SF6 density table data abnormity self-diagnosis method, storage medium and equipment

Abstract

The invention relates to an SF6 density table data abnormity self-diagnosis method, a storage medium and equipment, and the method comprises the following steps: obtaining a data collection time sequence corresponding to a to-be-diagnosed SF6 density table based on the uploading data of the to-be-diagnosed SF6 density table, and at each detection moment, obtaining a data collection time sequence corresponding to the to-be-diagnosed SF6 density table based on the real-time density time sequence; judging whether the change of the density measurement value exceeds a set fluctuation threshold value or not, and if so, judging the change direction of the density measurement value; judging whether air leakage exists or not based on the air pressure time sequence, and if yes, generating an air leakage alarm signal; and calculating a correlation coefficient of the real-time environment temperature time sequence and the density time sequence, judging whether the density value change and the environment temperature change have high negative correlation or not, and if so, judging that the density value change is caused by the fact that the environment temperature at the installation position of the density meter is higher or lower than the air chamber temperature of the electrical equipment body. Compared with the prior art, the method has the advantage that the change reason of the SF6 density value can be accurately and quickly judged.

Description

Technical Field

[0001] The present invention relates to the technical field of SF6 density monitoring, and in particular to a method, storage medium and equipment for self-diagnosis of abnormal data of a SF6 density meter. Background Art

[0002] In high-voltage electrical equipment, SF6 gas is widely used as an insulating medium and arc-extinguishing medium. In order to ensure the safe and stable operation of the equipment, the density of SF6 gas must be monitored. Traditional density monitoring usually relies on the SF6 density meter installed in the equipment. The working principle of the SF6 density meter is based on the physical properties of the gas, and the operating status of the equipment is judged by real-time monitoring of the gas density. However, when the ambient temperature of the SF6 density meter is higher or lower than the temperature of the equipment body, over-compensation or under-compensation may occur, which causes a deviation between the gas density displayed by the density meter and the actual gas density, thereby affecting the safe operation and maintenance decisions of the equipment. The over-compensation phenomenon makes the gas density displayed on the density meter lower than the actual value. For example, when the temperature difference between day and night is large, and the sunlight is direct in the early morning, the heat capacity of the SF6 gas density relay is small, and the temperature rises quickly, while the heat capacity of the equipment gas chamber is large, and the temperature rises slowly. This leads to a large temperature difference between the two, which can easily cause false alarms. The under-compensation phenomenon makes the gas density displayed on the density meter higher than the actual value. For example, when the load is large, the equipment in the high-pressure gas chamber, such as knife switches, switches, etc., will generate heat, causing the temperature in the gas chamber to rise, resulting in the temperature of the SF6 density meter being lower than the temperature in the gas chamber, which can easily cause gas leaks but no alarm.

[0003] In the existing technology, the reason for the change of the SF6 density meter value is usually determined by on-site verification, but this method has the problems of high cost, complex operation, and the need for the equipment to be offline. Therefore, there is an urgent need for a method that can evaluate and diagnose the SF6 density meter data in real time to identify abnormal data and thus improve the safety and reliability of the power system. Summary of the invention

[0004] The purpose of the present invention is to provide a method, storage medium and device for self-diagnosis of abnormal data of SF6 density meter, so as to accurately judge the cause of change of SF6 density value through the historical data uploaded by the density meter.

[0005] The purpose of the present invention can be achieved by the following technical solutions:

[0006] A method for self-diagnosis of abnormal data of SF6 density meter, comprising the following steps:

[0007] Based on the uploaded data of the SF6 density table to be diagnosed, a time series of collected data corresponding to the SF6 density table to be diagnosed is obtained, wherein the time series of collected data includes an ambient temperature time series, an air pressure time series, and a density time series;

[0008] At each detection moment, based on the real-time density time series, determine whether the change in the density measurement value exceeds the set fluctuation threshold, if so, diagnose the cause of the change, if not, continue to update the acquisition data time series;

[0009] The process of diagnosing the cause of the change includes the following steps:

[0010] 1) Determine the change direction of the density measurement value, if it exceeds the fluctuation threshold downward, execute step 2), otherwise execute step 3);

[0011] 2) judging whether there is leakage based on the air pressure time series, and if so, generating a leakage alarm signal, and if not, executing step 3);

[0012] 3) Calculate the correlation coefficient between the real-time ambient temperature time series and the density time series, and determine whether there is a high negative correlation between the density value change and the ambient temperature change based on the correlation coefficient. If so, determine that the change in density value is caused by the ambient temperature at the installation location of the density meter being higher or lower than the temperature of the air chamber of the electrical equipment body. If not, determine that the change in density value is caused by other reasons.

[0013] Furthermore, when the collected data time series is updated, the length of the series remains unchanged.

[0014] Before calculating the correlation coefficient, the ambient temperature time series and the density time series are normalized.

[0015] Furthermore, the maximum and minimum value method is used to implement the normalization processing.

[0016] Further, when the correlation coefficient is less than -0.95, it is determined that there is a high negative correlation between the density value change and the ambient temperature change.

[0017] Furthermore, step 3) also includes: generating a corresponding alarm signal.

[0018] Furthermore, the process of diagnosing the cause of the change also includes the following steps:

[0019] 4) Obtain the ambient temperature time series and density time series corresponding to a normal density meter, calculate the difference between the ambient temperature time series and the density time series corresponding to the SF6 density meter to be diagnosed, generate an ambient temperature difference sequence and a density difference sequence, calculate the correlation coefficient between the ambient temperature difference sequence and the density difference sequence, and determine whether there is a high negative correlation between the change in density value and the change in ambient temperature based on the correlation coefficient. If so, determine that the change in density value is caused by the ambient temperature at the installation location of the density meter being higher or lower than the temperature of the gas chamber of the electrical equipment body. If not, determine that the change in density value is caused by other reasons.

[0020] Further, when the correlation coefficient is less than -0.95, it is determined that there is a high negative correlation between the density value change and the ambient temperature change.

[0021] The present invention also provides a computer-readable storage medium, comprising one or more programs for execution by one or more processors of an electronic device, wherein the one or more programs include instructions for executing the SF6 density meter data abnormality self-diagnosis method as described above.

[0022] The present invention also provides an electronic device, comprising one or more processors, a memory and one or more programs stored in the memory, wherein the one or more programs include instructions for executing the SF6 density meter data abnormality self-diagnosis method as described above.

[0023] Compared with the prior art, the present invention has the following beneficial effects:

[0024] 1. The present invention is based on real-time processing of the remotely uploaded historical data of the SF6 density meter to quickly and accurately determine whether the change in the density value is caused by the ambient temperature at the installation location of the density meter being not equal to the temperature of the gas chamber of the electrical equipment body, thereby reducing the burden on the staff, timely discovering equipment problems and taking corresponding measures, and being able to improve the intelligence level and accuracy of the SF6 density meter status assessment, helping maintenance personnel to timely understand the operating status of the equipment, and having great practical value.

[0025] 2. The present invention diagnoses the cause of density value changes based on the correlation coefficient of the data time series, or further diagnoses based on the sequence correlation coefficient of the difference with the normal meter, and can quickly and accurately obtain the relationship with the ambient temperature at the installation location of the density meter, and improve reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0027] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0028] Example 1

[0029] This embodiment provides a method for self-diagnosis of abnormal data of SF6 density meter, such as Figure 1 As shown, the following steps are included:

[0030] S1. Based on the uploaded data of the SF6 density table to be diagnosed, a time series of collected data corresponding to the SF6 density table to be diagnosed is obtained, wherein the time series of collected data includes an ambient temperature time series, an air pressure time series and a density time series.

[0031] In this embodiment, a time series of ambient temperature (°C), air pressure (MPa), and density (expressed as air pressure at 20°C) (MPa) is constructed, including m data, which are respectively expressed as:

[0032] Ambient temperature time series: T = {T1, T2, ..., T m};

[0033] Air pressure time series: P = {P1, P2, ..., P m};

[0034] Density time series: ρ = {ρ1, ρ2, …, ρ m};

[0035] Among them, T m is the ambient temperature at time point m, P m is the air pressure at time point m, ρ m is the density value at time point m. When the ambient temperature, air pressure, and density time series receive new data, a set of historical data will be discarded at the same time to keep the number of elements m in the time series unchanged.

[0036] S2. At each detection moment, based on the real-time density time series, determine whether the change in the density measurement value exceeds the set fluctuation threshold. Under normal circumstances, the density measurement value should remain stable. If so, diagnose the cause of the change and execute step S3. If not, return to step S1 and use the newly added data to continue updating the collected data time series.

[0037] S3. Determine the direction of change of the density measurement value, whether it exceeds the fluctuation threshold downward or upward. If it exceeds the fluctuation threshold downward, execute step S4; if it exceeds the fluctuation threshold upward, execute step S5.

[0038] S4. Determine whether there is an air leakage based on the air pressure time series. If so, determine that an air leakage has occurred and generate an air leakage alarm signal. If not, execute step S5.

[0039] S5. Calculate the correlation coefficient between the real-time ambient temperature time series and the density time series.

[0040] Because the ambient temperature and density belong to different dimensions, in order to be comparable, it is necessary to normalize and remove the dimensions. In this embodiment, the maximum and minimum value method is used to convert the data into the range of [0, 1]. The specific normalization process is shown in formula (1) and formula (2):

[0041]

[0042] Among them, T i ′、ρ i ′ is the normalized ambient temperature and density value at time point i.

[0043] Based on the above correlation coefficient, it is determined whether there is a high negative correlation between the change in density value and the change in ambient temperature. If so, it is determined that the change in density value is caused by the ambient temperature at the installation location of the density meter being higher or lower than the temperature of the air chamber of the electrical equipment body. If not, it is determined that the change in density value is caused by other reasons.

[0044] In this embodiment, when the correlation coefficient is less than -0.95, it is determined that there is a high negative correlation between the change in density value and the change in ambient temperature. It can be considered that the change in density value is caused by the ambient temperature at the installation location of the density meter being higher or lower than the temperature of the air chamber of the electrical equipment body, and a corresponding alarm signal is issued. If the correlation coefficient is not less than -0.95, it can be considered that the change in density value is caused by other types of faults or defects, and other alarm signals are issued.

[0045] Furthermore, in a preferred embodiment, in order to more accurately determine the cause of the change in density value, step S6 may be performed on the basis of step S5:

[0046] Obtain an ambient temperature time series and a density time series corresponding to a normal density meter, calculate the difference between the ambient temperature time series and the density time series corresponding to the SF6 density meter to be diagnosed, generate an ambient temperature difference sequence and a density difference sequence, calculate the correlation coefficient between the ambient temperature difference sequence and the density difference sequence, and determine whether there is a high negative correlation between the density value change and the ambient temperature change based on the correlation coefficient. If so, determine that the change in density value is caused by the ambient temperature at the installation location of the density meter being higher or lower than the temperature of the gas chamber of the electrical equipment body. If not, determine that the change in density value is caused by other reasons.

[0047] If there are other SF6 density meters on the electrical equipment where the meter to be tested is installed, and the density value displayed by it is within the set fluctuation threshold, it can be regarded as a normal meter. The time series of the normal meter and the time series of the problematic meter can be used to construct a time series of the difference, because the change in the ambient temperature of the problematic meter may not completely cause the temperature deviation from the gas chamber of the electrical equipment body, and part of it is a normal change. The ambient temperature of the normal meter that can display the correct density can be regarded as the correct ambient temperature, and the difference can better represent the deviation caused by the ambient temperature of the problematic meter. Therefore, the correlation coefficient of the difference may be more effective. If there is data from a normal meter that can be used in actual applications, step S6 can be executed when the evaluation index is not met in step S5. If the evaluation index is also not met, it can be determined that the change in the density value is caused by other types of faults or defects.

[0048] Similarly, when the correlation coefficient between the ambient temperature difference sequence and the density difference sequence is less than -0.95, it is determined that there is a high negative correlation between the density value change and the ambient temperature change. It can be considered that the change in density value is caused by the ambient temperature at the installation location of the density meter being higher or lower than the temperature of the air chamber of the electrical equipment body, and a corresponding alarm signal is issued. If the correlation coefficient is not less than -0.95, it can be considered that the change in density value is caused by other types of faults or defects, and other alarm signals are issued.

[0049] If the above method is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0050] Example 2

[0051] This embodiment provides an electronic device, including one or more processors, a memory and one or more programs stored in the memory, wherein the one or more programs include instructions for executing the SF6 density meter data abnormality self-diagnosis method as described in Example 1.

[0052] 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. Moreover, 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 codes. The schemes in the embodiments of the present invention may be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0053] 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.

[0054] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for self-diagnosis of abnormal data of SF6 density meter, characterized in that: The following steps are involved: Based on the uploaded data of the SF6 density table to be diagnosed, a time series of collected data corresponding to the SF6 density table to be diagnosed is obtained, wherein the time series of collected data includes an ambient temperature time series, an air pressure time series, and a density time series; At each detection moment, based on the real-time density time series, determine whether the change in the density measurement value exceeds the set fluctuation threshold, if so, diagnose the cause of the change, if not, continue to update the acquisition data time series; The process of diagnosing the cause of the change includes the following steps: 1) Determine the change direction of the density measurement value, if it exceeds the fluctuation threshold downward, execute step 2), otherwise execute step 3); 2) judging whether there is leakage based on the air pressure time series, and if so, generating a leakage alarm signal, and if not, executing step 3); 3) Calculate the correlation coefficient between the real-time ambient temperature time series and the density time series, and determine whether there is a high negative correlation between the density value change and the ambient temperature change based on the correlation coefficient. If so, determine that the change in density value is caused by the ambient temperature at the installation location of the density meter being higher or lower than the temperature of the air chamber of the electrical equipment body. If not, determine that the change in density value is caused by other reasons.

2. The SF6 density meter data abnormality self-diagnosis method according to claim 1, characterized in that: When the collected data time series is updated, the length of the series remains unchanged.

3. The SF6 density meter data abnormality self-diagnosis method according to claim 1, characterized in that: Before calculating the correlation coefficient, the ambient temperature time series and the density time series are normalized.

4. The SF6 density meter data abnormality self-diagnosis method according to claim 3 is characterized in that: The normalization process is implemented by using the maximum and minimum value method.

5. The SF6 density meter data abnormality self-diagnosis method according to claim 1, characterized in that: When the correlation coefficient is less than -0.95, it is determined that there is a high negative correlation between the density value change and the ambient temperature change.

6. The SF6 density meter data abnormality self-diagnosis method according to claim 1, characterized in that: Step 3) also includes: generating a corresponding alarm signal.

7. The SF6 density meter data abnormality self-diagnosis method according to claim 1, characterized in that: The process of diagnosing the cause of the change also includes the following steps: 4) Obtain the ambient temperature time series and density time series corresponding to a normal density meter, calculate the difference between the ambient temperature time series and the density time series corresponding to the SF6 density meter to be diagnosed, generate an ambient temperature difference sequence and a density difference sequence, calculate the correlation coefficient between the ambient temperature difference sequence and the density difference sequence, and determine whether there is a high negative correlation between the change in density value and the change in ambient temperature based on the correlation coefficient. If so, determine that the change in density value is caused by the ambient temperature at the installation location of the density meter being higher or lower than the temperature of the gas chamber of the electrical equipment body. If not, determine that the change in density value is caused by other reasons.

8. The SF6 density meter data abnormality self-diagnosis method according to claim 7, characterized in that: When the correlation coefficient is less than -0.95, it is determined that there is a high negative correlation between the density value change and the ambient temperature change.

9. A computer-readable storage medium, characterized in that: It comprises one or more programs for execution by one or more processors of an electronic device, wherein the one or more programs comprise instructions for executing the method for self-diagnosis of abnormal SF6 density meter data as claimed in any one of claims 1 to 8.

10. An electronic device, characterized in that: The method comprises one or more processors, a memory and one or more programs stored in the memory, wherein the one or more programs comprise instructions for executing the method for self-diagnosis of abnormal SF6 density meter data as claimed in any one of claims 1 to 8.

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