SF6 density sensor working state on-line monitoring method

Abstract

The invention discloses an SF6 density sensor working state on-line monitoring method, and relates to the technical field of sensor monitoring, and the method comprises the steps: deploying an SF6 density sensor at a key position of monitoring equipment according to a topological relation, and integrating a miniature dual-channel temperature detection unit and a miniature humidity sensor beside each SF6 density sensor probe, forming a sensor network; gradient compensation is carried out on 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 sensor; locating a leakage source or fault equipment based on the abnormal data; associating the fault area with the sensor information; and pushing the high-risk equipment information to an operation and maintenance platform, triggering sound-light alarm, and marking fault equipment in the topological map. According to the invention, the problem of false alarm caused by moisture and main air temperature difference in SF6 density sensor state monitoring is solved.

Description

Technical Field

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

[0002] As the main insulating and arc-extinguishing medium, SF6 gas is widely used in high-voltage power transmission and substation systems due to its excellent electrical insulation and arc-extinguishing capabilities. However, the performance of SF6 gas is affected by factors such as ambient temperature, pressure, and trace moisture. Any slight density change may indicate a change in insulation status or a potential risk of equipment failure. For example, moisture in SF6 gas may affect the performance of the sensor, resulting in inaccurate measurements. In addition, since the installation location of the sensor may have temperature or airflow differences from the SF6 main gas chamber, the sampling data cannot fully reflect the actual conditions in the main gas chamber. Uneven temperature or water vapor migration may cause deviations in online monitoring results.

[0003] Traditional SF6 density detection methods usually rely on periodic detection and offline analysis, which makes it difficult to reflect the operating status of the equipment in real time and to warn 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 high-precision, real-time online monitoring method for the working status of the SF6 density sensor not only helps to fully grasp the health status of the equipment, but also can promptly discover the aging, drift or failure problems of the sensor itself, thereby providing a scientific basis for subsequent maintenance and equipment replacement. To this end, the present invention proposes an online monitoring method for the working status of an SF6 density sensor. Summary of the invention

[0004] The object 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 raised in the above-mentioned background technology.

[0005] To achieve the above object, 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: 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.

[0006] The present invention is further improved in that 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.

[0007] The present invention is further improved in that the temperature difference between air and room ΔT=Tsam-Tref 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 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 replace the original data, are stored in the central database, and the compensation mark is displayed in the topological map.

[0008] The present invention is further improved in that 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.

[0009] The present invention is further improved in that step S3 specifically comprises the following steps: 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.

[0010] A further improvement of the present invention is 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.

[0011] The present invention is further improved 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.

[0012] The present invention is further improved in that step S5 specifically comprises the following steps: 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.

[0013] On the other hand, the present invention provides an SF6 density sensor working state online monitoring system, comprising: 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 anomalies and evaluate the health status of compensated temperature sensors; locate the source of leakage 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.

[0014] Compared with the prior art, the present invention has the following beneficial effects: The present invention firstly adopts a micro-temperature compensation strategy to calculate the temperature difference ΔT in real time. When 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, which not only makes the data closer to the actual working conditions, but also can provide early warning of local overheating, insulation degradation or gas leakage and other potential faults according to the dynamic changes of abnormal temperature or humidity. The system compares and detects anomalies in the data of adjacent sensors in the sensor network, and uses weighted algorithms and temperature rise rate analysis to accurately locate local faults or leakage areas, avoiding misjudgments caused by a single data source, and providing clear fault area information for operation and maintenance personnel, greatly improving the accuracy of fault diagnosis and maintenance response speed. At the same time, the system's continuous assessment of the health status of the sensor can promptly detect anomalies in the sensor itself, ensuring the long-term stable operation of the entire monitoring network. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a flow chart of an online monitoring method for the working state of an SF6 density sensor of the present invention; Figure 2 A health status assessment flow chart of an online monitoring method for the working status of a SF6 density sensor according to the present invention; Figure 3 This is a framework diagram of an online monitoring system for the working status of an SF6 density sensor of the present invention. DETAILED DESCRIPTION

[0016] The technical solution of the present invention is described in detail below through 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. The embodiments of the present invention and the technical features in the embodiments may be combined with each other unless there is a conflict.

[0017] The term "and / or" is only a description of the association relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " generally indicates that the related objects are in an "or" relationship.

[0018] Example 1 Figure 1A flow chart of an online monitoring method for the working status of an SF6 density sensor disclosed in this embodiment is shown, and the steps are as follows: 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; The sensor network includes obtaining equipment topology maps, marking sensor deployment locations and nearby equipment information, such as valves and pipeline connection points, 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.

[0019] Example 2 Based on the inventive concept of Example 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: S2. Gradient compensation is performed on the data in the sensor network through micro-temperature compensation strategy and moisture compensation strategy; the error caused by data deviation caused by the temperature difference between the sampling point and the main air temperature is dynamically compensated.

[0020] 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 (0.0035 / ℃), is the pressure temperature coefficient (0.00367 / °C); 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.

[0021] Through temperature gradient compensation, the density measurement deviation caused by the temperature difference between the sensor installation position and the main air chamber is eliminated; the compensation coefficient is automatically adjusted according to the real-time ΔT to adapt to the temperature fluctuation caused by the change of equipment load; abnormal increase of ΔT can be used as an early indicator of local overheating or insulation degradation (such as local temperature rise caused by poor contact of contacts); Trace moisture in SF6 gas may cause the insulation performance of the equipment to deteriorate, and because moisture molecules occupy the volume of the gas, the presence of moisture will affect the density measurement of the gas. Therefore, a moisture compensation strategy is established 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.

[0022] Through moisture correction, the interference of humidity on density data is eliminated, and the risk of reduced insulation performance of equipment is warned in advance by combining moisture content with operating time. Moisture data is jointly judged with density and temperature anomalies to distinguish leakage from humidity failures. For example, in the case of leakage, density decreases while moisture increases, while in the case of sensor failure, only density is abnormal.

[0023] Example 3 Figure 2 The health status assessment flow chart of an online monitoring method for the working status of an SF6 density sensor of the present invention is shown. Based on the inventive concept of Embodiment 1 and Embodiment 2, in this embodiment, S3, abnormality detection and sensor health status assessment are performed on the compensated temperature sensor; the specific steps include: 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 diagram, such as the air chamber entrance, the middle section (current position), and the exit, and i indicates the i-th group; S32, calculate the abnormal value of the current position sensor comparison group. 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, triggers an alarm and ignores the current sensor data. 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.

[0024] S4: Locate the leakage source or faulty equipment based on the abnormal data, extract the faulty sensor data in step S3, and calculate the position confirmation value ,in represents the historical density mean of the k-1th sensor, which is used as a reference value to eliminate the impact of instantaneous fluctuations. 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; Through the comprehensive calculation of density change and temperature rise rate, the leakage source or fault area can be accurately located; density change reflects the local density drop caused by gas leakage, and temperature rise rate reflects the temperature anomaly caused by local overheating or equipment failure; through the topological relationship, the scope of the fault area is clearly defined, which is convenient for operation and 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 fault area. Combining density change and temperature rise rate can avoid misjudgment of a single data source (such as relying only on density change may ignore local overheating faults).

[0025] S5. Associating the fault area with the sensor information. The specific steps include: S51. Record sensor information in the fault area, such as equipment model, maintenance record, and design leakage rate; 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”.

[0026] 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.

[0027] The threshold and weight may be set by default according to the present invention, or may be set by an operator.

[0028] Example 4 Figure 3 The framework diagram of an online monitoring system for the working state of an SF6 density sensor of the present invention is shown. Based on the same inventive concept as that of Embodiments 1 to 3, the present invention provides an online monitoring system for the working state of an SF6 density sensor, comprising: 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 anomalies and evaluate the health status of compensated temperature sensors; locate the source of leakage 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.

[0029] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may 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.

[0030] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes 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 a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0031] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0032] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0033] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the enlightenment of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims, which all fall within the protection 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.

2. The method for online monitoring of the working state of a SF6 density sensor according to claim 1 is characterized in that: 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.

3. The method for online monitoring of the working state of a SF6 density sensor according to claim 2 is characterized in that: 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 replace the original data, are stored in the central database, and the compensation mark is displayed in the topological map.

4. The method for online monitoring of the working state of a SF6 density sensor according to claim 3 is characterized in that: 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.

5. 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.

6. The method for online monitoring of the working state of a SF6 density sensor according to claim 5, 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.

7. The method for online monitoring of the working state of a SF6 density sensor according to claim 1, 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.

8. The method for online monitoring of the working state of a SF6 density sensor according to claim 1, 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.

9. 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 8, 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.

Images