A transformer intelligent oil extraction control method and system

Abstract

The present invention relates to the field of transformer oil extraction control, and discloses a transformer intelligent oil extraction control method and system, which analyzes the oil extraction operation mode through the collected oil extraction related operation data, detects whether the oil extraction operation mode meets the preset standard conditions, and optimizes and adjusts the oil extraction method according to the detection result of the oil extraction operation mode, monitors and analyzes the oil extraction amount in real time, analyzes the oil level decline trend during oil extraction, controls the oil extraction amount to be kept above the minimum oil level line, plans the oil extraction time by establishing an oil extraction time prediction model based on historical oil extraction data, executes the oil extraction operation according to the planned oil extraction time, and then analyzes and calculates the compensated oil extraction time based on the oil extraction environment, dynamically optimizes and adjusts the oil extraction time, and keeps the oil extraction time within the standard range. Finally, the control analysis result is fed back, which is conducive to further improving the transformer oil extraction accuracy and intelligence level.

Description

Technical Field

[0001] The present invention relates to the technical field of transformer oil extraction control, and more particularly to a transformer intelligent oil extraction control method and system. Background Art

[0002] The inside of the transformer is filled with insulating oil, which not only acts as an insulator to prevent arc discharge between internal high-voltage components, but also acts as a coolant to help dissipate the heat generated by the transformer during operation. Over time, the transformer oil will age due to thermal, electrical, chemical and environmental influences, and its performance will decline. In order to ensure the healthy operation of the transformer, it is necessary to regularly monitor the quality of the insulating oil, including its dielectric strength, moisture content, acid value, etc. Traditional transformer oil extraction usually relies on manual operation. This method has safety risks, low efficiency, and environmental pollution. With the development of automation and intelligent technology, intelligent transformer oil extraction control methods have emerged. This method aims to realize the automation and intelligent control of the transformer oil extraction process through modern sensing technology, automatic control technology, communication technology, etc., so as to improve the safety and efficiency of oil extraction operations, reduce human errors, and enhance the intelligence level of transformer maintenance and management;

[0003] As described in the Chinese patent application CN115728096A, a transformer multi-mode layered automatic oil sampling monitoring method is provided. The present invention provides a transformer multi-mode automatic oil sampling monitoring method, comprising: an oil sampling system is installed on the side of the transformer casing, the oil sampling port of the transformer is connected to a pneumatic filling device for cooperating with the oil sampling system to extract oil; a controller has a data transmission function, which is triggered according to the received instructions or the mode set by itself, or the current detection data to control the pneumatic filling device and various components in the oil sampling system to start / stop in a timed manner; the method of the present invention can realize unmanned monitoring, automatically monitor the transformer and regularly take oil samples. After the samples are taken, the user can go to the site to take them away, realizing digital intelligent duty.

[0004] However, the above process still has the following disadvantages:

[0005] First, the existing intelligent oil extraction control method for transformers adopts a fixed oil extraction method, lacks dynamic analysis and adjustment of the operation mode, and cannot optimize and adjust the oil extraction method, resulting in low efficiency and accuracy of the oil extraction operation;

[0006] Second, the existing intelligent oil extraction control method for transformers only controls the oil extraction amount through a simple liquid level switch, which has low control accuracy and lacks analysis of the oil level drop trend;

[0007] Third, the existing intelligent oil extraction control method for transformers may adopt a fixed oil extraction time interval, without fully considering the historical oil extraction rules and the impact of the oil extraction environment on the oil extraction time, resulting in invalid oil extraction or missed detection. Summary of the Invention

[0008] In order to overcome the above-mentioned defects in the prior art, the present invention provides a transformer intelligent oil extraction control method and system to solve the problems existing in the above-mentioned background technology.

[0009] The present invention provides the following technical solution: a transformer intelligent oil extraction control method, comprising:

[0010] S1: used to collect relevant operation data of each oil extraction. When the push rod feedback signal is received, the sensor is triggered to collect the relevant operation data of the oil extraction during the oil extraction;

[0011] S2: Analyze the oil extraction operation mode based on the oil extraction related operation data, detect whether the oil extraction operation mode meets the preset standard conditions, and optimize and adjust the oil extraction method according to the detection results of the oil extraction operation mode;

[0012] S3: Based on real-time monitoring and analysis of the oil extraction volume, the oil level drop trend during oil extraction is analyzed, and the oil extraction volume is controlled to be kept above the minimum oil level line;

[0013] S4: used to collect historical oil extraction data over a period of time, establish an oil extraction time prediction model based on the historical oil extraction data, plan the oil extraction time according to the oil extraction time prediction model, and execute the oil extraction operation according to the planned oil extraction time;

[0014] S5: performing compensation analysis on the oil extraction time during the oil extraction operation based on the oil extraction environment to obtain the compensated oil extraction time, and dynamically optimizing and adjusting the oil extraction time based on the compensated oil extraction time to keep the oil extraction time within the standard range;

[0015] S6: Provide real-time feedback of the control adjustment results and the entire control analysis process to the inspector's terminal.

[0016] Preferably, after receiving the feedback signal from the push rod, the S1 immediately sends control information to the sensor through the solenoid valve to trigger the sensor to collect oil extraction related operation data during oil extraction and record the data collection time. The collected oil extraction related operation data include the ambient temperature, humidity, oil temperature, oil pressure, oil extraction flow rate, oil extraction volume, oil level and gas content in the oil during oil extraction. The collected oil extraction related operation data are classified and stored in the database according to the recorded collection time.

[0017] Preferably, the S2 monitors and records the environmental conditions during the oil extraction operation and whether a sealed or semi-sealed state is adopted, collects two samples of the same oil sample in a fully exposed and semi-sealed state respectively, and uses gas chromatography to quantitatively analyze the hydrogen, carbon dioxide and carbon monoxide contents in the two samples. Based on the results of the quantitative analysis, a statistical method is used to calculate the mean, standard deviation and coefficient of variation of each gas content in the two operating modes respectively, and a difference comparison analysis is performed on the data of each gas content in the oil calculated in the fully exposed and semi-sealed operating modes, and then a comprehensive calculation is made of the overall difference evaluation value of all gas contents to detect whether the differences between the oil extraction operation modes meet the preset standard conditions. If it is detected that the differences between the oil extraction operation modes do not meet the preset standard conditions, the inspection personnel are prompted to dynamically adjust the oil extraction operation method based on the analysis results of the operation differences.

[0018] Preferably, the S3 is based on the optimization result of the oil extraction operation mode, analyzes the oil level data collected, and calculates the oil level drop rate and oil level drop cycle to identify the speed and trend of the oil level drop, and sets a minimum oil level line based on the safe operation requirements of the transformer. , and set a safety margin based on the oil level drop rate, cycle and operating error , and then monitor the oil level in real time to keep it at no less than If the oil level is close to or below , the alarm is triggered immediately and the oil volume is adjusted. When the oil level is close to or below When the oil level deviation is calculated, the PID algorithm is used to calculate the control output value based on the calculated oil level deviation, and the oil intake is adjusted according to the control output value to ensure that the oil level returns to above.

[0019] Preferably, the S4 collects and stores historical oil extraction data of the transformer over a period of time, including oil extraction time, oil extraction amount, oil level changes and environmental conditions, selects a machine learning algorithm to establish an oil extraction time prediction model, uses historical data to train the model, uses historical oil extraction data to train the prediction model, and adjusts the model parameters to minimize the prediction error, then inputs the currently collected oil extraction data into the trained prediction model, and outputs the oil extraction prediction time, plans the optimal oil extraction time point according to the oil extraction prediction time, and performs the oil extraction operation.

[0020] Preferably, the S5 is based on the analysis results of the oil collection time planning, and uses statistical methods to analyze the correlation between the oil collection environmental condition parameters and the oil collection time. Based on the correlation analysis results, a mathematical model of environmental factors and oil collection time is established, and the current environmental conditions are input into the data model. The compensated oil collection time is calculated, and then the compensated oil collection time is compared with the current actual oil collection time to dynamically adjust the oil collection time and control the oil collection time within the standard range.

[0021] Preferably, the S6 feeds back the oil extraction operation mode control adjustment results, the oil extraction quantity control adjustment results and the oil extraction time control adjustment results to the inspector's terminal in real time, and automatically generates a control monitoring report based on the entire control analysis process and sends it to the inspector's terminal device, and allows the inspector to perform query and confirmation operations on the terminal device.

[0022] To achieve the above objectives, the present invention provides the following technical solutions: a transformer intelligent oil extraction control system, which implements the above transformer intelligent oil extraction control method, comprising:

[0023] Data collection module: used to collect relevant operation data of each oil extraction. When the push rod feedback signal is received, the sensor is triggered to collect the relevant operation data of the oil extraction during the oil extraction;

[0024] Oil extraction operation control module: Analyzes the oil extraction operation mode based on the oil extraction related operation data, detects whether the oil extraction operation mode meets the preset standard conditions, and optimizes and adjusts the oil extraction method according to the detection results of the oil extraction operation mode;

[0025] Oil extraction control module: Based on real-time monitoring and analysis of the oil extraction volume, the module analyzes the downward trend of the oil level during oil extraction and controls the oil extraction volume to remain above the minimum oil level line;

[0026] The oil extraction time planning module is used to collect historical oil extraction data over a period of time, establish an oil extraction time prediction model based on the historical oil extraction data, plan the oil extraction time according to the oil extraction time prediction model, and execute the oil extraction operation according to the planned oil extraction time;

[0027] Oil extraction time control module: Based on the oil extraction environment, the oil extraction time during the oil extraction operation is compensated and analyzed to obtain the compensated oil extraction time. The oil extraction time is dynamically optimized and adjusted according to the compensated oil extraction time to keep the oil extraction time within the standard range;

[0028] Control result feedback module: Provides real-time feedback of control adjustment results and the entire control analysis process to the inspector terminal.

[0029] The technical effects and advantages of the present invention are as follows:

[0030] Based on the analysis of oil extraction related operation data, it detects whether the operation mode meets the preset standard conditions, and optimizes and adjusts the oil extraction method according to the test results. It can adapt to the oil extraction needs in different situations and further improve the accuracy and efficiency of the oil extraction operation.

[0031] Based on real-time monitoring and analysis of oil extraction volume and oil level decline trend, it is possible to predict and control the oil extraction volume in advance to keep it above the minimum oil level line, avoiding excessive oil extraction leading to low oil level and affecting the normal operation of the transformer, thereby improving the safety and stability of transformer operation.

[0032] An oil extraction time prediction model is established based on historical oil extraction data over a period of time. The oil extraction time is planned according to the model, which can arrange oil extraction operations more scientifically, avoid unnecessary frequent oil extraction or long periods of no oil extraction, and perform compensation analysis on the oil extraction time according to the oil extraction environment to obtain the compensated oil extraction time. The oil extraction time is dynamically optimized and adjusted based on the compensation results to ensure that the oil extraction time always remains within the standard range, further improving the accuracy and reliability of the oil extraction operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 A diagram showing the steps of the method of the present invention.

[0034] Figure 2 This is a system structure diagram of the present invention.

[0035] Figure 3 This is the transformer oil extraction control flow chart of this embodiment. DETAILED DESCRIPTION

[0036] The technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the present invention. In addition, the forms of the various structures described in the following embodiments are merely examples. The intelligent oil extraction control method and system for transformers involved in the present invention are not limited to the various structures described in the following embodiments. All other embodiments obtained by ordinary technicians in this field without making creative work fall within the scope of protection of the present invention.

[0037] like Figure 1 This embodiment provides a transformer intelligent oil extraction control method, including:

[0038] S1: used to collect relevant operation data of each oil extraction. When the push rod feedback signal is received, the sensor is triggered to collect the relevant operation data of the oil extraction during the oil extraction.

[0039] In this embodiment, after receiving the feedback signal from the push rod, the S1 immediately sends control information to the sensor through the solenoid valve to trigger the sensor to collect oil extraction related operation data during oil extraction and record the data collection time. The collected oil extraction related operation data includes the ambient temperature, humidity, oil temperature, oil pressure, oil extraction flow rate, oil extraction volume, oil level and gas content in the oil during oil extraction. The collected oil extraction related operation data are classified and stored in the database according to the recorded collection time.

[0040] It should be specifically explained that by installing sensors on the oil extraction equipment to monitor the environmental conditions (temperature, humidity, air pressure) and operating status (whether it is sealed or semi-sealed) in real time, before the oil extraction begins, check whether all sensors (temperature sensor, humidity sensor, pressure sensor, oil level sensor, flow sensor and gas sensor), solenoid valves, push rods, data acquisition units and control units are installed in place and wired correctly, and configure the control system software to ensure that it can receive the signal from the solenoid valve and trigger sensor data acquisition; when the operator starts the push rod and performs the oil extraction operation, the built-in sensor or limit switch generates an electrical signal as feedback and transmits it to the solenoid valve. The solenoid valve receives the feedback signal from the push rod After being activated, it sends a control signal to the control system. After receiving the signal from the solenoid valve, the control system immediately sends a data acquisition start signal to all relevant sensors to collect the environmental parameters, oil sample parameters and gas content in the oil during oil extraction. The environmental parameters during oil extraction include the ambient temperature recorded by the temperature sensor and the ambient humidity recorded by the humidity sensor. The oil sample parameters include the oil sample temperature monitored and recorded by the oil temperature sensor, the oil pressure recorded by the pressure sensor and the oil extraction flow measured and recorded by the flow sensor. The gas content in the oil includes the content of hydrogen, carbon dioxide, carbon monoxide and other gases in the oil monitored in real time by the gas analysis sensor. At the same time, the current UTC time or local time is recorded as the starting timestamp of data collection.

[0041] S2: Analyze the oil extraction operation mode based on the oil extraction related operation data, detect whether the oil extraction operation mode meets the preset standard conditions, and optimize and adjust the oil extraction method according to the detection results of the oil extraction operation mode.

[0042] In this embodiment, the S2 monitors and records the environmental conditions during the oil extraction operation and whether a sealed or semi-sealed state is adopted, collects two samples of the same oil sample in a fully exposed and semi-sealed state respectively, and uses a gas chromatography method to quantitatively analyze the hydrogen, carbon dioxide and carbon monoxide contents in the two samples. Based on the results of the quantitative analysis, a statistical method is used to calculate the mean, standard deviation and coefficient of variation of each gas content in the two operating modes respectively, and a difference comparison analysis is performed on the data of each gas content in the oil calculated in the fully exposed and semi-sealed operating modes, and then a comprehensive calculation is made of the overall difference evaluation value of all gas contents to detect whether the differences between the oil extraction operation modes meet the preset standard conditions. If it is detected that the differences between the oil extraction operation modes do not meet the preset standard conditions, the inspection personnel are prompted to dynamically adjust the oil extraction operation method based on the analysis results of the operation differences.

[0043] It should be specifically noted that sampling equipment should be prepared to ensure that sampling can be performed in both fully exposed and semi-sealed states. Multiple samples of the same oil sample should be collected separately in the fully exposed state, and samples should be taken again using semi-sealed measures under the same environmental conditions. The oil samples should be pre-treated according to the requirements of gas chromatography analysis, such as degassing and filtration. The gas chromatograph should then be configured, appropriate detection parameters should be set, and the configured instrument should be run to quantitatively analyze the hydrogen, carbon dioxide, and carbon monoxide in each oil sample, thereby determining the hydrogen, carbon dioxide, and carbon monoxide content in each sample. The data obtained from the gas chromatography analysis should be organized into a table, including sample number, operating mode (fully exposed / semi-sealed), environmental conditions (temperature, humidity, air pressure), hydrogen content, carbon dioxide content, and carbon monoxide content. Based on the data obtained from the quantitative analysis, the mean, standard deviation, and coefficient of variation of each gas should be calculated respectively. The independent sample t-test should be used to compare the differences in the content of each gas under the two operating modes. The calculation formula for the t-test is: ,in, Indicates the average value of each gas content under full exposure conditions, Indicates the average value of each gas content in a semi-sealed state. Indicates the standard deviation of gas content in the fully exposed state, Indicates the standard deviation of gas content in semi-sealed state, represents the number of samples collected independently under full exposure conditions, Indicates the number of samples collected independently in a semi-sealed state;

[0044] The differences in each gas content calculated under the fully exposed and semi-sealed operating modes are comprehensively compared and analyzed to calculate the overall difference evaluation value of all gas contents. The specific calculation formula is: ,in, The weight coefficient represents the difference in content of each gas, and G represents the number of gases. A difference threshold is determined based on previous operating data and experimental results. The overall difference evaluation value of all gas contents is compared with the difference threshold. If the overall difference evaluation value of all gas contents is lower than the difference threshold, it is considered that the difference between the operating modes meets the preset standard conditions and no adjustment of the operating steps is required. If the overall difference evaluation value of all gas contents is higher than the difference threshold, it indicates that the difference between the operating modes does not meet the preset standard conditions. The operating modes in this case are analyzed to find out the causes of the differences, and adjustment suggestions are given. An early warning prompt message is immediately issued to the detection personnel, prompting them to further investigate and adjust the operating steps that lead to these differences according to the adjustment suggestions.

[0045] S3: Based on real-time monitoring and analysis of the oil extraction volume, the downward trend of the oil level during oil extraction is analyzed, and the oil extraction volume is controlled to remain above the minimum oil level line.

[0046] In this embodiment, the S3 is based on the optimization result of the oil extraction operation mode, analyzes the oil level data collected, and calculates the oil level drop rate and oil level drop cycle to identify the speed and trend of the oil level drop, and sets a minimum oil level line based on the safe operation requirements of the transformer. , and set a safety margin based on the oil level drop rate, cycle and operating error , and then monitor the oil level in real time to keep it at no less than If the oil level is close to or below , the alarm is triggered immediately and the oil volume is adjusted. When the oil level is close to or below When the oil level deviation is calculated, the PID algorithm is used to calculate the control output value based on the calculated oil level deviation, and the oil intake is adjusted according to the control output value to ensure that the oil level returns to above.

[0047] It should be noted that the oil level sensor collects real-time oil level data, records the timestamp of data collection, selects a suitable time window, and uses the oil level change within the window to calculate the oil level. Divide by the duration of the time window , calculate the oil level drop rate, the specific calculation formula is: The oil level data is analyzed in time series using the Fourier transform function to determine the oil level drop period. , that is, the period from the highest point to the next same height of the oil level, and then based on the oil level drop rate , Oil level drop cycle and operational errors (Due to errors caused by sensor accuracy, environmental factors and human operation), the safety margin is calculated as , according to the set minimum oil level line and safety margin , define a safe oil level threshold for oil extraction , based on the safety oil level threshold, determine whether to adjust the oil intake. When the oil level actually drops and approaches When the oil level deviation is calculated , that is, the current oil level and The difference between the two values ​​is calculated based on the oil level deviation, and the control output value is calculated using the PID algorithm. ,in, Indicates the control output value, that is, the adjustment amount of oil extraction. Indicates oil level difference, Represents the proportional gain, which is used to adjust the impact of the current deviation. Indicates the integral gain, which is used to adjust the cumulative effect of the deviation. Represents the differential gain, which is used to predict the changing trend of the deviation. Represents a time variable, used to describe the change of oil level difference over time. Adjust the oil intake to ensure that the oil level is kept at The above, and continuously monitor the oil level, and adjust the PID parameters according to the actual situation to optimize the control effect.

[0048] S4: used to collect historical oil extraction data within a period of time, establish an oil extraction time prediction model based on the historical oil extraction data, plan the oil extraction time according to the oil extraction time prediction model, and perform the oil extraction operation according to the planned oil extraction time.

[0049] In this embodiment, the S4 collects and stores historical oil extraction data of the transformer over the past period of time, including oil extraction time, oil extraction volume, oil level changes and environmental conditions, selects a machine learning algorithm to establish an oil extraction time prediction model, uses historical data to train the model, uses historical oil extraction data to train the prediction model, and adjusts the model parameters to minimize the prediction error. The currently collected oil extraction data is then input into the trained prediction model, and the predicted oil extraction time is output. According to the predicted oil extraction time, the optimal oil extraction time point is planned to perform the oil extraction operation.

[0050] It should be noted that by performing feature selection on the collected historical oil extraction data, key factors affecting the oil extraction volume, such as time, oil level drop rate, and environmental conditions, are selected. A time series analysis algorithm is then used to establish an oil extraction time prediction model. The historical oil extraction data is used to train the established oil extraction time prediction model. The specific formula is as follows: , where F represents the predicted oil extraction time, X represents the selected feature vector, Represents the model parameters, and the accuracy of the model prediction is evaluated by the loss function. The specific formula is as follows: ,in, Indicates the actual oil extraction time. Represents the predicted oil extraction time, represents the number of collected data points, and at the same time, uses the optimization algorithm to adjust the model parameters , to minimize the loss function, and then use cross-validation to evaluate the performance of the model. Adjust the model parameters until satisfactory prediction accuracy is achieved;

[0051] The currently collected oil extraction data is input as the input feature vector into the trained model, and the model is used to make predictions and output the predicted oil extraction time. The predicted oil extraction time output by the prediction model is used as the basis, combined with the operation window and buffer time, to calculate the optimal oil extraction time point. The specific calculation formula is: , where F represents the predicted oil extraction time, Indicates the start time of the operation window, Indicates the end time of the operation window. Indicates the buffer time.

[0052] S5: Based on the oil extraction environment, a compensation analysis is performed on the oil extraction time during the oil extraction operation to obtain the compensated oil extraction time. The oil extraction time is dynamically optimized and adjusted according to the compensated oil extraction time to keep the oil extraction time within the standard range.

[0053] In this embodiment, the S5 is based on the analysis results of the oil collection time planning. By using statistical methods to analyze the correlation between the oil collection environmental condition parameters and the oil collection time, a mathematical model of environmental factors and oil collection time is established based on the correlation analysis results. The current environmental conditions are input into the data model, and the compensated oil collection time is calculated. The compensated oil collection time is then compared with the current actual oil collection time to dynamically adjust the oil collection time and control the oil collection time within the standard range.

[0054] It should be noted that the correlation between environmental condition parameters and oil extraction time is analyzed by Pearson correlation coefficient. The specific analysis and calculation formula is: ,in, represents the correlation coefficient, and represent the observed values ​​of environmental parameters and oil extraction time respectively, and Represent the average values ​​of the environmental parameter observations and the oil extraction time observations respectively. Then, based on the correlation analysis results, a mathematical model of environmental factors and oil extraction time is established to calculate the compensated oil extraction time. For example, if there is a linear relationship between the environmental condition parameters and the oil extraction time, a linear regression model can be used to construct a mathematical model of environmental factors and oil extraction time. The collected environmental condition parameters are used as the characteristic parameters of the input linear regression model to calculate the compensated oil extraction time. The specific calculation formula is: , where T represents the oil extraction time after compensation, represents the model parameters, Indicates temperature, Indicates humidity, Indicates the oil viscosity, then compares the compensated oil extraction time with the current actual oil extraction time, and dynamically adjusts the oil extraction time to ,in, Indicates the lower limit of oil extraction time. Indicates the upper limit of the oil extraction time, and according to the final oil extraction time To perform the oil extraction operation.

[0055] S6: Provide real-time feedback of the control adjustment results and the entire control analysis process to the inspector's terminal.

[0056] In this embodiment, the S6 feeds back the oil extraction operation mode control adjustment results, the oil extraction quantity control adjustment results, and the oil extraction time control adjustment results to the inspector's terminal in real time, and automatically generates a control monitoring report based on the entire control analysis process and sends it to the inspector's terminal device, and allows the inspector to perform query and confirmation operations on the terminal device.

[0057] like Figure 2 The embodiment shown provides an implementation system corresponding to the article anti-counterfeiting method based on local feature visual information, including a data collection module, an oil extraction operation control module, an oil extraction quantity control module, an oil extraction time planning module, an oil extraction time control module and a control result feedback module. The data collection module is connected to the oil extraction operation control module, the oil extraction operation control module is connected to the oil extraction quantity control module, the oil extraction operation control module is connected to the oil extraction time planning module, the oil extraction time planning module is connected to the oil extraction time control module, and the oil extraction operation control module, the oil extraction quantity control module and the oil extraction time control module are connected to the control result feedback module.

[0058] The data collection module is used to collect relevant operation data of each oil extraction. When the push rod feedback signal is received, the sensor is triggered to collect the relevant operation data of the oil extraction during the oil extraction;

[0059] The oil extraction operation control module analyzes the oil extraction operation mode based on the oil extraction related operation data, detects whether the oil extraction operation mode meets the preset standard conditions, and optimizes and adjusts the oil extraction method according to the detection result of the oil extraction operation mode;

[0060] The oil extraction control module performs real-time monitoring and analysis of the oil extraction amount, analyzes the oil level drop trend during oil extraction, and controls the oil extraction amount to remain above the minimum oil level line;

[0061] The oil extraction time planning module is used to collect historical oil extraction data within a period of time, establish an oil extraction time prediction model based on the historical oil extraction data, plan the oil extraction time according to the oil extraction time prediction model, and perform the oil extraction operation according to the planned oil extraction time;

[0062] The oil extraction time control module performs compensation analysis on the oil extraction time during the oil extraction operation based on the oil extraction environment to obtain the compensated oil extraction time, and dynamically optimizes and adjusts the oil extraction time according to the compensated oil extraction time to keep the oil extraction time within the standard range;

[0063] The control result feedback module provides real-time feedback of the control adjustment results and the entire control analysis process to the detection personnel terminal.

[0064] like Figure 3 The figure shows the transformer oil extraction control process of this embodiment, and the specific execution steps include:

[0065] Step 1: When the push rod feedback signal is received, the sensor is triggered to collect the oil extraction related operation data during oil extraction;

[0066] Step 2: Analyze the oil extraction operation mode through the oil extraction related operation data to determine whether the oil extraction operation mode meets the preset standard conditions. If not, optimize and adjust the oil extraction method;

[0067] Step 3: Analyze the oil level drop trend during oil extraction by the oil extraction volume, and detect whether the oil level line is close to or below the safety oil level threshold. If it is close to or below the safety oil level threshold, adjust the oil extraction volume to return it to above the safety oil level threshold;

[0068] Step 4: Predict and plan the optimal oil extraction time based on the oil extraction time prediction model;

[0069] Step 5: Calculate the compensated fuel extraction time and compare it with the current actual fuel extraction time to dynamically adjust the fuel extraction time and control it within the standard range;

[0070] Step 6: Provide real-time feedback of the control process to the inspector terminal.

[0071] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

[0072] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A transformer intelligent oil extraction control method, characterized in that: include: S1: used to collect relevant operation data of each oil extraction. When the push rod feedback signal is received, the sensor is triggered to collect the relevant operation data of the oil extraction during the oil extraction; S2: Analyze the oil extraction operation mode based on the oil extraction related operation data, detect whether the oil extraction operation mode meets the preset standard conditions, and optimize and adjust the oil extraction method according to the detection results of the oil extraction operation mode. The S2 monitors and records the environmental conditions during the oil extraction operation and whether the sealing or semi-sealed state measures are adopted, collects two samples of the same oil sample in the fully exposed and semi-sealed states respectively, and uses the gas chromatography method to quantitatively analyze the hydrogen, carbon dioxide and carbon monoxide contents in the two samples. Based on the results of the quantitative analysis, use the statistical method to calculate the mean, standard deviation and coefficient of variation of each gas content in the two operation modes respectively, and compare and analyze the difference of each gas content data in the oil calculated under the fully exposed and semi-sealed operation modes, and then comprehensively calculate the overall difference evaluation value of all gas contents to detect whether the differences between the oil extraction operation modes meet the preset standard conditions. If it is detected that the differences between the oil extraction operation modes do not meet the preset standard conditions, then according to the analysis results of the operation differences, prompt the inspection personnel to dynamically adjust the oil extraction operation method; S3: Based on real-time monitoring and analysis of the oil extraction volume, the oil level drop trend during oil extraction is analyzed, and the oil extraction volume is controlled to be kept above the minimum oil level line; S4: used to collect historical oil extraction data over a period of time, establish an oil extraction time prediction model based on the historical oil extraction data, plan the oil extraction time according to the oil extraction time prediction model, and perform the oil extraction operation according to the planned oil extraction time. The S4 collects and stores historical oil extraction data of the transformer over a period of time, including oil extraction time, oil extraction volume, oil level changes and environmental conditions, selects a machine learning algorithm to establish an oil extraction time prediction model, uses historical data to train the model, uses historical oil extraction data to train the prediction model, and adjusts the model parameters to minimize the prediction error. The currently collected oil extraction data is then input into the trained prediction model, and the predicted oil extraction time is output. The optimal oil extraction time point is planned according to the predicted oil extraction time to perform the oil extraction operation; S5: Based on the oil extraction environment, a compensation analysis is performed on the oil extraction time during the oil extraction operation to obtain a compensated oil extraction time. The oil extraction time is dynamically optimized and adjusted based on the compensated oil extraction time to keep the oil extraction time within the standard range. S5 is based on the analysis results of the oil extraction time planning and uses statistical methods to analyze the correlation between the oil extraction environment condition parameters and the oil extraction time. Based on the correlation analysis results, a mathematical model of environmental factors and oil extraction time is established. The current environmental conditions are input into the data model to calculate the compensated oil extraction time. The compensated oil extraction time is then compared with the current actual oil extraction time to dynamically adjust the oil extraction time to control the oil extraction time within the standard range. S6: Provide real-time feedback of the control adjustment results and the entire control analysis process to the inspector's terminal.

2. A transformer intelligent oil extraction control method according to claim 1, characterized in that: After receiving the feedback signal from the push rod, the S1 immediately sends control information to the sensor through the solenoid valve to trigger the sensor to collect oil extraction-related operation data during oil extraction and record the data collection time. The collected oil extraction-related operation data includes the ambient temperature, humidity, oil temperature, oil pressure, oil extraction flow rate, oil extraction volume, oil level and gas content in the oil during oil extraction. The collected oil extraction-related operation data are classified and stored in the database according to the recorded collection time.

3. A transformer intelligent oil extraction control method according to claim 2, characterized in that: The S3 is based on the optimization result of the oil extraction operation mode, analyzes the oil level data collected, and calculates the oil level drop rate and oil level drop cycle to identify the speed and trend of the oil level drop, and sets a minimum oil level line based on the safe operation requirements of the transformer. , and set a safety margin based on the oil level drop rate, cycle and operating error , and then monitor the oil level in real time to keep it at no less than If the oil level is close to or below , the alarm is triggered immediately and the oil volume is adjusted. When the oil level is close to or below When the oil level deviation is calculated, the PID algorithm is used to calculate the control output value based on the calculated oil level deviation, and the oil intake is adjusted according to the control output value to ensure that the oil level returns to above.

4. A transformer intelligent oil extraction control method according to claim 3, characterized in that: The S6 feeds back the oil extraction operation mode control adjustment results, oil extraction quantity control adjustment results and oil extraction time control adjustment results to the inspector's terminal in real time, and automatically generates a control monitoring report based on the entire control analysis process and sends it to the inspector's terminal device, and allows the inspector to perform query and confirmation operations on the terminal device.

5. A transformer intelligent oil extraction control system, implementing a transformer intelligent oil extraction control method according to any one of claims 1 to 4, characterized in that: include: Data collection module: used to collect relevant operation data of each oil extraction. When the push rod feedback signal is received, the sensor is triggered to collect the relevant operation data of the oil extraction during the oil extraction; Oil extraction operation control module: Analyzes the oil extraction operation mode based on the oil extraction related operation data, detects whether the oil extraction operation mode meets the preset standard conditions, and optimizes and adjusts the oil extraction method according to the detection results of the oil extraction operation mode; Oil extraction control module: Based on real-time monitoring and analysis of the oil extraction volume, the module analyzes the downward trend of the oil level during oil extraction and controls the oil extraction volume to remain above the minimum oil level line; The oil extraction time planning module is used to collect historical oil extraction data over a period of time, establish an oil extraction time prediction model based on the historical oil extraction data, plan the oil extraction time according to the oil extraction time prediction model, and execute the oil extraction operation according to the planned oil extraction time; Oil extraction time control module: Based on the oil extraction environment, the oil extraction time during the oil extraction operation is compensated and analyzed to obtain the compensated oil extraction time. The oil extraction time is dynamically optimized and adjusted according to the compensated oil extraction time to keep the oil extraction time within the standard range; Control result feedback module: Provides real-time feedback of control adjustment results and the entire control analysis process to the inspector terminal.

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