An online monitoring method for the working state of an SF6 density sensor

Abstract

The present invention discloses an online monitoring method for the working state of an SF6 density sensor, which relates to the technical field of sensor monitoring. The steps of the method include: deploying SF6 density sensors at key positions of the monitoring device according to the topological relationship, and integrating a micro two-channel temperature detection unit and a micro humidity sensor beside the probe of each SF6 density sensor to form a sensor network; performing gradient compensation on the data in the sensor network through a micro temperature compensation strategy and a moisture compensation strategy; performing anomaly detection and sensor health state evaluation on the compensated temperature sensors; locating the leakage source or faulty device based on the abnormal data; associating the fault area with the sensor information; pushing the information of high-risk devices to the operation and maintenance platform, triggering an audible and visual alarm, and marking the faulty devices in the topological map. The present invention solves the problem of false alarms caused by moisture and the main air temperature difference in the state monitoring of SF6 density sensors.

Description

Technical Field

[0001] The present invention relates to the technical field of sensor monitoring, and particularly to an online monitoring method for the working state of an SF6 density sensor. Background Art

[0002] As a main insulating and arc-extinguishing medium, SF6 gas is widely used in high-voltage power transmission and transformation systems due to its excellent electrical insulation performance and arc-extinguishing ability. However, the performance of SF6 gas is affected by factors such as environmental temperature, pressure, and trace moisture. Any slight density change may imply a change in the insulation state or a potential fault risk of the equipment. For example, moisture in SF6 gas may affect the performance of the sensor, resulting in inaccurate measurement; and due to the possible temperature or airflow differences between the installation position of the sensor and the main SF6 gas chamber, the sampled data cannot fully reflect the true situation in the main gas chamber. Temperature non-uniformity or water vapor migration phenomena may both cause deviations in the online monitoring results.

[0003] Traditional SF6 density detection methods usually rely on periodic detection and offline analysis, and it is difficult to reflect the equipment operation state in real time and give early warnings of potential faults in a timely manner. With the development of monitoring technology and Internet of Things technology, online monitoring has become an inevitable choice to improve the safety and reliability of power systems. Therefore, constructing a method for high-precision, real-time online monitoring of the working state of SF6 density sensors not only helps to comprehensively understand the equipment health status, but also can timely detect the aging, drift, or fault problems of the sensors themselves, thus providing a scientific basis for subsequent maintenance and equipment replacement. For this reason, the present invention proposes an online monitoring method for the working state of an SF6 density sensor. Summary of the Invention

[0004] The purpose of the present invention is to provide an online monitoring method for the working state of an SF6 density sensor to solve the existing problems mentioned in the above background art.

[0005] To achieve the above purpose, the present invention provides the following technical solution: An online monitoring method for the working state of an SF6 density sensor, comprising the following steps:

[0006] S1. Deploy SF6 density sensors at key positions of the monitoring equipment according to the topological relationship, and integrate a micro dual-channel temperature detection unit and a micro humidity sensor beside each SF6 density sensor probe to form a sensor network;

[0007] S2. Perform gradient compensation on the data in the sensor network through a micro temperature compensation strategy and a moisture compensation strategy;

[0008] S3. Perform anomaly detection and sensor health state assessment on the compensated temperature sensors;

[0009] S4. Locate the leakage source or faulty equipment based on abnormal data;

[0010] S5. Associate the fault area with sensor information;

[0011] S6. Push the high-risk equipment information to the operation and maintenance platform, trigger an audible and visual alarm, and mark the faulty equipment on the topology map.

[0012] A further improvement of the present invention lies in that the sensor network includes obtaining an equipment topology map, marking the deployment positions of sensors and the information of adjacent equipment, and each sensor collects SF6 density , temperature , and pressure data in real time, and dynamically displays the data status of each sensor on the topology map, marking the temperature gradient detection unit of each sensor and the heat conduction path to the main gas chamber; the micro dual-channel temperature detection unit includes measuring the temperature Tsam at the connection of the measurement gas chamber, and connecting it to the inside of the main gas chamber through a micro heat conduction belt to obtain the reference temperature Tref at the center of the gas chamber, calibrating the initial temperature difference ΔT0 between Tsam and Tref, and storing it as a reference value in the central database; marking the positions of the micro humidity sensors of each sensor, and obtaining the corresponding real-time moisture content , and performing standardization processing on all the data in the central database.

[0013] A further improvement of the present invention lies in that the temperature difference ΔT = Tsam - Tref of the gas chamber is calculated in real time, and a temperature difference threshold is set. When |ΔT - ΔT0| is greater than the temperature difference threshold, the micro temperature compensation strategy is triggered. According to the SF6 gas state equation, the density and pressure are corrected to obtain the temperature-compensated density and the temperature-compensated pressure , where is the SF6 density temperature coefficient, is the pressure temperature coefficient; the temperature-compensated density and the temperature-compensated pressure are used to replace the original data, stored in the central database, and a compensation mark is displayed on the topology map.

[0014] A further improvement of the present invention lies in that the moisture compensation strategy further corrects the density compensation value according to the real-time moisture content : , where is the corrected density value, is the moisture saturation concentration of SF6 gas at the current temperature, dynamically correcting the density value, replacing the originally compensated density, and storing it in the central database.

[0015] A further improvement of the present invention lies in that the specific steps of step S3 include:

[0016] S31. Form a comparison group from the data of three adjacent density sensors, which are respectively represented as , , , where the superscript 1 represents the position of the sensor in the topology diagram, and i represents the i-th group;

[0017] S32. Calculate the outlier value of the comparison group of the sensor at the current position. If the outlier value of the comparison group is greater than the set outlier threshold, it is determined that the current sensor is faulty or there is a gas leak, and an alarm is triggered and the data of the current sensor is ignored.

[0018] In a further improvement of the present invention, the calculation formula for the outlier value Acg of the comparison group of the sensor at the current position is expressed as:

[0019] ;

[0020] Among them, , , , , represent the weight coefficients, , , is the rated pressure of the sensor.

[0021] In a further improvement of the present invention, step S4 includes extracting the data of the faulty sensor in step S3 and calculating the position confirmation value , where represents the historical density average value of the (k - 1)-th sensor, represents the temperature rise rate of the k-th sensor, ; Sort in descending order, and the area between the sensor corresponding to the maximum value and the previous sensor is determined to be a leakage or area fault.

[0022] In a further improvement of the present invention, the specific steps of step S5 include:

[0023] S51. Record the sensor information in the faulty area;

[0024] S52. Calculate the abnormal probability of the sensor , where A is the set of historical fault characteristics of the device, B is the set of current abnormal data, and the sensor with the maximum Q value is marked as a high-risk sensor.

[0025] On the other hand, the present invention provides an on-line monitoring system for the working state of an SF6 density sensor, including:

[0026] The sensor network deployment module is used to deploy SF6 density sensors at key positions of the monitoring device according to the topological relationship, and integrate a micro dual-channel temperature detection unit and a micro humidity sensor beside each SF6 density sensor probe to form a sensor network;

[0027] The data compensation module is used to perform gradient compensation on the data in the sensor network through a micro temperature compensation strategy and a moisture compensation strategy;

[0028] The fault location module is used to perform anomaly detection and sensor health status assessment on the compensated temperature sensors; locate the leakage source or faulty device based on the abnormal data;

[0029] The device association module associates the fault area with the sensor information;

[0030] The alarm and visualization module pushes the high-risk device information to the operation and maintenance platform, triggers an audible and visual alarm, and marks the faulty device on the topological map.

[0031] Compared with the prior art, the beneficial effects of the present invention are:

[0032] The present invention first adopts a micro temperature compensation strategy. By calculating the temperature difference ΔT in real time, when the change in the temperature difference exceeds the set threshold, the density and pressure data are dynamically corrected according to the SF6 gas state equation; combined with the moisture correction strategy, the interference of humidity on density measurement is further eliminated, not only making the data closer to the actual working conditions, but also being able to early warn of potential faults such as local overheating, insulation deterioration or gas leakage according to the dynamic changes of temperature difference anomalies or humidity anomalies;

[0033] The system realizes the accurate location of local faults or leakage areas through the comparison and anomaly detection of the data of adjacent sensors in the sensor network, adopts a weighted algorithm and the analysis of the temperature rise rate, avoids misjudgment caused by a single data source, provides clear fault area information for the operation and maintenance personnel, greatly improves the accuracy of fault diagnosis and the maintenance response speed. At the same time, the continuous assessment of the sensor health status by the system can timely detect the anomalies of the sensors themselves and ensure the long-term stable operation of the entire monitoring network. Description of the Drawings

[0034] Figure 1 It is a flowchart of an online monitoring method for the working state of an SF6 density sensor of the present invention;

[0035] Figure 2 It is a flowchart of the health status assessment of an online monitoring method for the working state of an SF6 density sensor of the present invention;

[0036] Figure 3 It is a framework diagram of an online monitoring system for the working state of an SF6 density sensor of the present invention. Detailed Embodiments

[0037] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations on the technical solution of the present invention. Without conflict, the technical features in the embodiments of the present invention and the embodiments can be combined with each other.

[0038] The term "and / or" is merely a description of the association relationship of associated objects, indicating that three relationships can exist. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " generally represents an "or" relationship between the associated objects before and after.

[0039] Embodiment 1

[0040] Figure 1 The flowchart of an online monitoring method for the working state of an SF6 density sensor disclosed in this embodiment is shown as follows:

[0041] S1. Deploy SF6 density sensors at key positions of the monitoring device according to the topological relationship, and integrate a micro dual-channel temperature detection unit and a micro humidity sensor beside each SF6 density sensor probe to form a sensor network;

[0042] The sensor network includes obtaining a device topology map, marking the deployment positions of the sensors and information of adjacent devices, such as valves and pipe connection points. Each sensor collects SF6 density , temperature , and pressure data in real time, and dynamically displays the data status of each sensor in the topology map, marking the heat conduction path of the temperature gradient detection unit of each sensor and the main gas chamber; the micro dual-channel temperature detection unit includes measuring the temperature Tsam at the connection of the measurement chamber, and connecting it to the inside of the main gas chamber through a micro heat conduction belt to obtain the reference temperature Tref at the center of the gas chamber, calibrating the initial temperature difference ΔT0 between Tsam and Tref, and storing it as a reference value in the central database; marking the position of the micro humidity sensor of each sensor, and obtaining the corresponding real-time moisture content , and standardizing all the data in the central database.

[0043] Embodiment 2

[0044] Based on the inventive concept of Embodiment 1, this embodiment proposes a micro temperature compensation strategy and a moisture compensation strategy to perform gradient compensation on the data in the sensor network. The specific steps include:

[0045] S2. Perform gradient compensation on the data in the sensor network through a micro temperature compensation strategy and a moisture compensation strategy; dynamically compensate for the error caused by the data deviation resulting from the temperature difference between the sampling point and the main gas chamber.

[0046] Calculate the temperature difference ΔT = Tsam - Tref between the gas and the room temperature in real time, and set a temperature difference threshold. When |ΔT - ΔT0| is greater than the temperature difference threshold, trigger the micro temperature compensation strategy. According to the SF6 gas state equation, correct the density and pressure to obtain the temperature-compensated density and the temperature-compensated pressure , where is the SF6 density temperature coefficient (0.0035 / °C), is the pressure temperature coefficient (0.00367 / °C); replace the original data with the temperature-compensated density and temperature-compensated pressure, store them in the central database, and display a compensation flag on the topology map.

[0047] Through temperature gradient compensation, eliminate the density measurement deviation caused by the temperature difference between the sensor installation position and the main gas chamber; automatically adjust the compensation coefficient according to the real-time ΔT to adapt to the temperature fluctuations caused by changes in equipment load; the abnormal increase in ΔT can be used as an early indicator of local overheating or insulation deterioration (such as local temperature rise caused by poor contact of the contact).

[0048] Trace moisture in SF6 gas may cause a decline in the insulation performance of the equipment, and since water molecules occupy the gas volume, the presence of moisture will affect the density measurement of the gas. Therefore, establish a moisture compensation strategy, and further correct the density compensation value according to the real-time moisture content : , where is the corrected density value, is the moisture saturation concentration of SF6 gas at the current temperature, dynamically correct the density value, replace the original compensated density, and store it in the central database.

[0049] Through moisture correction, eliminate the interference of humidity on density data, combine the moisture content with the operating time, and early warn of the risk of decline in equipment insulation performance; jointly determine the moisture data with abnormal density and temperature to distinguish between leakage and humidity faults, such as when there is a leak, the density decreases and the moisture increases, and when there is a sensor fault, only the density is abnormal.

[0050] Example 3

[0051] Figure 2 Shows the flowchart of the health status assessment of an online monitoring method for the working state of an SF6 density sensor according to the present invention. Based on the inventive concept of Example 1 and Example 2, in this example, S3. Perform abnormal detection and sensor health status assessment on the compensated temperature sensor; the specific steps include:

[0052] S31. Form a comparison group with the data of three adjacent density sensors, which are respectively represented as , , , where the superscript 1 represents the position of the sensor in the topology diagram, such as the gas chamber inlet, the middle section (current position), and the outlet, and i represents the i-th group;

[0053] S32. Calculate the outlier of the comparison group of the sensor at the current position. If the outlier of the comparison group is greater than the set outlier threshold, it is determined that the current sensor is faulty or there is a gas leak, trigger an alarm and ignore the data of the current sensor. The calculation formula for the outlier Acg of the comparison group of the sensor at the current position is expressed as:

[0054] ;

[0055] Among them, , , , , represent the weight coefficients, , , is the rated pressure of the sensor.

[0056] S4. Locate the leakage source or faulty equipment based on the abnormal data, extract the data of the faulty sensor in step S3, and calculate the position confirmation value , where represents the historical density average value of the (k - 1)-th sensor, which is used as a reference value to eliminate the influence of instantaneous fluctuations, represents the temperature rise rate of the k-th sensor, ; Arrange in descending order, and the area between the sensor corresponding to the maximum value and the previous sensor is determined as the leakage or regional fault;

[0057] Through the comprehensive calculation of density change and temperature rise rate, accurately locate the leakage source or faulty area; the density change reflects the local density decrease caused by gas leakage, and the temperature rise rate reflects the temperature anomaly caused by local overheating or equipment failure; through the topological relationship, clarify the scope of the faulty area, which is convenient for maintenance personnel to handle quickly; Arrange in descending order, and the area between the sensor corresponding to the maximum value and the previous sensor is determined as the leakage or faulty area. Combining density change and temperature rise rate can avoid misjudgment of a single data source (such as only relying on density change may ignore local overheating faults).

[0058] S5. Associate the faulty area with the sensor information; the specific steps include:

[0059] S51. Record the sensor information in the fault area, such as device model, maintenance record, and designed leakage rate;

[0060] S52. Calculate the probability of sensor anomaly , where A is the set of historical fault characteristics of the device, B is the set of current abnormal data, and the sensor with the maximum Q value is marked as the "high-risk sensor".

[0061] S6. Push the high-risk device information to the operation and maintenance platform, trigger an audible and visual alarm, and mark the faulty device on the topology map.

[0062] The setting of the threshold and weight can be based on the default settings of the present invention or can be set by the operator himself.

[0063] Embodiment 4

[0064] Figure 3 It shows a framework diagram of an online monitoring system for the working state of an SF6 density sensor according to the present invention. Based on the same inventive concept as Embodiments 1 to 3, the present invention provides an online monitoring system for the working state of an SF6 density sensor, including:

[0065] A sensor network deployment module for deploying SF6 density sensors at key positions of the monitoring device according to the topological relationship, and integrating a micro dual-channel temperature detection unit and a micro humidity sensor beside each SF6 density sensor probe to form a sensor network;

[0066] A data compensation module for performing gradient compensation on the data in the sensor network through a micro temperature compensation strategy and a moisture compensation strategy;

[0067] A fault location module for performing anomaly detection and sensor health status evaluation on the compensated temperature sensors; locating the leakage source or faulty device based on the abnormal data;

[0068] A device association module for associating the fault area with the sensor information;

[0069] An alarm and visualization module for pushing the high-risk device information to the operation and maintenance platform, triggering an audible and visual alarm, and marking the faulty device on the topology map.

[0070] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0071] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, and combinations of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device to produce a machine, such that the instructions executed by the processor of the computer or other programmable data processing device generate means for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0072] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including instruction means that implement the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0073] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0074] The embodiments of the present invention have been described above in conjunction with the accompanying drawings. However, the present invention is not limited to the above specific embodiments. The above specific embodiments are merely illustrative and not restrictive. Under the inspiration of the present invention, those of ordinary skill in the art can also make many forms without departing from the spirit and scope protected by the present invention and the claims. All of these fall within the protection scope of the present invention.

Claims

1. An online monitoring method for the working state of an SF6 density sensor, characterized in that: The following steps are involved: S1. Deploy SF6 density sensors at key locations of monitoring equipment according to topological relationships, and integrate a miniature dual-channel temperature detection unit and a miniature humidity sensor next to each SF6 density sensor probe to form a sensor network; S2, gradient compensation of data in the sensor network through micro temperature compensation strategy and moisture compensation strategy; S3, performing abnormality detection and sensor health status assessment on the compensated temperature sensor; S4. Locate the leakage source or faulty equipment based on abnormal data; S5, associating the fault area with the sensor information; S6: Push high-risk equipment information to the operation and maintenance platform, trigger an audible and visual alarm, and mark the faulty equipment on the topology map; The sensor network includes obtaining a device topology map, marking the sensor deployment location and adjacent device information, and each sensor collects SF6 density in real time. ,temperature , Pressure data , and dynamically display the data status of each sensor in the topological map, marking the thermal conductivity path between the temperature gradient detection unit of each sensor and the main air chamber; the micro dual-channel temperature detection unit includes measuring the temperature Tsam at the connection of the air chamber, and connecting to the inside of the main air chamber through a micro thermal conductivity belt, obtaining the reference temperature Tref of the center of the air chamber, calibrating the initial temperature difference ΔT0 between Tsam and Tref, and storing it in the central database as a reference value; marking the position of the micro humidity sensor of each sensor, and obtaining the corresponding real-time moisture content and standardize all data in the central database; Calculate the temperature difference between air and room in real time ΔT=Tsam-Tref, and set the temperature difference threshold. When |ΔT-ΔT0| is greater than the temperature difference threshold, the micro temperature compensation strategy is triggered. According to the SF6 gas state equation, the density and pressure Correction is performed to obtain temperature compensated density and temperature compensated pressure ,in, is the SF6 density temperature coefficient, is the pressure temperature coefficient; the temperature compensated density and temperature compensated pressure are used to replace the original data, stored in the central database, and a compensation mark is displayed in the topological map; The moisture compensation strategy is based on the real-time moisture content Further calibration of the density compensation value: ,in is the corrected density value, The density value is dynamically corrected for the water saturation concentration of SF6 gas at the current temperature, replacing the original compensated density and storing it in the central database.

2. The method for online monitoring of the working state of a SF6 density sensor according to claim 1 is characterized in that: Step S3 specifically includes: S31, grouping the data of three adjacent density sensors into comparison groups, represented as , , , where the superscript 1 indicates the position of the sensor in the topological map, and i indicates the i-th group; S32, calculating the abnormal value of the comparison group of the current position sensor, if the abnormal value of the comparison group is greater than the set abnormal threshold, it is determined that the current sensor is faulty or gas leaks, triggering an alarm and ignoring the current sensor data.

3. The method for online monitoring of the working state of a SF6 density sensor according to claim 2 is characterized in that: The calculation formula of the abnormal value Acg of the current position sensor comparison group is expressed as: ； in, , , , , represents the weight coefficient, , , Rated pressure for the sensor.

4. The method for online monitoring of the working state of a SF6 density sensor according to claim 1 is characterized in that: Step S4 includes extracting the fault sensor data in step S3 and calculating the position confirmation value ,in represents the historical density mean of the k-1th sensor, represents the temperature rise rate of the kth sensor, ;Will Arranged in descending order, the sensor corresponding to the maximum value and the area inspected by the previous sensor are judged as leaking or having regional failure.

5. The method for online monitoring of the working state of a SF6 density sensor according to claim 1 is characterized in that: Step S5 specifically includes: S51, recording sensor information in the fault area; S52, calculate sensor abnormality probability , where A is the historical fault feature set of the equipment, B is the current abnormal data set, and the sensor with the maximum Q value is marked as a high-risk sensor.

6. An online monitoring system for the working state of an SF6 density sensor, used to execute an online monitoring method for the working state of an SF6 density sensor according to any one of claims 1 to 5, characterized in that: include: The sensor network deployment module is used to deploy SF6 density sensors at key locations of monitoring equipment according to topological relationships, and integrate a miniature dual-channel temperature detection unit and a miniature humidity sensor next to each SF6 density sensor probe to form a sensor network; A data compensation module, used to perform gradient compensation on the data in the sensor network through a micro temperature compensation strategy and a moisture compensation strategy; Fault location module, used to detect abnormalities and assess the health status of the compensated temperature sensor; Locate the leak source or faulty equipment based on abnormal data; Equipment association module, which associates fault areas with sensor information; The alarm and visualization module pushes high-risk equipment information to the operation and maintenance platform, triggers sound and light alarms, and marks faulty equipment on the topology map.

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