A microcontroller-based control method for transformer cooling systems

Abstract

This invention relates to the field of transformer cooling control technology and discloses a microcontroller-based control method for a transformer cooling system. The method acquires a multi-dimensional real-time temperature dataset of the transformer body through a group of temperature sensors; then, the dataset is calibrated and filtered by the microcontroller's built-in preprocessing module to obtain processed temperature data; subsequently, based on a preset temperature field reconstruction algorithm, the overall thermal field distribution of the transformer is calculated from the processed temperature data to generate a dynamic thermal field distribution map; next, the dynamic thermal field distribution map is analyzed through a preset cooling strategy decision model to generate a target cooling control command set; finally, the cooling actuator group is controlled to perform coordinated cooling operations according to the command set. This method overcomes the limitations of traditional single-temperature point monitoring and fixed threshold control, achieving precise and efficient transformer cooling, adapting to different operating conditions, and ensuring stable transformer operation.

Description

Technical Field

[0001] This invention relates to the field of transformer cooling control technology, specifically a microcontroller-based control method for transformer cooling systems. Background Technology

[0002] A transformer is a device that uses the principle of electromagnetic induction to change alternating current voltage. Its main components are the primary coil, secondary coil, and iron core (magnetic core). A transformer is a device that uses electromagnetic mutual induction to transform voltage, current, and impedance. The components of a transformer include the transformer body (iron core, windings, insulation, leads), transformer oil, oil tank and cooling device, voltage regulating device, protection device (dehumidifier, safety vent, gas relay, oil conservator and temperature measuring device, etc.) and outgoing bushings. Among them, the iron core is the main magnetic circuit part of the transformer, and the windings are the electrical circuit part of the transformer, which are made of double-insulated flat wire or enameled round wire.

[0003] In practical applications, transformers, as the core equipment for energy conversion and transmission in power systems, have their operating temperature directly affecting their operational stability and service life. With the continuous growth of power load and the increasing complexity of power grid structure, transformers often operate under high load conditions, and the resulting losses are converted into heat. If the heat cannot be dissipated in time, it will lead to accelerated aging of insulation materials, and may even cause faults such as winding burnout and oil deterioration, resulting in large-scale power outages. Therefore, the cooling system, as an important auxiliary device for transformers, plays an irreplaceable role in ensuring the safe operation of transformers.

[0004] Most mainstream transformer cooling systems on the market rely on a single temperature monitoring point for control. Common monitoring locations include the top oil temperature or the winding hot spot temperature. Cooling devices are triggered to start and stop by setting a fixed temperature threshold. For example, traditional oil-immersed transformers often use a simple control logic: "start the cooling fan when the oil temperature reaches 65℃, and stop when it drops to 55℃." While this method achieves basic temperature regulation, it has significant limitations. A single temperature point cannot comprehensively reflect the overall thermal distribution of the transformer. Different parts of the transformer (such as windings, core, and tank walls) have significant temperature differences. Controlling based solely on local temperature data can easily lead to "localized overcooling" or "localized overheating." For instance, when the winding hot spot temperature is close to the safety limit, the top oil temperature may still be below the threshold, causing the cooling device to fail to start in time and miss the optimal cooling opportunity. Conversely, if the top oil temperature reaches the activation threshold but the winding temperature is low, continuous operation of the cooling device will result in energy waste.

[0005] Traditional cooling system control often relies on relays or simple logic circuits, lacking effective temperature data processing capabilities and unable to handle interference and noise in temperature signals. In the power system operating environment, temperature sensors are susceptible to electromagnetic interference, mechanical vibration, and other factors, often resulting in fluctuating temperature data. Directly relying on raw data for control leads to frequent start-stop cycles of the cooling device, reducing equipment lifespan and causing system instability. Furthermore, existing cooling control strategies lack dynamic adjustment capabilities, failing to optimize cooling schemes based on real-time operating conditions such as transformer load changes and ambient temperature fluctuations. For example, in high-temperature summer environments, transformer heat dissipation efficiency is inherently low; using fixed temperature threshold control may cause the cooling device to operate at full load for extended periods, exacerbating energy consumption. Conversely, in low-temperature winter environments, where natural heat dissipation would suffice, rigid control strategies can cause the cooling device to erroneously start.

[0006] While some microcontroller-based cooling control systems possess certain data processing potential, they suffer from shortcomings in data application. These systems typically only perform simple filtering of temperature data, failing to construct a comprehensive thermal field model of the transformer. This makes it difficult to accurately grasp the heat transfer paths and accumulation areas within the transformer, resulting in a lack of targeted operation of cooling actuators (such as cooling fans and submersible pumps). For example, when heat is mainly concentrated in the lower part of the windings, if all cooling fans maintain the same speed, it will cause uneven distribution of cooling resources, with some areas under-cooled and others over-cooled. This not only fails to achieve the desired cooling effect but also further increases operating costs. With the development of smart grids, higher demands are placed on the refined and efficient control of transformer cooling systems. Existing technologies are insufficient to meet the needs of safe and economical power system operation, necessitating a control method capable of multi-dimensional temperature monitoring, precise thermal field analysis, and dynamic cooling regulation. Summary of the Invention

[0007] The purpose of this invention is to provide a microcontroller-based control method for a transformer cooling system to solve the problems mentioned in the background art.

[0008] To achieve the above objectives, the present invention provides a microcontroller-based control method for a transformer cooling system, the method comprising:

[0009] A multi-dimensional real-time temperature dataset of the transformer body is obtained through a set of temperature sensors.

[0010] The multi-dimensional real-time temperature dataset is calibrated and filtered using the preprocessing module built into the microcontroller to obtain processed temperature data.

[0011] The overall thermal field distribution of the transformer is calculated based on the processed temperature data according to the preset temperature field reconstruction algorithm, and a dynamic thermal field distribution map is generated.

[0012] The dynamic thermal field distribution map is analyzed by a preset cooling strategy decision model to generate a target cooling control command set;

[0013] The target cooling control command set controls the cooling actuator group to perform coordinated cooling operations.

[0014] Preferably, the step of analyzing the dynamic thermal field distribution map through a preset cooling strategy decision model to generate a target cooling control command set includes:

[0015] High-temperature regions are identified in the dynamic thermal field distribution map to obtain feature data of multiple high-temperature regions;

[0016] Calculate the temperature rise rate and heat accumulation corresponding to the characteristic data of each high-temperature region;

[0017] Based on the temperature rise rate and heat accumulation, a cooling priority sequence for the characteristic data of each high-temperature region is determined;

[0018] A target cooling control instruction set, including cooling intensity level and cooling direction, is generated according to the cooling priority sequence.

[0019] Preferably, the calculation of the temperature rise rate and heat accumulation corresponding to the characteristic data of each high-temperature region includes:

[0020] Extract historical temperature data sequences corresponding to high-temperature regions from the multi-dimensional real-time temperature dataset;

[0021] The instantaneous temperature rise rate curve is obtained by performing a first-order differential calculation in the time dimension on the historical temperature data sequence.

[0022] The instantaneous temperature rise rate curve is integrated over time to obtain a heat accumulation distribution map;

[0023] The instantaneous temperature rise rate curve and heat accumulation distribution map are spatially mapped and matched with the high-temperature region characteristic data.

[0024] Preferably, determining the cooling priority sequence of characteristic data for each high-temperature region based on the temperature rise rate and heat accumulation includes:

[0025] Set the temperature rise rate threshold and the heat accumulation threshold;

[0026] High-temperature region feature data where the instantaneous temperature rise rate exceeds the temperature rise rate threshold and the heat accumulation also exceeds the heat accumulation threshold are marked as first-level priority;

[0027] High-temperature region feature data that exceed the temperature rise rate threshold or the heat accumulation threshold are marked as secondary priority;

[0028] The remaining high-temperature region characteristic data are sorted in descending order of heat accumulation value to form a priority level of three or below.

[0029] Preferably, generating a target cooling control instruction set including cooling intensity level and cooling direction according to the cooling priority sequence includes:

[0030] The cooling intensity level corresponding to the characteristic data of each high-temperature region is determined according to the priority level, where the first priority level corresponds to the maximum cooling intensity level.

[0031] The cooling direction parameters are determined based on the spatial location of the high-temperature region characteristics on the transformer body.

[0032] The cooling intensity level and cooling direction parameters are combined to generate independent control commands for the characteristic data of each high-temperature region.

[0033] All independent control commands are sorted and integrated according to their execution time sequence to form the target cooling control command set.

[0034] Preferably, after controlling the cooling actuator group to perform coordinated cooling operations according to the target cooling control command set, the method further includes:

[0035] Real-time acquisition of operating status data of the cooling actuator group;

[0036] The execution effect is evaluated by the feedback processing module of the microcontroller using the running status data;

[0037] The results of the performance evaluation are compared and analyzed with the expected cooling target to generate cooling effect deviation data;

[0038] The parameters of the cooling strategy decision model are corrected based on the cooling effect deviation data.

[0039] Preferably, the step of evaluating the execution effect of the operating status data through the feedback processing module of the microcontroller includes:

[0040] Extract the actual temperature change curve from the operating status data;

[0041] Calculate the degree of agreement between the actual temperature change curve and the expected temperature decrease curve.

[0042] Monitor the ratio of power consumption data to cooling effect of the cooling actuator group;

[0043] The overall consistency index and ratio relationship are used to generate an evaluation score for the implementation effect.

[0044] Preferably, the step of correcting the parameters of the cooling strategy decision model based on the cooling effect deviation data includes:

[0045] Establish a mapping table between cooling effect deviation data and control parameters of the cooling strategy decision model;

[0046] Determine the types and adjustment ranges of the control parameters that need to be adjusted based on the mapping table;

[0047] A gradual adjustment strategy is adopted to iteratively optimize the control parameters of the cooling strategy decision model;

[0048] Record the history of parameter adjustments and the corresponding improvements in cooling performance.

[0049] Preferably, the step of acquiring a multi-dimensional real-time temperature dataset of the transformer body through a group of temperature sensors includes:

[0050] Multiple temperature sensors located at different positions on the transformer body synchronously collect temperature data;

[0051] The collected temperature data is encoded according to spatial location information;

[0052] Add timestamp information to create a multi-dimensional real-time temperature dataset with spatiotemporal labels;

[0053] The multi-dimensional real-time temperature dataset is transmitted to the microcontroller's data receiving buffer via the data bus.

[0054] Preferably, the step of performing data calibration and filtering on the multi-dimensional real-time temperature dataset using the preprocessing module built into the microcontroller includes:

[0055] The collected data is corrected for deviations based on the calibration coefficients of each temperature sensor;

[0056] A sliding window filtering algorithm is used to eliminate high-frequency noise interference in temperature data;

[0057] The filtered data is validated to remove outlier data points.

[0058] Compared with the prior art, the beneficial effects of the present invention are:

[0059] From the perspective of temperature data acquisition and processing, this method obtains a multi-dimensional real-time temperature dataset of the transformer body through a temperature sensor group. Compared with traditional single-temperature-point monitoring, it can comprehensively capture the temperature changes of different parts such as the transformer winding, core, and tank wall, avoiding control deviations caused by the one-sidedness of local temperature data. At the same time, the preprocessing module built into the microcontroller calibrates and filters the multi-dimensional temperature data, effectively eliminating invalid data caused by factors such as electromagnetic interference and sensor errors, improving the accuracy and reliability of temperature data, and providing a high-quality data basis for subsequent thermal field analysis and control decision-making. This process breaks the limitation of the "extensive" application of temperature data in traditional control methods, making the basis for cooling control more in line with the actual operating state of the transformer and avoiding misoperations of the cooling device caused by data distortion.

[0060] At the level of thermal field analysis, based on the preset temperature field reconstruction algorithm, the processed temperature data is used to calculate the overall thermal field distribution and generate a dynamic thermal field distribution map, realizing the visualization and accurate perception of the internal thermal state of the transformer. Traditional cooling control cannot grasp the internal thermal distribution law of the transformer and can only rely on empirical thresholds for blind regulation. However, through the dynamic thermal field distribution map, this method can clearly present the heat aggregation area, transfer path, and temperature gradient change inside the transformer, enabling maintenance personnel and the control system to intuitively understand the thermal load conditions of each part of the transformer. For example, when the thermal field distribution map shows local high-temperature aggregation in the middle of the winding, the control system can adjust the cooling actuator in the corresponding area specifically, avoiding the waste of resources caused by "full cooling" in traditional cooling methods, making the cooling operation more targeted, and improving the cooling efficiency.

[0061] From the perspective of cooling strategy decision-making and execution, the preset cooling strategy decision model analyzes the dynamic thermal field distribution map and generates a target cooling control instruction set, which can dynamically optimize the cooling scheme according to the real-time thermal field state of the transformer, getting rid of the rigid limitation of traditional fixed-threshold control. The decision model can comprehensively consider factors such as the temperature difference of each part of the transformer, the trend of thermal field change, and external factors such as ambient temperature and load change, and formulate the most suitable cooling strategy for the current working condition, rather than simply triggering start and stop according to a single temperature value. At the same time, according to the control instruction set, the cooling actuator group is controlled to perform coordinated cooling operations, which can achieve the linkage and cooperation between multiple cooling devices, rather than the independent operation and lack of coordination of each device in the traditional method. For example, when the thermal field distribution map shows high-temperature areas simultaneously in the upper part of the transformer tank and the lower part of the winding, the decision model can generate a coordinated control instruction of "increasing the rotation speed of the upper cooling fan and enhancing the flow rate of the lower submersible pump", enabling each cooling actuator to perform its own duties and cooperate with each other to form an all-round and multi-level cooling system, which not only ensures that each high-temperature area can be effectively cooled but also avoids energy consumption waste caused by over-operation of some devices.

[0062] This method relies on a microcontroller to achieve fully automated control throughout the entire process. It completes a closed-loop process of temperature acquisition, data processing, thermal field analysis, strategy decision-making, and execution without manual intervention, significantly improving the response speed and control accuracy of the cooling system. Compared to traditional cooling methods that rely on manual adjustments, it can react quickly in the early stages of transformer temperature anomalies, promptly curbing the temperature rise, reducing insulation material aging and loss, and extending transformer lifespan. Simultaneously, through precise thermal field analysis and coordinated cooling control, it avoids ineffective operation of the cooling device, reducing energy consumption and aligning with the current development needs of energy conservation and emission reduction in the power system. Whether in extreme operating scenarios under high load and high temperature conditions or in complex operating conditions with frequent load fluctuations, this method maintains stable and efficient cooling control, adapting to the transformer cooling needs under different operating conditions and providing strong support for the safe and stable operation of the power system. Attached Figure Description

[0063] Figure 1 This is a schematic diagram illustrating the working principle of the microcontroller-based transformer cooling system control method described in this invention.

[0064] Figure 2 A flowchart generated for the target cooling control instruction set;

[0065] Figure 3 A flowchart for determining the cooling priority sequence. Detailed Implementation

[0066] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0067] Please see Figure 1This invention provides a microcontroller-based control method for a transformer cooling system. The method involves real-time acquisition of multi-dimensional temperature data from the transformer body using a temperature sensor array composed of multiple digital temperature sensors positioned at different locations on the transformer. A synchronous acquisition mechanism ensures data consistency over time. The microcontroller receives the temperature dataset with spatiotemporal tags via a data bus and performs data calibration and filtering using a built-in preprocessing module. The calibration process corrects measurement deviations based on the calibration coefficients of each sensor, and the filtering process uses a sliding window algorithm to eliminate high-frequency noise. The processed data is then input into a temperature field reconstruction algorithm module, which calculates the overall thermal field distribution of the transformer based on finite element analysis, generating a dynamic thermal field distribution map containing temperature gradient information. A cooling strategy decision model performs feature analysis on the thermal field distribution map, identifies high-temperature regions, calculates cooling parameters, and generates a control instruction set including intensity level and direction parameters. The microcontroller controls the cooling actuator group to perform coordinated cooling operations via a drive circuit, achieving precise control of the transformer's thermal field.

[0068] Example 1: See Figure 2 The system identifies high-temperature regions in a dynamic thermal field distribution map using a temperature threshold-based region growing algorithm. This algorithm expands outward from the highest temperature point, merging regions continuously exceeding a set threshold into high-temperature regions. Each identified high-temperature region records its boundary coordinates, centroid location, area, and average temperature value; these parameters constitute the high-temperature region feature data and are stored as a structure array. Eight-neighbor connectivity detection is used during region identification to ensure the accuracy of region boundaries while filtering out noise regions with excessively small areas. Feature data extraction includes statistical calculations, recording the maximum, minimum, and variance values ​​of each region's temperature, providing a complete thermal characteristic description for subsequent analysis. When calculating the temperature rise rate corresponding to the feature data of each high-temperature region, the system retrieves the temperature sampling sequence of that region from the historical database over a past period, with the time window length set according to the transformer's thermal time constant. The temperature rise rate is calculated using the central difference method, numerically differentiating the historical temperature data sequence to obtain a smooth instantaneous temperature rise rate curve. A five-point Stokes algorithm is used to improve calculation accuracy during the differentiation calculation, while a Savitzky-Golay filter is used to eliminate numerical noise. The instantaneous temperature rise rate curve is stored in array form, containing timestamps and corresponding rate values, with the sampling interval consistent with the temperature acquisition cycle.

[0069] The heat accumulation is calculated by numerically integrating the instantaneous temperature rise rate curve using a composite trapezoidal rule. The integration process calculates the heat accumulation from the onset of the temperature anomaly to the current moment. The specific heat capacity and density parameters of the transformer material are considered during the integration process, converting the temperature change into an actual accumulated heat energy value. The heat accumulation distribution map is stored in matrix form, with dimensions corresponding to the thermal field distribution. Figure 1Each pixel stores its corresponding cumulative heat value. The calculation of cumulative heat value includes environmental temperature compensation to eliminate the influence of environmental factors on the heat calculation and improve the accuracy of the results. The instantaneous temperature rise rate curve and cumulative heat distribution map are spatially mapped and matched with the high-temperature region feature data, and the system establishes a coordinate transformation mapping table. The matching process uses a nearest neighbor interpolation algorithm to map the temperature rise rate and cumulative heat value data to the corresponding high-temperature regions. For irregularly shaped high-temperature regions, an area-weighted average method is used to calculate the overall thermal parameters of the region. The mapped data is organized into a unified data structure, containing region identifiers, spatial coordinates, temperature characteristics, temperature rise rate values, and cumulative heat value. The data structure is stored in memory alignment to improve the access efficiency of the microcontroller.

[0070] The high-temperature region characteristic data is updated synchronously with the generation of the thermal field distribution map. All thermal dynamic parameters are recalculated each time a new thermal field distribution map is generated. The system maintains a historical state queue for high-temperature regions, recording the changing trends of temperature parameters in each region. All calculations are performed in the microcontroller's digital signal processing unit, employing fixed-point arithmetic to optimize computational efficiency. A circular buffer is used to store intermediate results during data processing, optimizing memory usage and preventing data overflow. The calculation of the temperature rise rate also includes data validity verification, removing invalid data points caused by sensor malfunctions to ensure the reliability of the calculation results. The calculation of heat accumulation considers changes in the transformer's operating load, using real-time load data from the monitoring system as a calculation correction factor.

[0071] The system establishes a thermal behavior model for each high-temperature region. This model predicts the temperature change trend of the region based on historical data of temperature rise rate and heat accumulation. The thermal behavior model employs an autoregressive algorithm, and model parameters are identified online using recursive least squares to continuously improve prediction accuracy. All thermal parameter data is transmitted to the cooling strategy decision module via a data bus, providing data support for the generation of cooling control commands. A CRC check mechanism is used during data transmission to ensure data integrity and accuracy, preventing decision-making errors due to transmission mistakes. The high-temperature region identification and thermal parameter calculation processes employ a priority scheduling mechanism, with the microcontroller's real-time operating system allocating computing resources to ensure that critical tasks are executed first. The parameters of the calculation algorithm can be configured according to the transformer model and operating environment, with threshold parameters and calculation coefficients set through configuration files. The system's operating status and calculation results are displayed in real-time on the human-machine interface, allowing operators to monitor changes in thermal state and system operation. All historical data is recorded in non-volatile memory, forming an operating log for subsequent analysis and system optimization.

[0072] Example 2: See Figure 3The process of setting the temperature rise rate threshold and heat accumulation threshold is based on transformer thermal characteristic analysis and statistical research of historical operating data. These threshold parameters are dynamically configured according to the transformer insulation class, cooling medium characteristics, and environmental conditions. The temperature rise rate threshold mainly reflects the urgency of temperature changes, and its value is determined by analyzing the thermal tolerance characteristics of the transformer insulation material and past operating data. A typical value is set in the range of 2-3 degrees Celsius per minute. The heat accumulation threshold characterizes the degree of thermal stress accumulation. Its setting takes into account the transformer's heat capacity and heat dissipation characteristics, and the value range is usually between 1000-2000 kJ / m². The threshold parameters are stored in the microcontroller's non-volatile memory, supporting online modification and adaptive adjustment. The system periodically optimizes and updates the thresholds based on operating results. When high-temperature area characteristic data where the instantaneous temperature rise rate exceeds the temperature rise rate threshold and the heat accumulation also exceeds the heat accumulation threshold are marked as first-priority, the system uses a parallel comparison circuit to simultaneously monitor the over-limit status of the two parameters. When a region is detected to simultaneously meet both over-limit conditions, the corresponding bit in the priority flag register is immediately set, triggering the first-priority processing flow. Level 1 priority areas will receive the highest response privileges. The system will interrupt currently executing low-priority cooling tasks and prioritize handling these emergency hotspots. Each area marked as Level 1 priority will generate an emergency event log, detailing the exceeded limits, the time of occurrence, and the area's location. These logs will be used for subsequent analysis and system optimization. The system will allocate dedicated processing resources to Level 1 priority areas to ensure rapid response and execution of cooling operations.

[0073] For high-temperature areas where the instantaneous temperature rise rate exceeds the temperature rise rate threshold or the heat accumulation exceeds the heat accumulation threshold, the system uses a logical OR condition to determine priority. If either condition is met, the area is marked as a secondary priority area. Secondary priority areas are determined using a sequential scanning method: first, the temperature rise rate exceeds the limit for all areas; then, the heat accumulation is checked; and finally, the priority level is determined through logical operations. While these areas are not as urgent as primary priority areas, they still require a relatively fast response. The system will schedule cooling operations immediately after processing primary priority areas. Secondary priority area data is stored in a dedicated buffer and sorted according to the degree of exceedance to ensure a reasonable processing order. The system sets processing time limits for secondary priority areas, requiring cooling operations to be completed within a specified time. The characteristic data of other high-temperature areas are sorted in descending order of heat accumulation value to form a third-level and lower priority level. The sorting algorithm uses an improved quicksort method to ensure that areas with higher heat accumulation are processed first. The spatial relationship between areas is also considered during the sorting process; adjacent areas are grouped for processing to improve cooling efficiency. Third-level and lower priority areas are processed using a polling method, with the system gradually completing the cooling tasks for these areas during idle periods. All priority information is integrated into a priority mapping table, which is updated in real time to reflect the latest status of each area, providing a basis for cooling decisions. The system sets different refresh frequencies for different priority areas to ensure that the status of high-priority areas is updated in a timely manner.

[0074] When determining the cooling intensity level corresponding to the characteristic data of each high-temperature region based on priority levels, the system adopts a hierarchical control strategy, dividing the cooling intensity into multiple discrete levels. The highest priority level corresponds to the maximum cooling intensity level, during which the cooling system operates at maximum power, including activating all backup cooling equipment and increasing the operating parameters of existing equipment. The calculation of the cooling intensity level is based on a thermodynamic model, considering the difference between the region temperature and the target temperature, the region's heat capacity, and the maximum cooling capacity of the cooling system. Each intensity level corresponds to a combination of equipment operating parameters, including specific control values ​​such as fan speed, pump flow rate, and coolant flow rate. The system sets duration parameters for each intensity level to ensure the effectiveness and safety of the cooling operation. When determining the cooling direction parameters based on the spatial location of the high-temperature region's characteristic data on the transformer body, the system first establishes a transformer coordinate system, dividing the transformer body into several cooling regions. The center coordinates of each high-temperature region are converted into control angles for the cooling equipment; the cooling direction parameters include two dimensions: horizontal deflection angle and vertical tilt angle. The direction calculation uses a geometric projection method, mapping the three-dimensional spatial position to a two-dimensional control plane to ensure that the cooling medium accurately covers the target area. For high-temperature regions with complex shapes, the system calculates multiple direction parameters to achieve multi-angle coordinated cooling. The mechanical characteristics of the cooling equipment are taken into account during the calculation of directional parameters to avoid exceeding the equipment's range of motion.

[0075] When generating independent control commands for each high-temperature region by combining cooling intensity levels and cooling direction parameters, the system employs a command encoding protocol to convert analog control quantities into digital control signals. Each command includes fields such as target device identifier, operating parameter values, and execution duration, which are packaged into data packets according to a predetermined format. During command generation, the system checks the device status to ensure the feasibility and safety of the commands, preventing control failures due to device malfunctions. All commands are timestamped and have sequence numbers for easy tracking and error recovery during execution. The system sets a priority flag for each command to ensure that high-priority commands are processed first. When sorting and integrating all independent control commands according to their execution time sequence, the system uses a time-slice round-robin scheduling algorithm to arrange the execution order based on the urgency and execution time requirements of the commands. First-priority commands are inserted at the front of the execution queue, second-priority commands are placed in the middle, and third-priority and lower commands are placed at the end. During command integration, control commands for the same device are merged to reduce the number of device start-ups and shutdowns and improve system efficiency. The final target cooling control command set is sent to the cooling actuator via an industrial bus protocol. The command set includes header verification information and the command body to ensure the integrity and reliability of transmission. The system monitors the execution status of commands and dynamically adjusts the content of subsequent commands based on the execution results.

[0076] Example 3: Real-time acquisition of the operating status data of the cooling actuator group is achieved through various sensors installed on the cooling equipment. These sensors include a Hall effect sensor to monitor the fan motor speed, an electromagnetic flowmeter to detect the pump circulation rate, and a power metering module to record energy consumption data. Sensor data is converted into digital signals by an isolated analog-to-digital converter and transmitted to the microcontroller's data acquisition card via the CAN bus protocol. The sampling frequency is dynamically adjusted according to the cooling intensity level; a sampling rate of 100 times per second is used during high-intensity cooling, while it is reduced to 10 times per second during low-intensity cooling to reduce processing load. Cyclic redundancy check is used during transmission to ensure data integrity. Each data packet contains a timestamp, device identifier, and numerical information, stored in a circular buffer according to a predefined data structure.

[0077] When evaluating the performance of operational status data through the microcontroller's feedback processing module, the system first preprocesses the raw data, including removing outliers and applying moving average filtering, and then extracts key performance indicators. The evaluation process is based on a multi-parameter fusion algorithm to establish a cooling effect evaluation model.

[0078]

[0079] in: This represents the evaluation coefficient for cooling effect. To assess the length of the time window, It is the number of sampling points within the time window. This represents the measured temperature value at the j-th sampling point. That is the corresponding desired temperature value. Indicates the sampling time interval. This represents the device power consumption during that time period. This is the cooling capacity coefficient. The model comprehensively considers temperature tracking accuracy and energy efficiency, obtaining a quantitative evaluation result through weighted calculation. The calculation process utilizes a floating-point unit for accelerated processing, and the evaluation results are stored in a structure format, containing a comprehensive score and various sub-indicators. When comparing the performance evaluation results with the expected cooling target, the system loads preset performance benchmark values ​​from the storage module. These benchmark values ​​are pre-configured based on the transformer model and operating environment. The comparison algorithm employs a fuzzy logic control strategy, defining multiple evaluation levels, each with a corresponding deviation tolerance range. The analysis process generates multi-dimensional cooling effect deviation data, including parameters such as temperature deviation coefficient, response time delay, and energy efficiency ratio, which are encapsulated in a deviation data structure. The comparison results are displayed in real-time through a human-machine interface module, allowing operators to intuitively understand the system's operating status.

[0080] When calibrating the cooling strategy decision model based on cooling effect deviation data, the system employs a progressive parameter adjustment algorithm. The calibration module first parses the deviation data structure to identify the types of parameters requiring adjustment, including temperature rise rate thresholds, heat accumulation thresholds, and cooling intensity level mapping relationships. The parameter adjustment amount is determined through regression analysis based on historical data, establishing a non-linear mapping relationship between the deviation value and the adjustment magnitude. The calibration process follows a small-step, gradual approach, with each adjustment not exceeding 5% of the current value to prevent over-calibration and system oscillations. All parameter adjustment records are encrypted and stored in the calibration log, including adjustment time, parameter identifier, original value, new value, and adjustment reason. The system establishes a parameter adjustment rollback mechanism; if performance deteriorates for three consecutive evaluation cycles after calibration, the system automatically reverts to the previous parameter settings. Digital filtering technology is used during calibration to smooth parameter change curves, avoiding the impact of abrupt adjustments on the system. For critical safety parameters, modification permission levels are set, requiring secondary authorization confirmation for adjustments to important parameters. The parameter calibration module and the main control program are loosely coupled, exchanging data through a message queue to ensure the real-time performance of the system's main functions remains unaffected.

[0081] The parameter calibration of the cooling strategy decision model also considers environmental factors for adaptation. The system integrates temperature and humidity sensor data, automatically compensating for parameter adjustments when environmental conditions change. The calibration algorithm incorporates a forgetting factor, giving new data higher weight and ensuring the system can adapt to changes in operating conditions. All parameters are protected by upper and lower limits to prevent out-of-bounds settings during calibration, maintaining safe system operation. A parameter version management function records detailed information for each adjustment, supports importing and exporting parameter configurations, and facilitates parameter synchronization across multiple devices. The calibration effect is continuously monitored during system operation, and the effectiveness of the calibration is verified by comparing changes in performance indicators before and after calibration. A parameter sensitivity matrix is ​​established to identify the key parameters with the greatest impact on system performance, prioritizing fine-tuning of these parameters. The calibration cycle is dynamically adjusted according to operating status, using an hourly interval during normal operation and shortening to once per minute under abnormal conditions. The maintenance interface provides a manual calibration function, allowing experienced engineers to adjust parameters based on professional judgment; manual adjustment records are also fully documented and audited.

[0082] Example 4: When evaluating the performance of the operating status data through the microcontroller's feedback processing module, the system acquires real-time operating parameters of the cooling equipment from the data acquisition unit. These parameters include measured fan speed, pump flow meter readings, energy consumption data collected by the power sensor, and post-cooling temperature feedback from the temperature sensor. The data preprocessing unit filters and normalizes these raw data to eliminate the effects of measurement noise and unit inconsistencies. The processed data is stored in a circular buffer for subsequent analysis. The core of the evaluation algorithm calculates performance indicators by comparing the degree of fit between the actual cooling curve and the expected cooling curve, while simultaneously analyzing the ratio of energy consumption to cooling effect. The evaluation process employs a sliding time window mechanism, with each window containing data from 60 consecutive sampling points.

[0083] The actual temperature change curve is extracted using a sliding window sampling technique, recording the temperature values ​​of key areas of the transformer at minute intervals to form a time-temperature series data. Curve smoothing employs a moving average algorithm to eliminate random fluctuations. The expected temperature drop curve is derived from the output of a thermodynamic model, which calculates the theoretical cooling trajectory based on transformer load, ambient temperature, and initial thermal state. Model parameters are continuously optimized through a combination of offline training and online learning. The consistency index is calculated using an area comparison method, using the area difference between two curves as the evaluation criterion, while also considering time delay factors to correct for errors caused by asynchrony. The result is expressed as a percentage of the degree of matching. When monitoring the ratio of power consumption data to cooling effect of the cooling actuator group, the system uses smart meters to meter each device, accurately recording the power consumption of each cooling device. The cooling effect is quantified by the temperature drop per unit time. The energy efficiency ratio is calculated using a weighted average method, comprehensively considering the contribution of different devices. The ratio reflects the energy utilization efficiency of the cooling system. The system sets an energy efficiency baseline; when the actual ratio deviates from the baseline by more than a set range, an early warning mechanism is triggered, prompting operators to check the equipment status or adjust operating parameters.

[0084] The performance evaluation score is generated using a multi-dimensional weighted scoring method, with the consistency index accounting for 60% of the weight and the energy efficiency ratio accounting for 40%. The weighting coefficients can be dynamically adjusted according to the operating mode. The scoring algorithm sets multiple level thresholds and uses a moving average to process the scores over three consecutive evaluation periods to eliminate the impact of random fluctuations. The evaluation results are stored in a structured format, including the total score, scores for each sub-item, evaluation timestamp, and confidence index. The system also records the original data samples used during the evaluation process for easy traceability and verification. All evaluation data is stored encrypted to ensure security. Cooling effect deviation data is generated based on the difference between the evaluation score and the target value. The target value is dynamically adjusted according to the transformer's operating level. The deviation data includes both absolute and relative deviation dimensions. The system establishes a correlation mapping between deviation data and operating parameters, identifies the main influencing factors causing the deviation through correlation analysis, and stores the deviation data sequence in a time-series database, supporting rapid retrieval and trend analysis. The deviation analysis results are visualized through a human-machine interface, helping operators intuitively understand the system performance status.

[0085] When establishing a mapping table between cooling effect deviation data and control parameters of the cooling strategy decision-making model, the system uses multiple regression analysis to determine the influence weight of each parameter. The mapping table uses a matrix structure to store adjustment coefficients and influencing factors. This table is updated regularly, and the mapping relationship is optimized through machine learning algorithms to improve the accuracy of parameter adjustments. Table version management ensures that each adjustment is traceable and supports historical version rollback. The mapping relationship maintenance module provides manual editing functionality, allowing experienced engineers to adjust the mapping relationship based on professional judgment. When determining the category and adjustment range of control parameters to be adjusted based on the mapping relationship table, the system first performs pattern recognition on the current deviation data to determine the main characteristics and trends of the deviation. The parameter adjustment decision adopts a fuzzy reasoning mechanism, considering multiple factors such as deviation magnitude, duration, and trend. A conservative coefficient is introduced into the calculation of the adjustment range to prevent over-adjustment from causing system instability. For critical safety parameters, an upper limit for the adjustment range is set to ensure that the system operates within a safe range. After the adjustment plan is generated, it needs to be verified through simulation before practical application.

[0086] When iteratively optimizing the control parameters of the cooling strategy decision model using a gradual adjustment strategy, the system breaks down large adjustments into multiple small-step changes, observing the effect feedback for two evaluation periods after each adjustment. The optimization process employs a gradient descent algorithm to gradually approach the optimal parameter combination. Exit conditions are set during iteration; if performance is not improved after three consecutive adjustments, an expert intervention mode is activated. Detailed log information is recorded during the parameter adjustment process, including parameter values ​​before and after adjustment, adjustment time, executor identification, and explanation of the adjustment reason. When recording the parameter adjustment history and corresponding cooling effect improvement, the system uses blockchain technology to ensure data immutability, and the recorded content includes a comparison of performance indicators before and after adjustment and an assessment of the degree of improvement. Historical data provides multi-dimensional query functions, supporting filtering by time range, parameter type, adjustment effect, etc. The data visualization module displays the relationship curve between parameter adjustment history and performance changes in chart form. The system periodically generates parameter adjustment effect analysis reports, summarizing adjustment experience and optimization directions, providing a reference for subsequent decision-making.

[0087] Table 1: Cooling System Parameter Adjustment Record

[0088] Adjust time Parameter name Original value New value Adjustment range Effect evaluation Enforcement personnel 2023-08-0110:30:00 Temperature rise rate threshold 3.0℃ / min 2.8℃ / min -6.7% good The system automatically 2023-08-0111:15:00 Cooling intensity coefficient 0.85 0.88 +3.5% generally The system automatically 2023-08-0112:20:00 Response time delay 5.0s 4.5s -10.0% excellent Engineer A 2023-08-0114:05:00 Energy efficiency weighting coefficient 0.40 0.35 -12.5% good The system automatically 2023-08-0115:40:00 Heat accumulation threshold 1500kJ / m² 1450kJ / m² -3.3% excellent The system automatically

[0089] Referring to Table 1, the system analyzes these parameter adjustment records to optimize subsequent adjustment strategies and improve the accuracy and effectiveness of parameter calibration. All adjustment operations are simulated and verified before being applied to the actual system, reducing adjustment risks. The system maintains a parameter adjustment knowledge base, accumulating adjustment experience under different operating conditions. The parameter calibration module and the main control system use a two-way communication mechanism; after the calibration command is sent, execution confirmation feedback is received. The calibration effect monitoring cycle is dynamically set according to the adjustment range. The system provides a parameter adjustment simulation function, allowing for pre-simulation of adjustment effects to verify the rationality of the adjustment scheme. All calibration operations are recorded in the audit log, meeting the safety and compliance requirements of industrial control systems.

[0090] Example 5: In the process of synchronously collecting temperature data from multiple temperature sensors located at different positions on the transformer body, the system adopts a distributed sensor network architecture. This network includes 24 digital temperature sensor nodes evenly distributed at key locations such as the transformer tank surface, winding hot spots, and radiator inlets and outlets. Each sensor node uses a high-precision digital temperature sensor with a measurement range covering -55℃ to +150℃ and an accuracy of ±0.3℃. The sensor nodes are networked via a digital bus protocol. The bus controller sends synchronous acquisition commands, and all nodes simultaneously initiate temperature conversion upon receiving the commands, ensuring consistency in acquisition time. The sensor nodes are powered by isolated DC-DC converter modules to avoid measurement errors introduced by grounding loops. Shielded twisted-pair cables are used for signal transmission to enhance anti-interference capabilities. The sampling frequency is dynamically adjusted according to the operating status, reaching a maximum of 100 samples per second.

[0091] When encoding the collected temperature data according to spatial location information, the system assigns a unique location identifier to each sensor, which includes a region code, installation level, and coordinate information. The location encoding adopts a three-level structure: the first level represents the transformer area (such as the upper part of the tank, winding area, radiator, etc.), the second level indicates the installation height level, and the third level provides specific coordinate values. Encoded data is combined with temperature measurements to form data frames. Each data frame includes a start symbol, sensor ID, temperature value, checksum, and end symbol. The data frame format follows industry standard communication protocol specifications, facilitating integration with existing monitoring systems. The encoding process is completed locally at the data acquisition node, reducing the processing burden on the main controller. The data encoding process also includes sensor status information, such as battery level, signal strength, and running time. When adding timestamp information to form a multi-dimensional real-time temperature dataset with spatiotemporal labels, the system uses a high-precision real-time clock chip to provide the reference time, with a clock synchronization accuracy of 1 millisecond. Each data packet is immediately stamped with a 64-bit timestamp after acquisition, containing the date, hours, minutes, seconds, and milliseconds. The spatiotemporal label dataset also includes an environmental parameter index, linking to the ambient temperature and humidity data collected at the same time. The data set is organized in a structured format, supporting nested data and array representations for easier subsequent parsing and processing. The timestamp addition process is guaranteed to be atomic, avoiding time inconsistencies in a multi-threaded environment. Time synchronization signals are broadcast to all nodes via a bus, ensuring time consistency across the entire system.

[0092] When transmitting multi-dimensional real-time temperature datasets to the microcontroller's data receiving buffer via the data bus, the system employs an industrial bus physical layer protocol with a transmission rate of 250kbps. Data transmission follows a producer-consumer model, with sensor nodes acting as data producers and the microcontroller's data receiving buffer as a consumer. The buffer is designed as a dual-ring structure, with each ring having a capacity of 1024 data packets, enabling non-blocking read and write operations. The bus controller implements an arbitration mechanism to ensure high-priority data is transmitted first, avoiding data congestion. The transport layer protocol includes a retransmission mechanism to guarantee data transmission reliability. The data receiving buffer uses a ping-pong operation mode, allowing the processing unit to read data from one buffer while the other is receiving data. When correcting for deviations in the collected data based on the calibration coefficients of each temperature sensor, the system stores a calibration parameter table for each sensor in non-volatile memory. Calibration parameters include zero-point offset, slope correction coefficient, and nonlinearity compensation value. These parameters are determined through factory calibration experiments. The correction algorithm uses a piecewise linear interpolation method, selecting the appropriate correction parameters based on the current temperature value. The calibration process also considers the impact of ambient temperature on the sensor, further correcting the measured values ​​using a temperature compensation curve. The calibration process is executed automatically and periodically to ensure consistent measurement accuracy. The calibration parameter table supports remote updates, allowing modification of calibration parameters without system downtime.

[0093] When using a sliding window filtering algorithm to eliminate high-frequency noise interference in temperature data, the system sets a configurable window size, with a default value of 8 sampling points. The filtering algorithm employs a weighted average method, with the most recent data having a higher weight. The weight coefficients decrease exponentially, and filtering is performed in real time during data reception, updating the filtering result with each new data point. The algorithm implementation uses fixed-point arithmetic optimization to reduce the computational load on the microcontroller. Filtering parameters can be dynamically adjusted according to noise characteristics to achieve optimal filtering results. The filtering window size can be adaptively adjusted based on signal characteristics; increasing the window size improves smoothness when the signal is stable, and decreasing it improves response speed when the signal changes drastically. For validating the filtered data, the system sets multiple verification rules, including numerical range checks (-50℃ to 150℃), rate of change limits (no more than 10℃ change per minute), and consistency checks (comparison with adjacent sensor readings). Anomaly detection uses statistical methods to calculate the deviation between the current value and the historical mean; values ​​exceeding three standard deviations are marked as anomalies. The verification process also checks data integrity to ensure the correct data frame structure and checksum matching. Abnormal data is logged in the system log for sensor health monitoring and maintenance alerts. The data verification process employs a multi-level verification mechanism: first, a basic range check is performed; then, statistical analysis is conducted; and finally, cross-validation between devices is performed.

[0094] When the processed data is reorganized into a standardized temperature data matrix according to spatial dimensions, the system establishes a three-dimensional matrix model. The first dimension represents the region number, the second dimension represents the height level, and the third dimension represents the horizontal coordinate. Matrix filling uses bilinear interpolation to calculate the temperature values ​​of virtual grid points based on the actual sensor locations. The matrix data is stored as 16-bit integers, achieving a resolution of 0.01℃. The matrix structure includes header information, recording the matrix dimensions, update timestamp, and data version number. Matrix data is transferred to the temperature field reconstruction module via DMA, without consuming CPU resources during the transfer process. Matrix updates use an incremental update method, updating only the data regions that have changed. The standardized temperature data matrix is ​​stored with double buffers in memory to ensure that the reconstruction module can access complete data snapshots. The system periodically compresses and archives the matrix data, preserving historical temperature distribution records. The matrix access interface provides multiple data extraction methods, supporting queries by region, time slices, and filtering by temperature range. The entire data acquisition and processing flow adopts an event-driven architecture, with each processing stage triggered by the output event of the previous stage. The system uses a watchdog timer to monitor the real-time performance of the processing flow, ensuring that all data processing tasks are completed within the specified time. Error handling mechanisms include automatic retry and degradation strategies to guarantee normal system operation even when some sensors fail. The execution status of all processing steps is recorded in the system log, supporting operational status monitoring and fault diagnosis. Log data is stored cyclically, automatically overwriting the earliest record.

[0095] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A control method for a transformer cooling system based on a microcontroller, characterized in that, The microcontroller-based transformer cooling system control method includes: A multi-dimensional real-time temperature dataset of the transformer body is obtained by a temperature sensor group. The multi-dimensional real-time temperature dataset includes the transformer winding temperature, core temperature and oil tank temperature. The multi-dimensional real-time temperature dataset is calibrated and filtered using the preprocessing module built into the microcontroller to obtain processed temperature data. The overall thermal field distribution of the transformer is calculated based on the processed temperature data according to the preset temperature field reconstruction algorithm, and a dynamic thermal field distribution map is generated. The dynamic thermal field distribution map is analyzed by a preset cooling strategy decision model to generate a target cooling control command set; The target cooling control command set controls the cooling actuator group to perform coordinated cooling operations.

2. The microcontroller-based transformer cooling system control method according to claim 1, characterized in that, The step involves analyzing the dynamic thermal field distribution map using a preset cooling strategy decision model to generate a target cooling control command set, including: High-temperature regions are identified in the dynamic thermal field distribution map to obtain feature data of multiple high-temperature regions; Calculate the temperature rise rate and heat accumulation corresponding to the characteristic data of each high-temperature region; Based on the temperature rise rate and heat accumulation, a cooling priority sequence for the characteristic data of each high-temperature region is determined; A target cooling control instruction set, including cooling intensity level and cooling direction, is generated according to the cooling priority sequence.

3. The microcontroller-based transformer cooling system control method according to claim 2, characterized in that, The calculation of the temperature rise rate and heat accumulation corresponding to the characteristic data of each high-temperature region includes: Extract historical temperature data sequences corresponding to high-temperature regions from the multi-dimensional real-time temperature dataset; The instantaneous temperature rise rate curve is obtained by performing a first-order differential calculation in the time dimension on the historical temperature data sequence. The instantaneous temperature rise rate curve is integrated over time to obtain a heat accumulation distribution map; The instantaneous temperature rise rate curve and heat accumulation distribution map are spatially mapped and matched with the high-temperature region characteristic data.

4. The microcontroller-based transformer cooling system control method according to claim 3, characterized in that, The step of determining the cooling priority sequence of characteristic data for each high-temperature region based on the temperature rise rate and heat accumulation includes: Set the temperature rise rate threshold and the heat accumulation threshold; High-temperature region feature data where the instantaneous temperature rise rate exceeds the temperature rise rate threshold and the heat accumulation also exceeds the heat accumulation threshold are marked as first-level priority. High-temperature region feature data that exceed the temperature rise rate threshold or the heat accumulation threshold are marked as secondary priority; The remaining high-temperature region characteristic data are sorted in descending order of heat accumulation value to form a priority level of three or below.

5. The microcontroller-based transformer cooling system control method according to claim 4, characterized in that, The step of generating a target cooling control instruction set containing cooling intensity level and cooling direction according to the cooling priority sequence includes: The cooling intensity level corresponding to the characteristic data of each high-temperature region is determined according to the priority level, where the first priority level corresponds to the maximum cooling intensity level. The cooling direction parameters are determined based on the spatial location of the high-temperature region characteristics on the transformer body. The cooling intensity level and cooling direction parameters are combined to generate independent control commands for the characteristic data of each high-temperature region. All independent control commands are sorted and integrated according to their execution time sequence to form the target cooling control command set.

6. The microcontroller-based transformer cooling system control method according to claim 1, characterized in that, After controlling the cooling actuator group to perform coordinated cooling operations according to the target cooling control command set, the method further includes: Real-time acquisition of operating status data of the cooling actuator group; The execution effect is evaluated by the feedback processing module of the microcontroller using the running status data; The results of the performance evaluation are compared and analyzed with the expected cooling target to generate cooling effect deviation data; The parameters of the cooling strategy decision model are corrected based on the cooling effect deviation data.

7. The microcontroller-based transformer cooling system control method according to claim 6, characterized in that, The step of evaluating the execution effect of the running status data through the microcontroller's feedback processing module includes: Extract the actual temperature change curve from the operating status data; Calculate the degree of agreement between the actual temperature change curve and the expected temperature decrease curve. Monitor the ratio of power consumption data to cooling effect of the cooling actuator group; The overall consistency index and ratio relationship are used to generate an evaluation score for the implementation effect.

8. The microcontroller-based transformer cooling system control method according to claim 6, characterized in that, The parameter correction of the cooling strategy decision model based on cooling effect deviation data includes: Establish a mapping table between cooling effect deviation data and control parameters of the cooling strategy decision model; Determine the types and adjustment ranges of the control parameters that need to be adjusted based on the mapping table; A gradual adjustment strategy is adopted to iteratively optimize the control parameters of the cooling strategy decision model; Record the history of parameter adjustments and the corresponding improvements in cooling performance.

9. The microcontroller-based transformer cooling system control method according to claim 1, characterized in that, The process of acquiring a multi-dimensional real-time temperature dataset of the transformer body through a group of temperature sensors includes: Multiple temperature sensors located at different positions on the transformer body synchronously collect temperature data; The collected temperature data is encoded according to spatial location information; Add timestamp information to create a multi-dimensional real-time temperature dataset with spatiotemporal labels; The multi-dimensional real-time temperature dataset is transmitted to the microcontroller's data receiving buffer via the data bus.

10. The microcontroller-based transformer cooling system control method according to claim 1, characterized in that, The process of performing data calibration and filtering on the multi-dimensional real-time temperature dataset using the microcontroller's built-in preprocessing module includes: The collected data is corrected for deviations based on the calibration coefficients of each temperature sensor; A sliding window filtering algorithm is used to eliminate high-frequency noise interference in temperature data; The filtered data is validated to remove outlier data points.

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