Multi-voltage output precision control method and system of multi-winding isolation transformer
By configuring independent sampling channels and load prediction models for multi-winding isolation transformers, combined with hierarchical control and voltage compensation, the problem of transformer voltage output accuracy under complex working conditions is solved, and high-precision and high-reliability power supply control is achieved.
Patent Information
- Application Number
- CN202510734979.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-04
- Publication Date
- 2025-09-05
AI Technical Summary
Traditional multi-winding isolation transformers have low voltage output accuracy under load changes and ambient temperature fluctuations, and electromagnetic coupling between multiple windings causes output errors, making it difficult to meet the needs of high-precision and high-dynamic complex experiments.
An independent sampling channel is configured for each winding, and coarse and fine adjustments are performed using the load prediction model. The voltage compensation amount is calculated based on temperature fluctuations and coupled interference, and high-precision voltage output is achieved through hierarchical control.
The power output accuracy and reliability of multi-winding transformers under complex working conditions are improved, ineffective switching losses are reduced, and the safety and stability of the equipment are improved.
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Figure CN120601781A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of transformer precision control, and in particular to a method and system for controlling the precision of multi-voltage outputs of a multi-winding isolation transformer. Background Art
[0002] With the increasing sophistication of industrial equipment and the complexity of power electronics systems, experimental multi-voltage, multi-winding dry-type isolation transformers are widely used in research laboratories, precision instrument power supplies, and medical device testing. They require multiple independent and stable voltage outputs for different loads. Traditional voltage control methods rely primarily on fixed-threshold PID feedback regulation or manual tap switching, passively correcting the output voltage through a single closed-loop circuit. While these methods can achieve basic voltage regulation, their response speed, anti-interference capabilities, and multi-objective collaborative control performance cannot meet the needs of complex, high-precision, and dynamic experiments. Furthermore, the different voltage outputs of multi-winding isolation transformers are typically provided by multiple secondary windings. Because these windings often share a common core or are arranged in close proximity, electromagnetic coupling between the windings can occur. Sudden load changes or ambient temperature fluctuations can affect the voltage accuracy between different windings. In particular, changes in the load on one winding can affect the output voltages of other windings through coupling effects, leading to errors in the accuracy of the multiple voltage outputs. Therefore, a method for controlling the accuracy of the multiple voltage outputs of multi-winding isolation transformers is urgently needed. Summary of the Invention
[0003] The present invention provides a method and system for controlling the precision of multi-voltage outputs of a multi-winding isolation transformer, which can effectively solve the problems in the background technology.
[0004] In order to achieve the above object, the technical solution adopted by the present invention is: A method for controlling the precision of multi-voltage outputs of a multi-winding isolation transformer, the method comprising: Configure an independent sampling channel for each winding of the transformer to collect the running status data of the winding; Inputting the operating status data into a load prediction model to obtain a load prediction result; Setting a coarse adjustment trigger condition to coarsely adjust the output voltage according to the load prediction result; Calculating a voltage compensation amount based on temperature fluctuations and coupling interference, and adding the voltage compensation amount to a target voltage to obtain a corrected target voltage; The output voltage after coarse adjustment is fine-adjusted according to the corrected target voltage.
[0005] Furthermore, the output voltage is controlled in stages, a coarse adjustment loop and a fine adjustment loop are set in the transformer, and a dead time is set in the control algorithm.
[0006] Furthermore, configuring an independent sampling channel for each winding of the transformer includes: Designing an architecture for independent sampling channels, and adopting isolation measures to electrically isolate the independent sampling channels; Establish a synchronous sampling mechanism to synchronously sample the running status data of the windings; A data calibration system is configured for the independent sampling channel to verify the collected operating status data.
[0007] Furthermore, building a load forecasting model includes: Collect historical operating status data and historical load data of the winding and build a historical data set; Select the TCN-Transformer hybrid timing model as the load prediction model and design a hierarchical architecture of the hybrid timing model; The hybrid time series model is trained and optimized using the historical data set to obtain the load prediction model.
[0008] Furthermore, the hybrid time series model is trained using the historical data set, including: Freeze all layers except the TCN layer in the hybrid time series model, and perform basic training on the TCN layer using the historical dataset; Unfreeze the Transformer layer and feature fusion layer, perform data expansion on historical data, and use the expanded data for joint fine-tuning; The hybrid time series model is completely unfrozen, adversarial samples are generated according to the historical data set, and adversarial training is performed on the hybrid time series model.
[0009] Furthermore, the setting of the coarse adjustment trigger condition includes setting a dynamic threshold using fuzzy logic, forming a trigger condition by the load prediction change rate and the predicted load rate, and setting a minimum action time interval.
[0010] Furthermore, based on the temperature fluctuation and coupling interference, the voltage compensation amount is calculated including: ; in, To correct the target voltage, is the original target voltage, For temperature compensation, For coupling compensation.
[0011] Furthermore, a safety margin is established and a voltage compensation value limit is set.
[0012] A multi-voltage output precision control system for a multi-winding isolation transformer, the system comprising: Data acquisition module: configures an independent sampling channel for each winding of the transformer to collect the running status data of the winding; Load prediction module: inputs the operating status data into the load prediction model to obtain a load prediction result; Voltage coarse adjustment module: sets coarse adjustment trigger conditions and performs coarse adjustment on the output voltage according to the load prediction result; Voltage correction module: calculates voltage compensation based on temperature fluctuation and coupling interference, and adds the voltage compensation to the target voltage to obtain a corrected target voltage; Voltage fine adjustment module: fine-adjusts the output voltage after coarse adjustment according to the corrected target voltage.
[0013] Furthermore, the data acquisition module includes: Channel architecture design unit: designs the architecture of independent sampling channels and uses isolation measures to electrically isolate the independent sampling channels; Sampling mechanism establishment unit: establishes a synchronous sampling mechanism to synchronously sample the running status data of the winding; Calibration system configuration unit: configures a data calibration system for the independent sampling channel to verify the collected operating status data.
[0014] The technical solution of the present invention can achieve the following technical effects: It effectively solves the control bottleneck of power output accuracy of multi-winding transformers under complex working conditions such as cross interference and temperature fluctuations, and realizes high-precision and high-reliability control of multi-winding transformers. At the same time, it reduces invalid switching losses through load prediction and hierarchical coordination mechanism, significantly improving equipment safety and long-term stability in complex experimental scenarios, and providing reliable protection for precision scientific research testing and high-demand industrial applications.
[0015] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS
[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.
[0017] Figure 1 Schematic diagram of a flow chart of a method for controlling the precision of multi-voltage outputs of a multi-winding isolation transformer; Figure 2 This is a flowchart for configuring independent sampling channels; Figure 3 The diagram is a structural diagram of a multi-voltage output precision control system for a multi-winding isolation transformer. DETAILED DESCRIPTION
[0018] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.
[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this invention pertains. The terms used in this specification are for the purpose of describing specific embodiments only and are not intended to limit the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.
[0020] Example 1: like Figure 1 As shown, a method for controlling the precision of multi-voltage outputs of a multi-winding isolation transformer includes: S1: Configure an independent sampling channel for each winding of the transformer to collect the running status data of the winding; Specifically, to avoid interference or sampling lag introduced by multiple windings sharing a channel and ensure the independence and sampling accuracy of each winding's data, an independent sampling channel can be configured for each winding of the transformer to enable real-time monitoring of each winding and obtain key parameters such as temperature, current, voltage, and switching signals, ensuring the accuracy and timeliness of data acquisition. It is important to note that independent sampling of multiple windings can avoid measurement errors caused by cross-interference and is particularly suitable for isolation transformers with phase coupling. However, attention should be paid to sampling synchronization issues, and a timestamp alignment mechanism can be added to ensure data timing consistency.
[0021] S2: Input the operating status data into the load prediction model to obtain the load prediction result; When building a load prediction model, you can choose learning models such as long short-term memory networks, recurrent neural networks, and gated recurrent units. Lightweight models are preferred to ensure real-time predictions. This step predicts future load changes, allowing for rough adjustments before they occur. This ensures that the output voltage does not deviate significantly due to sudden load changes, thereby improving overall system stability and accuracy.
[0022] S3: Set the coarse adjustment trigger condition and make coarse adjustment to the output voltage according to the load prediction result; S4: Calculate the voltage compensation amount based on the temperature fluctuation and coupling interference, and add the voltage compensation amount to the target voltage to obtain the corrected target voltage; Specifically, temperature changes can affect resistance, insulation material properties, and other factors, leading to voltage deviations. The physical proximity of multiple windings can generate electromagnetic coupling interference, which can cause load changes in one winding to affect other windings. Temperature fluctuations and coupling interference are important factors affecting the accuracy of multi-voltage outputs. Directly measuring or predicting them and then compensating and superimposing them on the target voltage can significantly reduce errors caused by the external environment and mutual coupling effects, ensuring that the output is closer to the expected value.
[0023] S5: Fine-adjust the coarsely adjusted output voltage according to the corrected target voltage.
[0024] Coarse tuning can quickly respond to load changes and quickly adjust the output voltage close to the target value, laying the foundation for subsequent fine tuning. The trigger condition in this step is set to initiate the coarse tuning process only when the load forecast indicates a significant change, avoiding frequent adjustments during small fluctuations. After coarse tuning, fine tuning can eliminate any remaining minor errors. Based on real-time feedback data and a sophisticated control algorithm, closed-loop control of the fine tuning process is achieved. In a hierarchical control system, using load forecasting in the coarse tuning loop and voltage compensation in the fine tuning loop is an effective method for optimizing dynamic response and efficiency. Based on real-time signals, the coarse tuning loop predicts short-term load demand and, by anticipating future load changes, adjusts the transformer taps or power supply topology in advance to bring the system operating point close to the target voltage. The fine tuning loop, based on the coarse tuning, fine-tunes the voltage using a closed-loop algorithm to ensure output voltage accuracy. Coarse tuning achieves rapid response through load forecasting, while fine tuning provides micro-compensation for temperature drift and electromagnetic coupling. This balances dynamic response with steady-state accuracy, improving the overall control efficiency of multi-winding systems.
[0025] This invention establishes an independent sampling channel for each winding, accurately collecting real-time operating data from each winding. Using a load prediction model, it predicts future load changes. Based on the predictions, it performs coarse adjustments when certain trigger conditions are met, proactively addressing large load fluctuations. Furthermore, it calculates the corresponding voltage compensation based on actual temperature fluctuations and potential coupling interference between windings, allowing for fine adjustments to the already coarsely adjusted output, achieving even higher output accuracy.
[0026] The present invention effectively solves the control bottleneck of power supply output accuracy of multi-winding transformers under complex working conditions such as cross-interference and temperature fluctuations, realizes high-precision and high-reliability control of multi-winding transformers, and reduces invalid switching losses through load prediction and hierarchical coordination mechanism, significantly improving equipment safety and long-term stability in complex experimental scenarios, and providing reliable protection for precision scientific research testing and high-demand industrial applications.
[0027] In order to achieve the control of the regulation accuracy of multiple output voltages, the output voltages are controlled in stages, a coarse adjustment loop and a fine adjustment loop are set in the transformer, and a dead time is set in the control algorithm.
[0028] Specifically, in order to solve the problems of large-scale rapid adjustment and fine voltage regulation, a coarse adjustment loop and a fine adjustment loop can be set in the transformer. The coarse adjustment loop and the fine adjustment loop are each responsible for different control tasks. The two work in coordination to enable the output voltage to quickly approach the target while achieving high-precision steady-state control. The dead time is set to smooth the switching between the two, thereby avoiding system instability caused by too frequent adjustments.
[0029] As a preferred embodiment of this invention, Figure 2 As shown in Figure 1, an independent sampling channel is configured for each winding of the transformer, including: S11: Design the architecture of independent sampling channels and use isolation measures to electrically isolate the independent sampling channels; In this embodiment, a separate sampling signal chain is designed for each winding, including a sensor, signal conditioning circuit, and ADC subsystem. Each channel uses a separate PCB trace to minimize cross-interference between adjacent channels. Isolation amplifiers, optocouplers, or digital isolators are used in each sampling path to ensure that each sampling circuit does not interfere with each other electrically. At the same time, good shielding (such as a metal shield) and grounding method (such as single-point grounding) are designed to reduce common-mode interference.
[0030] S12: Establish a synchronous sampling mechanism to synchronously sample the running status data of the winding; In a multi-channel system, only by ensuring that all data channels are collected at the same time can the operating status of each winding be truly reflected. Especially when load coupling exists in the system, synchronous sampling is necessary to accurately determine the mutual influence between channels. Therefore, a synchronous sampling mechanism can be established using methods such as synchronous trigger circuits and clock distribution. This facilitates multi-channel joint correction, error comparison, and composite compensation, which in turn facilitates the subsequent development of load prediction or compensation algorithms.
[0031] S13: Configure a data calibration system for the independent sampling channels to verify the collected operating status data.
[0032] Specifically, data calibration can eliminate systematic errors caused by the system and environment, making the collected data closer to the true value, thereby improving the accuracy of the entire voltage control system; and a sampling channel redundancy verification mechanism can be designed. When a channel fails, it will automatically switch to the adjacent winding sampling value for estimation, and trigger a graded alarm at the same time to avoid single-point failures causing system crashes.
[0033] Furthermore, building a load forecasting model includes: S21: Collect historical operating status data and historical load data of the winding to build a historical data set; S22: Select the TCN-Transformer hybrid timing model as the load prediction model and design a layered architecture for the hybrid timing model. S23: Use the historical data set to train and optimize the hybrid time series model to obtain a load prediction model.
[0034] Specifically, load data often contains significant local periodic fluctuations as well as long-term trends or mutations. TCN excels at extracting local features, while Transformer can capture global temporal dependencies. The combination of the two can more comprehensively describe data characteristics and improve prediction accuracy. Therefore, the TCN-Transformer hybrid time series model combines the advantages of both models to more comprehensively model the various time series characteristics in load and operating status data, thereby obtaining more accurate load prediction results. After selecting the TCN-Transformer hybrid time series model as the load prediction model, the architecture of the hybrid time series model can be designed, including the input layer, TCN feature extraction layer, Transformer global modeling layer, feature fusion layer, and output layer. This can fully leverage the advantages of both models and ultimately achieve accurate load prediction.
[0035] Based on the above embodiment, the hybrid time series model is trained using a historical data set, including: S231: Freeze all layers except the TCN layer in the hybrid time series model and perform basic training on the TCN layer using the historical dataset. For model training, in the early stages of the model, if the TCN layer and the Transformer layer are trained simultaneously, the gradients of the two may interfere with each other, resulting in slow or unstable convergence. It is possible to focus on the training of the TCN layer first, so that the TCN layer can learn the local timing pattern of the load, initially establish trend perception capabilities, and avoid early interference from complex modules; in the initial stage, only adjusting the TCN layer parameters can reduce the dimension of parameter updates, reduce interference during training, and facilitate faster model convergence.
[0036] S232: Unfreeze the Transformer layer and feature fusion layer, perform data expansion on historical data, and use the expanded data for joint fine-tuning; Based on the trained TCN layer, after unfreezing the Transformer layer and the feature fusion layer, joint fine-tuning allows the model to simultaneously optimize local features (TCN) and global dependencies (Transformer), achieving better synergy between features. TCN's local features (such as short-term trends) may conflict with Transformer's global features (such as long-term dependencies). Data expansion can introduce diversity, prompting the fusion layer (such as the gating mechanism) to dynamically adjust the weight allocation strategy.
[0037] S233: Completely unfreeze the hybrid time series model, generate adversarial samples based on historical data sets, and perform adversarial training on the hybrid time series model.
[0038] Specifically, the purpose of adversarial training is to allow the model to globally adjust parameter distribution under adversarial perturbations and enhance robustness to input noise and non-steady-state features. During the adversarial training phase, all trainable parameters of the model are in an updateable state. If some layers are frozen, the perturbation generation will not be able to reflect the sensitive areas of the complete model, reducing the effect of adversarial training.
[0039] Furthermore, setting the coarse adjustment trigger condition includes setting a dynamic threshold using fuzzy logic, forming a trigger condition by the load prediction change rate and the predicted load rate, and setting a minimum action time interval.
[0040] In this embodiment, fuzzy logic is used to set a dynamic threshold. The power change rate and predicted load rate are fuzzified through a membership function. A trigger intensity coefficient is generated according to a preset rule base. When the coefficient exceeds the dynamically adjusted threshold, coarse adjustment is triggered. This step dynamically sets the threshold parameters through a fuzzy logic controller. The input variables of the fuzzy logic controller include the load prediction change rate and the predicted load rate. When the load prediction change rate exceeds the current dynamic threshold and the predicted load rate is greater than a set percentage, the trigger condition is determined to be met. A minimum gear switching interval of no less than 1 second is set to suppress false triggering caused by transient disturbances. The setting of the coarse adjustment trigger condition ensures that the system only initiates coarse adjustment when a significant load change is detected, avoiding frequent adjustments. At the same time, it ensures that large-scale adjustments are made first, followed by detailed corrections when necessary.
[0041] Furthermore, based on the temperature fluctuation and coupling interference, the voltage compensation amount is calculated including: ; in, To correct the target voltage, is the original target voltage, For temperature compensation, For coupling compensation.
[0042] Specifically, temperature compensation can be achieved by placing distributed temperature sensors at key heat-generating points (such as winding ends and core joints) to construct a three-dimensional thermal field model. The compensation algorithm needs to take into account the thermal inertia effect and introduce an advance compensation strategy to avoid the phase lag caused by pure feedback control. For coupling interference, a mutual inductance matrix between windings should be established, and the coupling interference should be quantified through real-time flux observation. Finite element simulation can be used to obtain initial parameters, and then dynamic corrections can be made through online parameter identification to improve the calculation accuracy of the compensation amount.
[0043] On the basis of the above embodiments, a safety margin is established and a voltage compensation value limit is set.
[0044] Specifically, in voltage compensation control based on temperature fluctuations and coupling interference, the dynamic adjustment of the compensation amount may lead to overcompensation or undercompensation due to model errors, sensor noise or extreme working conditions. A safety boundary can be established to set the voltage compensation value limit to be less than or equal to 5% of the original target voltage to prevent oscillation caused by overcompensation.
[0045] Example 2: like Figure 3 As shown, a multi-voltage output precision control system for a multi-winding isolation transformer includes: Data acquisition module: configures an independent sampling channel for each winding of the transformer to collect the running status data of the winding; Load prediction module: inputs the operating status data into the load prediction model to obtain the load prediction results; Voltage coarse adjustment module: sets the coarse adjustment trigger conditions and performs coarse adjustment on the output voltage according to the load prediction results; Voltage correction module: Calculates voltage compensation based on temperature fluctuations and coupling interference, adds the voltage compensation to the target voltage, and obtains the corrected target voltage. Voltage fine-tuning module: fine-tunes the output voltage after coarse adjustment according to the corrected target voltage.
[0046] The above-mentioned adjustment system in the present invention can effectively implement the multi-voltage output precision control method of the multi-winding isolation transformer. The technical effects that can be achieved are as described in the above-mentioned embodiments and will not be repeated here.
[0047] Furthermore, the data acquisition module includes: Channel architecture design unit: Design the architecture of independent sampling channels and use isolation measures to electrically isolate independent sampling channels; Sampling mechanism establishment unit: establishes a synchronous sampling mechanism to synchronously sample the running status data of the winding; Calibration system configuration unit: configures the data calibration system for independent sampling channels to verify the collected operating status data.
[0048] Similarly, the above-mentioned optimization schemes for the system can also respectively achieve the corresponding optimization effects of the method in Example 1, which will not be repeated here.
[0049] Although the present application has been described with reference to specific features and embodiments thereof, it is apparent that various modifications and combinations may be made thereto without departing from the spirit and scope of the present application. Accordingly, this specification and drawings are merely illustrative of the present application as defined herein and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. It is apparent that various modifications and variations of the present application may be made by those skilled in the art without departing from the scope of the present application. Thus, the present application is intended to include such modifications and variations as fall within the scope of the present application and its equivalents.
Claims
1. A method for controlling the multi-voltage output accuracy of a multi-winding isolation transformer, characterized in that: The method comprises: Configure an independent sampling channel for each winding of the transformer to collect the running status data of the winding; Inputting the operating status data into a load prediction model to obtain a load prediction result; Setting a coarse adjustment trigger condition to coarsely adjust the output voltage according to the load prediction result; Calculating a voltage compensation amount based on temperature fluctuations and coupling interference, and adding the voltage compensation amount to a target voltage to obtain a corrected target voltage; The output voltage after coarse adjustment is fine-adjusted according to the corrected target voltage.
2. The method for controlling the multi-voltage output accuracy of a winding isolation transformer according to claim 1, characterized in that: The output voltage is controlled in stages, a coarse adjustment loop and a fine adjustment loop are set in the transformer, and a dead time is set in the control algorithm.
3. The method for controlling the multi-voltage output accuracy of a winding isolation transformer according to claim 1, characterized in that: Configuring an independent sampling channel for each winding of the transformer includes: Designing an architecture for independent sampling channels, and adopting isolation measures to electrically isolate the independent sampling channels; Establish a synchronous sampling mechanism to synchronously sample the running status data of the windings; A data calibration system is configured for the independent sampling channel to verify the collected operating status data.
4. The method for controlling the multi-voltage output accuracy of a winding isolation transformer according to claim 1, characterized in that: Building a load forecasting model includes: Collect historical operating status data and historical load data of the winding and build a historical data set; Select the TCN-Transformer hybrid timing model as the load prediction model and design a hierarchical architecture of the hybrid timing model; The hybrid time series model is trained and optimized using the historical data set to obtain the load prediction model.
5. The method for controlling the multi-voltage output accuracy of a winding isolation transformer according to claim 4, characterized in that: Training the hybrid time series model using the historical data set includes: Freeze all layers except the TCN layer in the hybrid time series model, and perform basic training on the TCN layer using the historical dataset; Unfreeze the Transformer layer and feature fusion layer, perform data expansion on historical data, and use the expanded data for joint fine-tuning; The hybrid time series model is completely unfrozen, adversarial samples are generated according to the historical data set, and adversarial training is performed on the hybrid time series model.
6. The method for controlling the multi-voltage output accuracy of a winding isolation transformer according to claim 1, characterized in that: The setting of the coarse adjustment triggering condition includes setting a dynamic threshold using fuzzy logic, forming a triggering condition by the load prediction change rate and the predicted load rate, and setting a minimum action time interval.
7. The method for controlling the multi-voltage output accuracy of a winding isolation transformer according to claim 1, characterized in that: Based on temperature fluctuations and coupling interference, the voltage compensation is calculated including: ； in, To correct the target voltage, is the original target voltage, For temperature compensation, For coupling compensation.
8. The method for controlling the multi-voltage output accuracy of a winding isolation transformer according to claim 7, characterized in that: Establish safety margins and set voltage compensation value limits.
9. A multi-voltage output precision control system for a multi-winding isolation transformer, characterized in that: The system comprises: Data acquisition module: configures an independent sampling channel for each winding of the transformer to collect the running status data of the winding; Load prediction module: inputs the operating status data into the load prediction model to obtain a load prediction result; Voltage coarse adjustment module: sets coarse adjustment trigger conditions and performs coarse adjustment on the output voltage according to the load prediction result; Voltage correction module: calculates voltage compensation based on temperature fluctuation and coupling interference, and adds the voltage compensation to the target voltage to obtain a corrected target voltage; Voltage fine adjustment module: fine-adjusts the output voltage after coarse adjustment according to the corrected target voltage.
10. The multi-voltage output precision control system of a multi-winding isolation transformer according to claim 9, characterized in that: The data acquisition module includes: Channel architecture design unit: designs the architecture of independent sampling channels and uses isolation measures to electrically isolate the independent sampling channels; Sampling mechanism establishment unit: establishes a synchronous sampling mechanism to synchronously sample the running status data of the winding; Calibration system configuration unit: configures a data calibration system for the independent sampling channel to verify the collected operating status data.