Integrated detection method, system and equipment of oil chromatography online monitoring device

Abstract

The invention relates to the technical field of power equipment monitoring, and discloses an integrated detection method, system and equipment of an oil chromatography online monitoring device, and the method comprises the following steps: obtaining standard oil monitoring data of the oil chromatography online monitoring device in a transformer substation, and calculating a measurement error and measurement repeatability; acquiring monitoring data of the on-line monitoring device on the oil samples with different concentrations, and taking the concentration value of the oil sample meeting the condition as the minimum detection concentration; the measurement error, the measurement repeatability and the minimum detection concentration are compared with different threshold values respectively, and whether the oil chromatography online monitoring device is qualified or not is judged; wherein the threshold values are adjusted on the basis of the initial set value, and the adjustment amount is related to the type of the detected object, is in positive correlation with the voltage grade of the transformer substation, and is in positive correlation with the difference value between the historical monitoring data mean value and the initial set value. The method can adapt to influences caused by changes of manufacturers, models and the like of the oil chromatography online monitoring device, and the function detection precision of the oil chromatography online monitoring device is improved.

Description

Technical Field

[0001] This invention relates to the field of power equipment monitoring technology, specifically to an integrated detection method, system, and equipment for online oil chromatography monitoring. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] The reliability of large power equipment in substations is crucial for ensuring the safe and stable operation of the power grid. Equipment failure can lead to widespread power outages, endangering social safety and causing huge economic losses.

[0004] The real-time performance, intelligent analysis, maintenance optimization, and efficiency improvement of online monitoring devices for power equipment make them a key means to ensure the stable operation of the power system.

[0005] Among them, online oil chromatography monitoring devices play a crucial role in the power system, not only ensuring the safe operation of the power grid, but also improving the operating efficiency and maintenance level of power equipment.

[0006] Currently, the testing conducted on online oil chromatography monitoring devices, whether in laboratory testing, on-site handover and acceptance, or periodic calibration, mainly focuses on performance analysis with standard oil calibration. However, even after passing the performance analysis, the device manufacturer's staff still needs to coordinate with the substation's backend manufacturer after installation, and a significant amount of commissioning work is required, which is time-consuming and labor-intensive.

[0007] The current detection mode of online oil chromatography monitoring devices has the following problems: First, the testing standards are incomplete and the testing methods are not comprehensive, lacking a full range of testing standards for the functions and data communication dimensions of online oil chromatography monitoring devices; Secondly, the results analysis is inefficient. The collection and comparison of test data all rely on manual labor, which is labor-intensive and makes it difficult to meet the needs of horizontal and vertical comparisons. Third, the testing standards are mostly set arbitrarily and are difficult to change according to the manufacturer or specifications of the online oil chromatography monitoring device. Summary of the Invention

[0008] To address the aforementioned problems, this invention provides an integrated detection method, system, and equipment for online oil chromatography monitoring devices. The detection standard is adjusted based on the initial set value, adapting to changes such as the substation voltage level, the difference between the average historical monitoring data and the initial set value. This avoids the drawbacks of thresholds being entirely manually set and can accommodate changes in the manufacturer and model of the online oil chromatography monitoring device, thereby improving the accuracy of functional detection of the online oil chromatography monitoring device.

[0009] To achieve the above objectives, the present invention adopts the following technical solution: The first aspect of the present invention provides an integrated detection method for an online oil chromatography monitoring device, comprising: Acquire monitoring data of the standard oil from the online oil chromatography monitoring device in the substation; calculate the difference between the monitoring data and the standard data to obtain the measurement error; calculate the relative standard deviation of the monitoring data acquired in several consecutive times to obtain the measurement repeatability. The monitoring data of oil samples of different concentrations obtained by the online oil chromatography monitoring device in the substation are used as the minimum detection concentration for the oil sample that meets the conditions. The measurement error, measurement repeatability, and minimum detection concentration are compared with the first threshold, the second threshold, and the third threshold, respectively, to determine whether the online oil chromatography monitoring device is qualified. Among them, the first threshold, the second threshold and the third threshold are all adjusted based on the initial set value. The adjustment amount is related to the type of detection object, positively correlated with the voltage level of the substation, and positively correlated with the difference between the average of historical monitoring data and the initial set value.

[0010] Furthermore, it also includes: calculating the average acquisition time of several consecutive monitoring data acquisitions to obtain the minimum detection cycle, and judging whether the online oil chromatography monitoring device is qualified based on the minimum detection cycle.

[0011] Furthermore, it also includes: responding to the data alarm signal sent by the online oil chromatography monitoring device, analyzing historical monitoring data, and determining the cause of the data alarm signal: if the latest monitoring data shows a data jump, it can be determined that the data fluctuation is caused by an abnormality of the online oil chromatography monitoring device; if the historical monitoring data shows a peak value, it can be determined that the data fluctuation is caused by the unstable performance of the online oil chromatography monitoring device; otherwise, it is determined whether the data warning logic of the online oil chromatography monitoring device is incorrect.

[0012] Furthermore, the judgment steps of the data early warning logic of the oil chromatography online monitoring device include: A set of test data is randomly selected, imported into the online oil chromatography monitoring device, and the hourly, daily, and weekly increments and data warning signals of each test data in the set are obtained from the online oil chromatography monitoring device. If the data calculated by the online oil chromatography monitoring device is compared with the built-in data, and a single data point does not match, it is determined that the data warning logic of the online oil chromatography monitoring device is incorrect.

[0013] Furthermore, it also includes: summoning a setpoint to obtain a first threshold group returned by the online oil chromatography monitoring device; modifying the first threshold group by adding a setpoint to each threshold in the first threshold group to form a second threshold group; after sending the second threshold group to the online oil chromatography monitoring device, summoning the setpoint again to obtain a third threshold group returned by the online oil chromatography monitoring device; if the third threshold group is consistent with the second threshold group, the threshold modification function of the online oil chromatography monitoring device is normal; if the third threshold group is consistent with the first threshold group, the threshold modification function of the online oil chromatography monitoring device is abnormal.

[0014] Furthermore, it also includes: obtaining the data model file of the online oil chromatography monitoring device, verifying whether the dataset in the data model file is missing any required options, whether the dataset is missing any optional conditions, whether the data format conforms to the specifications, and whether the data structure is missing any required attributes, and determining whether the data model of the online oil chromatography monitoring device is abnormal.

[0015] Furthermore, the monitoring data includes concentration data for acetylene, hydrogen, and total hydrocarbons.

[0016] A second aspect of the present invention provides an integrated detection system for an online oil chromatography monitoring device, comprising: The first calculation module is configured to: acquire monitoring data of the standard oil from the online oil chromatography monitoring device in the substation; calculate the difference between the monitoring data and the standard data to obtain the measurement error; and calculate the relative standard deviation of the monitoring data acquired in several consecutive times to obtain the measurement repeatability. The second calculation module is configured to: acquire monitoring data of oil samples of different concentrations from the online oil chromatography monitoring device in the substation, and take the concentration value of the oil sample that meets the conditions as the minimum detection concentration; The comparative analysis module is configured to compare the measurement error, measurement repeatability, and minimum detection concentration with the first threshold, the second threshold, and the third threshold, respectively, to determine whether the online oil chromatography monitoring device is qualified. Among them, the first threshold, the second threshold and the third threshold are all adjusted based on the initial set value. The adjustment amount is related to the type of detection object, positively correlated with the voltage level of the substation, and positively correlated with the difference between the average of historical monitoring data and the initial set value.

[0017] Furthermore, the monitoring data includes concentration data of acetylene, hydrogen, and total hydrocarbons.

[0018] A third aspect of the present invention provides a computer device including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the program to implement the steps in the integrated detection method of an online oil chromatography monitoring device as described above.

[0019] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention provides an integrated detection method for an online oil chromatography monitoring device. The detection standard is adjusted based on the initial set value. It can adapt to changes such as the voltage level of the substation, the average value of historical monitoring data, and the difference between the initial set value. This avoids the drawbacks of the threshold being set entirely manually and can adapt to the influence of changes in the manufacturer and model of the online oil chromatography monitoring device, thereby improving the accuracy of functional detection of the online oil chromatography monitoring device.

[0020] This invention provides an integrated detection method for an online oil chromatography monitoring device, which can analyze data alarm signals, perform data early warning logic verification, and realize comprehensive detection of the performance, function, and data communication of the online oil chromatography monitoring device for power equipment. Attached Figure Description

[0021] The accompanying drawings, which constitute a part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute a limitation thereof.

[0022] Figure 1 This is an architectural diagram of an integrated detection method for an online oil chromatography monitoring device according to Embodiment 1 of the present invention; Figure 2 This is a flowchart illustrating the detection process of the value distribution function in Embodiment 1 of the present invention. Detailed Implementation

[0023] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0024] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0025] Unless otherwise specified, the embodiments and features in the embodiments of the present invention can be combined with each other. The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0026] Example 1 The purpose of this first embodiment is to provide an integrated detection method for an online oil chromatography monitoring device.

[0027] This embodiment provides an integrated detection method for an online oil chromatography monitoring device, which enables comprehensive detection of the device's performance, functions, and data communication, while also achieving full-process digitalization of data acquisition and analysis.

[0028] This embodiment provides an integrated detection method for an online oil chromatography monitoring device, which is configured on an integrated detection platform.

[0029] This embodiment provides an integrated detection method for an online oil chromatography monitoring device, such as... Figure 1 As shown, it includes the following steps: Step 1: Connect the oil chromatography online monitoring device (hereinafter referred to as the online monitoring device) to the integrated detection platform through a switch / router.

[0030] The online monitoring device can be composed of multiple online oil chromatography devices, including online oil chromatography monitoring device 1, online oil chromatography monitoring device 2, ... and online oil chromatography monitoring device N.

[0031] Step 2: Import the data model file (ICD file) of the online oil chromatography monitoring device into the integrated detection platform and set the configuration parameters (IP address, port).

[0032] Step 3: The integrated detection platform connects to the online oil chromatography monitoring device according to the configuration parameters and displays whether the online monitoring device is successfully connected.

[0033] Step 4: If the connection is successful, proceed to step 5; if the connection fails, the online monitoring device's communication function is abnormal, and the communication test ends.

[0034] Step 5: The integrated detection platform verifies the dataset and data structure in the ICD file. Verification includes: whether the dataset is missing any required or optional data points; whether the data format conforms to specifications; and whether the data structure is missing any required attributes (timestamp, quality bit). If the ICD file verification passes, proceed to Step 6; otherwise, the online monitoring device's data model is abnormal, and this step ends.

[0035] Step 6: For each online monitoring device, the integrated monitoring platform automatically sends and calls for set values ​​in sequence. Based on the results returned by the online monitoring device, it checks whether the set value sending function is normal.

[0036] like Figure 2As shown, the steps for detecting abnormalities in the setpoint distribution function include: the integrated detection platform invokes setpoints and obtains the default first threshold group m (acetylene 0.5, hydrogen 75, total hydrocarbons 75) returned by the online monitoring device; the integrated detection platform modifies the first threshold group m by adding a set value (e.g., 1) to each threshold in the first threshold group m, changing it to the second threshold group n (acetylene 1.5, hydrogen 76, total hydrocarbons 76); after the integrated detection platform distributes the second threshold group n to the online monitoring device, it invokes setpoints again and obtains the third threshold group p returned by the online monitoring device. If the third threshold group p is consistent with the second threshold group n, the threshold modification function (i.e., the setpoint distribution function) is normal; if the third threshold group p is consistent with the first threshold group m, the threshold modification function is abnormal.

[0037] Step 7: The online monitoring device takes samples and uploads the oil sample data (i.e., monitoring data, including full telemetry (i.e., gas concentration data), remote signaling, spectral files, and log files) to the integrated monitoring platform via a switch / router.

[0038] In this case, the same standard oil sample was analyzed by both an online monitoring device and an offline chromatograph, with the offline chromatograph collecting the standard data.

[0039] Sampling is performed simultaneously through multiple oil lines, such as Figure 1 As shown, each oil chromatography unit samples standard oil data from one oil path.

[0040] Step 8: The integrated testing platform automatically receives the sampled data and analyzes it to assess the data format, data requirements, data latency, file naming, and file format.

[0041] Step 9: The integrated detection platform automatically receives standard data from the offline chromatograph, including gas concentration data such as acetylene, hydrogen, and total hydrocarbons.

[0042] Step 10: The integrated detection platform has built-in formulas for measurement error, measurement repeatability, minimum detection cycle, and minimum detection concentration. Based on the sampling data from the online monitoring device and the standard data from the offline chromatograph, it automatically calculates the measurement error, error level, measurement repeatability, minimum detection cycle, and minimum detection concentration. It then compares the measurement error, measurement repeatability, and minimum detection concentration with the first, second, and third thresholds, respectively, to determine whether the oil chromatography online monitoring device is qualified. Measurement error, measurement repeatability, minimum detection cycle, and minimum detection concentration are four indicators for evaluating the device's performance.

[0043] Among them, the first threshold, the second threshold, and the third threshold are all adjusted based on the initial set value. The adjustment amount is related to the type of detection object, positively correlated with the substation voltage level, and positively correlated with the difference between the average historical monitoring data and the initial set value. Where T is the first threshold, second threshold, or third threshold; T0 is the initial setting value, which varies for substations of different voltage levels and for different types of detection objects (acetylene, hydrogen, total hydrocarbons, etc.), and the T0 value corresponding to the measurement error, measurement repeatability, and minimum detection concentration is also different; k is the adjustment coefficient, which varies for different types of detection objects; AVG is the average value of the monitoring data (i.e., concentration value) of a certain type of detection object by an online oil chromatography monitoring device in a substation over a period of time; S is the voltage level of the substation, such as 750kV, 500kV, 330kV, etc.

[0044] The above calculations avoid the drawbacks of setting the first, second, and third thresholds entirely by human judgment. They can vary with the difference between the average historical monitoring data and the initial set value, and can adapt to changes in the manufacturer and model of the online oil chromatography monitoring device, thereby improving the detection accuracy of the online oil chromatography monitoring device.

[0045] The measurement error is the difference between the sampling data from the online monitoring device and the standard data from the offline chromatograph, including relative error and absolute error. ; Among them, C o It is data from an online monitoring device, C l This is standard data from an offline chromatograph, E a It is the absolute error, E r It is a relative error.

[0046] For example, the method for determining the error level is as follows: taking the acetylene C2H2 standard oil concentration range of 0.2-5µL / L as an example, if the measurement error is within ±0.2µL / L or ±30%, it is classified as A1; if it exceeds the previous error limit but is within ±0.5µL / L or ±30%, it is classified as A2; if it exceeds the previous error limit but is within ±1µL / L or ±30%, it is classified as B; if it exceeds the previous error limit but is within ±1.5µL / L or ±30%, it is classified as C. For newly built equipment, online monitoring devices for 750kV and above substations should meet the requirements of A1 (i.e., the first threshold is ±0.2µL / L), online monitoring devices for 500kV substations should meet the requirements of A2 (i.e., the first threshold is ±0.5µL / L), and online monitoring devices for 330kV and below substations should meet the requirements of not less than B (i.e., the first threshold is ±1µL / L).

[0047] The formula for measuring repeatability is as follows: Where n is the number of measurements, and C i This represents the result of the i-th measurement (sampling data from the online monitoring device). Let i be the arithmetic mean of n measurements, where i is the measurement number.

[0048] The same oil sample shall be continuously monitored and analyzed no less than 8 times, and the results of 6 consecutive measurements shall be taken. Repeatability shall be expressed as the relative standard deviation (RSD) of the total hydrocarbon measurement results.

[0049] For example, the measurement repeatability of online oil chromatography monitoring devices in 750kV and above substations should not exceed 3% (i.e., the second threshold is 3%); the measurement repeatability of online oil chromatography monitoring devices in 500kV and below substations should not exceed 5% (i.e., the second threshold is 5%); exceeding this specification will result in disqualification.

[0050] If the RSD of total hydrocarbons is within acceptable limits, but the RSDs of methane, ethane, ethylene, and acetylene are all within acceptable limits, the test item should be deemed unacceptable.

[0051] The minimum detection cycle algorithm works as follows: for each online monitoring device, the data sampling time of three consecutive oil samples is recorded, and the average value is automatically calculated as the minimum detection cycle.

[0052] In this embodiment, if the minimum detection cycle of the online monitoring device is no more than 2 hours after more than Q detections, it is considered qualified; if the minimum detection cycle of the online monitoring device is more than 12 hours after less than P detections, it is considered qualified. Q and P are both set values.

[0053] The monitoring data of oil samples with different concentrations obtained by the online oil chromatography monitoring device in the substation are used to determine the minimum detection concentration. Specifically, the minimum detection concentration is calculated as follows: For each online oil chromatography monitoring device, a blank oil sample is first tested; the concentration data for acetylene and hydrogen, etc., should all be 0. Then, oil sample 1 is tested, with three consecutive tests. If all three sets show a non-zero response value, the concentration value corresponding to oil sample 1 is the minimum detection concentration. If the three consecutive test data for oil sample 1 are zero, oil sample 2 is used for testing, and three consecutive tests should also show a non-zero response value; the concentration value of oil sample 2 is then the minimum detection concentration. This process continues. Oil sample 1 has the lowest concentration, oil sample 2 has the second lowest concentration, and so on.

[0054] For example, for online oil chromatography monitoring devices in 750kV and above substations, the minimum detectable concentration of acetylene in the oil should not exceed 0.2 μL / L (i.e., the third threshold is 0.2 μL / L), and the minimum detectable concentration of hydrogen in the oil should not exceed 2 μL / L (i.e., the third threshold is 2 μL / L); for online oil chromatography monitoring devices in 500kV and below substations, the minimum detectable concentration of acetylene in the oil should not exceed 0.5 μL / L (i.e., the third threshold is 0.5 μL / L), and the minimum detectable concentration of hydrogen in the oil should not exceed 5 μL / L (i.e., the third threshold is 5 μL / L). Exceeding these specifications will result in the minimum detectable concentration being deemed unqualified.

[0055] Step 11: After receiving the alarm remote signal from the online monitoring device, the integrated detection platform determines the online monitoring device as faulty if the data value corresponding to this alarm remote signal is -99999 and the alarm remote signal display is normal. If the data value corresponding to this alarm remote signal is not -99999 and the alarm remote signal display is abnormal, the online monitoring device is considered a false alarm. Otherwise, the online monitoring device's early warning function is normal. For example, for the alarm remote signal "oil sample sampling abnormality alarm," a value of 0 represents normal, and 1 represents abnormality, meaning oil inlet timeout, abnormal oil circuit pressure, abnormal oil tank level, level rod malfunction, abnormal oil pump speed, etc. If the oil sample sampling abnormality alarm is 0 and the corresponding data value is -99999, the online monitoring device is considered faulty. If the oil sample sampling abnormality alarm is 1 and the data value is not -99999, the online monitoring device is considered a false alarm.

[0056] Step 12: After receiving the data alarm signal from the online monitoring device, determine the cause of the data alarm signal through data analysis. For example: Upon receiving data from the online monitoring device, an acetylene alarm of attention value 2 is detected. By checking the acetylene data, it is found that its concentration is 1.3, which exceeds the attention value 2 threshold of 1.0, thus triggering the attention value 2 data alarm signal.

[0057] The data analysis steps include: the integrated detection platform analyzes the historical curves of acetylene, hydrogen, total hydrocarbons, and other data transmitted by the online monitoring device. If the historical curve is relatively stable (the data deviation for 7 consecutive cycles is within 5%), but the latest data shows a jump (more than 50% higher than the average of the previous 7 cycles), it can be determined that the data fluctuation is caused by the online monitoring device. If the historical curve fluctuates significantly and shows obvious peaks (the data fluctuation for two adjacent cycles exceeds 50%), it can be determined that the large amount of data fluctuation is caused by the unstable performance of the online monitoring device itself. If the historical curve is relatively stable and there is no data jump in the latest data, then proceed to step 13 to determine whether the data warning logic of the online monitoring device is incorrect.

[0058] Step 13: Verification of data alerts.

[0059] (1) Prepare several sets of test data, randomly select one set of test data, import it into the online monitoring device, and the online monitoring device automatically calculates the hourly increment, daily increment, weekly increment and data warning signal of each data in the set.

[0060] (2) All test data are already available in the integrated testing platform, and the hourly increment, daily increment, weekly increment and data warning signal are automatically calculated according to the formula.

[0061] (3) The integrated detection platform automatically compares the data sent by the online monitoring device with the built-in data. If the calculated value of a data does not correspond to the calculated value of the platform (for example, the online monitoring device calculates the weekly acetylene increment of the group data at 1:00 on August 12, 2022 as 0.5, while the integrated detection platform calculates the weekly acetylene increment of the same group data as 2.77), then it is determined that the data warning logic of the online monitoring device is wrong.

[0062] The weekly increment, daily increment, 4-hour increment, and 2-hour increment are calculated as follows: △C=C i,2 -C i,1 In the formula: △C represents the increment per week, day, 4h, or 2h, in μL / L; C i,2 This indicates the latest measurement data for the corresponding characteristic gas, in μL / L; C i,1 This indicates the reference value for the corresponding characteristic gas, in μL / L.

[0063] Among them, the calculation of weekly increment of online monitoring data: C i,1 Take the arithmetic mean of the measurement data within the period of 336 hours to 168 hours prior to this data point (i.e., 14 days to 7 days prior). Outliers in the measurement data should be removed before calculation.

[0064] Among them, the daily increment calculation of online monitoring data: C i,1 Take the arithmetic mean of the measurement data within the previous 48 hours to the previous 24 hours (after removing outliers).

[0065] Among them, the incremental calculation of online monitoring data every 4 hours and every 2 hours: For a data collection cycle of 4 hours, C i,1 Take the arithmetic mean of the four measurements taken 4 hours ago (after removing outliers), and calculate the increment ΔC every 4 hours. 4h For a data acquisition period of 2 hours, C i,1 Take the arithmetic mean of the four measurements taken 2 hours ago (after removing outliers), and calculate the increment ΔC every 2 hours. 2h It also calculates the increment ΔC every 4 hours. 4h For the retest data, the same C value is used as for the data to be retested. i,1 .

[0066] Table 1 shows two sets of test data.

[0067] Table 1. Two sets of test data

[0068] This embodiment provides an integrated detection method for an online oil chromatography monitoring device. The main innovation lies in the integrated detection platform being connected to the online monitoring device and the offline chromatograph. This replaces the previous manual data collection, analysis, and judgment work, achieving "automatic degassing, automatic sample injection, automatic analysis, automatic calculation" and the ability to simultaneously detect multiple oil chromatography devices. This realizes full-process automation and solves the problem of low detection efficiency caused by "manual degassing, manual sample injection, manual data entry, and calculation errors".

[0069] This embodiment provides an integrated detection method for an online oil chromatography monitoring device, which can analyze data alarm signals, perform data early warning logic verification, and realize comprehensive detection of the performance, function, and data communication of the online oil chromatography monitoring device for power equipment.

[0070] Example 2 The purpose of this second embodiment is to provide an integrated detection system for an online oil chromatography monitoring device, including: The first calculation module is configured to: acquire monitoring data of the standard oil from the online oil chromatography monitoring device in the substation; calculate the difference between the monitoring data and the standard data to obtain the measurement error; and calculate the relative standard deviation of the monitoring data acquired in several consecutive times to obtain the measurement repeatability. The second calculation module is configured to: acquire monitoring data of oil samples of different concentrations from the online oil chromatography monitoring device in the substation, and take the concentration value of the oil sample that meets the conditions as the minimum detection concentration; The comparative analysis module is configured to compare the measurement error, measurement repeatability, and minimum detection concentration with the first threshold, the second threshold, and the third threshold, respectively, to determine whether the online oil chromatography monitoring device is qualified. Among them, the first threshold, the second threshold and the third threshold are all adjusted based on the initial set value. The adjustment amount is related to the type of detection object, positively correlated with the voltage level of the substation, and positively correlated with the difference between the average of historical monitoring data and the initial set value.

[0071] Furthermore, it also includes a third calculation module, which is configured to: calculate the average acquisition time of several consecutive monitoring data acquisitions to obtain the minimum detection cycle, and determine whether the oil chromatography online monitoring device is qualified based on the minimum detection cycle.

[0072] Furthermore, it also includes an alarm analysis module, which is configured to: respond to the data alarm signal sent by the online oil chromatography monitoring device, analyze historical monitoring data, and determine the cause of the data alarm signal: if the latest monitoring data shows a data jump, it can be determined that the data fluctuation is caused by an abnormality of the online oil chromatography monitoring device; if the historical monitoring data shows a peak value, it can be determined that the data fluctuation is caused by the unstable performance of the online oil chromatography monitoring device; otherwise, it can be determined whether the data warning logic of the online oil chromatography monitoring device is incorrect.

[0073] Furthermore, the judgment steps of the data early warning logic of the oil chromatography online monitoring device include: A set of test data is randomly selected, imported into the online oil chromatography monitoring device, and the hourly, daily, and weekly increments and data warning signals of each test data in the set are obtained from the online oil chromatography monitoring device. If the data calculated by the online oil chromatography monitoring device is compared with the built-in data, and a single data point does not match, it is determined that the data warning logic of the online oil chromatography monitoring device is incorrect.

[0074] Furthermore, it also includes a threshold modification function detection module, which is configured to: invoke a setpoint to obtain a first threshold group returned by the online oil chromatography monitoring device; modify the first threshold group by adding a setpoint to each threshold in the first threshold group to form a second threshold group; after sending the second threshold group to the online oil chromatography monitoring device, invoke the setpoint again to obtain a third threshold group returned by the online oil chromatography monitoring device; if the third threshold group is consistent with the second threshold group, the threshold modification function of the online oil chromatography monitoring device is normal; if the third threshold group is consistent with the first threshold group, the threshold modification function of the online oil chromatography monitoring device is abnormal.

[0075] Furthermore, it also includes a data model detection module, which is configured to: acquire the data model file of the online oil chromatography monitoring device, verify whether the dataset in the data model file is missing any required items, whether the dataset is missing any optional conditions, whether the data format conforms to the specifications, and whether the data structure is missing any required attributes, and determine whether the data model of the online oil chromatography monitoring device is abnormal.

[0076] Furthermore, the monitoring data includes concentration data of acetylene, hydrogen, and total hydrocarbons.

[0077] It should be noted that each module in this embodiment corresponds one-to-one with each step in Embodiment 1, and their specific implementation processes are the same, so they will not be repeated here.

[0078] Example 3 This embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the program, it implements the steps in the integrated detection method of an online oil chromatography monitoring device as described in Embodiment 1 above.

[0079] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0080] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. An integrated detection method for an online oil chromatography monitoring device, characterized in that, include: Acquire monitoring data of the standard oil from the online oil chromatography monitoring device in the substation; For monitoring data, the difference between the data and the standard data is calculated to obtain the measurement error; the relative standard deviation of the monitoring data acquired in several consecutive data acquisitions is calculated to obtain the measurement repeatability. The monitoring data of oil samples of different concentrations obtained by the online oil chromatography monitoring device in the substation are used as the minimum detection concentration for the oil sample that meets the conditions. The measurement error, measurement repeatability, and minimum detection concentration are compared with the first threshold, the second threshold, and the third threshold, respectively, to determine whether the online oil chromatography monitoring device is qualified. Among them, the first threshold, the second threshold and the third threshold are all adjusted based on the initial set value. The adjustment amount is related to the type of detection object, positively correlated with the voltage level of the substation, and positively correlated with the difference between the average of historical monitoring data and the initial set value.

2. The integrated detection method of the online oil chromatography monitoring device as described in claim 1, characterized in that, Also includes: The average acquisition time of several consecutive monitoring data acquisitions is calculated to obtain the minimum detection cycle. Based on the minimum detection cycle, it is determined whether the online oil chromatography monitoring device is qualified.

3. The integrated detection method of the online oil chromatography monitoring device as described in claim 1, characterized in that, Also includes: In response to the data alarm signal sent by the online oil chromatography monitoring device, analyze historical monitoring data to determine the cause of the data alarm signal: if the latest monitoring data shows a data jump, it can be determined that the data fluctuation is caused by an abnormality of the online oil chromatography monitoring device; if the historical monitoring data shows a peak value, it can be determined that the data fluctuation is caused by the unstable performance of the online oil chromatography monitoring device; otherwise, determine whether the data warning logic of the online oil chromatography monitoring device is wrong.

4. The integrated detection method of the online oil chromatography monitoring device as described in claim 3, characterized in that, The judgment steps of the data early warning logic of the oil chromatography online monitoring device include: A set of test data is randomly selected, imported into the online oil chromatography monitoring device, and the hourly, daily, and weekly increments and data warning signals of each test data in the set are obtained from the online oil chromatography monitoring device. If the data calculated by the online oil chromatography monitoring device is compared with the built-in data, and a single data point does not match, it is determined that the data warning logic of the online oil chromatography monitoring device is incorrect.

5. The integrated detection method of the online oil chromatography monitoring device as described in claim 1, characterized in that, Also includes: Call the setpoint and obtain the first threshold set returned by the online oil chromatography monitoring device; Modify the first threshold group by adding the set value to each threshold in the first threshold group to create the second threshold group; after sending the second threshold group to the online oil chromatography monitoring device, call the set value again to obtain the third threshold group returned by the online oil chromatography monitoring device; if the third threshold group is consistent with the second threshold group, the threshold modification function of the online oil chromatography monitoring device is normal; if the third threshold group is consistent with the first threshold group, the threshold modification function of the online oil chromatography monitoring device is abnormal.

6. The integrated detection method of the online oil chromatography monitoring device as described in claim 1, characterized in that, Also includes: Obtain the data model file of the online oil chromatography monitoring device, verify whether the dataset in the data model file is missing any required items, whether the dataset is missing any optional conditions, whether the data format conforms to the specifications, and whether the data structure is missing any required attributes, and determine whether the data model of the online oil chromatography monitoring device is abnormal.

7. The integrated detection method of the online oil chromatography monitoring device as described in claim 1, characterized in that, The monitoring data includes concentration data for acetylene, hydrogen, and total hydrocarbons.

8. An integrated detection system for an online oil chromatography monitoring device, characterized in that, include: The first calculation module is configured to: acquire the monitoring data of the standard oil from the online oil chromatography monitoring device in the substation; For monitoring data, the difference between the data and the standard data is calculated to obtain the measurement error; the relative standard deviation of the monitoring data acquired in several consecutive data acquisitions is calculated to obtain the measurement repeatability. The second calculation module is configured to: acquire monitoring data of oil samples of different concentrations from the online oil chromatography monitoring device in the substation, and take the concentration value of the oil sample that meets the conditions as the minimum detection concentration; The comparative analysis module is configured to compare the measurement error, measurement repeatability, and minimum detection concentration with the first threshold, the second threshold, and the third threshold, respectively, to determine whether the online oil chromatography monitoring device is qualified. Among them, the first threshold, the second threshold and the third threshold are all adjusted based on the initial set value. The adjustment amount is related to the type of detection object, positively correlated with the voltage level of the substation, and positively correlated with the difference between the average of historical monitoring data and the initial set value.

9. The integrated detection system of the online oil chromatography monitoring device as described in claim 8, characterized in that, The monitoring data includes concentration data for acetylene, hydrogen, and total hydrocarbons.

10. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the integrated detection method of the online oil chromatography monitoring device as described in any one of claims 1-7.

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