is used to extract the number corresponding to the outlier value;

[0105] A comparison module, used for comparing the number corresponding to the abnormal value with a preset number threshold;

[0106] A signal generation time acquisition module, used for retrieving the signal generation time corresponding to each abnormal value when the number corresponding to the abnormal value exceeds a preset number threshold;

[0107] An abnormal value generation coefficient acquisition module is used to obtain the abnormal value generation coefficient by using the signal generation time corresponding to each abnormal value;

[0108] The outlier generation coefficient is obtained by the following formula:

[0109]

[0110] Where J represents the outlier generation coefficient; n represents the number of outliers; T i+1 represents the time when the i+1th abnormal value is generated; T i represents the time when the ith abnormal value is generated; T c Indicates the preset time interval reference value; Y i represents the abnormal data value corresponding to the i-th abnormal value; Y i+1 represents the abnormal data value corresponding to the i+1th abnormal value; X p Indicates the average value corresponding to the temperature measurement signal; Y max Indicates the maximum value of abnormal data corresponding to n abnormal values; Y min Indicates the minimum value of abnormal data corresponding to n abnormal values;

[0111] a first coefficient comparison module, used for comparing the abnormal value generation coefficient with a preset generation coefficient threshold;

[0112] The abnormality severity determination module is used to determine the abnormality severity of the abnormal value when the abnormal value generation coefficient exceeds a preset generation coefficient threshold.

[0113] The technical effect of the above technical solution is: through the outlier number extraction module, the technical solution can automatically detect and count the number of outliers in the statistical data set. This is the basic step of anomaly detection and provides key information for subsequent analysis. The comparison module compares the number of outliers with the preset number threshold. This step helps to screen out those situations where there are too many outliers and may represent more serious problems. When the number of outliers exceeds the threshold, the signal generation time acquisition module will call the signal generation time corresponding to each outlier. This helps to analyze the distribution of outliers from the time dimension and further understand the pattern and trend of anomaly occurrence. The outlier generation coefficient acquisition module uses the generation time of the outlier, the abnormal data value, the average value of the temperature measurement signal, and the maximum and minimum values ​​of the abnormal data to calculate the outlier generation coefficient through a specific formula. This coefficient combines information from multiple dimensions and can more comprehensively reflect the characteristics and severity of the outliers. The first coefficient comparison module compares the calculated outlier generation coefficient with the preset generation coefficient threshold. If the coefficient exceeds the threshold, the abnormal severity determination module will determine the abnormal severity of the outlier. This step helps to grade the abnormal situation so as to take more appropriate countermeasures.

[0114] In summary, this technical solution can more accurately identify potential abnormal problems by calculating the outlier generation coefficient by integrating information from multiple dimensions. Comprehensive analysis of outliers from the time dimension and numerical dimension helps to gain a deeper understanding of the patterns and causes of anomalies. By setting thresholds and comparison coefficients, abnormal situations can be graded, so that more accurate and effective response measures can be taken. Through modular design, this technical solution realizes the automated processing of anomaly detection, analysis and judgment, and improves the efficiency and accuracy of data processing.

[0115] Specifically, the abnormal severity determination module includes:

[0116] A temperature measurement signal value extraction module, used to extract signal values ​​corresponding to two valid temperature measurement signals adjacent to each abnormal value generation time before and after as a first temperature measurement signal value and a second temperature measurement signal value;

[0117] An abnormality degree coefficient acquisition module, used to obtain the abnormality degree coefficient by using the first temperature measurement signal value and the second temperature measurement signal value corresponding to each abnormal value;

[0118] The abnormality degree coefficient is obtained by the following formula:

[0119]

[0120] Among them, R represents the abnormality coefficient; n represents the number of abnormal values; Y i represents the abnormal data value corresponding to the i-th abnormal value; X xi and X hi represents the first temperature measurement signal value and the second temperature measurement signal value corresponding to the i-th abnormal value; J represents the abnormal value generation coefficient; J0 represents the preset generation coefficient threshold; X p represents the average value corresponding to the temperature measurement signal; S represents the adjustment coefficient, and the adjustment coefficient is obtained by the following formula:

[0121]

[0122] Among them, S represents the adjustment coefficient; n represents the number of outliers; Y max Indicates the maximum value of abnormal data corresponding to n abnormal values; Y min represents the minimum value of abnormal data corresponding to n abnormal values; X max and X min Indicates the maximum and minimum values ​​corresponding to the temperature measurement signal; Y b represents the standard deviation of abnormal data corresponding to n abnormal values; X b Indicates the standard deviation of the temperature measurement signal; Y i represents the abnormal data value corresponding to the i-th abnormal value; X xi and X hirepresents the first temperature measurement signal value and the second temperature measurement signal value corresponding to the i-th abnormal value; X p Indicates the average value corresponding to the temperature measurement signal;

[0123] A second coefficient comparison module, used for comparing the abnormal degree coefficient with a preset degree coefficient threshold;

[0124] The abnormal value serious warning module is used to determine that an abnormal value serious warning is needed when the abnormal degree coefficient exceeds a preset degree coefficient threshold.

[0125] The technical effect of the above technical solution is: through the temperature measurement signal value extraction module, the technical solution can extract the effective temperature measurement signal value adjacent to each abnormal value generation time, that is, the first temperature measurement signal value and the second temperature measurement signal value. This information provides key data for the subsequent calculation of the abnormal degree coefficient. The abnormal degree coefficient acquisition module uses the extracted temperature measurement signal value and the abnormal data value to calculate the abnormal degree coefficient through a specific formula. This coefficient combines multiple factors such as the size of the abnormal value itself, the fluctuation of the temperature measurement signal, and the abnormal value generation coefficient, and can more accurately reflect the severity of the abnormality. The introduction of the adjustment coefficient makes the calculation of the abnormal degree coefficient more flexible and accurate. The coefficient can dynamically adjust the calculation of the abnormal degree coefficient by considering multiple factors such as the maximum and minimum values ​​of the abnormal data, the maximum and minimum values ​​of the temperature measurement signal, the standard deviation of the abnormal data, and the standard deviation of the temperature measurement signal, so that it can better adapt to different abnormal situations. Through the second coefficient comparison module and the abnormal value serious warning module, the technical solution realizes the hierarchical warning of the abnormal degree. When the abnormal degree coefficient exceeds the preset degree coefficient threshold, the system will trigger a serious warning, prompting the user or system administrator to take necessary countermeasures. This technical solution achieves rapid detection, analysis and early warning of abnormal values ​​through automation and intelligence. This not only improves the efficiency of abnormality processing, but also reduces the risk of false positives and false negatives, allowing the system to respond to abnormal situations more accurately.

[0126] In summary, this technical solution improves the accuracy of anomaly detection: by integrating information from multiple dimensions to calculate the degree of anomaly coefficient, it can more accurately identify potential serious anomaly problems. The introduction of information such as adjustment coefficients and temperature measurement signal values ​​makes anomaly analysis more comprehensive and in-depth. Grading warnings based on the size of the degree of anomaly coefficients helps to take more appropriate and timely response measures. By processing abnormal values ​​in an automated and intelligent manner, the response speed and accuracy of the system are improved.

[0127] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0128] While the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that many changes, modifications, substitutions and alterations may be made to the embodiments without departing from the principles and spirit of the invention.

Claims

1. The power cable safety monitoring system based on Raman temperature measurement system is characterized by: include: The light source module is used to emit a laser signal using a high-speed pulse light source. The pulse modulation circuit adjusts the pulse width according to the size of the laser signal. After the adjustment is completed, the laser signal is transmitted to the sensor module and returned to the spectrometer module through the sensor module; The sensor module is used to receive the laser signal emitted by the light source module, generate back Raman scattered light using the laser signal transmitted by the light source module, and transmit the back Raman scattered light back to the light splitting module; A light splitting module, used for splitting the back Raman scattered light generated in the sensing module into Raman-Stokes light and anti-Stokes light through a wavelength division multiplexer, and transmitting the Raman-Stokes light and the anti-Stokes light to the photoelectric conversion module; A temperature calibration module is used to measure the temperature of the sensor module in real time and calibrate according to the real-time monitored temperature; A photoelectric conversion module, used for converting Raman-Stokes light and anti-Stokes light into analog voltage signals, amplifying the analog voltage signals, and transmitting the converted analog voltage signals to a data acquisition module; The data acquisition module is used to read the analog voltage signal of the spectrophotometer, convert the analog voltage signal into a digital signal, process the digital signal, and transmit the digital signal to the computer after the processing is completed; A power module is used to provide power supply for the light source module, the spectrometer module, the temperature calibration module, the photoelectric conversion module and the data acquisition module; The computer is used to process the digital voltage signal to obtain the temperature distribution along the sensing optical fiber of the distributed optical fiber Raman temperature measurement system, analyze the monitoring results of the power cable according to the temperature distribution results, identify abnormal temperature rise points or hot spots, and display them on the display interface.

2. The power cable safety monitoring system based on the Raman temperature measurement system according to claim 1 is characterized in that: The temperature calibration module is composed of a DS1624 temperature sensor, a controller, a calibration unit and an optical fiber fixing frame, and the sensor is separated from the controller, and the controller is separated from the calibration unit. The calibration module includes: The first constant temperature reference module is used to perform preliminary calibration on the temperature data of the DS1624 temperature sensor to obtain preliminary calibration data of the DS1624 temperature sensor; The second constant temperature reference module is used to perform secondary calibration on the DS1624 temperature sensor during the period of measuring the temperature of the sensing module by using the DS1624 temperature sensor to obtain secondary calibration data of the DS1624 temperature sensor; The comprehensive calibration module is used to perform comprehensive calibration on the temperature of the sensor module using the preliminary calibration data and the secondary calibration data to obtain the actual temperature data of the sensor module.

3. The power cable safety monitoring system based on the Raman temperature measurement system according to claim 2 is characterized in that: The sensing module includes a reference optical fiber, a measuring optical fiber and a sensing optical fiber. The front end of the measuring optical fiber is provided with a reference optical fiber, and the rear end of the measuring optical fiber is provided with a sensing optical fiber. The central wavelength of the light source module is 1550nm, the frequency is 10kHz, and it is equipped with an SMA interface for receiving data acquisition module signals, a 5V power supply interface and a multimode optical fiber FC / APC connector.

4. The power cable safety monitoring system based on the Raman temperature measurement system according to claim 3 is characterized in that: The optical splitting module comprises: A receiving module, used for receiving the backscattered Raman light returned by the sensing module; The wavelength division multiplexer is used for dividing the back Raman scattered light into Raman Stokes light and anti-Stokes light by using the wavelength division multiplexer.

5. The power cable safety monitoring system based on the Raman temperature measurement system according to claim 4 is characterized in that: The wavelength division multiplexer is equipped with a 1550nm interface adapted to the light source, a COM port connected to the sensing optical fiber, and 1450nm and 1650nm interfaces adapted to the APD module.

6. The power cable safety monitoring system based on the Raman temperature measurement system according to claim 5 is characterized in that: The photoelectric conversion module is equipped with 1450nm and 1650nm optical path interfaces connected to the wavelength division multiplexer, and two SMA interfaces connected to the data acquisition module, and also has ±5V power supply and grounding interfaces.

7. The power cable safety monitoring system based on the Raman temperature measurement system according to claim 1 is characterized in that: The data acquisition module comprises: A data acquisition module is used to acquire the analog voltage signal converted by the pulse modulation circuit and the photoelectric conversion module using the high-speed data acquisition module; The data processing module is used to perform noise reduction processing on the temperature measurement signal output by the temperature calibration module by combining three noise reduction algorithms: cumulative averaging, statistical sorting and wavelet transform; The data conversion module is used to convert the analog voltage signal after data processing into a digital signal and transmit the digital signal to the computer.

8. The power cable safety monitoring system based on the Raman temperature measurement system according to claim 7 is characterized in that: The data processing module comprises: Use the cumulative average method to measure each group of temperature measurement signals multiple times and accumulate them to obtain the average value; The statistical sorting method is used to sort the temperature measurement signals after cumulative averaging to remove abnormal values; Use wavelet transform to decompose the processed temperature measurement signal, decompose the signal into high-frequency and low-frequency components, perform threshold processing on the high-frequency components, and reconstruct the temperature measurement signal after the processing; The temperature measurement signal after noise reduction is verified to determine whether the temperature measurement signal meets the requirements. If it does not meet the requirements, the cumulative average, statistical sorting and wavelet transform processing are repeated until the temperature measurement signal meets the requirements.

9. The power cable safety monitoring system based on the Raman temperature measurement system according to claim 7 is characterized in that: The data processing module further includes: An outlier number extraction module is used to extract the number corresponding to the outlier value; A comparison module, used for comparing the number corresponding to the abnormal value with a preset number threshold; A signal generation time acquisition module, used for retrieving the signal generation time corresponding to each abnormal value when the number corresponding to the abnormal value exceeds a preset number threshold; An abnormal value generation coefficient acquisition module is used to obtain the abnormal value generation coefficient by using the signal generation time corresponding to each abnormal value; The outlier generation coefficient is obtained by the following formula: Where J represents the outlier generation coefficient; n represents the number of outliers; T i+1 represents the time when the i+1th abnormal value is generated; T i represents the time when the ith abnormal value is generated; T c Indicates the preset time interval reference value; Y i represents the abnormal data value corresponding to the i-th abnormal value; Y i+1 represents the abnormal data value corresponding to the i+1th abnormal value; X p Indicates the average value corresponding to the temperature measurement signal; Y max Indicates the maximum value of abnormal data corresponding to n abnormal values; Y min Indicates the minimum value of abnormal data corresponding to n abnormal values; a first coefficient comparison module, used for comparing the abnormal value generation coefficient with a preset generation coefficient threshold; The abnormality severity determination module is used to determine the abnormality severity of the abnormal value when the abnormal value generation coefficient exceeds a preset generation coefficient threshold.

10. The power cable safety monitoring system based on the Raman temperature measurement system according to claim 9 is characterized in that: The abnormal severity determination module further includes: A temperature measurement signal value extraction module, used to extract signal values ​​corresponding to two valid temperature measurement signals adjacent to each abnormal value generation time before and after as a first temperature measurement signal value and a second temperature measurement signal value; An abnormality degree coefficient acquisition module, used to obtain the abnormality degree coefficient by using the first temperature measurement signal value and the second temperature measurement signal value corresponding to each abnormal value; The abnormality degree coefficient is obtained by the following formula: Among them, R represents the abnormality coefficient; n represents the number of abnormal values; Y i represents the abnormal data value corresponding to the i-th abnormal value; X xi and X hi represents the first temperature measurement signal value and the second temperature measurement signal value corresponding to the i-th abnormal value; J represents the abnormal value generation coefficient; J0 represents the preset generation coefficient threshold; X p represents the average value corresponding to the temperature measurement signal; S represents the adjustment coefficient, and the adjustment coefficient is obtained by the following formula: Among them, S represents the adjustment coefficient; n represents the number of outliers; Y max Indicates the maximum value of abnormal data corresponding to n abnormal values; Y min represents the minimum value of abnormal data corresponding to n abnormal values; X max and X min Indicates the maximum and minimum values ​​corresponding to the temperature measurement signal; Y b represents the standard deviation of abnormal data corresponding to n abnormal values; X b Indicates the standard deviation of the temperature measurement signal; Y i represents the abnormal data value corresponding to the i-th abnormal value; X xi and X hi represents the first temperature measurement signal value and the second temperature measurement signal value corresponding to the i-th abnormal value; X p Indicates the average value corresponding to the temperature measurement signal; A second coefficient comparison module, used for comparing the abnormal degree coefficient with a preset degree coefficient threshold; The abnormal value serious warning module is used to determine that an abnormal value serious warning is needed when the abnormal degree coefficient exceeds a preset degree coefficient threshold.

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