Leakage monitoring system for oil-filled cable pipeline

Abstract

The invention relates to a leakage monitoring system for an oil-filled cable pipeline, and the system comprises a calculation alarm unit and a plurality of leakage detection modules which are respectively disposed at oil ducts at two ends of a cable, and each leakage detection module comprises a pressure sensor, an oil duct flowmeter, a signal collection unit, a time synchronizer, a wireless transmission unit, and a micro-flow testing unit. The pressure sensor and the oil duct flow meter are subjected to time synchronization through the time synchronizer and then are transmitted to the calculation alarm unit through the wireless transmission unit; the micro flow testing unit comprises a bypass micro flow channel arranged on one side of the oil channel, a switching valve is arranged at the joint of the bypass micro flow channel and the oil channel, a plurality of branches are arranged, and each branch is provided with a flow limiting valve; the calculation alarm unit judges the leakage condition according to the received detection data of the pressure sensor and the oil duct flowmeter; and system calibration is carried out by starting the micro flow test unit. Compared with the prior art, the method has the advantages of early detection, accurate positioning, self-calibration and the like of leakage.

Description

Technical Field

[0001] This invention relates to the field of high-voltage oil-filled cable monitoring technology, and in particular to a leakage monitoring system for oil-filled cable pipelines. Background Technology

[0002] High-voltage oil-filled cables are critical equipment in urban power grids and large energy hubs. Their hydraulic pipeline systems are responsible for the circulation and cooling of the insulating oil, ensuring the stable operation of the cables. Leaks can not only damage the cable's insulation, leading to major power outages, but also cause environmental pollution and enormous economic losses.

[0003] Currently, the main technical challenges and shortcomings in monitoring this type of pipeline leak are as follows: 1. Difficulty in detecting minute leaks: Traditional pressure or flow threshold alarm systems are not sensitive to slow-developing minute leaks and cannot detect them in the early stages. Often, the alarm is only triggered when the leak is large, which is too late.

[0004] 2. Poor positioning accuracy: The positioning method based on negative pressure waves is easily affected by changes in the characteristics of fluids in the pipeline (such as temperature and viscosity) and various on-site interferences (such as valve start-up and shutdown, pump operation), resulting in inaccurate wave velocity calculation and difficulty in signal recognition, and large positioning error.

[0005] 3. Weak anti-interference capability: Complex on-site working conditions can produce pressure fluctuations similar to the characteristics of leakage signals. Existing systems have difficulty effectively distinguishing between real leakage and operational interference, resulting in a high false alarm rate.

[0006] 4. Lack of self-calibration capability: After long-term operation, the accuracy of the sensor will drift, but the existing system lacks effective online self-calibration methods, which leads to a decrease in monitoring reliability over time.

[0007] Therefore, there is an urgent need in this field for an online monitoring system for leaks in oil-filled cable ducts that can achieve early, accurate, reliable, and self-diagnostic capabilities to address the aforementioned technical deficiencies. Summary of the Invention

[0008] The purpose of this invention is to overcome the shortcomings of the existing technology and provide a precise, reliable leak monitoring system for oil-filled cable ducts with self-diagnostic function.

[0009] The objective of this invention can be achieved through the following technical solutions: A leakage monitoring system for an oil-filled cable pipeline includes a calculation alarm unit and multiple leakage detection modules respectively installed at the oil inlet and outlet of the oil passage at both ends of the cable. Each leakage detection module includes a pressure sensor, an oil passage flow meter, a signal acquisition unit, a time synchronizer, a wireless transmission unit, and a micro-flow test unit. The detection ends of the pressure sensor and the oil passage flow meter are connected to the oil passage, and the output ends are connected to the signal acquisition unit. After being time-synchronized by the time synchronizer, the signal is transmitted to the calculation alarm unit by the wireless transmission unit. The micro-flow test unit includes a bypass micro-flow channel set on one side of the oil passage. A switching valve is provided at the connection between the bypass micro-flow channel and the oil passage. The bypass micro-flow channel has multiple branches, and each branch is equipped with a flow limiting valve to simulate pipeline leakage. A micro-flow meter is connected to the bypass micro-flow channel. The calculation alarm unit determines the leakage situation based on the received detection data from the pressure sensor and the oil flow meter; and performs system calibration by activating the micro-flow test unit.

[0010] Furthermore, the pressure sensor is used to capture negative pressure waves within the oil passage; The signal acquisition unit is used to identify negative pressure waves caused by leakage through wavelet transform, and to distinguish and eliminate pressure fluctuations caused by normal valve opening and closing operations. The calculation alarm unit identifies and locates leaks based on the negative pressure wave processed by the signal acquisition unit.

[0011] Furthermore, the leakage detection module also includes a temperature sensor and a viscosity sensor, both of which are installed on the oil passage to monitor the temperature and viscosity data of the insulating oil. The calculation alarm unit uses the temperature and viscosity data of the insulating oil, combined with the negative pressure wave data, to perform leakage identification and location through a data fusion algorithm.

[0012] Furthermore, the leakage detection module also includes a pipe wall vibration sensor and a distributed fiber optic temperature sensor. The pipe wall vibration sensor is used to measure the pipe wall vibration signal to distinguish between minor leaks and background noise; the distributed fiber optic temperature sensor is used to provide continuous temperature profile data along the entire length of the cable. The calculation alarm unit integrates the temperature and viscosity data of the insulating oil, the negative pressure wave data, the pipe wall vibration signal, and the continuous temperature profile data of the entire cable length to identify and locate leaks.

[0013] Furthermore, the signal acquisition unit also uses machine learning algorithms to select the optimal wavelet basis and scale of wavelet transform based on historical data of negative pressure waves, and dynamically updates them.

[0014] Furthermore, the machine learning algorithm is a genetic algorithm or a particle swarm optimization algorithm.

[0015] Furthermore, during the calibration process of the calculation alarm unit, the leakage monitoring system allows oil to leak through the bypass microchannel by switching the valve of the microflow test unit, and the flow rate is measured by the microflow meter. This allows the calculation alarm unit to identify and locate the leak, and the system is calibrated based on the detection results of the calculation alarm unit.

[0016] Furthermore, the computational alarm unit is deployed on a cloud platform.

[0017] Furthermore, the calculation alarm unit also includes: pre-establishing a feature library of leakage types and interference signals, and determining the corresponding leakage type by pattern matching on the detection data fed back by the leakage detection module.

[0018] Furthermore, the leakage monitoring system also includes a digital twin module for simulating and calibrating the oil-filled cable duct, the alarm calculation unit, and the leakage detection module.

[0019] Compared with the prior art, the present invention has the following advantages: (1) Early detection and precise location of leaks: This invention achieves early detection and precise location of leaks by working together with a high-frequency dynamic pressure sensor and a high-precision micro-flow test unit, combined with the negative pressure wave method and the flow method. It can quickly respond to sudden large leaks and also keenly capture slow micro leaks, realizing full-range, high-precision monitoring and location of leaks from micro to severe. The micro-leak simulation achieved by the micro-flow test unit can calibrate the leak monitoring and location algorithms of the negative pressure wave method and the flow method, thereby improving the accuracy of the algorithms.

[0020] (2) Greatly improves anti-interference ability and identification reliability: The present invention adopts advanced signal processing technology such as wavelet transform modulus maxima method, which can effectively distinguish and eliminate pressure fluctuations caused by normal operations such as valve opening and closing and load changes, and accurately capture negative pressure wave singularities caused by leakage, thereby greatly reducing the false alarm rate of the system.

[0021] (3) An innovative online self-calibration function has been introduced: By setting multiple branches of the bypass micro-channel, different levels of leakage can be simulated periodically to calibrate and verify the entire monitoring system (including sensors and algorithms) online, ensuring the measurement accuracy and reliability of the system in long-term operation and solving the problem of accuracy decline caused by sensor drift.

[0022] (4) Improved system adaptability and intelligence: This invention further eliminates the need for a negative pressure wave propagation correction model that considers multiple factors such as temperature and viscosity, as well as an algorithm that adaptively selects wavelet basis and scale, so that the system can adapt to different working conditions and environmental changes, automatically optimize parameters, and exhibit stronger environmental adaptability and intelligence.

[0023] (5) Remote and visual management is realized: It can integrate remote communication modules and visualization software platforms to deploy calculation alarm units, support remote real-time monitoring, data analysis and historical trend query, and accurately locate the leak point on the map, which greatly improves the convenience of operation and maintenance and management efficiency. Attached Figure Description

[0024] Figure 1 This is a schematic diagram of a leakage monitoring system for an oil-filled cable duct provided in an embodiment of the present invention. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0026] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0027] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0028] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship in which the product of this invention is usually placed during use. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0029] It should be noted that the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0030] Furthermore, terms such as "horizontal" and "vertical" do not imply that components must be absolutely horizontal or suspended, but rather that they can be slightly tilted. For example, "horizontal" simply means that its direction is more horizontal than "vertical," and does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0031] Example 1 like Figure 1 As shown, this embodiment provides a leakage monitoring system for an oil-filled cable pipeline, including a calculation alarm unit and multiple leakage detection modules respectively installed at the oil inlet and outlet of the oil passage at both ends of the cable. The leakage detection module includes a pressure sensor, an oil passage flow meter, a signal acquisition unit, a time synchronizer, a wireless transmission unit, and a micro-flow test unit. The detection ends of the pressure sensor and the oil passage flow meter are both connected to the oil passage, and the output ends are both connected to the signal acquisition unit. After being synchronized by the time synchronizer, the signal is transmitted to the calculation alarm unit by the wireless transmission unit. The micro-flow test unit includes a bypass micro-flow channel located on one side of the oil passage. A switching valve is installed at the connection between the bypass micro-flow channel and the oil passage. The bypass micro-flow channel has multiple branches, each equipped with a flow-limiting valve to simulate pipeline leakage. A micro-flow meter is connected to the bypass micro-flow channel for normal oil supply and accurate flow measurement. It is used to determine micro-leakage, assist in localization, and conduct long-term dynamic evaluation through the flow method.

[0032] The alarm calculation unit determines the leakage situation based on the detection data received from the pressure sensor and the oil flow meter; and performs system calibration by activating the micro-flow test unit.

[0033] Preferably, the pressure sensor is a high-frequency dynamic pressure sensor with a frequency response greater than 3kHz, ensuring the capture of rapid changes in negative pressure waves.

[0034] Preferably, the micro-flow test unit also includes a leakage correction unit branch, which contains three branches, each with a flow limiting valve for a standard leak, which is periodically opened to simulate leakage and leakage amount for system calibration.

[0035] The flow resistance of the micro-flow test unit is pre-calibrated and converted into length according to the fourth power of the ratio to the cable oil pipeline, and the leakage point is calculated.

[0036] The micro-flow testing unit designed in this scheme can achieve dual-purpose functionality with a single valve. During normal operation, the oil flows through the main direct oil passage while the bypass micro-oil passage is closed, resulting in minimal flow resistance and no impact on the main system. During testing, the valve is switched, and the oil flows through the precision micro-oil passage, where it is measured by a high-precision micro-flow meter. This design cleverly resolves the contradiction between online measurement and system interference.

[0037] During the calibration of the calculation alarm unit in the leakage monitoring system, the oil flow is allowed to leak through the bypass micro-channel by switching the valve of the micro-flow test unit, and the flow rate is measured by the micro-flow meter. The leakage is then identified and located by the calculation alarm unit, and the system is calibrated based on the detection results of the calculation alarm unit.

[0038] The signal acquisition unit is used to identify negative pressure waves caused by leakage through wavelet transform, and to distinguish and eliminate pressure fluctuations caused by normal valve opening and closing operations. Preferably, the signal acquisition unit also uses machine learning algorithms to automatically select the optimal wavelet basis and scale based on historical pressure wave data, improving the accuracy of wavelet transform in capturing signal abrupt changes and reducing the false alarm rate. Specifically, a genetic algorithm or particle swarm optimization algorithm is used to dynamically adjust the wavelet parameters.

[0039] Preferably, the signal acquisition unit also employs a deep learning-based time-series signal analysis model (such as LSTM or Transformer) to directly process the original pressure waveform and automatically extract features, thereby reducing the reliance on manual selection of traditional wavelet transform parameters (basis and scale) and improving the adaptability and recognition accuracy under different working conditions.

[0040] The alarm calculation unit identifies and locates leaks based on the negative pressure wave processed by the signal acquisition unit.

[0041] In a preferred embodiment, the leakage detection module also includes a temperature sensor and a viscosity sensor, both of which are installed on the oil passage to monitor the temperature and viscosity data of the insulating oil. The alarm calculation unit uses data on the temperature and viscosity of the insulating oil, combined with negative pressure wave data, to perform leakage identification and location through a data fusion algorithm.

[0042] Specifically, the system integrates temperature and viscosity sensors to monitor the temperature and viscosity changes of the insulating oil in real time. Combined with negative pressure wave data, a data fusion algorithm improves the accuracy and reliability of leak location. A temperature-viscosity-wave velocity correlation model is established to dynamically correct the negative pressure wave propagation speed.

[0043] In a preferred embodiment, the leak detection module also includes a pipe wall vibration sensor and a distributed fiber optic temperature sensor. The pipe wall vibration sensor is used to measure the pipe wall vibration signal to distinguish between minor leaks and background noise; the distributed fiber optic temperature sensor is used to provide continuous temperature profile data along the entire length of the cable. The alarm unit calculates and integrates data on the temperature and viscosity of the insulating oil, negative pressure wave data, pipe wall vibration signals, and continuous temperature profile data along the entire length of the cable to identify and locate leaks.

[0044] That is, in addition to temperature and viscosity sensors, pipe wall vibration (acoustic / acceleration) sensors and distributed fiber optic temperature sensors (DTS) are added. Vibration sensors can effectively distinguish minor leaks from background noise, while DTS can provide a continuous temperature profile along the entire length of the cable, which is very helpful in detecting localized overheating or thermal changes accompanied by leaks. By fusing this data, a more comprehensive fault diagnosis model can be built.

[0045] Example 2 This embodiment is largely the same as Embodiment 1, except that the alarm calculation unit is located on a cloud platform.

[0046] Specifically, a three-tiered "cloud-edge-device" processing architecture is proposed. Terminal devices (such as various sensors) only perform basic signal processing and alarm functions; edge computing gateways (such as signal acquisition units) are deployed on-site and are responsible for complex data fusion and real-time analysis; the cloud platform is responsible for big data storage, historical trend analysis, model optimization, and cross-line collaborative diagnostics. This reduces local computing pressure and enables large-scale operation and maintenance.

[0047] It also provides self-diagnostic capabilities, enabling it to monitor whether the pressure sensor itself has failed or drifted. For example, by comparing multiple sensor readings or performing periodic self-tests using a built-in calibration branch, and reporting a "sensor failure" alarm instead of a "leakage" alarm in the event of a fault, it greatly improves system reliability.

[0048] As a preferred implementation, the alarm calculation unit further includes: pre-establishing a feature library of leakage types and interference signals, and determining the corresponding leakage type by pattern matching on the detection data fed back by the leakage detection module.

[0049] For example, a feature library of typical leaks and interference signals can be established (such as valve start / stop, pump vibration, normal flow regulation, etc.). Through pattern matching, it is possible not only to identify "whether there is a leak", but also to preliminarily determine the type (pinhole, crack) and severity of the leak, providing richer information for operation and maintenance decisions.

[0050] As a preferred implementation, the leak monitoring system also includes a digital twin module for simulating and calibrating the oil-filled cable duct, the alarm unit, and the leak detection module.

[0051] For example, a hydraulic dynamic model (digital twin) can be created for a specific cable pipeline. Before physical calibration, extensive virtual simulation calibration can be performed on the model to quickly determine key parameters under different operating conditions, reduce the workload of on-site calibration, and optimize sensor layout.

[0052] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.

Claims

1. A leakage monitoring system for oil-filled cable ducts, characterized in that, It includes a calculation alarm unit and multiple leakage detection modules respectively set at the oil inlet and outlet of the cable at both ends. The leakage detection module includes a pressure sensor, an oil flow meter, a signal acquisition unit, a time synchronizer, a wireless transmission unit and a micro flow test unit. The detection ends of the pressure sensor and the oil flow meter are connected to the oil channel, and the output ends are connected to the signal acquisition unit. After being synchronized by the time synchronizer, the signal is transmitted to the calculation alarm unit by the wireless transmission unit. The micro-flow test unit includes a bypass micro-flow channel set on one side of the oil passage. A switching valve is provided at the connection between the bypass micro-flow channel and the oil passage. The bypass micro-flow channel has multiple branches, and each branch is equipped with a flow limiting valve to simulate pipeline leakage. A micro-flow meter is connected to the bypass micro-flow channel. The calculation alarm unit determines the leakage situation based on the received detection data from the pressure sensor and the oil flow meter; and performs system calibration by activating the micro-flow test unit.

2. The leakage monitoring system for oil-filled cable ducts according to claim 1, characterized in that, The pressure sensor is used to capture negative pressure waves within the oil passage. The signal acquisition unit is used to identify negative pressure waves caused by leakage through wavelet transform, and to distinguish and eliminate pressure fluctuations caused by normal valve opening and closing operations. The calculation alarm unit identifies and locates leaks based on the negative pressure wave processed by the signal acquisition unit.

3. The leakage monitoring system for oil-filled cable ducts according to claim 2, characterized in that, The leakage detection module also includes a temperature sensor and a viscosity sensor, both of which are installed on the oil passage to monitor the temperature and viscosity data of the insulating oil. The calculation alarm unit uses the temperature and viscosity data of the insulating oil, combined with the negative pressure wave data, to perform leakage identification and location through a data fusion algorithm.

4. The leakage monitoring system for oil-filled cable ducts according to claim 3, characterized in that, The leakage detection module also includes a pipe wall vibration sensor and a distributed fiber optic temperature sensor. The pipe wall vibration sensor is used to measure the pipe wall vibration signal to distinguish between minor leaks and background noise. The distributed fiber optic temperature sensor is used to provide continuous temperature profile data along the entire length of the cable. The calculation alarm unit integrates the temperature and viscosity data of the insulating oil, the negative pressure wave data, the pipe wall vibration signal, and the continuous temperature profile data of the entire cable length to identify and locate leaks.

5. The leakage monitoring system for oil-filled cable ducts according to claim 2, characterized in that, The signal acquisition unit also uses machine learning algorithms to select the optimal wavelet basis and scale of wavelet transform based on historical data of negative pressure waves, and dynamically updates them.

6. The leakage monitoring system for oil-filled cable ducts according to claim 5, characterized in that, The machine learning algorithm is either a genetic algorithm or a particle swarm optimization algorithm.

7. The leakage monitoring system for oil-filled cable ducts according to claim 1, characterized in that, During the calibration process of the calculation alarm unit, the leakage monitoring system allows oil to leak through the bypass microchannel by switching the valve of the microflow test unit, and the flow rate is measured by the microflow meter. The leakage is then identified and located by the calculation alarm unit, and the system is calibrated based on the detection results of the calculation alarm unit.

8. The leakage monitoring system for oil-filled cable ducts according to claim 1, characterized in that, The computational alarm unit is deployed on a cloud platform.

9. The leakage monitoring system for oil-filled cable ducts according to claim 1, characterized in that, The calculation alarm unit also includes: pre-establishing a feature library of leakage types and interference signals, and determining the corresponding leakage type by pattern matching on the detection data fed back by the leakage detection module.

10. The leakage monitoring system for oil-filled cable ducts according to claim 1, characterized in that, The leakage monitoring system also includes a digital twin module, which is used to simulate and calibrate the oil-filled cable duct, the alarm calculation unit, and the leakage detection module.

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