A detection method of high-efficiency heat dissipation low-voltage current transformer

Abstract

This invention belongs to the field of instrument transformer technology, specifically relating to a detection method for a high-efficiency heat dissipation low-voltage current transformer. This invention utilizes multi-dimensional temperature monitoring and thermal field analysis: integrating temperature sensors and thermocouples, combined with infrared thermal imaging technology, it achieves comprehensive and high-precision monitoring of the internal and external temperatures of the transformer, enabling timely detection and location of hotspot areas. Through phased data acquisition and comparative analysis, it not only evaluates the electrical performance stability of the transformer under different temperature conditions but also captures the dynamic response characteristics of the transformer through transient response testing, ensuring its reliable operation under various operating conditions. This detection method, through highly integrated monitoring technology, intelligent thermal management strategies, and a comprehensive performance evaluation system, significantly improves the thermal management and performance monitoring level of low-voltage current transformers in complex operating environments, providing strong technical support for ensuring the safe and efficient operation of power systems.

Description

Technical Field

[0001] This invention belongs to the field of instrument transformer technology, specifically relating to a detection method for a high-efficiency heat dissipation low-voltage current transformer. Background Technology

[0002] As power systems evolve towards intelligence and high efficiency, low-voltage current transformers, as key components in power systems, directly impact the safety and stability of the system. Especially in modern power networks, the compact design and long-term operation under high load conditions make heat dissipation a growing concern for transformers. Traditional current transformer designs often prioritize electrical performance while neglecting heat dissipation capabilities, leading to overheating under prolonged high-current operation, which affects the transformer's measurement accuracy and lifespan.

[0003] Existing current transformer testing methods mostly focus on electrical performance testing, with insufficient attention to assessing the thermal performance of transformers under complex operating conditions, especially high-temperature environments. Specifically, this manifests as a lack of precise monitoring of the local and overall heat distribution of the transformer, the ability to dynamically adjust thermal management strategies, and in-depth analysis of the transformer's transient response characteristics. Therefore, how to efficiently and accurately evaluate the thermal and electrical performance of low-voltage current transformers under actual operating conditions, and to proactively identify and address potential overheating and performance degradation issues, has become an urgent technical challenge. Summary of the Invention

[0004] The purpose of this invention is to provide a detection method for a high-efficiency heat dissipation low-voltage current transformer. Through integrated monitoring technology, intelligent thermal management strategy and comprehensive performance evaluation system, the thermal management and performance monitoring level of low-voltage current transformers in complex working environments is significantly improved, thereby solving the problems in the prior art mentioned in the background.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a detection method for a high-efficiency heat dissipation low-voltage current transformer, comprising the following steps:

[0006] S1: A low-voltage current transformer is provided, wherein the transformer has at least one coil for sensing current and is equipped with a temperature sensor and multiple distributed thermocouples to monitor local and overall temperature changes.

[0007] S2: Apply a preset low-voltage AC or DC current to the input terminal of the transformer under test to activate the built-in temperature sensor and thermocouple, start real-time monitoring of the temperature rise of the transformer, and record the initial temperature data.

[0008] S3: Using infrared thermal imaging, a non-contact temperature scan is performed on the outside of the current transformer to form an initial thermal field distribution map. Then, the output voltage signal of the current transformer is acquired through the data acquisition module as the induction feedback of the primary current.

[0009] S4: Implement the first stage of data processing, convert the acquired voltage signal into the corresponding current value, and evaluate the linearity and accuracy of the current transformer by comparing the converted current value with the expected applied current.

[0010] S5: Enter the continuous heating stage, gradually increase the input current to near but not exceeding the rated current value of the transformer, continuously monitor temperature changes during this period, analyze thermocouple and temperature sensor data, and identify hot spots inside and on the surface of the transformer.

[0011] S6: Activate the thermal management system, dynamically adjust the built-in micro fan or liquid cooling circulation system based on the hot spot analysis results, and use machine learning algorithms to predict the temperature rise trend of the current transformer in future working conditions by combining historical data.

[0012] S7: After the current transformer reaches the predetermined stable operating temperature, the second stage of data acquisition is carried out to record the current and voltage characteristic curves at this time. The data of the first stage and the second stage are compared to evaluate the performance stability of the current transformer under different temperature conditions.

[0013] S8: Implement transient response testing by suddenly changing the input current magnitude, recording the transformer response time and recovery process, analyzing the response time data, optimizing the data acquisition frequency to ensure that all key transient changes are captured, and using advanced data analysis algorithms to automatically identify potential fault signs from the data.

[0014] S9: Based on all test results, generate a comprehensive evaluation report, including thermal performance, electrical performance and reliability evaluation of the instrument transformer.

[0015] Preferably, in S1, the current transformer further includes: a circuit board, on which signal conditioning circuits for temperature sensors and thermocouples are integrated, and a circuit-sensor interface.

[0016] Preferably, in step S2, a preset low-voltage AC or DC current is applied to the input terminal of the transformer under test, including:

[0017] According to the test requirements of the instrument transformer, select the appropriate current source equipment and set the preset low-voltage AC or DC current value. The current value is between the lower limit and the middle limit of the rated operating range of the instrument transformer.

[0018] Connect the current source correctly to the input terminal of the transformer. At the same time, check the connection lines of all temperature sensors and thermocouples.

[0019] Activate the built-in temperature sensor and thermocouple of the current transformer, start data acquisition, apply current and record initial data;

[0020] Verify the validity of the initial temperature data. If any abnormal data is found, immediately check whether the sensor is working properly or recalibrate it.

[0021] Preferably, in S3, infrared thermal imaging is used to perform a non-contact temperature scan of the outside of the transformer to form an initial thermal field distribution map, including:

[0022] Based on the characteristics of the current transformer, set appropriate temperature measurement range, emissivity value and image quality parameters on the thermal imager, place the infrared thermal imager in a safe and unobstructed position, and adjust the distance between the thermal imager and the current transformer.

[0023] The infrared thermal imager is activated to perform a full-range non-contact temperature scan. The raw thermal image obtained from the scan is then converted into a color thermal image.

[0024] Preferably, in S4, the first stage data processing step includes:

[0025] Collect the output voltage signal data of the current transformer under different input currents, process the raw voltage signal data, and remove any obvious outliers or noise interference.

[0026] The collected voltage signal is converted into the corresponding current value, and the formula is: I_measured = V_output / K, where K is the turns ratio;

[0027] The converted actual current value is compared one-to-one with the expected applied current value, and the deviation between the two is calculated. The deviation value is I_expected - I_actual.

[0028] Plot a scatter plot of the expected and measured current values, fit a straight line to represent the ideal linear relationship, evaluate the linearity by calculating the coefficient of determination (the closer the coefficient of determination is to 1, the better the linearity), and evaluate the accuracy of the transformer by calculating the average and maximum deviations of all measurement points.

[0029] Results summary and reporting: Based on the evaluation results of linearity and accuracy, an analysis report is written.

[0030] Preferably, in S5, identifying hotspot areas inside and on the surface of the current transformer includes:

[0031] Before applying current, the ambient temperature and initial surface temperature of the transformer are recorded using thermocouples and temperature sensors as reference data.

[0032] Start increasing the input current gradually from zero, following the principle of small increments and slow speeds each time.

[0033] Throughout the heating process, thermocouples are continuously used to monitor the internal temperature of the transformer, while temperature sensors are used to monitor the surface temperature.

[0034] The system analyzes the collected data in real time, compares the temperature distribution at different time points, assesses the temperature rise trend and potential overheating risk, and marks abnormally high temperature areas.

[0035] Preferably, in S6, a machine learning algorithm is used to predict the temperature rise trend of the current transformer in future operating conditions, combined with historical data, including:

[0036] Data collection and preprocessing: Collect real-time data on the current operating temperature, ambient temperature, and load status of the current transformer from the sensors, and integrate historical temperature records;

[0037] Hotspot analysis and location: Infrared imaging is used to analyze hotspots on the surface of the equipment, identify the high-heat areas of the current transformer, and record the location and temperature data of these hotspots;

[0038] Establish a machine learning model: Based on historical temperature data, load conditions, and environmental factors, train a model to predict the future temperature rise trend of the transformer.

[0039] Preferably, in S7, after the current transformer reaches a predetermined stable operating temperature, the second stage of data acquisition is performed, including:

[0040] Set the corresponding data acquisition parameters according to the test requirements of the second stage, and perform the second stage current and voltage characteristic curve measurement;

[0041] The voltage value is recorded in real time for each current change, forming the current and voltage characteristic curve data for the second stage;

[0042] The current and voltage characteristic curves obtained in the second stage are compared with those in the first stage to analyze the differences between the two and evaluate the performance stability of the transformer under different temperature conditions.

[0043] Preferably, in S8, a transient response test is performed, including:

[0044] Set up a transient response test plan, including the magnitude of the current change, the duration of the change, and the time schedule for the current to return to the original level;

[0045] Adjust the sampling rate of the data acquisition system based on the expected characteristics of the transient response;

[0046] After collecting the data, analyze the response time data to identify the transient response characteristics of the current transformer, including response speed, overshoot, and number of oscillations, and use charts to illustrate the behavior.

[0047] Preferably, in S9, a comprehensive evaluation report is generated, including:

[0048] Data collection and organization: Collect data from all testing phases, standardize the data format, and integrate it into a single spreadsheet;

[0049] Based on industry standards and testing specifications, calculate various key performance indicators and compare the calculated performance indicators with the manufacturer's declared values, industry standard requirements, and the performance of similar products.

[0050] Based on the problems and abnormal data found in the test, failure mode and effect analysis is carried out to identify potential causes that may lead to transformer failure, assess their impact and probability of occurrence, propose corresponding risk mitigation measures, and generate a comprehensive assessment report.

[0051] Technical effects and advantages of the present invention: The detection method for a high-efficiency heat dissipation low-voltage current transformer proposed in this invention has the following advantages compared with the prior art:

[0052] This invention utilizes multi-dimensional temperature monitoring and thermal field analysis: integrating temperature sensors and thermocouples with infrared thermal imaging technology, it achieves comprehensive and high-precision monitoring of the internal and external temperatures of current transformers, enabling timely detection and location of hotspot areas and providing accurate data support for thermal management. Through phased data acquisition and comparative analysis, it not only evaluates the electrical performance stability of current transformers under different temperature conditions but also captures the dynamic response characteristics of current transformers through transient response testing, ensuring their reliable operation under various conditions. This detection method, through highly integrated monitoring technology, intelligent thermal management strategies, and a comprehensive performance evaluation system, significantly improves the thermal management and performance monitoring level of low-voltage current transformers in complex operating environments, providing strong technical support for ensuring the safe and efficient operation of power systems. Attached Figure Description

[0053] Figure 1 This is a flowchart of a detection method for a high-efficiency heat dissipation low-voltage current transformer according to the present invention. Detailed Implementation

[0054] 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. The specific embodiments described herein are merely used to explain the present invention and are not intended to limit the present invention. 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.

[0055] This invention provides, for example Figure 1 The method for testing a high-efficiency heat dissipation low-voltage current transformer, as shown, includes the following steps:

[0056] S1: A low-voltage current transformer is provided, wherein the transformer has at least one built-in coil for sensing current and is equipped with a temperature sensor and multiple distributed thermocouples to monitor local and overall temperature changes; the method steps for implementing the low-voltage current transformer are as follows:

[0057] Design and Selection: Based on the expected operating environment and requirements of the current transformer, design the number of turns, material, and dimensions of the coil to ensure efficient current sensing under low-voltage conditions. Select high-temperature resistant, high-sensitivity temperature sensors and thermocouples to accurately monitor temperature changes.

[0058] Structural integration: Induction coils are precisely arranged inside the current transformer to ensure a reliable connection between the coils and the circuit. Simultaneously, temperature sensors are installed at key heat-generating areas of the current transformer, such as near the coils, while multiple distributed thermocouples are strategically arranged within and around the transformer casing to cover potential heat flow paths for comprehensive temperature monitoring.

[0059] Circuit and sensor interface design: Design a circuit board that integrates signal conditioning circuits for temperature sensors and thermocouples, including amplification, filtering, and analog-to-digital conversion functions, to ensure that the temperature signal can be accurately read and transmitted to the data processing unit.

[0060] Software Development and Algorithm Design: Develop data acquisition and processing software, including algorithms for real-time monitoring and analysis of data collected from temperature sensors and thermocouples, enabling temperature anomaly early warning. Simultaneously, design algorithms to fuse local and overall temperature information to assess the thermal state of the current transformer.

[0061] Calibration and Verification: Under laboratory conditions, the current transformer is calibrated using a standard current source, while the accuracy and response speed of the temperature sensor and thermocouple are verified using a known temperature source. Software parameters are adjusted to ensure the reliability of the measurement results.

[0062] On-site installation and commissioning: Install current transformers in the actual application scenario, and fine-tune the thermal management system parameters according to site conditions, such as fan speed or liquid cooling flow rate, to ensure effective heat dissipation under various operating conditions. Verify the stability and accuracy of the entire system through initial operational testing, and perform fine-tuning and optimization as necessary.

[0063] By following the above steps, we can ensure that the provided low-voltage current transformer has both efficient current sensing capabilities and accurate and comprehensive temperature monitoring capabilities, thereby effectively preventing overheating risks and improving the stability and safety of the equipment.

[0064] S2: Apply a preset low-voltage AC or DC current to the input terminal of the transformer under test to activate the built-in temperature sensor and thermocouple, and start real-time monitoring of the transformer's temperature rise, recording the initial temperature data; the specific implementation procedure for this step is as follows:

[0065] Current source setting: Select a suitable current source device according to the test requirements of the instrument transformer and set a preset low-voltage AC or DC current value. This current value should be between the lower and middle limits of the rated operating range of the instrument transformer to ensure safe and effective testing.

[0066] Connection and Inspection: Correctly connect the current source to the input terminal of the transformer, ensuring a secure connection. Simultaneously, check the connections of all temperature sensors and thermocouples to ensure unobstructed signal transmission.

[0067] Activate the monitoring system: Activate the temperature sensor and thermocouple system built into the transformer through the control panel or software interface to ensure that they are in standby mode and ready to receive and record data.

[0068] Start real-time monitoring: Start the data acquisition software and set the monitoring parameters, including sampling rate and data recording interval, to ensure that the temperature change of the transformer after the current is applied can be accurately captured.

[0069] Apply current and record initial data: Slowly and smoothly input the preset current into the transformer while simultaneously recording the initial readings of the temperature sensor and thermocouple. This initial data will serve as the baseline for subsequent temperature change analysis.

[0070] Data Verification and Adjustment: Verify the rationality of the initial temperature data and confirm that there are no abnormal readings. If any abnormal data is found, immediately check whether the sensor is working properly or recalibrate it. If necessary, adjust the current input and record the initial data again until all data are accurate.

[0071] The above steps ensure that the temperature rise of the transformer can be monitored in a timely and accurate manner after the preset current is applied, and valuable initial temperature data is recorded, providing a reliable basis for subsequent temperature change analysis and thermal management.

[0072] S3: Using infrared thermal imaging, a non-contact temperature scan is performed on the outside of the current transformer to form an initial thermal field distribution map. Then, the output voltage signal of the current transformer is acquired through the data acquisition module as the induction feedback of the primary current.

[0073] Regarding infrared thermal imaging and the formation of the initial thermal field distribution map, the specific implementation steps are as follows:

[0074] Infrared thermal imager preparation: Select a suitable infrared thermal imager, ensuring its resolution, temperature measurement range, and accuracy meet the transformer detection requirements. Check that the imager lens is clean, the battery is fully charged, and that the equipment is in optimal working condition.

[0075] Parameter setting and calibration: Based on the material, size, and expected operating temperature range of the current transformer, set appropriate temperature range, emissivity value, and image quality parameters on the thermal imager. Perform necessary calibration operations to ensure the accuracy of the measurement results.

[0076] Positioning and Distance Adjustment: Place the infrared thermal imager in a safe and unobstructed location, ensuring the entire external surface of the transformer is within the field of view. Adjust the distance between the thermal imager and the transformer, following the equipment's recommended optimal viewing distance, to obtain a clear, distortion-free thermal image.

[0077] Non-contact temperature scanning: Activate the infrared thermal imager and perform a full-range non-contact temperature scan. Move the thermal imager slowly (if necessary) to ensure that every area is fully covered, while avoiding image blurring caused by rapid movement.

[0078] Thermal distribution map generation: Import the raw thermal images obtained from the scan using the analysis software built into the thermal imager or third-party professional software. Apply a color mapping algorithm to convert the temperature data into a color thermal map, visually displaying the temperature distribution outside the transformer. Adjust the color levels to highlight temperature differences for easier analysis.

[0079] Analysis and Recording: Analyze the thermal distribution map to identify hot spots, uniformity, and potential anomalies on the transformer surface. Record key temperature data, including the highest temperature, average temperature, and temperature gradient, to provide a basis for subsequent thermal management and performance evaluation. Simultaneously, save thermal images and analysis reports for future reference.

[0080] After completing the above steps, a detailed thermal field distribution map of the external environment of the transformer will be obtained, providing visual data support for further thermal performance analysis and optimization.

[0081] S4: Implement the first stage of data processing, convert the acquired voltage signal into the corresponding current value, and evaluate the linearity and accuracy of the current transformer by comparing the converted current value with the expected applied current.

[0082] The first stage of data processing steps, specifically the implementation process, is as follows:

[0083] Data Acquisition and Processing: Ensure the data acquisition module is operating stably and collect the output voltage signals of the current transformer under different input currents. Export this raw data to the analysis software for preliminary processing, removing any obvious outliers or noise interference.

[0084] Voltage-to-current conversion calculation: Based on the rated transformation ratio of the transformer (i.e., the ratio of primary current to secondary voltage), the acquired voltage signal is converted into the corresponding current value. The formula is usually: I_measured = V_output / K (where K is the transformation ratio). Ensure the calculation process is accurate.

[0085] Data comparison and deviation calculation: The converted actual current value (I_measured) is compared one-to-one with the expected applied current value (I_expected), and the deviation between the two is calculated. Deviation value = I_expected - I_measured.

[0086] Linearity analysis: Plot a scatter plot of the expected and measured current values, and fit a straight line to represent the ideal linear relationship. Linearity is evaluated by calculating R² (coefficient of determination); the closer R² is to 1, the better the linearity.

[0087] Accuracy assessment: The accuracy of the current transformer is evaluated by calculating the average and maximum deviations of all measurement points. Generally, the smaller the average deviation, the higher the accuracy. Furthermore, the deviation distribution is analyzed to determine whether systematic or random errors exist.

[0088] Results Summary and Report: Based on the evaluation results of linearity and accuracy, write an analysis report. The report should include data processing methods, key calculation steps, evaluation indicators (such as R² value, average deviation, and maximum deviation), and conclusions. If the transformer performance is found to be non-compliant with standards, improvement measures or maintenance recommendations should also be proposed.

[0089] By following the steps above, the performance of the current transformer in the first stage can be systematically analyzed, providing a scientific basis for subsequent optimization or fault diagnosis.

[0090] R² (coefficient of determination) calculation in linearity analysis is a method to quantify how well data points fit a straight line. The following are the specific steps for calculating R², based on the relationship between the expected current value (theoretical value) and the measured current value:

[0091] The specific steps for calculating R² are as follows:

[0092] Data preparation: Collect data during the current transformer testing process, including expected current values ​​(X-axis, usually standard or theoretical values) and corresponding measured current values ​​(Y-axis, experimental or actual measured values).

[0093] Calculate the mean: Calculate the average of the expected current values ​​(X) respectively. The average value of the measured current (Y)

[0094] Calculate the sum of squared deviations: Total sum of squared deviations (SST): Calculates the sum of squares of the differences between all measured values ​​and the mean of Y.

[0095]

[0096] Sum of Squared Residuals (SSE): Calculates the sum of squares of the differences between the measured values ​​and the predicted values ​​based on linear regression. First, the slope (b1) and intercept (b0) of the best-fit line are obtained using the least squares method. Then, the predicted value for each point is calculated. Finally, calculate SSE.

[0097]

[0098] Calculate R²: Use the sum of squared deviations above to calculate R², which represents the proportion of variance explained by the model to the total variance.

[0099] The value of R² ranges from 0 to 1. The closer it is to 1, the stronger the linear relationship between the measured value and the expected value, that is, the better the linearity.

[0100] Implementation example (not direct code calculation, only logical explanation):

[0101] Step 1: Assume you have n sets of data points (X1,Y1), (X1,Y1),...,(X1,Y1).

[0102] Step 2: Calculate the average of X and Y.

[0103] Step 3: Use the least squares formula to solve for the slope and intercept of the line. The formula is:

[0104]

[0105]

[0106] Step 4: Calculate the predicted value for each point based on the slope and intercept mentioned above.

[0107] Step 5: Calculate SST and SSE using the above formulas, and then calculate R2.

[0108] Step 6: Interpret the R2 value. If R2 is close to 1, it indicates that there is a good linear correlation between the measured current value and the expected current value.

[0109] S5: Enter the continuous heating stage, gradually increase the input current to near but not exceeding the rated current value of the transformer, continuously monitor temperature changes during this period, analyze thermocouple and temperature sensor data, and identify hot spots inside and on the surface of the transformer.

[0110] The specific implementation steps for the continuous heating phase of the current transformer are as follows:

[0111] Preparation and Safety Checks: Confirm that all test equipment is connected correctly, including current sources, thermocouples, temperature sensors, and data acquisition systems. Conduct a safety check to ensure all electrical connections comply with specifications and that all personnel on site are wearing appropriate personal protective equipment.

[0112] Initial state recording: Before applying current, the ambient temperature and initial surface temperature of the transformer are recorded using thermocouples and temperature sensors as baseline data. Simultaneously, it is confirmed that the data acquisition system is properly configured to monitor and record temperature changes in real time.

[0113] Gradually increase the current: Start from zero and gradually increase the input current, following the principle of small increments and slow speeds to avoid sudden current surges that could impact the transformer. Closely monitor the current value to ensure it gradually approaches but does not exceed the transformer's rated current value.

[0114] Real-time monitoring and recording: Throughout the heating process, thermocouples are continuously used to monitor the internal temperature of the transformer, while temperature sensors are used to monitor the surface temperature. The data acquisition system should automatically record time, current values, and corresponding temperature data to generate a time-temperature curve.

[0115] Hotspot Identification and Analysis: Real-time analysis of collected data, using data analysis software to identify hotspot areas inside and on the surface of the transformer. Comparison of temperature distribution at different time points to assess temperature rise trends and potential overheating risks. Abnormally high-temperature areas are marked for further analysis or appropriate countermeasures.

[0116] Assessment and Decision-Making: After the current approaches its rated value and stabilizes for a period of time, comprehensively evaluate the thermal performance of the instrument transformer. Analyze whether the hot spot area exceeds the design allowable range and determine whether the instrument transformer can operate safely under rated conditions. Based on the test results, decide whether it is necessary to adjust the test conditions, maintain the instrument transformer, or conduct more in-depth troubleshooting.

[0117] S6: Activate the thermal management system, dynamically adjust the built-in micro fan or liquid cooling circulation system based on hotspot analysis results, and use machine learning algorithms combined with historical data to predict the temperature rise trend of the current transformer in future operating conditions. The specific steps for the thermal management system activation and dynamic adjustment process, combined with the use of machine learning algorithms to predict the temperature rise trend of the current transformer, are as follows:

[0118] Data Collection and Preprocessing: First, real-time data such as the current operating temperature of the current transformer, ambient temperature, and load status are collected from sensors, and historical temperature records are integrated. The data is then cleaned, outliers are removed, and the data is standardized for use in training machine learning models.

[0119] Hotspot Analysis and Location: Using infrared imaging or other non-contact temperature detection technologies, hotspot analysis is performed on the equipment surface to identify high-heat areas of the current transformer. The location and temperature data of these hotspots are recorded as a basis for adjusting thermal management strategies.

[0120] Establish a machine learning model: Select a suitable machine learning algorithm (such as ARI MA, LSTM, etc. for time series prediction) and train a model based on multi-dimensional features such as historical temperature data, load conditions, and environmental factors to predict the future temperature rise trend of the transformer.

[0121] Model validation and optimization: Use methods such as cross-validation to verify the accuracy of the model, adjust the model parameters based on the validation results, and ensure the reliability of the prediction results. If necessary, introduce new data to retrain the model to improve prediction accuracy.

[0122] Real-time monitoring and early warning settings: Implement continuous temperature monitoring. When the real-time temperature approaches or exceeds the preset safety threshold, trigger the early warning system to promptly notify maintenance personnel to intervene and take measures.

[0123] Dynamic adjustment strategy formulation: Based on the temperature rise trend predicted by the machine learning model and the current hotspot analysis results, a thermal management strategy is designed. For example, if a sharp increase in temperature is predicted in the future, the cooling capacity is increased in advance.

[0124] Perform thermal management operations: Activate the built-in micro fan or liquid cooling circulation system, dynamically adjusting its operating speed or flow rate according to the strategy. For hot spots, targeted local cooling can be enhanced to maintain overall temperature balance.

[0125] Feedback and Adaptive Learning: After implementing thermal management, new temperature data and system response effects are collected and fed back to the machine learning model for self-optimization and learning. This helps the model continuously adapt to the changing operating characteristics of the transformer over time, improving the accuracy of prediction and regulation. Through this series of steps, intelligent prediction and proactive management of transformer temperature rise trends are achieved, effectively preventing overheating, extending equipment lifespan, and ensuring safe operation.

[0126] The thermal management system mainly consists of the following key components:

[0127] Temperature sensor: Installed around the current transformer to monitor temperature changes in these parts in real time and provide accurate temperature data.

[0128] Control Unit: Receives data from temperature sensors and determines the appropriate cooling measures based on preset algorithms or strategies. Modern thermal management systems typically employ Electronic Control Units (ECUs), capable of executing complex control logic and algorithms.

[0129] Cooling medium circulation system: This includes coolant pumps, radiators, heat exchangers, etc., used for liquid circulation cooling. In some systems, this may be a closed liquid cooling loop, where the coolant absorbs and removes heat generated by the heat source, and then discharges it into the environment through the radiator.

[0130] Fan system: This typically includes one or more fans responsible for accelerating airflow and improving heat dissipation efficiency. Modern thermal management systems often feature intelligently controlled fans that can adjust their speed as needed, resulting in both high efficiency and energy savings.

[0131] Heat exchanger / radiator: Used in liquid cooling systems, it is a device that exchanges heat between the coolant and the outside air, accelerating heat dissipation by increasing the surface area.

[0132] Valves and actuators: used to control the direction and flow rate of coolant or gas, ensuring that heat can be effectively transferred from the heat source to the heat dissipation device.

[0133] Working principle:

[0134] Monitoring and Analysis: The system first continuously monitors the temperature of key components through temperature sensors, and the control unit analyzes this data to identify hot spots or potential overheating risks.

[0135] Decision-making and control: Based on the analysis results, the control unit decides whether cooling measures need to be activated or adjusted. For example, if the temperature in a hot spot area is too high, the system may instruct the fan to run faster to increase airflow, or adjust the coolant circulation rate to accelerate heat transfer.

[0136] Dynamic adjustment: In liquid cooling systems, the control unit may adjust the speed and direction of the coolant pump, or change the circulation path of the coolant through valves, to allocate cooling resources in the most efficient way. In air-cooled systems, fan speed is adjusted to adapt to different heat dissipation requirements.

[0137] Prediction and optimization: Advanced thermal management systems may also integrate predictive algorithms that use historical data and real-time operating conditions to predict future heat load changes and adjust thermal management strategies in advance to achieve more efficient thermal energy management.

[0138] In summary, the thermal management system, through the integration of hardware and software, achieves intelligent monitoring and regulation of temperature, ensuring that the equipment maintains the optimal operating temperature under various working conditions, thereby improving efficiency, extending lifespan, and ensuring safety.

[0139] In the S6 scheme, the specific steps and principles of using machine learning algorithms to predict the temperature rise trend of the current transformer are as follows: Steps:

[0140] Data collection and preprocessing: Collect historical data of the current transformer during operation, including but not limited to current intensity, voltage, ambient temperature, and historical temperature rise records.

[0141] Data preprocessing: Clean the data, remove outliers and missing values, and perform normalization or standardization to facilitate algorithm training.

[0142] Feature engineering: Select or construct feature variables related to the temperature rise of the transformer, such as current-time series, ambient temperature, statistical characteristics of past temperature rise curves, etc.

[0143] It may also include encoding historical hotspot analysis data to reflect the thermal sensitivity of specific parts of the transformer.

[0144] Model selection: Choose an appropriate machine learning model based on the characteristics of the problem, such as time series prediction models (e.g., ARIMA, Prophet), regression models (e.g., linear regression, random forest), or deep learning models (e.g., LSTM, GRU).

[0145] Training and test set division: The collected data is divided into training and test sets, usually with 70% or 80% used for training and the remainder used to test model performance.

[0146] Model training: Train the selected machine learning model using the training set data, and adjust the model parameters to minimize the prediction error.

[0147] Cross-validation: Apply cross-validation techniques (such as k-fold cross-validation) to evaluate the generalization ability of the model and avoid overfitting.

[0148] Model Evaluation and Optimization: Use a test set to evaluate the accuracy of the model in predicting temperature rise trends. Adjust model parameters or replace the model based on the evaluation results to improve prediction accuracy. Visualize the prediction results and actual temperature rise data, analyze prediction errors, and understand under which conditions the model performs poorly.

[0149] Deployment and Real-time Prediction: The optimized model is deployed into the thermal management system to receive the latest data in real time and predict the future temperature rise trend of the current transformer. Based on the prediction results, the thermal management system (such as fan speed and liquid cooling cycle intensity) is dynamically adjusted to prevent overheating.

[0150] Brief Principle: Machine learning algorithms learn patterns from historical data, establishing a complex model of the relationship between inputs (such as current and environmental conditions) and outputs (temperature rise trends). Through iterative optimization algorithms (such as gradient descent), model parameters are adjusted to minimize the prediction error on the training data. The predictive model utilizes these learned patterns to predict temperature rise trends under unknown future conditions, providing intelligent decision-making support for thermal management systems. By combining historical data with real-time monitoring information, the algorithm can more accurately estimate the real-time thermal status of current transformers, thereby guiding thermal management strategies, effectively avoiding overheating risks, and improving equipment stability and safety.

[0151] S7: After the current transformer reaches the predetermined stable operating temperature, the second stage of data acquisition is carried out to record the current and voltage characteristic curves at this time. The data of the first stage and the second stage are compared to evaluate the performance stability of the current transformer under different temperature conditions.

[0152] The specific steps for the second stage of data acquisition after the current transformer reaches the predetermined stable operating temperature are as follows:

[0153] Confirm stable operating condition: Monitor the temperature sensor data of the current transformer to confirm that the temperature in all critical areas has reached and stabilized at the predetermined target value. Ensure this state is maintained for a period of time, such as a few minutes to a few hours, to ensure that thermal stability is fully established.

[0154] Set data acquisition parameters: In the data acquisition system, set the corresponding acquisition parameters according to the test requirements of the second stage, including but not limited to acquisition frequency, sampling time interval, data record length, etc., to ensure that the performance of the current transformer can be accurately recorded at a stable temperature.

[0155] Perform the second stage current-voltage characteristic curve measurement: Similar to step S4, apply a series of currents at different levels (it is recommended to cover multiple points from low to high), and record the output voltage of the transformer at each current level. Note that the current change should be gradual to avoid transient surges.

[0156] Data recording and processing: Real-time recording of voltage values ​​at each current change to form the second-stage current-voltage characteristic curve data. Ensuring data completeness, accuracy, and no omissions, and performing preliminary processing such as outlier removal and smoothing.

[0157] Compare the data from the two stages: Compare the current-voltage characteristic curve obtained in the second stage with the characteristic curve in the first stage (under initial temperature conditions). Analyze the differences between the two, including linearity, slope change, and offset, to evaluate the performance stability of the transformer under different temperature conditions.

[0158] Performance Evaluation and Analysis Report: Based on the comparison results, evaluate the temperature stability of the instrument transformer, including key indicators such as linearity change rate and temperature coefficient. Analyze the specific impact of temperature changes on the instrument transformer performance, such as whether it causes a significant increase in nonlinearity or a decrease in accuracy.

[0159] Prepare a detailed analysis report summarizing the results of the two-phase testing, including data comparison charts, performance stability assessments, possible cause analyses, and improvement suggestions or maintenance measures for any performance degradation found. This will provide important reference for subsequent optimization design, maintenance planning, or operational adjustments.

[0160] By following the steps above, the performance stability of the current transformer under different temperature conditions can be comprehensively evaluated, providing a scientific basis for ensuring its long-term reliable operation.

[0161] S8: Implement transient response testing by suddenly changing the input current magnitude, recording the transformer response time and recovery process, analyzing the response time data, optimizing the data acquisition frequency to ensure that all key transient changes are captured, and using advanced data analysis algorithms to automatically identify potential fault signs from the data.

[0162] The specific steps for conducting transient response testing are as follows:

[0163] Preparation before testing: Ensure that the current transformer is in a stable working state, and that all monitoring equipment (such as current source, voltage measurement equipment, and data acquisition system) has been connected and warmed up, so that conditions are ready for recording transient response.

[0164] Define test parameters: Set the specific scheme for transient response testing, including the magnitude of the current change (such as a rapid jump from 50% to 100% or higher of the rated current), the duration of the change (such as an instantaneous jump or a step change), and the time arrangement for returning to the original current level.

[0165] Optimize data acquisition settings: Based on the expected characteristics of the transient response, adjust the sampling rate of the data acquisition system to a sufficiently high level to ensure accurate capture of every critical moment in the instantaneous voltage fluctuations caused by current changes and the transformer response. Typically, the sampling frequency should be much higher than twice the shortest period of the expected transient event.

[0166] Perform a transient response test: suddenly change the input current magnitude, strictly following the pre-set procedure. Simultaneously, immediately start data logging, closely monitoring the transformer's response time (the delay from current change to voltage response) and any abnormal behavior during the recovery process.

[0167] Response time analysis: After collecting data, analyze the response time data to identify the transient response characteristics of the current transformer, including response speed, overshoot, and number of oscillations. Use charts (such as time-domain plots and step response curves) to visually display the transient behavior of the current transformer.

[0168] Advanced Data Analysis and Fault Sign Identification: Utilizing advanced data analysis algorithms such as time series analysis, spectrum analysis, and pattern recognition, this feature automatically searches for anomalous patterns or trends in transient response data. This includes, but is not limited to, abnormally prolonged response times, irregular oscillation patterns, and atypical behavior during the recovery process—all indicators of potential faults.

[0169] Based on the analysis results, assess the transient response quality of the instrument transformers and identify any signs that may indicate performance degradation or impending failure. If necessary, adjust the test plan or data acquisition strategy and repeat the test to further confirm the analysis results.

[0170] By following the steps above, we can not only comprehensively evaluate the transient response characteristics of the instrument transformer, but also promptly identify and warn of potential fault risks, providing important information for maintaining and optimizing the performance of the instrument transformer.

[0171] The specific steps for automatically identifying potential fault signs using advanced data analysis algorithms in the S8 solution are as follows:

[0172] Data preprocessing: First, the collected transient response test data is preprocessed, including removing outliers, smoothing noise, and imputing missing values ​​to ensure data quality. This step is fundamental to subsequent analysis and helps reduce misinterpretations during the analysis process.

[0173] Feature extraction: Key features are extracted from the preprocessed data. These features may include response time, overshoot, oscillation frequency, settling time, and recovery speed. Feature selection is based on an understanding of the transformer's performance and its failure modes.

[0174] Time series analysis: Applying time series analysis methods, such as autocorrelation analysis, Fourier transform, or wavelet analysis, to identify periodicity, trends, or anomalous fluctuations in data. These analyses help reveal hidden patterns in transient responses, especially complex dynamics that may be related to faults.

[0175] Pattern Recognition and Classification: Machine learning algorithms (such as Support Vector Machines, Neural Networks, Random Forests, etc.) are used to classify extracted features, establishing pattern recognition models for normal and abnormal responses (potential signs of failure). Training the model requires known normal and fault data as training samples.

[0176] Anomaly detection: Apply anomaly detection algorithms, such as statistical Z-score methods, Isolation Forest, or clustering algorithms (such as DBSCAN), to identify outliers in the data, which may be caused by abnormal transformer performance or potential faults.

[0177] Result Interpretation and Verification: A detailed analysis of identified potential fault signs is conducted, considering their physical meaning and actual operating conditions, to assess the probability of their authenticity. If necessary, retesting or verification using other detection methods (such as physical inspection, vibration analysis, etc.) is performed to improve diagnostic accuracy.

[0178] Through the steps described above, advanced data analysis algorithms can automatically filter out potential fault signs from transient response data, providing early warnings for maintenance teams and facilitating preventative maintenance and improving the reliability of instrument transformers.

[0179] S9: Based on all test results, generate a comprehensive evaluation report, including thermal performance, electrical performance and reliability evaluation of the instrument transformer.

[0180] To generate a comprehensive evaluation report covering the thermal performance, electrical performance, and reliability assessment of the instrument transformer, the specific methodological steps are as follows:

[0181] Data collection and organization: Collect data from all testing phases, including results from transient response tests, long-term stability tests, thermal cycling tests, and insulation withstand voltage tests. Format this data uniformly and integrate it into a central database or spreadsheet for easy analysis and comparison.

[0182] Performance index calculation: Based on industry standards and testing specifications, calculate various key performance indicators. For example, for thermal performance, calculate the maximum temperature rise, thermal stability, and insulation resistance change after thermal cycling; for electrical performance, calculate accuracy, linearity, saturation voltage, etc.; reliability can be measured by indicators such as mean time between failures (MTBF) and failure rate.

[0183] Performance Comparison and Benchmark Setting: Compare the calculated performance indicators with manufacturer-declared values, industry standard requirements, and the performance of similar products. Establish reasonable performance benchmarks, clarifying which indicators meet the standards and which require further attention or improvement.

[0184] Failure Mode and Effects Analysis (FMEA): Based on problems and anomalies discovered during testing, Failure Mode and Effects Analysis is performed. Potential causes that may lead to transformer failure are identified, their impact and probability of occurrence are assessed, and corresponding risk mitigation measures are proposed.

[0185] Comprehensive Assessment Writing: Based on the above analysis, write a comprehensive assessment report. The report should include an introduction, test overview, thermal performance assessment, electrical performance assessment, reliability evaluation, failure analysis and recommendations, and conclusions and recommendations. Ensure the report content is objective and clear, and that charts and data effectively support the analytical conclusions.

[0186] Review and Feedback Cycle: After the initial draft is completed, a cross-departmental review meeting is organized, inviting members from the design, production, quality control, and maintenance teams to participate. Feedback is collected, and the report is revised as necessary to ensure a comprehensive and thorough evaluation. The final draft is then submitted to management for approval and archived for future reference and to track improvements.

[0187] Through the above steps, a comprehensive and objective evaluation report on current transformers can be systematically generated, providing solid data support for product improvement, quality control, and subsequent research and development.

[0188] In summary, this invention achieves comprehensive and high-precision monitoring of the internal and external temperatures of current transformers through multi-dimensional temperature monitoring and thermal field analysis. By integrating temperature sensors and thermocouples with infrared thermal imaging technology, it promptly identifies and locates hotspots, providing accurate data support for thermal management. Through phased data acquisition and comparative analysis, it not only evaluates the electrical performance stability of the current transformer under different temperature conditions but also captures the dynamic response characteristics of the transformer through transient response testing, ensuring its reliable operation under various conditions. This detection method, through highly integrated monitoring technology, intelligent thermal management strategies, and a comprehensive performance evaluation system, significantly improves the thermal management and performance monitoring level of low-voltage current transformers in complex operating environments, providing strong technical support for ensuring the safe and efficient operation of power systems.

[0189] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A detection method for a high-efficiency heat dissipation low-voltage current transformer, characterized in that, Includes the following steps: S1: A low-voltage current transformer is provided, wherein the transformer has at least one coil for sensing current and is equipped with a temperature sensor and multiple distributed thermocouples to monitor local and overall temperature changes. S2: Apply a preset low-voltage AC or DC current to the input terminal of the transformer under test to activate the built-in temperature sensor and thermocouple, start real-time monitoring of the temperature rise of the transformer, and record the initial temperature data. S3: Using infrared thermal imaging, a non-contact temperature scan is performed on the outside of the current transformer to form an initial thermal field distribution map. Then, the output voltage signal of the current transformer is acquired through the data acquisition module as the induction feedback of the primary current. S4: Implement the first stage of data processing, convert the acquired voltage signal into the corresponding current value, and evaluate the linearity and accuracy of the current transformer by comparing the converted current value with the expected applied current. S5: Enter the continuous heating stage, gradually increase the input current to near but not exceeding the rated current value of the transformer, continuously monitor temperature changes during this period, analyze thermocouple and temperature sensor data, and identify hot spots inside and on the surface of the transformer. S6: Activate the thermal management system, dynamically adjust the built-in micro fan or liquid cooling circulation system based on the hot spot analysis results, and use machine learning algorithms to predict the temperature rise trend of the current transformer in future working conditions by combining historical data. S7: After the current transformer reaches the predetermined stable operating temperature, the second stage of data acquisition is carried out to record the current and voltage characteristic curves at this time. The data of the first stage and the second stage are compared to evaluate the performance stability of the current transformer under different temperature conditions. S8: Implement transient response testing by suddenly changing the input current magnitude, recording the transformer response time and recovery process, analyzing the response time data, optimizing the data acquisition frequency to ensure that all key transient changes are captured, and using advanced data analysis algorithms to automatically identify potential fault signs from the data. S9: Based on all test results, generate a comprehensive evaluation report, including thermal performance, electrical performance and reliability evaluation of the instrument transformer.

2. The detection method for a high-efficiency heat dissipation low-voltage current transformer according to claim 1, characterized in that, In S1, the current transformer also includes: a circuit board, on which signal conditioning circuitry for temperature sensors and thermocouples is integrated, and a circuit and sensor interface.

3. The detection method for a high-efficiency heat dissipation low-voltage current transformer according to claim 1, characterized in that, In S2, a preset low-voltage AC or DC current is applied to the input terminal of the transformer under test, including: According to the test requirements of the instrument transformer, select the appropriate current source equipment and set the preset low-voltage AC or DC current value. The current value is between the lower limit and the middle limit of the rated operating range of the instrument transformer. Connect the current source correctly to the input terminal of the transformer. At the same time, check the connection lines of all temperature sensors and thermocouples. Activate the built-in temperature sensor and thermocouple of the current transformer, start data acquisition, apply current and record initial data; Verify the validity of the initial temperature data. If any abnormal data is found, immediately check whether the sensor is working properly or recalibrate it.

4. The detection method for a high-efficiency heat dissipation low-voltage current transformer according to claim 1, characterized in that, In S3, infrared thermal imaging is used to perform a non-contact temperature scan of the outside of the current transformer, forming an initial thermal field distribution map, including: Based on the characteristics of the current transformer, set appropriate temperature measurement range, emissivity value and image quality parameters on the thermal imager, place the infrared thermal imager in a safe and unobstructed position, and adjust the distance between the thermal imager and the current transformer. The infrared thermal imager is activated to perform a full-range non-contact temperature scan. The raw thermal image obtained from the scan is then converted into a color thermal image.

5. The detection method for a high-efficiency heat dissipation low-voltage current transformer according to claim 1, characterized in that, In S4, the first stage of data processing includes: Collect the output voltage signal data of the current transformer under different input currents, process the raw voltage signal data, and remove any obvious outliers or noise interference. The collected voltage signal is converted into the corresponding current value, and the formula is: I_measured = V_output / K, where K is the turns ratio; The converted actual current value is compared one-to-one with the expected applied current value, and the deviation between the two is calculated. The deviation value is I_expected - I_actual. Plot a scatter plot of the expected and measured current values, fit a straight line to represent the ideal linear relationship, evaluate the linearity by calculating the coefficient of determination (the closer the coefficient of determination is to 1, the better the linearity), and evaluate the accuracy of the transformer by calculating the average and maximum deviations of all measurement points. Results summary and reporting: Based on the evaluation results of linearity and accuracy, an analysis report is written.

6. The detection method for a high-efficiency heat dissipation low-voltage current transformer according to claim 1, characterized in that, In S5, hotspot areas inside and on the surface of the current transformer are identified, including: Before applying current, the ambient temperature and initial surface temperature of the transformer are recorded using thermocouples and temperature sensors as reference data. Start increasing the input current gradually from zero, following the principle of small increments and slow speeds each time. Throughout the heating process, thermocouples are continuously used to monitor the internal temperature of the transformer, while temperature sensors are used to monitor the surface temperature. The system analyzes the collected data in real time, compares the temperature distribution at different time points, assesses the temperature rise trend and potential overheating risk, and marks abnormally high temperature areas.

7. The detection method for a high-efficiency heat dissipation low-voltage current transformer according to claim 1, characterized in that, In S6, machine learning algorithms are used to predict the temperature rise trend of the current transformer under future operating conditions, based on historical data, including: Data collection and preprocessing: Collect real-time data on the current operating temperature, ambient temperature, and load status of the current transformer from the sensors, and integrate historical temperature records; Hotspot analysis and location: Infrared imaging is used to analyze hotspots on the surface of the equipment, identify the high-heat areas of the current transformer, and record the location and temperature data of these hotspots; Establish a machine learning model: Based on historical temperature data, load conditions, and environmental factors, train a model to predict the future temperature rise trend of the transformer.

8. The detection method for a high-efficiency heat dissipation low-voltage current transformer according to claim 1, characterized in that, In S7, after the current transformer reaches the predetermined stable operating temperature, the second stage of data acquisition is performed, including: Set the corresponding data acquisition parameters according to the test requirements of the second stage, and perform the second stage current and voltage characteristic curve measurement; The voltage value is recorded in real time for each current change, forming the current and voltage characteristic curve data for the second stage; The current and voltage characteristic curves obtained in the second stage are compared with those in the first stage to analyze the differences between the two and evaluate the performance stability of the transformer under different temperature conditions.

9. The detection method for a high-efficiency heat dissipation low-voltage current transformer according to claim 1, characterized in that, In S8, transient response testing is performed, including: Set up a transient response test plan, including the magnitude of the current change, the duration of the change, and the time schedule for the current to return to the original level; Adjust the sampling rate of the data acquisition system based on the expected characteristics of the transient response; After collecting the data, analyze the response time data to identify the transient response characteristics of the current transformer, including response speed, overshoot, and number of oscillations, and use charts to illustrate the behavior.

10. The detection method for a high-efficiency heat dissipation low-voltage current transformer according to claim 1, characterized in that, In S9, a comprehensive evaluation report is generated, including: Data collection and organization: Collect data from all testing phases, standardize the data format, and integrate it into a single spreadsheet; Based on industry standards and testing specifications, calculate various key performance indicators and compare the calculated performance indicators with the manufacturer's declared values, industry standard requirements, and the performance of similar products. Based on the problems and abnormal data found in the test, failure mode and effect analysis is carried out to identify potential causes that may lead to transformer failure, assess their impact and probability of occurrence, propose corresponding risk mitigation measures, and generate a comprehensive assessment report.

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