Transformer loss monitoring system based on Internet of Things

Abstract

The invention relates to the field of transformer loss monitoring, in particular to a transformer loss monitoring system based on the Internet of Things, which comprises a transformation data acquisition module, a load analysis module, a loss analysis module, an early warning processing module and a remote monitoring module, the load analysis module evaluates transformer load loss from apparent power, abnormal power duration, iron core loss difference and load difference, calculates a load abnormal comprehensive coefficient and compares the load abnormal comprehensive coefficient with a threshold value to judge whether the load is abnormal, and the early warning processing module carries out early warning according to different set loss levels and corresponding threshold values. According to the method, the defects of a traditional monitoring mode can be effectively overcome, the transformer loss monitoring efficiency and precision are improved, and operation of a power system is guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of transformer loss monitoring, specifically a transformer loss monitoring system based on the Internet of Things. Background Art

[0002] In the power system, the transformer is one of the key devices, and its operating state directly affects the stability, economy, and safety of the power system. The traditional transformer loss monitoring method mainly relies on manual regular inspections. Manual inspections are difficult to achieve real-time monitoring, have a long inspection cycle, and cannot capture the instantaneous changes in transformer losses in a timely manner. It may lead to the situation that when problems are discovered, the transformer is already in a relatively serious state, increasing the failure risk and maintenance cost. Manual measurement and data recording are easily affected by human factors, such as measurement errors and inaccurate recording, making it difficult to guarantee the accuracy and reliability of the data.

[0003] In recent years, the power system has continuously increased its demand for intelligence and automation. The rapid development of Internet of Things technology has provided a new solution for transformer loss monitoring. Internet of Things technology can realize the real-time collection, transmission, and processing of data. By installing various sensors on the transformer, the operating parameters of the transformer, such as current, voltage, magnetic flux, and temperature, can be obtained in real time, providing rich data support for accurately analyzing transformer losses. In this context, researching and developing a transformer loss monitoring system based on the Internet of Things has important practical significance. It can effectively overcome the deficiencies of traditional monitoring methods, improve the efficiency and accuracy of transformer loss monitoring, and ensure the reliable operation of the power system. Summary of the Invention

[0004] To solve the technical problems raised in the above background art, the present invention provides a transformer loss monitoring system based on the Internet of Things.

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

[0006] The present invention is a transformer loss monitoring system based on the Internet of Things, including a transformer data acquisition module, a load analysis module, a loss analysis module, a warning processing module, a remote monitoring module, and a data center.

[0007] The transformer data acquisition module collects the operating state data of the transformer according to various types of sensors, and sends the collected data to each module for analysis. The specific acquisition process is as follows:

[0008] Install various types of sensors at the winding and iron core positions of the transformer. The various types of sensors include current sensors, voltage sensors, and magnetic flux sensors. Real-time monitor the current data information of the current transformer through the current sensor to obtain the current parameter value of the current transformer. Real-time monitor the voltage data information of the current transformer through the voltage sensor to obtain the voltage parameter value of the current transformer. Real-time collect the magnetic flux data of the current transformer through the magnetic flux sensor to obtain the magnetic flux value of the current transformer.

[0009] The output end of the transformer data acquisition module is connected to the input ends of the load analysis module and the loss analysis module, and sends the current parameter value and the voltage parameter value to the load analysis module, and sends the magnetic flux value to the loss analysis module.

[0010] The load analysis module analyzes the load loss of the transformer according to the obtained data information. The specific acquisition process is as follows:

[0011] K1: The input end of the load analysis module is connected to the output end of the data center, and the output end of the load analysis module is connected to the input end of the early warning processing module. Set the operation cycle of each transformer as Y1, and set the transformer as Ci, i = 1, 2,..., n, where i represents the number of the corresponding transformer, and n represents the total number of transformers. Obtain the current parameter values and voltage parameter values of each transformer at each monitoring time point within Y1 to get the current parameter values and voltage parameter values of each transformer at each monitoring time point. Based on the current parameter values and voltage parameter values, obtain the apparent power values of each transformer at each monitoring time point. Extract the preset apparent power threshold within the data center, compare each apparent power value with the preset apparent power threshold, mark the apparent power value corresponding to the value greater than the preset apparent power threshold as the abnormal power consumption value, and perform an average calculation on each abnormal power value to obtain the abnormal power average value, marked as YE.

[0012] K2: Based on each abnormal power, obtain the corresponding abnormal power duration period, marked as the abnormal power duration. Add up each abnormal power duration to obtain the total abnormal power duration, marked as YT.

[0013] K3: Set the preset transformer iron core monitoring period, divide the preset transformer iron core monitoring period into several preset transformer iron core monitoring sub-periods, obtain the iron core loss values corresponding to each preset transformer iron core monitoring sub-period, the iron core loss values corresponding to each preset transformer iron core monitoring sub-period. Arrange each iron core loss value from largest to smallest, obtain the maximum iron core loss value and the minimum iron core loss value, and subtract the minimum loss value from the maximum loss value to obtain the iron core loss difference value, marked as YU.

[0014] K4: Monitor the load of each transformer within the total duration of abnormal power, obtain the load value of each transformer, mark it as abnormal load value, extract the standard load value of the data center, calculate the difference between the standard load value and the abnormal load value to obtain the transformer load difference, marked as TH;

[0015] K5: Calculate using the obtained average abnormal power, total abnormal power duration, core loss difference and transformer load difference, using the formula The load anomaly comprehensive coefficient TC of the transformer is obtained, where T1, T2, T3 and T4 are preset proportional coefficients, the preset load anomaly comprehensive threshold of the data center is extracted, and the actual load anomaly comprehensive coefficient is compared with the preset load anomaly comprehensive threshold. If the actual load anomaly comprehensive coefficient is greater than the preset load anomaly comprehensive threshold, a load anomaly signal is generated and sent to the early warning processing module.

[0016] The loss analysis module analyzes the transformer loss based on the collected magnetic flux value and various data information. The specific analysis process is as follows:

[0017] The input end of the loss analysis module is connected to the output end of the data center, and the output end of the loss analysis module is connected to the input end of the early warning processing module. The operation time point when the current transformer is put into use is obtained, marked as zero time, and then the current system time point is cut off, marked as the end time, and the zero time and the end time are subtracted to obtain the cumulative operation time of the current transformer, marked as ZL 累计时长 ; Divide the accumulated running time into several progressive running time periods, obtain the flux values ​​at all acquisition times, match one progressive running time period with one flux value, and calculate using the first-order difference formula Get the flux change rate FA 变化 , where T n and T n-1 Represented as two adjacent progressive operation time periods, CK n and CK n-1 It is expressed as the flux value of two adjacent progressive operation time periods;

[0018] The temperature data of each position point of the transformer at each monitoring time point is collected in real time through a temperature sensor. Each position point includes the iron core, primary winding, and secondary winding inside the transformer, and the temperature values of each position point of the transformer at each monitoring time point are obtained. The positions of the transformer are matched with the corresponding set high-efficiency operation thermal amplitude intervals to obtain the high-efficiency operation thermal amplitude intervals of each position point of the transformer. The temperature values of each position of the transformer at each monitoring time point are compared with the corresponding high-efficiency operation thermal amplitude intervals. The position monitoring time points greater than the maximum value of the high-efficiency operation thermal amplitude interval are marked as abnormal time points. The abnormal time points are integrated to obtain the abnormal temperature duration period, and the maximum temperature value and temperature change amount corresponding to the abnormal temperature duration period are obtained. Based on the abnormal temperature duration period, the maximum temperature value, and the temperature change amount, a temperature abnormality evaluation index is obtained, marked as KQ 指数 ; Obtain the material consumption value at the start time point corresponding to the abnormal temperature duration period, marked as the initial loss value, and then obtain the material consumption value at the end time point, marked as the final loss value. Subtract the initial loss value from the final loss value to obtain the temperature difference material loss value CA 损耗 ;

[0019] Calculate according to the obtained cumulative operation duration, temperature difference material loss value, temperature abnormality evaluation index, and magnetic flux change rate, and use the formula to obtain the fusion loss measurement value GIY, where J1, J2, J3, and J4 are preset proportionality coefficients, and FB 预设变化 represents the preset magnetic flux change rate, and CH 预设损耗 represents the preset temperature difference material loss value. Extract the preset fusion loss measurement threshold of the data center, and compare the actual fusion loss measurement value with the preset fusion loss measurement threshold. If the actual fusion loss measurement value is greater than the preset fusion loss measurement threshold, an abnormal loss signal is generated and sent to the warning processing module.

[0020] The warning processing module sets a warning processing mechanism for different loss degrees of the transformer according to the received signal and processes them separately. The specific acquisition process is as follows:[[]]

[0021] The output end of the warning processing module is connected to the input end of the data center. The warning processing module consists of a data reception sub-module, a threshold setting sub-module, and a warning output sub-module. The data reception sub-module receives the load abnormal signal and the abnormal loss signal, and sends the load abnormal signal and the abnormal loss signal to the level setting sub-module. The level setting sub-module receives the load abnormal signal and the abnormal loss signal and sets different loss levels. The loss levels include normal operation level, mild abnormal level, moderate abnormal level, and severe abnormal level. The standard loss threshold range is set. The normal operation level is specifically that the transformer loss is within the standard loss threshold range, and a normal signal is generated and sent to the warning output sub-module. The warning output sub-module receives the normal signal and continues to monitor the data information;

[0022] The mild loss level is specifically 20% exceeding the standard loss threshold range. A mild warning signal is generated and sent to the warning output sub-module, and the current transformer status is automatically obtained and sent to the operation and maintenance personnel to prompt the operation and maintenance personnel to pay attention to the operation status of the transformer.

[0023] The moderate loss level is specifically 50% exceeding the standard loss threshold range. A moderate warning signal is generated and sent to the warning output sub-module. When the warning output sub-module receives the moderate warning signal, it obtains the corresponding transformer positioning information. Taking the transformer positioning as the center point, it obtains the mobile terminals of the operation and maintenance personnel within the radius. The positions of the transformer and each operation and maintenance personnel are connected to obtain the straight-line distances. The shortest straight-line distance is obtained and the corresponding operation and maintenance personnel are marked as selected personnel. The corresponding transformer position information is sent to the operation and maintenance personnel for maintenance.

[0024] The severe loss level is specifically 80% exceeding the standard loss threshold range. A severe warning signal is generated and sent to the warning output sub-module. When the warning output sub-module receives the severe warning signal, it obtains the corresponding transformer position, connects to the power dispatching center within the radius of the corresponding transformer, performs an emergency power outage on the transformer, and sends the severe warning signal to the mobile terminal of the maintenance personnel.

[0025] The remote monitoring module monitors the appearance of the transformer and the handling of the maintenance personnel through a high-definition camera, and feeds back the handling results to the monitoring system interface. The specific acquisition process is as follows:

[0026] The appearance operation status of each transformer is monitored in real time through a high-definition camera. The surface area of the transformer oil stain is identified through the color area to obtain the surface areas of the oil stains of each transformer. The total number of each oil stain corresponding to the surface areas of the oil stains is counted. The total oil stain area is obtained based on the surface areas of the oil stains and the total number of oil stains. The preset total oil stain area in the data center is extracted. The total oil stain area is compared with the preset total oil stain area. If the total oil stain area is greater than the preset total oil stain area, a cleaning signal is generated and sent to the mobile terminal of the operation and maintenance personnel.

[0027] The handling video of the operation and maintenance personnel is monitored in real time, and the handling video is decomposed to obtain the corresponding handling results and sent to the monitoring system interface.

[0028] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention uses a variety of sensors through a variable voltage data acquisition module to collect the operation data of the transformer in real time, providing data for accurate analysis; the loss analysis module, based on the collected data, comprehensively considers multiple factors such as the cumulative operation duration, the magnetic flux change rate, the temperature abnormality situation, and the temperature difference material loss, and calculates the integrated loss measurement value through calculation, which can accurately reflect the loss status of the transformer, overcome the problem of poor real-time performance of traditional monitoring methods, ensure that the operation and maintenance personnel can timely master the operation status of the transformer, and provide a basis for timely maintenance. The load analysis module evaluates the load loss of the transformer from multiple dimensions such as apparent power, abnormal power duration, core loss difference, and load difference, calculates the comprehensive load abnormality coefficient and compares it with the threshold value, and can judge whether the load is abnormal, ensuring the operation efficiency of the transformer.

[0029] The early warning processing module intelligently judges and classifies and warns the loss degree of the transformer according to different set loss levels and corresponding threshold values. From the prompt attention for mild abnormalities, to the accurate positioning for moderate abnormalities and notifying the nearby operation and maintenance personnel, and then to the linkage with the power dispatching center for emergency power outage in case of severe abnormalities, it realizes all-round and multi-level safety protection, effectively reduces the fault risk of the transformer, prevents the expansion of faults, reduces the impact on the power system caused by transformer faults, and improves the reliability of power supply. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. The following drawings are not deliberately drawn to scale in actual size, and the focus is on showing the gist of the present invention.

[0031] Figure 1 It is a principle block diagram of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0032] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings. Obviously, the described embodiments are only partial embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts also belong to the scope of protection of the present invention.

[0033] Please refer to Figure 1 As shown, the present invention is a transformer loss monitoring system based on the Internet of Things, including a variable voltage data acquisition module, a load analysis module, a loss analysis module, an early warning processing module, a remote monitoring module, and a data center.

[0034] The variable voltage data acquisition module collects the operation status data of the transformer according to various types of sensors, and sends the collected data to each module for analysis. The specific acquisition process is as follows:

[0035] Install various types of sensors at the winding and core positions of the transformer. The various types of sensors include current sensors, voltage sensors, and flux sensors. Monitor the current data information of the current transformer in real time through the current sensor to obtain the current parameter value of the current transformer. Monitor the voltage data information of the current transformer in real time through the voltage sensor to obtain the voltage parameter value of the current transformer. Collect the flux data of the current transformer in real time through the flux sensor to obtain the flux value of the current transformer.

[0036] The output end of the transformer data acquisition module is connected to the input ends of the load analysis module and the loss analysis module, and sends the current parameter value and the voltage parameter value to the load analysis module, and sends the flux value to the loss analysis module.

[0037] The load analysis module analyzes the load loss of the transformer according to the obtained data information. The specific acquisition process is as follows:

[0038] K1: The input end of the load analysis module is connected to the output end of the data center, and the output end of the load analysis module is connected to the input end of the early warning processing module. Set the operation cycle of each transformer as Y1, and set the transformer as Ci, where i = 1, 2,..., n. i represents the number of the corresponding transformer, and n represents the total number of transformers. Obtain the current parameter value and the voltage parameter value of each transformer at each monitoring time point within Y1 to obtain the current parameter value and the voltage parameter value of each transformer at each monitoring time point. Based on the current parameter value and the voltage parameter value, obtain the apparent power value of each transformer at each monitoring time point. Extract the preset apparent power threshold in the data center, compare each apparent power value with the preset apparent power threshold, mark the apparent power value corresponding to the value greater than the preset apparent power threshold as the abnormal power consumption value, calculate the average value of each abnormal power value to obtain the abnormal power average value, and mark it as YE.

[0039] K2: Based on each abnormal power, obtain the corresponding abnormal power duration period, mark it as the abnormal power duration, and add up each abnormal power duration to obtain the total abnormal power duration, marked as YT.

[0040] K3: Set the preset transformer core monitoring period, divide the preset transformer core monitoring period into several preset transformer core monitoring sub-periods, obtain the core loss value corresponding to each preset transformer core monitoring sub-period, the core loss values corresponding to each preset transformer core monitoring sub-period, arrange each core loss value from largest to smallest, obtain the maximum core loss value and the minimum core loss value, and subtract the minimum loss value from the maximum loss value to obtain the core loss difference value, marked as YU.

[0041] K4: Monitor the load of each transformer within the total duration of abnormal power, obtain the load value of each transformer, mark it as abnormal load value, extract the standard load value of the data center, calculate the difference between the standard load value and the abnormal load value to obtain the transformer load difference, marked as TH;

[0042] K5: Calculate using the obtained average abnormal power, total abnormal power duration, core loss difference and transformer load difference, using the formula The load anomaly comprehensive coefficient TC of the transformer is obtained, where T1, T2, T3 and T4 are preset proportional coefficients, the preset load anomaly comprehensive threshold of the data center is extracted, and the actual load anomaly comprehensive coefficient is compared with the preset load anomaly comprehensive threshold. If the actual load anomaly comprehensive coefficient is greater than the preset load anomaly comprehensive threshold, a load anomaly signal is generated and sent to the early warning processing module.

[0043] The loss analysis module analyzes the transformer loss based on the collected magnetic flux value and various data information. The specific analysis process is as follows:

[0044] The input end of the loss analysis module is connected to the output end of the data center, and the output end of the loss analysis module is connected to the input end of the early warning processing module. The operation time point when the current transformer is put into use is obtained, marked as zero time, and then the current system time point is cut off, marked as the end time, and the zero time and the end time are subtracted to obtain the cumulative operation time of the current transformer, marked as ZL 累计时长 ; Divide the accumulated running time into several progressive running time periods, obtain the flux values ​​at all acquisition times, match one progressive running time period with one flux value, and calculate using the first-order difference formula Get the flux change rate FA 变化 , where T n and T n-1 Represented as two adjacent progressive operation time periods, CK n and CK n-1 It is expressed as the flux value of two adjacent progressive operation time periods;

[0045] The temperature data of each position point of the transformer at each monitoring time point is collected in real time through a temperature sensor. Each position point includes the iron core, primary winding, and secondary winding inside the transformer, and the temperature values of each position point of the transformer at each monitoring time point are obtained. The positions of the transformer are matched with the corresponding set high-efficiency operation thermal amplitude intervals to obtain the high-efficiency operation thermal amplitude intervals of each position point of the transformer. The temperature values of each position of the transformer at each monitoring time point are compared with the corresponding high-efficiency operation thermal amplitude intervals. The position monitoring time points greater than the maximum value of the high-efficiency operation thermal amplitude interval are marked as abnormal time points. The abnormal time points are integrated to obtain the abnormal temperature duration period, and the maximum temperature value and temperature change amount corresponding to the abnormal temperature duration period are obtained. Based on the abnormal temperature duration period, maximum temperature value, and temperature change amount, a temperature abnormality evaluation index is obtained and marked as KQ 指数 ; Obtain the material consumption value at the start time point corresponding to the abnormal temperature duration period, marked as the initial loss value, and then obtain the material consumption value at the end time point, marked as the final loss value. Subtract the initial loss value from the final loss value to obtain the temperature difference material loss value CA 损耗 ;

[0046] Calculations are performed based on the obtained cumulative operation duration, temperature difference material loss value, temperature abnormality evaluation index, and magnetic flux change rate, using the formula to obtain the fusion loss measurement value GIY, where J1, J2, J3, and J4 are preset proportionality coefficients, FB 预设变化 represents the preset magnetic flux change rate, and CH 预设损耗 represents the preset temperature difference material loss value. The preset fusion loss measurement threshold of the data center is extracted, and the actual fusion loss measurement value is compared with the preset fusion loss measurement threshold. If the actual fusion loss measurement value is greater than the preset fusion loss measurement threshold, an abnormal loss signal is generated and sent to the warning processing module.

[0047] The warning processing module sets a warning processing mechanism for different loss degrees of the transformer according to the received signal and processes them separately. The specific acquisition process is as follows:

[0048] The output end of the warning processing module is connected to the input end of the data center. The warning processing module consists of a data receiving sub-module, a threshold setting sub-module, and a warning output sub-module. The data receiving sub-module receives the load abnormal signal and the abnormal loss signal, and sends the load abnormal signal and the abnormal loss signal to the level setting sub-module. The level setting sub-module receives the load abnormal signal and the abnormal loss signal and sets different loss levels. The loss levels include normal operation level, mild abnormal level, moderate abnormal level, and severe abnormal level. The standard loss threshold range is set. The normal operation level is specifically that the transformer loss is within the standard loss threshold range, and a normal signal is generated and sent to the warning output sub-module. The warning output sub-module receives the normal signal and continues to monitor the data information;

[0049] The mild loss level is specifically 20% exceeding the standard loss threshold range. A mild warning signal is generated and sent to the warning output sub-module, and the current transformer status is automatically obtained and sent to the operation and maintenance personnel to prompt them to pay attention to the operation status of the transformer.

[0050] The moderate loss level is specifically 50% exceeding the standard loss threshold range. A moderate warning signal is generated and sent to the warning output sub-module. After receiving the moderate warning signal, the warning output sub-module obtains the corresponding transformer positioning information. Taking the transformer positioning as the origin, the mobile terminals of the operation and maintenance personnel within the radius are obtained, and the positions of the transformer and each operation and maintenance personnel are connected to obtain the straight-line distances. The shortest straight-line distance is obtained and the corresponding operation and maintenance personnel are marked as the selected personnel, and the corresponding transformer position information is sent to the operation and maintenance personnel for maintenance.

[0051] The severe loss level is specifically 80% exceeding the standard loss threshold range. A severe warning signal is generated and sent to the warning output sub-module. After receiving the severe warning signal, the warning output sub-module obtains the corresponding transformer position, connects it to the power dispatching center within the radius of the corresponding transformer, performs an emergency power outage on the transformer, and sends the severe warning signal to the mobile terminal of the maintenance personnel.

[0052] The remote monitoring module monitors the appearance of the transformer and the handling of the maintenance personnel through a high-definition camera, and feeds back the handling results to the monitoring system interface. The specific acquisition process is as follows:

[0053] The appearance operation status of each transformer is monitored in real time through a high-definition camera. The surface area of the transformer oil stain is identified through the color area, and the surface areas of the oil stains of each transformer are obtained. The total number of each oil stain corresponding to each oil stain surface area is counted, and the total oil stain area is obtained based on the surface areas of the oil stains and the total number of oil stains. The preset total oil stain area in the data center is extracted, and the total oil stain area is compared with the preset total oil stain area. If the total oil stain area is greater than the preset total oil stain area, a cleaning signal is generated and sent to the mobile terminal of the operation and maintenance personnel.

[0054] The handling video of the operation and maintenance personnel is monitored in real time, and the corresponding handling results are obtained by decomposing the handling video and sent to the monitoring system interface.

[0055] The foregoing is a description of the invention and should not be construed as limiting thereof. Although several exemplary embodiments of the invention have been described, those skilled in the art will readily appreciate that many modifications can be made to the exemplary embodiments without departing from the novel teachings and advantages of the invention. Accordingly, all such modifications are intended to be included within the scope of the invention as defined by the claims. It should be understood that the foregoing is a description of the invention and should not be considered limited to the specific embodiments disclosed, and modifications to the disclosed embodiments as well as other embodiments are intended to be included within the scope of the appended claims. The invention is defined by the claims and their equivalents.

Claims

1. A transformer loss monitoring system based on the Internet of Things, including a transformer data acquisition module, a load analysis module, an early warning processing module, a remote monitoring module and a data center, characterized in that: Also includes loss analysis module; The loss analysis module analyzes the transformer loss according to the collected magnetic flux value and various data information, obtains the operation time point when the current transformer is put into use, marks it as zero time, and then cuts off the current system time point, marks it as the end time, and subtracts the zero time and the end time to obtain the cumulative operation time ZL of the current transformer. 累计时长 ; Divide the accumulated running time into several progressive running time periods, obtain the magnetic flux values ​​at all acquisition times, and calculate using the formula Get the flux change rate FA 变化 , the temperature data of each position point at each monitoring time point of the transformer is collected in real time by the temperature sensor, and the temperature value of each position point at each monitoring time point of the transformer is obtained. Each position point of the transformer is matched with the corresponding set high-efficiency operation thermal amplitude interval to obtain the high-efficiency operation thermal amplitude interval of each position point of the transformer, and the temperature value of each position at each monitoring time point of the transformer is compared with the corresponding high-efficiency operation thermal amplitude interval, and the position monitoring time point greater than the maximum value of the high-efficiency operation thermal amplitude interval is marked as an abnormal time point, and each abnormal time point is integrated to obtain the abnormal temperature duration period, and the corresponding maximum temperature value and temperature change amount of the abnormal temperature duration period are obtained. Based on the abnormal temperature duration period, the maximum temperature value and the temperature change amount, the temperature abnormality evaluation index is obtained, which is marked as KQ 指数 ; Obtain the material consumption value at the start time point corresponding to the abnormal temperature duration period, marked as the initial loss value, obtain the material consumption value at the end time point, marked as the final loss value, and subtract the initial loss value from the final loss value to obtain the temperature difference material loss value CA 损耗 ; Using the formula The fusion loss calculation value GIY is obtained, where J1, J2, J3 and J4 are preset proportional coefficients. The preset fusion loss calculation threshold of the data center is extracted, and the actual fusion loss calculation value is compared with the preset fusion loss calculation threshold. If the actual fusion loss calculation value is greater than the preset fusion loss calculation threshold, an abnormal loss signal is generated and sent to the early warning processing module.

2. The transformer loss monitoring system based on the Internet of Things according to claim 1 is characterized in that: The load analysis module analyzes the load loss of the transformer based on the obtained data information. The specific process is as follows: K1: The input end of the load analysis module is connected to the output end of the data center, and the output end of the load analysis module is connected to the input end of the early warning processing module. The operation cycle of each transformer is set to Y1, and the transformer is set to Ci. The current parameter value and voltage parameter value of each transformer at each monitoring time point in Y1 are obtained, and the current parameter value and voltage parameter value of each transformer at each monitoring time point are obtained. Based on the current parameter value and the voltage parameter value, the apparent power value of each transformer at each monitoring time point is obtained, and the preset apparent power threshold in the data center is extracted. Each apparent power value is compared with the preset apparent power threshold, and the apparent power value corresponding to the preset apparent power threshold is marked as an abnormal power consumption value, and the average value of each abnormal power value is calculated to obtain the abnormal power average value, which is marked as YE; K2: Based on each abnormal power, the corresponding abnormal power duration is obtained, which is marked as the abnormal power duration, and the durations of each abnormal power are added together to obtain the total duration of the abnormal power, which is marked as YT; K3: Set a preset transformer core monitoring period, divide the preset transformer core monitoring period into several preset transformer core monitoring sub-periods, obtain the core loss value corresponding to each preset transformer core monitoring sub-period, and each core loss value corresponding to each preset transformer core monitoring sub-period, arrange each core loss value from large to small, obtain the maximum core loss value and the minimum core loss value, and make a difference between the maximum loss value and the minimum core loss value to obtain the core loss difference value, marked as YU; K4: Monitor the load of each transformer within the total duration of abnormal power, obtain the load value of each transformer, mark it as abnormal load value, extract the standard load value of the data center, calculate the difference between the standard load value and the abnormal load value to obtain the transformer load difference, marked as TH; K5: Calculate using the obtained average abnormal power, total abnormal power duration, core loss difference and transformer load difference, using the formula The load anomaly comprehensive coefficient TC of the transformer is obtained, where T1, T2, T3 and T4 are preset proportional coefficients, the preset load anomaly comprehensive threshold of the data center is extracted, and the actual load anomaly comprehensive coefficient is compared with the preset load anomaly comprehensive threshold. If the actual load anomaly comprehensive coefficient is greater than the preset load anomaly comprehensive threshold, a load anomaly signal is generated and sent to the early warning processing module.

3. The transformer loss monitoring system based on the Internet of Things according to claim 1 is characterized in that: The early warning processing module sets up early warning processing mechanisms for different loss levels of transformers according to the received signals and processes them separately. The specific process is as follows: The output end of the early warning processing module is connected to the input end of the data center. The early warning processing module is composed of a data receiving submodule, a threshold setting submodule and an early warning output submodule. The data receiving submodule receives the load abnormality signal and the abnormal loss signal, and sends the load abnormality signal and the abnormal loss signal to the level setting submodule. The level setting submodule receives the load abnormality signal and the abnormal loss signal and sets different loss levels. The loss level includes a normal operation level, a slight abnormality level, a moderate abnormality level and a serious abnormality level. The standard loss threshold range is set. The normal operation level is specifically that the transformer loss is within the standard loss threshold range. A normal signal is generated and sent to the early warning output submodule. The early warning output submodule receives the normal signal and continues to monitor data information; The specific level of mild loss is 20% more than the standard loss threshold range, generating a mild warning signal and sending it to the warning output submodule, automatically obtaining the current transformer status and sending it to the operation and maintenance personnel, prompting the operation and maintenance personnel to pay attention to the transformer operation status; The specific moderate loss level is that the loss exceeds the standard loss threshold range by 50%. A moderate warning signal is generated and sent to the warning output submodule. The warning output submodule receives the moderate warning signal, obtains the corresponding transformer location information, takes the transformer location as the dot, obtains the mobile phone terminal of the operation and maintenance personnel within the radius, connects the transformer and the location of each operation and maintenance personnel, obtains each straight-line distance, obtains the nearest straight-line distance and marks the corresponding operation and maintenance personnel as the selected personnel, and sends the corresponding transformer location information to the operation and maintenance personnel for maintenance; The severe loss level is specifically when it exceeds the standard loss threshold range by 80%. A severe warning signal is generated and sent to the warning output submodule. The warning output submodule receives the severe warning signal, obtains the corresponding transformer location, connects to the power dispatching center within the radius of the corresponding transformer, performs an emergency power outage on the transformer, and sends the severe warning signal to the maintenance personnel's mobile terminal.

4. The transformer loss monitoring system based on the Internet of Things according to claim 1 is characterized in that: The remote monitoring module monitors the transformer appearance and maintenance personnel's processing through a high-definition camera, and feeds back the processing results to the monitoring system interface. The specific process is as follows: The appearance and operating status of each transformer is monitored in real time through a high-definition camera. The surface area of ​​the transformer oil stains is identified through the color area, and the surface area of ​​each transformer oil stain is obtained. The total number of oil stains corresponding to each oil stain surface area is counted. The total area of ​​oil stains is obtained based on the surface area of ​​each oil stain and the total number of oil stains. The preset total area of ​​oil stains in the data center is extracted, and the total area of ​​oil stains is compared with the preset total area of ​​oil stains. If the total area of ​​oil stains is greater than the preset total area of ​​oil stains, a cleaning signal is generated and sent to the mobile terminal of the operation and maintenance personnel. Monitor the processing video of the operation and maintenance personnel in real time, decompose the processing video to obtain the corresponding processing results and send them to the monitoring system interface.

5. The transformer loss monitoring system based on the Internet of Things according to claim 1 is characterized in that: The transformer data acquisition module collects the operating status data of the transformer according to various types of sensors and sends the collected data to each module for analysis. The specific process is as follows: Various types of sensors are installed at the winding and core positions of the transformer, including current sensors, voltage sensors and flux sensors; the current data information of the current transformer is monitored in real time by the current sensor to obtain the current parameter value of the current transformer; the voltage data information of the current transformer is monitored in real time by the voltage sensor to obtain the voltage parameter value of the current transformer; the flux data of the current transformer is collected in real time by the flux sensor to obtain the flux value of the current transformer; the output end of the transformer data acquisition module is connected to the input end of the load analysis module and the loss analysis module, and the current parameter value and voltage parameter value are sent to the load analysis module, and the flux value is sent to the loss analysis module.

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