A method and system for adaptive switching of current transformer

Abstract

The present invention relates to the field of equipment control technology, and in particular to an adaptive switching method and system for a current transformer. The method comprises the following steps: collecting primary side parameters and secondary side parameters of a current transformer to obtain primary side collection parameters and secondary side collection parameters of the current transformer; initializing the current transformer state segmentation according to the primary side collection parameters and the secondary side collection parameters of the current transformer, thereby generating a classification result of the current transformer operation state; judging the switching result of the current transformer according to the classification result of the current transformer operation state, and when the classification result of the current transformer operation state is a critical state segment of the current transformer, parallel buffering is performed on the current transformer to generate critical current transformer switching data. The present invention improves the sensitivity and stability of current transformer switching through precise state classification, dynamic switching mechanism, timing optimization and visual display.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment control, and in particular to an adaptive switching method and system for a current transformer. Background Art

[0002] The core idea of ​​the adaptive switching method is to dynamically adjust the working mode or measurement range of the current transformer according to the changing characteristics of the current to achieve higher measurement accuracy and faster response speed. This method usually includes real-time monitoring and analysis of the current and automatic switching of the range or working mode based on a specific algorithm. The development of this technology originated from the need to optimize the performance of traditional current transformers. Early research focused on improving the signal processing and range extension technology of current transformers, and gradually achieved accurate tracking and dynamic adaptation of current changes. With the introduction of digital and intelligent technologies, adaptive switching methods have gradually received widespread attention. The adaptive switching of modern current transformers not only relies on hardware improvements, but also incorporates complex algorithms and artificial intelligence technologies, and further improves its adaptive ability in a variable current environment through machine learning and other means. However, at present, when the traditional current transformer is in a critical or abnormal state, there is usually no effective switching mechanism to ensure its normal operation. At the same time, traditional technologies often lack timing optimization of the current transformer switching process, resulting in delays or shocks during the switching process, which leads to low sensitivity and stability of the switching. Summary of the invention

[0003] Based on this, it is necessary to provide an adaptive switching method and system for a current transformer to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for adaptive switching of a current transformer is provided, the method comprising the following steps:

[0005] Step S1: collecting primary side parameters and secondary side parameters of the current transformer to obtain primary side collection parameters and secondary side collection parameters of the current transformer; initializing the current transformer state segmentation according to the primary side collection parameters and secondary side collection parameters of the current transformer, thereby generating a classification result of the current transformer operation state;

[0006] Step S2: according to the classification result of the current transformer operation state, the switching result of the current transformer is judged. When the classification result of the current transformer operation state is the critical state segment of the current transformer, the corresponding current transformer is parallel buffered to generate critical current transformer switching data; when the classification result of the current transformer operation state is the abnormal state segment of the current transformer, the corresponding current transformer is dynamically switched to the abnormal state to generate abnormal current transformer switching data; based on the critical current transformer switching data and the abnormal current transformer switching data, the current transformer is switched and executed to generate current transformer switching execution data;

[0007] Step S3: performing switching timing analysis on the current transformer through the current transformer switching execution data to generate current transformer switching timing data; performing adaptive switching simulation on the current transformer according to the current transformer switching timing data to generate current transformer switching simulation data; performing adaptive switching timing control on the current transformer using the current transformer switching simulation data and the current transformer switching timing data to generate current transformer switching timing control data;

[0008] Step S4: Switching and displaying the current transformer switching execution data based on the current transformer switching timing control data to visualize the current transformer switching execution data, so as to perform the adaptive switching control operation of the current transformer.

[0009] The present invention can obtain the working state data of the current transformer in real time by collecting the primary and secondary side parameters of the current transformer, and provide accurate basic data for subsequent state judgment and switching decision. Through state segmentation initialization, the operating state of the current transformer can be accurately classified, effectively avoiding misjudgment in a single state, and ensuring intelligent response under different current environments. According to the state classification result of the current transformer, switching judgment can be performed, and parallel buffering can be adopted when the current transformer is in a critical state to avoid equipment damage and improve the safety of the system; in an abnormal state, dynamic abnormal switching is implemented to reduce the risk of fault propagation, thereby improving the reliability and stability of the current transformer. The generation of switching execution data ensures the correctness and effectiveness of the switching process in actual operation. By performing timing analysis on the switching execution data, the timing in the switching process can be optimized, making the switching operation smoother and avoiding current shock during switching. Adaptive switching simulation further improves the dynamic adaptability during switching, and optimizes the control strategy according to the simulation results to ensure adaptive adjustment under different current environments, minimizing equipment damage and system fluctuations. Visualizing the switching execution data can intuitively display the current transformer switching process, facilitate operators to monitor and adjust the switching strategy in real time, and improve the convenience and controllability of operation. Through visualization, operators can timely discover potential problems during the switching process and respond quickly, further enhancing the stability and flexibility of the system. Therefore, the present invention improves the sensitivity and stability of current transformer switching through precise state classification, dynamic switching mechanism, timing optimization and visual display.

[0010] Preferably, step S1 comprises the following steps:

[0011] Step S11: collecting primary side parameters of the current transformer in the intelligent manufacturing equipment to obtain primary side collection parameters of the current transformer, wherein the primary side parameter collection includes working current collection, temperature collection and saturation state collection;

[0012] Step S12: collecting secondary side parameters of the current transformer in the intelligent manufacturing equipment based on the primary side collection parameters of the current transformer to obtain the secondary side collection parameters of the current transformer, wherein the secondary side parameter collection includes voltage collection, current collection and load power factor collection;

[0013] Step S13: performing data preprocessing on the primary side acquisition parameters of the current transformer and the secondary side acquisition parameters of the current transformer to generate the primary side acquisition parameters of the standard current transformer and the secondary side acquisition parameters of the standard current transformer;

[0014] Step S14: Initialize the current transformer status in sections according to the primary side acquisition parameters of the standard current transformer and the secondary side acquisition parameters of the standard current transformer, thereby generating a classification result of the current transformer operation status.

[0015] The present invention provides comprehensive current transformer operating status information by covering multiple key parameters of the primary and secondary sides (such as operating current, temperature, saturation state, voltage, current and load power factor). The primary side acquisition guides the secondary side acquisition, effectively combining the information of the two, and improving the accuracy and relevance of data acquisition. The acquisition parameters are standardized to solve the problems of noise and abnormal values ​​in the original data, ensuring the reliability of subsequent state analysis. The acquired parameters are initialized and classified in state segments, providing basic data support for real-time monitoring and early warning, and facilitating more efficient operation of intelligent manufacturing equipment. By segmenting and classifying the parameters, the monitoring strategy can be dynamically adjusted according to different working conditions to adapt to complex and changeable industrial environments. Based on the state classification results, equipment maintenance personnel can quickly locate problematic components, realize predictive maintenance, and reduce equipment failure rate and downtime.

[0016] Preferably, step S14 comprises the following steps:

[0017] Step S141: setting a state segmentation rule according to the primary side acquisition parameters of the current transformer and the secondary side acquisition parameters of the current transformer to generate a current transformer state segmentation rule;

[0018] Step S142: performing a comparison analysis on the primary side acquisition parameters of the current transformer and the secondary side acquisition parameters of the current transformer one by one according to the current transformer state segmentation rule, and obtaining current transformer difference comparison analysis data;

[0019] Step S143: classifying the operating status of the current transformer difference comparison analysis data based on the preset difference comparison threshold value, when the current transformer difference comparison analysis data is greater than the preset difference comparison threshold value, generating a current transformer abnormal status segment; when the current transformer difference comparison analysis data is equal to the preset difference comparison threshold value, generating a current transformer critical status segment;

[0020] Step S144: when the current transformer difference comparison analysis data is less than the preset difference comparison threshold, a current transformer normal state segment is generated; the current transformer abnormal state segment, the current transformer critical state segment and the current transformer normal state segment are integrated to generate current transformer state segment result data;

[0021] Step S145: Initialize the current transformer state segmentation for the current transformer primary side acquisition parameters and the current transformer secondary side acquisition parameters according to the current transformer state segmentation result data, thereby generating a current transformer operation state classification result.

[0022] The present invention provides a flexible state segmentation rule setting mechanism, so that the state classification of the current transformer can be dynamically adjusted according to the actual operation characteristics of the equipment and industrial needs, thereby enhancing the adaptability. Through one-by-one difference comparison analysis, the correlation and difference between the primary and secondary side acquisition parameters are fully explored, providing an accurate data basis for state classification. The difference comparison threshold setting realizes the standardization of state classification. When the difference data is in different intervals, the equipment state (abnormal, critical or normal) can be quickly clarified, reducing the error of human judgment and improving the monitoring efficiency. The three states of abnormal, critical and normal are integrated into state segmentation result data to form a state assessment from a global perspective, providing comprehensive data support for further operation analysis. Through the state segmentation result data, the state initialization is completed intelligently, providing a reliable basis for the operation state classification of the current transformer, which is helpful to quickly locate potential problems or optimize the equipment operation parameters. The classification of abnormal state segments and critical state segments helps to timely discover equipment hidden dangers, provides a prerequisite for predictive maintenance, and reduces the failure rate and downtime cost. Based on the state classification results, equipment managers can formulate equipment operation strategies and maintenance plans more scientifically, thereby improving the overall management level and resource utilization efficiency of the equipment.

[0023] Preferably, step S2 comprises the following steps:

[0024] Step S21: the current transformer operation state classification result is used to determine the current transformer switching result. When the current transformer operation state classification result is the current transformer normal state segment, the corresponding current transformer is not switched.

[0025] Step S22: when the classification result of the current transformer operation state is the current transformer critical state segment, parallel buffering is performed on the corresponding current transformer to generate a current transformer critical parallel buffering strategy; based on the current transformer critical parallel buffering strategy, the corresponding current transformer is pre-switched to generate critical current transformer switching data;

[0026] Step S23: when the classification result of the current transformer operation state is the current transformer abnormal state segment, dynamically switching the corresponding current transformer to the abnormal state, and generating abnormal current transformer switching data;

[0027] Step S24: Switching the current transformer is performed based on the critical current transformer switching data and the abnormal current transformer switching data to generate current transformer switching execution data.

[0028] The present invention avoids unnecessary operations by directly performing non-switching processing according to the normal state segment, ensures that the equipment operates in the optimal way under normal circumstances, thereby reducing the operating risk and switching cost. The parallel buffer strategy provides additional protection for the current transformer in the critical state segment, so that it can still maintain operation when the performance is degraded but not completely abnormal, reducing the risk of sudden failure of the equipment. A dynamic abnormal state switching strategy is adopted for the abnormal state segment, which can quickly isolate and replace the faulty equipment, prevent the further spread of the abnormality, and improve the safety and stability of the system. The switching data of the critical state and the abnormal state are unified and integrated to form a complete switching execution data, provide clear guidance for the execution process, and reduce delays and misoperations in the switching process. Through clear state distinction and precise response strategy, the interference in the normal operating state is minimized to ensure the efficient use of the equipment and the continuity of the system. The switching strategy is dynamically adjusted based on the real-time classification results, can adapt to complex and changeable operating conditions, and improve the flexibility and responsiveness of the system. The critical buffer and abnormal switching strategies work together to enable the system to recover quickly when a failure occurs, reduce downtime, and improve the overall stability and reliability of the system. Based on the execution results of the state switching strategy, equipment managers can better evaluate and optimize resource allocation, which helps to achieve more efficient equipment operation and maintenance.

[0029] Preferably, step S22 includes the following steps:

[0030] Step S221: when the classification result of the current transformer operation state is the critical state section of the current transformer, impedance matching demand calculation is performed on the corresponding current transformer primary side acquisition parameters and current transformer secondary side acquisition parameters to obtain critical buffer demand evaluation data;

[0031] Step S222: selecting a buffer transformer from a preset backup transformer pool based on the critical buffer demand assessment data, and generating buffer transformer matching data; configuring the parallel parameters of the current transformer using the buffer transformer matching data and the critical buffer demand assessment data, and generating parallel buffer configuration data;

[0032] Step S223: performing virtual access simulation on the parallel buffer configuration data to generate parallel connection test data; adjusting the buffer connection of the parallel buffer configuration data through the parallel connection test data, thereby generating a critical parallel buffer strategy for the current transformer;

[0033] Step S224: pre-switching the corresponding current transformer and buffer transformer based on the current transformer critical parallel buffering strategy to generate critical current transformer switching data.

[0034] The present invention generates critical buffer demand assessment data by calculating the impedance matching requirements of the primary and secondary side parameters, ensures that the buffer strategy is based on accurate parameters, and improves the accuracy of the buffer configuration. By using the critical buffer demand assessment data, the best buffer transformer is selected from the backup transformer pool, matching data is generated and parallel parameters are configured, dynamic optimization matching is achieved, and resource utilization efficiency and system flexibility are improved. By simulating and generating parallel connection test data through virtual access, the parallel buffer configuration is verified and adjusted, avoiding failures or incompatibility problems caused by direct physical connection, and significantly improving the reliability of the solution. Pre-switching is quickly performed based on the critical parallel buffer strategy to ensure that the current transformer can smoothly transition to the buffer state under the critical state, effectively reducing fluctuations and risks in operation. The load pressure is effectively dispersed, the overload risk of a single current transformer caused by the critical state is reduced, and the stability of the overall operation of the equipment is improved. When the equipment enters the critical state, the evolution of the critical state to the abnormal state is effectively prevented through rapid buffer configuration and switching, and the overall failure rate of the system is reduced. The backup resources are dynamically called in combination with actual needs, and the optimal configuration of equipment resources is achieved, avoiding excessive redundancy or waste of resources. The automated generation and application of critical parallel buffering strategies reduces manual intervention, improves the intelligence level of equipment management, and provides an innovative solution for complex equipment management in smart manufacturing environments.

[0035] Preferably, step S224 includes the following steps:

[0036] Step S2241: collecting the current rated current of the corresponding current transformer and buffer transformer to obtain the first current transformer rated current and the first buffer transformer rated current; pre-planning the switching path of the corresponding current transformer and buffer transformer based on the critical parallel buffering strategy of the current transformer to generate a pre-planned switching path;

[0037] Step S2242: updating the data of the first current transformer rated current and the first buffer transformer rated current according to the pre-planned switching path to generate the second current transformer rated current and the second buffer transformer rated current;

[0038] Step S2243: When the current ratio of the rated current of the second current transformer to the rated current of the first current transformer is greater than 90% or the current ratio of the rated current of the second buffer transformer to the rated current of the first buffer transformer is greater than 95%, the corresponding current transformer and buffer transformer are switched at zero crossing points to generate critical current transformer switching data; otherwise, the pre-planned switching path is adjusted.

[0039] The present invention generates the first current transformer rated current and the first buffer transformer rated current by collecting the current equipment rated current of the corresponding current transformer and the buffer transformer, thereby ensuring that the switching scheme is based on the accurate equipment operation status and reducing the risk of misjudgment. The switching path pre-planning based on the critical parallel buffer strategy provides clear switching operation guidance and improves the efficiency and reliability of switching execution. By updating the rated current of the first current transformer and the first buffer transformer, the second current transformer and the second buffer transformer rated current are generated to ensure real-time reflection of the state change of the equipment before and after the switching. The real-time update mechanism provides efficient data support for subsequent judgment and execution, further improving the accuracy of the switching operation. The current ratio (second to first rated current) is introduced as the core basis for switching judgment to ensure that the conditions of the switching operation are reasonable and scientific: when the current ratio condition is met (>90% or>95%), zero crossing point switching is performed to ensure the stability and accuracy of the switching and avoid sudden current or fluctuations during switching. When the conditions are not met, the switching process is further optimized by adjusting the pre-planned switching path to avoid the risks caused by blind switching. The application of zero-crossing switching technology significantly reduces transient impacts during switching, protects equipment and prolongs the service life of transformers and buffer transformers. The combination of switching path pre-planning and dynamic adjustment makes the switching process highly flexible and adaptable to the needs of different operating environments. The real-time collection and update of rated current data significantly improves the real-time and accuracy of switching decisions, ensuring that the equipment can quickly adapt to changes in critical conditions.

[0040] Preferably, step S23 includes the following steps:

[0041] Step S231: when the classification result of the current transformer operation state is the current transformer abnormal state segment, outputting a transformer signal to the corresponding current transformer to generate a current transformer abnormal state segment output signal; performing signal waveform analysis on the current transformer abnormal state output signal to generate a current transformer abnormal state segment waveform;

[0042] Step S232: performing waveform strength analysis on the waveform of the abnormal state segment of the current transformer to generate the waveform strength of the abnormal state segment of the current transformer; performing light load state identification on the waveform of the abnormal state segment of the current transformer according to the waveform strength of the abnormal state segment of the current transformer to generate light load abnormal state data of the current transformer;

[0043] Step S233: calculating the waveform amplitude of the abnormal state segment waveform of the current transformer to obtain the waveform amplitude of the abnormal state segment of the current transformer; identifying the overload state of the abnormal state segment waveform of the current transformer according to the waveform amplitude of the abnormal state segment of the current transformer to generate the overload abnormal state data of the current transformer;

[0044] Step S234: performing waveform distortion detection on the waveform of the abnormal state segment of the current transformer to obtain waveform distortion data of the abnormal state segment of the current transformer; performing saturation state identification on the waveform of the abnormal state segment of the current transformer according to the waveform distortion data of the abnormal state segment of the current transformer to generate saturation abnormal state data of the current transformer;

[0045] Step S235: performing light load error compensation on the corresponding current transformer based on the abnormal state segment waveform of the current transformer, and generating a light load abnormality response strategy; performing current limiting protection on the corresponding current transformer based on the abnormal overload state data of the current transformer, and generating an overload abnormality response strategy; performing excitation current adjustment on the corresponding current transformer based on the abnormal saturation state data of the current transformer, and generating a saturation abnormality response strategy;

[0046] Step S236: dynamically switching the abnormal state of the corresponding current transformer using the light load abnormality coping strategy, the overload abnormality coping strategy and the saturation abnormality coping strategy to generate abnormal current transformer switching data.

[0047] The present invention generates signals and waveforms of abnormal state segments through signal output and waveform analysis, providing accurate basic data for subsequent analysis. Through multi-dimensional analysis of waveform intensity, amplitude and distortion, the three main abnormal states of light load, overload and saturation are identified respectively: the stable operation of the transformer under low load is guaranteed, and misjudgment or unnecessary switching is avoided. The abnormal situation of excessive load is discovered in time to effectively prevent the equipment from overheating or damage. The waveform distortion is analyzed to solve the measurement error problem caused by magnetic saturation. Specific response strategies are formulated for different abnormal states: the measurement accuracy of the transformer under light load is improved through compensation mechanism to avoid data deviation. The current size is effectively limited to prevent the transformer from being damaged or causing secondary equipment failure due to overload. The excitation current is adjusted to reduce the magnetic saturation effect and improve the signal output quality and stability of the transformer. By integrating the three abnormal response strategies of light load, overload and saturation, the transformer switching under abnormal state is dynamically realized: the efficiency and accuracy of the switching process are guaranteed, and the impact on the overall operation of the system under abnormal state is avoided. The operating state of the transformer is optimized through the dynamic switching mechanism to improve the safety and reliability of the system. The automated processing method based on waveform analysis and data calculation reduces manual intervention and realizes the rapid detection and processing of abnormal status of transformers. The combination of dynamic switching and targeted response strategies significantly improves the processing efficiency under abnormal conditions. Through the accurate identification and strategic response of light load, overload and saturation, further damage to transformers and related equipment under abnormal conditions is avoided.

[0048] Preferably, step S3 comprises the following steps:

[0049] Step S31: performing switching timing analysis on the current transformer through the current transformer switching execution data to generate current transformer switching timing data; performing adaptive switching simulation on the current transformer according to the current transformer switching timing data to generate current transformer switching simulation data;

[0050] Step S32: performing a switching timing error evaluation on the current transformer switching simulation data according to the current transformer switching timing data to generate switching timing error data; performing a switching timing prediction on the switching timing error data to generate a current transformer switching timing prediction model;

[0051] Step S33: using the current transformer switching timing prediction model to predict the switching timing of the current transformer switching execution data, and generating current transformer switching timing prediction data; based on the current transformer switching timing prediction data, adaptively controlling the switching timing of the current transformer switching execution data, and generating current transformer switching timing control data.

[0052] The present invention generates the current transformer switching timing data through switching timing analysis, which can deeply understand the dynamic behavior of each moment in the switching process. Combined with adaptive switching simulation, the actual switching process is simulated to verify the rationality of the switching timing and optimize the switching scheme. Reduce the delay or conflict caused by improper timing planning and improve the switching efficiency. The switching timing error evaluation quantifies the error in the switching process by analyzing the simulation data, providing data support for subsequent optimization. The current transformer switching timing prediction model constructed based on the evaluation results can effectively predict future switching errors, plan adjustments in advance, and avoid the occurrence of potential problems. Through the switching timing prediction model, the switching execution data can be predicted in real time to generate forward-looking switching timing prediction data. Based on the prediction data, adaptive switching timing control is performed to realize dynamic adjustment and refined management of the switching process, which significantly improves the intelligent level of current transformer switching. The generation of switching timing control data ensures the coordination and controllability of the entire switching process: avoiding short circuits, misoperations or equipment damage caused by improper timing. Ensure a smooth transition of the switching process and reduce interference with other devices in the system. The adaptive control mechanism enables the system to respond to external environmental changes in real time and improves the reliability of switching.

[0053] Preferably, step S4 comprises the following steps:

[0054] Step S41: performing adaptive current transformer closed-loop control on current transformer switching execution data based on current transformer switching timing control data to generate a current transformer closed-loop control result;

[0055] Step S42: Visualizing the closed-loop control result of the current transformer to perform adaptive switching control of the current transformer.

[0056] The present invention performs adaptive closed-loop control on the switching execution data based on the current transformer switching timing control data: a real-time monitoring and dynamic feedback mechanism is formed to ensure the accuracy of the switching process. During the switching process, the control parameters are adjusted in time according to the actual operating status to eliminate error accumulation. The closed-loop control result of the current transformer effectively improves the stability of the switching process and reduces the probability of switching failures. The closed-loop control mechanism realizes a dynamic response to the operating status: adaptive adjustment enables the current transformer to adapt to complex working conditions such as load changes and external environmental disturbances. It helps to solve the problem of high environmental sensitivity in traditional switching control methods and improve the robustness of the system. Adaptive closed-loop control accurately manages the switching execution data: the flexibility of current transformer switching is improved. It reduces the dependence on manual intervention and realizes more efficient and intelligent control operations.

[0057] In this specification, an adaptive switching system of a current transformer is provided, which is used to execute the adaptive switching method of the current transformer mentioned above, and the adaptive switching system of the current transformer includes:

[0058] The state segmentation module is used to collect the primary side parameters and the secondary side parameters of the current transformer to obtain the primary side collection parameters and the secondary side collection parameters of the current transformer; the current transformer state segmentation initialization is performed according to the primary side collection parameters and the secondary side collection parameters of the current transformer, so as to generate the current transformer operation state classification result;

[0059] A switching execution module is used to judge the switching result of the current transformer according to the classification result of the current transformer operation state. When the classification result of the current transformer operation state is the critical state segment of the current transformer, the corresponding current transformer is buffered in parallel to generate critical current transformer switching data; when the classification result of the current transformer operation state is the abnormal state segment of the current transformer, the corresponding current transformer is dynamically switched to the abnormal state to generate abnormal current transformer switching data; based on the critical current transformer switching data and the abnormal current transformer switching data, the current transformer is switched and executed to generate current transformer switching execution data;

[0060] A switching timing control module is used to perform switching timing analysis on the current transformer through the current transformer switching execution data to generate current transformer switching timing data; perform adaptive switching simulation on the current transformer according to the current transformer switching timing data to generate current transformer switching simulation data; perform adaptive switching timing control on the current transformer using the current transformer switching simulation data and the current transformer switching timing data to generate current transformer switching timing control data;

[0061] The switching visualization module is used to perform switching display visualization on the current transformer switching execution data based on the current transformer switching timing control data, so as to perform the adaptive switching control operation of the current transformer.

[0062] The beneficial effect of the present invention is that by collecting the parameters of the primary and secondary sides of the current transformer and performing state segmentation initialization based on these data, the working state of the current transformer can be accurately judged. The clear division between different states (such as normal, critical, abnormal, etc.) enables the current transformer to make reasonable switching decisions according to different working environments and conditions, thereby improving the response speed and adaptability of the system. Switching discrimination is performed based on the state classification result of the current transformer, and parallel buffering can be performed when the current transformer is in a critical state to avoid damage to the equipment due to overload; in an abnormal state, dynamic abnormal state switching can be triggered to effectively protect the current transformer and ensure safe operation of the system. By generating specific switching data, the switching execution process of the current transformer in different states is ensured to be efficient and accurate, and the risk of failure and the spread of system failures are effectively reduced. The timing analysis and adaptive simulation of the switching execution data can optimize the timing in the switching process of the current transformer to avoid current shock or system instability caused by the switching operation. Through adaptive switching timing control, the switching strategy can be automatically adjusted according to different current states and external conditions to ensure a smoother switching process, reduce the need for human intervention, and improve the intelligence level of the system. By visually displaying the switching timing control data and execution data, the switching process of the current transformer is intuitively visible, and the operator can monitor the switching status and adjust the parameters in real time, and promptly discover and deal with potential problems. The visualization function improves the convenience and transparency of operation, enhances the controllability of the current transformer switching process, and helps operators make timely and accurate decisions, especially in complex environments, to ensure the safe operation of equipment and systems. Therefore, the present invention improves the sensitivity and stability of current transformer switching through precise state classification, dynamic switching mechanism, timing optimization and visual display. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 A schematic diagram of a process flow of an adaptive switching method for a current transformer;

[0064] Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart;

[0065] Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG.

[0066] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0067] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0068] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0069] It should be understood that although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit, and the term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0070] To achieve this, please refer to Figures 1 to 3 , an adaptive switching method for a current transformer, the method comprising the following steps:

[0071] Step S1: collecting primary side parameters and secondary side parameters of the current transformer to obtain primary side collection parameters and secondary side collection parameters of the current transformer; initializing the current transformer state segmentation according to the primary side collection parameters and secondary side collection parameters of the current transformer, thereby generating a classification result of the current transformer operation state;

[0072] Step S2: according to the classification result of the current transformer operation state, the switching result of the current transformer is judged. When the classification result of the current transformer operation state is the critical state segment of the current transformer, the corresponding current transformer is parallel buffered to generate critical current transformer switching data; when the classification result of the current transformer operation state is the abnormal state segment of the current transformer, the corresponding current transformer is dynamically switched to the abnormal state to generate abnormal current transformer switching data; based on the critical current transformer switching data and the abnormal current transformer switching data, the current transformer is switched and executed to generate current transformer switching execution data;

[0073] Step S3: performing switching timing analysis on the current transformer through the current transformer switching execution data to generate current transformer switching timing data; performing adaptive switching simulation on the current transformer according to the current transformer switching timing data to generate current transformer switching simulation data; performing adaptive switching timing control on the current transformer using the current transformer switching simulation data and the current transformer switching timing data to generate current transformer switching timing control data;

[0074] Step S4: Switching and displaying the current transformer switching execution data based on the current transformer switching timing control data to visualize the current transformer switching execution data, so as to perform the adaptive switching control operation of the current transformer.

[0075] The present invention can obtain the working state data of the current transformer in real time by collecting the primary and secondary side parameters of the current transformer, and provide accurate basic data for subsequent state judgment and switching decision. Through state segmentation initialization, the operating state of the current transformer can be accurately classified, effectively avoiding misjudgment in a single state, and ensuring intelligent response under different current environments. According to the state classification result of the current transformer, switching judgment can be performed, and parallel buffering can be adopted when the current transformer is in a critical state to avoid equipment damage and improve the safety of the system; in an abnormal state, dynamic abnormal switching is implemented to reduce the risk of fault propagation, thereby improving the reliability and stability of the current transformer. The generation of switching execution data ensures the correctness and effectiveness of the switching process in actual operation. By performing timing analysis on the switching execution data, the timing in the switching process can be optimized, making the switching operation smoother and avoiding current shock during switching. Adaptive switching simulation further improves the dynamic adaptability during switching, and optimizes the control strategy according to the simulation results to ensure adaptive adjustment under different current environments, minimizing equipment damage and system fluctuations. Visualizing the switching execution data can intuitively display the current transformer switching process, facilitate operators to monitor and adjust the switching strategy in real time, and improve the convenience and controllability of operation. Through visualization, operators can timely discover potential problems during the switching process and respond quickly, further enhancing the stability and flexibility of the system. Therefore, the present invention improves the sensitivity and stability of current transformer switching through precise state classification, dynamic switching mechanism, timing optimization and visual display.

[0076] In the embodiment of the present invention, reference Figure 1 FIG. 1 is a schematic diagram of a step flow chart of an adaptive switching method of a current transformer according to the present invention. In this example, the adaptive switching method of a current transformer includes the following steps:

[0077] Step S1: collecting primary side parameters and secondary side parameters of the current transformer to obtain primary side collection parameters and secondary side collection parameters of the current transformer; initializing the current transformer state segmentation according to the primary side collection parameters and secondary side collection parameters of the current transformer, thereby generating a classification result of the current transformer operation state;

[0078] In the embodiment of the present invention, the current sensor connected to the primary side of the current transformer is used to collect the primary side current signal. It includes basic parameters such as current amplitude, phase, frequency, and waveform characteristics. The current signal on the secondary side is obtained by connecting the measuring instrument on the secondary side of the current transformer, which also includes parameters such as current amplitude and phase, as well as the proportional relationship on the secondary side. High-precision data acquisition equipment, such as data acquisition cards, digital oscilloscopes, sensor interfaces, etc., is used to ensure the accuracy and timeliness of parameter data. The collected primary and secondary side parameters are preprocessed, such as filtering, denoising, time domain and frequency domain analysis, etc., to remove noise interference. The current signals on the primary and secondary sides are compared to ensure that the current value on the secondary side matches the primary side according to the transformation ratio of the current transformer. At this time, the current data on both sides can be calibrated by the transformation ratio factor to ensure consistency. The state segmentation initialization is performed according to the current characteristics of the primary and secondary sides. For example, different current value intervals are set, such as normal operating state, overload state, short circuit state, etc., to correspond to different operating states. Set parameter thresholds for each segment to determine when to enter a specific state. These thresholds can be based on the design specifications of standard current transformers or obtained through historical data analysis. Apply machine learning algorithms (such as K-means clustering, support vector machines, etc.) to classify the collected data. By analyzing the change patterns of primary and secondary currents and combining the characteristics of current transformers, they are divided into different operating states. Generate outputs of the operating state classification results for subsequent maintenance, fault diagnosis, monitoring, etc.

[0079] Step S2: according to the classification result of the current transformer operation state, the switching result of the current transformer is judged. When the classification result of the current transformer operation state is the critical state segment of the current transformer, the corresponding current transformer is parallel buffered to generate critical current transformer switching data; when the classification result of the current transformer operation state is the abnormal state segment of the current transformer, the corresponding current transformer is dynamically switched to the abnormal state to generate abnormal current transformer switching data; based on the critical current transformer switching data and the abnormal current transformer switching data, the current transformer is switched and executed to generate current transformer switching execution data;

[0080] In the embodiment of the present invention, by using the classification result of the current transformer operating state obtained in step S1, it is first determined whether the current current transformer is in a critical state segment or an abnormal state segment. The critical state segment generally refers to the operating parameters of the current transformer approaching the limit of its normal working range, and is about to enter an abnormal state, which requires special attention. The abnormal state segment refers to the operating parameters of the current transformer exceeding its normal working range, indicating that the current transformer is faulty or operating abnormally. When the classification result of the operating state of the current transformer indicates that it is in the critical state segment, parallel buffering is performed. By connecting multiple current transformers in parallel or adding additional buffer circuits, the load fluctuation or risk caused by the current transformer in the critical state is reduced, and the stable operation time of the current transformer is extended. The relevant switching data under the critical state is recorded, including key information such as the parallel buffering process, delay, and switching time, in preparation for subsequent switching operations. When the classification result of the operating state of the current transformer indicates that it enters the abnormal state segment, a dynamic switching operation is performed. At this time, the current transformer has exceeded the safe working range and needs to respond quickly to protect the equipment. Through the automatic switching device of the current transformer, the current transformer in abnormal state is disconnected from the system to ensure that no further system failure or damage will be caused. The switching data under abnormal state is recorded, including the current, time, switching action, etc. during the switching, so as to carry out subsequent inspection and maintenance of the current transformer. According to the generated critical current transformer switching data and abnormal current transformer switching data, the actual current transformer switching operation is carried out. The execution includes: issuing a switching instruction through the automatic control system, controlling the switching switch, protection device, buffer circuit and other hardware, and performing the switching operation. All operation data during the current transformer switching execution process are recorded, including the switching start time, switching end time, switching equipment, switching status, etc., to ensure that the switching process is traceable and safe. During the switching process, the system should monitor the operating status of the current transformer in real time, and feedback any abnormalities during the switching execution process to ensure that the working state of the current transformer after the switching is stable. After the switching execution is completed, all operation data and status are fed back to the monitoring system and archived for subsequent analysis, optimization and maintenance.

[0081] Step S3: performing switching timing analysis on the current transformer through the current transformer switching execution data to generate current transformer switching timing data; performing adaptive switching simulation on the current transformer according to the current transformer switching timing data to generate current transformer switching simulation data; performing adaptive switching timing control on the current transformer using the current transformer switching simulation data and the current transformer switching timing data to generate current transformer switching timing control data;

[0082] In an embodiment of the present invention, by collecting the time, switching order, switching state and other related data in the current transformer switching process according to the current transformer switching execution data generated in step S2. Use a timing analysis tool (such as a signal timing diagram, an event timing diagram, etc.) to analyze the switching execution data in detail. Analyze the time point, duration, operation type (parallel buffering, dynamic switching, etc.) of the switching operation and the dependency between operations. Determine the timing law of the current transformer switching, such as the change trend of the current state before and after the switching, the response time of the switching, the stabilization time, etc. The current transformer switching timing data is generated by the timing analysis of the switching process. The data includes: the timing sequence, time delay, duration, etc. of each switching operation; the current transformer state change at the critical moment; the synchronization relationship and conflict between each operation. According to the switching timing data, a simulation model of the current transformer switching is established. This model may include: the current response model of the current transformer; the impact of the switching operation on the system (for example, system load, current fluctuation, etc.); the impact of external interference and noise on the switching process. Use adaptive simulation algorithms (such as adaptive filtering, optimization algorithms, etc.) to simulate the impact of different switching strategies on the system, and adjust simulation parameters according to the real-time data of the current transformer. The simulation process should consider: the effect of different switching timings, such as early switching, delayed switching, etc.; the impact of the state of other current transformers in the system on switching. During the simulation process, collect simulation data of the current transformer switching timing, which includes: current changes, time response, stability, etc. during the switching process; faults or unstable factors that occur in the system during the simulation process; and the adaptability of the simulation system to different switching timings. Based on the simulation data and switching timing data, design an adaptive switching timing control strategy. The strategy includes: dynamically adjusting the timing of switching according to the system load and the working state of the current transformer; setting dynamic adjustment rules, for example, when a current transformer enters a critical state, it can be switched in advance; when the load of the current transformer exceeds the preset threshold, the switching is delayed. Combined with real-time monitoring data, adjust the switching timing control strategy. For example, if the system detects that the current transformer responds slowly in a certain state, the control system can adaptively adjust the switching order or timing to reduce system fluctuations and improve stability. Based on the adaptive control strategy, the actual switching sequence control is performed, which includes: determining the switching timing and operation sequence of the current transformers at each moment; ensuring the coordination and synchronization between the current transformers during the switching process; monitoring any abnormalities in the control process in real time, and adjusting the control strategy according to the feedback information to generate switching sequence control data.

[0083] Step S4: Switching and displaying the current transformer switching execution data based on the current transformer switching timing control data to visualize the current transformer switching execution data, so as to perform the adaptive switching control operation of the current transformer.

[0084] In the embodiment of the present invention, key information including the time, sequence, state and related control instructions of each switching operation is extracted from the current transformer switching timing control data generated in step S3. Specific information includes: the timing of the switching operation (timestamp, switching duration, etc.); the switching state of the current transformer (normal, buffered, abnormal, etc.); control signals (such as switch action, delay time, etc.). This information is stored in a structured format (such as a table, database or JSON format) to facilitate subsequent operations and visual display. The visual interface of the current transformer switching execution data is designed to help monitoring personnel understand the timing and process of the switching. Key objectives include: clearly displaying the state of each current transformer (such as before switching, during switching, and after switching); displaying the time nodes and state changes of each current transformer switching; and displaying the response time, stabilization time, delay, etc. of each switching operation. Select appropriate graphical elements to display the switching execution data. Common graphical elements include: displaying the timing of the switching operation, highlighting the time point and duration of each switching action. Display the status changes of the current transformer during the switching process (e.g. green for normal, yellow for critical, and red for abnormal) through status color changes, dynamic charts or flow charts. Display the performance indicators such as the switching duration and delay time of each current transformer. Display real-time data such as the load change and stability change of the current transformer during the switching process. Use real-time data streams (such as WebSocket, MQTT, etc.) to transmit the switching execution data to the visualization platform, and display the switching status through dynamic updates. During the switching operation, the system will continuously obtain information such as the status changes and control signals of the current transformer, and update the visualization interface in real time to ensure real-time display of the switching process. During the switching process, key indicators such as the response time and stabilization time of each current transformer are displayed in real time to ensure that the operator can monitor the switching effect in real time.

[0085] Preferably, step S1 comprises the following steps:

[0086] Step S11: collecting primary side parameters of the current transformer in the intelligent manufacturing equipment to obtain primary side collection parameters of the current transformer, wherein the primary side parameter collection includes working current collection, temperature collection and saturation state collection;

[0087] Step S12: collecting secondary side parameters of the current transformer in the intelligent manufacturing equipment based on the primary side collection parameters of the current transformer to obtain the secondary side collection parameters of the current transformer, wherein the secondary side parameter collection includes voltage collection, current collection and load power factor collection;

[0088] Step S13: performing data preprocessing on the primary side acquisition parameters of the current transformer and the secondary side acquisition parameters of the current transformer to generate the primary side acquisition parameters of the standard current transformer and the secondary side acquisition parameters of the standard current transformer;

[0089] Step S14: Initialize the current transformer status in sections according to the primary side acquisition parameters of the standard current transformer and the secondary side acquisition parameters of the standard current transformer, thereby generating a classification result of the current transformer operation status.

[0090] In an embodiment of the present invention, the working current data of the primary side of the current transformer is collected in real time through the sensor in the intelligent manufacturing equipment, including information such as its peak current, mean current and change frequency. The operating temperature of the primary side of the current transformer is monitored by the embedded temperature sensor, and the temperature change trend is recorded to determine its working stability. The saturation detection module inside the current transformer is used to monitor the magnetic saturation degree of the iron core to determine whether the current transformer is in an overload or nonlinear working state. The collected data is preliminarily processed by the edge computing device to generate the primary side acquisition parameters. The secondary side voltage value is measured by a high-precision voltage sensor, including instantaneous voltage, mean voltage and fluctuation amplitude. The secondary side current signal is obtained through the current sensing module, including effective value, harmonic components, etc. Based on the real-time monitored voltage and current signals, the power factor of the load is calculated, and the load operation of the secondary side is analyzed. All secondary side parameters are synchronously collected and transmitted by the edge computing device with the same parameters as the primary side. Remove abnormal values ​​and noise data in the collected data, and fill in missing data. The primary and secondary side acquisition parameters are normalized according to the preset range to eliminate dimensional differences. Extract key features from the collected parameters, such as the waveform characteristics of current and voltage, the temperature change rate, and the dynamic change of load power factor. After processing, generate the primary side collection parameters of the standard current transformer and the secondary side collection parameters of the standard current transformer. Based on the primary and secondary side parameters, set the state segmentation rules, for example: Normal state: Both the primary and secondary side parameters are within the safe range. Warning state: The temperature or current is close to the threshold, or the saturation state fluctuates greatly. Fault state: The parameters are beyond the safe range, or the saturation state is continuously abnormal. Using models such as logistic regression, decision tree, or neural network, input the primary and secondary side standard collection parameters into the model to generate the operation status classification results. Generate a current transformer operation status classification report based on the classification results, and provide equipment operation optimization suggestions.

[0091] Preferably, step S14 comprises the following steps:

[0092] Step S141: setting a state segmentation rule according to the primary side acquisition parameters of the current transformer and the secondary side acquisition parameters of the current transformer to generate a current transformer state segmentation rule;

[0093] Step S142: performing a comparison analysis on the primary side acquisition parameters of the current transformer and the secondary side acquisition parameters of the current transformer one by one according to the current transformer state segmentation rule, and obtaining current transformer difference comparison analysis data;

[0094] Step S143: classifying the operating status of the current transformer difference comparison analysis data based on the preset difference comparison threshold value, when the current transformer difference comparison analysis data is greater than the preset difference comparison threshold value, generating a current transformer abnormal status segment; when the current transformer difference comparison analysis data is equal to the preset difference comparison threshold value, generating a current transformer critical status segment;

[0095] Step S144: when the current transformer difference comparison analysis data is less than the preset difference comparison threshold, a current transformer normal state segment is generated; the current transformer abnormal state segment, the current transformer critical state segment and the current transformer normal state segment are integrated to generate current transformer state segment result data;

[0096] Step S145: Initialize the current transformer state segmentation for the current transformer primary side acquisition parameters and the current transformer secondary side acquisition parameters according to the current transformer state segmentation result data, thereby generating a current transformer operation state classification result.

[0097] In an embodiment of the present invention, the dynamic change rules of the primary side acquisition parameters (such as working current, temperature, saturation state) and the secondary side acquisition parameters (such as voltage, current, load power factor) of the current transformer are comprehensively analyzed. Using historical operation data and expert experience, segmentation rules are defined, including the following key conditions: Normal range: the parameter is in the standard operating range designed for the equipment. Critical range: the parameter is close to the threshold, but does not exceed the safety limit. Abnormal range: the parameter exceeds the design threshold or there is a significant abnormal fluctuation, and a set of current transformer state segmentation rules are generated as a benchmark for subsequent analysis. Based on the state segmentation rules, the primary side acquisition parameters and the secondary side acquisition parameters are compared one by one, and the difference value of each parameter is calculated, for example: D=|Pprimary−Psecondary|, where D is the difference value, Pprimary is the primary side parameter, and Psecondary is the secondary side parameter. The calculated difference value is normalized and the abnormal value is processed to generate the current transformer difference comparison analysis data. Based on actual operation experience and equipment design standards, the difference comparison threshold is set, including: normal state threshold: the difference value is less than the threshold. Critical state threshold: the difference value is equal to the threshold. Abnormal state threshold: the difference value is greater than the threshold. Classify the difference comparison analysis data one by one: when the difference value D>threshold, generate the abnormal state segment; when the difference value D=threshold, generate the critical state segment; when the difference value D<threshold, generate the normal state segment. Logically combine the abnormal state segment, critical state segment and normal state segment to generate complete current transformer state segmentation result data. Generate charts or reports using the state segmentation result data to intuitively display the operating state distribution of the current transformer. Based on the state segmentation result data, the primary side acquisition parameters and the secondary side acquisition parameters are marked with the state to generate the segment initialization results. For example: the normal segment is marked as "normal operation". The critical segment is marked as "operation warning". The abnormal segment is marked as "maintenance required". Integrate the initialization results to generate the current transformer operating state classification results, which are used to guide subsequent equipment optimization and maintenance.

[0098] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0099] Step S21: the current transformer operation state classification result is used to determine the current transformer switching result. When the current transformer operation state classification result is the current transformer normal state segment, the corresponding current transformer is not switched.

[0100] Step S22: when the classification result of the current transformer operation state is the current transformer critical state segment, parallel buffering is performed on the corresponding current transformer to generate a current transformer critical parallel buffering strategy; based on the current transformer critical parallel buffering strategy, the corresponding current transformer is pre-switched to generate critical current transformer switching data;

[0101] Step S23: when the classification result of the current transformer operation state is the current transformer abnormal state segment, dynamically switching the corresponding current transformer to the abnormal state, and generating abnormal current transformer switching data;

[0102] Step S24: Switching the current transformer is performed based on the critical current transformer switching data and the abnormal current transformer switching data to generate current transformer switching execution data.

[0103] In an embodiment of the present invention, key operating parameters (such as current, voltage, temperature, etc.) are collected from the current transformer in real time. The collected data is input into the classification module for judgment, and the classification module can use threshold rules (such as whether the current exceeds the set range) or machine learning models (such as decision trees, support vector machines) for judgment. The classification results are marked as "normal state segment", "critical state segment" or "abnormal state segment". If it is determined to be a "normal state segment", the current transformer is marked as "not switched", and its switching function is locked to avoid triggering unnecessary operations. The marking signal is written through the state monitoring module, and it is monitored regularly to ensure that its state continues to be within the normal range. When it is detected that the current transformer is in the "critical state segment", the operating pressure is reduced by connecting the standby transformer in parallel: select qualified equipment from the standby transformer (such as sufficient remaining capacity). Allocate part of the load to the standby transformer to form a buffering effect to avoid further deterioration of the state. Monitor the operating state during the buffering process, and record the load change data of the critical transformer in real time to provide a basis for pre-switching decisions. According to the load distribution after buffering, it is pre-determined whether switching needs to be performed: if the state after buffering tends to be stable, the buffering time is extended. If the status continues to deteriorate, the pre-switching operation is triggered, and the key data before the switching (such as load change trend, standby transformer usage, etc.) is recorded. The switching preparation data of the critical current transformer is output, including the device identification, load distribution results and buffer effect evaluation results. When the operation status classification result shows "abnormal state segment", the abnormal cause (such as overload, short circuit or external environmental impact) is analyzed first. Combined with the operation strategy of the power system, the target device most suitable for switching is selected from the list of standby transformers, and the device with large remaining capacity and high operation reliability is usually given priority. Adjust the switching timing to reduce the system impact: monitor the current load fluctuation and select the time period with smaller fluctuation for switching operation. Switching process: dynamically transfer the load of the abnormal transformer to the target device, and monitor the operation status of the standby transformer in real time during the switching process, and generate abnormal switching data, including abnormal transformer identification, target transformer identification, switching time and load change data before and after switching. Collect critical switching data (generated in step S22) and abnormal switching data (generated in step S23), and perform consistency check on them to ensure that the data is complete and correct. Generate the final switching plan according to the switching strategy, including the switching sequence and execution steps. Complete equipment switching step by step according to priority to avoid system instability caused by instantaneous large-scale load transfer. During the switching process, collect the load data of the backup transformer in real time. If there is an overload trend, adjust the switching strategy in time and dynamically optimize the load distribution. After the switching is completed, monitor the system operation status and evaluate whether the switching effect meets expectations: verify whether the load is evenly distributed and check whether the backup transformer is running stably. Record the final execution data, including switching time, equipment operation status and load distribution after switching, to provide a reference for subsequent optimization.

[0104] Preferably, step S22 includes the following steps:

[0105] Step S221: when the classification result of the current transformer operation state is the critical state section of the current transformer, impedance matching demand calculation is performed on the corresponding current transformer primary side acquisition parameters and current transformer secondary side acquisition parameters to obtain critical buffer demand evaluation data;

[0106] Step S222: selecting a buffer transformer from a preset backup transformer pool based on the critical buffer demand assessment data, and generating buffer transformer matching data; configuring the parallel parameters of the current transformer using the buffer transformer matching data and the critical buffer demand assessment data, and generating parallel buffer configuration data;

[0107] Step S223: performing virtual access simulation on the parallel buffer configuration data to generate parallel connection test data; adjusting the buffer connection of the parallel buffer configuration data through the parallel connection test data, thereby generating a critical parallel buffer strategy for the current transformer;

[0108] Step S224: pre-switching the corresponding current transformer and buffer transformer based on the current transformer critical parallel buffering strategy to generate critical current transformer switching data.

[0109] In an embodiment of the present invention, parameters (such as primary current, primary voltage, primary impedance, etc.) are collected from the primary side of the current transformer. Parameters (such as secondary current, secondary voltage, secondary load impedance, etc.) are collected from the secondary side. The impedance characteristics of the primary side and the secondary side are compared to determine whether there is a mismatch and calculate the impedance deviation rate. Considering the operating environment and load fluctuations, combined with the impedance deviation rate, the buffering demand in the critical state segment is evaluated. Critical buffering demand evaluation data including impedance deviation, load distribution, mismatch degree, etc. are output to provide input basis for subsequent steps. According to the critical buffering demand evaluation data, a buffer transformer that meets the requirements is selected from the preset backup transformer pool: the backup transformer should have sufficient capacity margin, low load level and good operating status. Considering factors such as geographical location and connection conditions, priority is given to transformers that can be quickly connected, and buffer transformer matching data is generated, including backup transformer identification, matching degree, expected buffering effect, etc. According to the buffer transformer matching data and critical buffering demand evaluation data, the parameters required for parallel connection are calculated: including parallel connection ratio, impedance adjustment coefficient, load distribution parameters, etc. Output parallel buffer configuration data and clarify the specific configuration plan. Import the parallel buffer configuration data into the virtual simulation platform to simulate the access process of the buffer transformer. Test the applicability of the parallel parameters in the virtual environment, observe the current and voltage distribution and system stability changes, and generate parallel connection test data. Analyze the parallel connection test data to identify potential problems (such as excessive impedance deviation and uneven load distribution). Adjust the parallel parameter configuration based on the test results: optimize impedance matching and adjust the load distribution ratio. Replace the spare transformer or modify the connection plan when necessary. Output the adjusted parallel buffer configuration data and finally generate the critical parallel buffer strategy for the current transformer. According to the critical parallel buffer strategy for the current transformer, control the connection between the main transformer and the buffer transformer: ensure that the buffer transformer gradually takes over part of the load of the main transformer to reduce the pressure on the main transformer. Monitor the operating status in real time during the switching process to ensure a smooth connection process. Record key data during the pre-switching process: load distribution of the main transformer and the buffer transformer. Trend of operating status changes during the switching process. Output critical current transformer switching data including switching scheme, load distribution results, operating status and other information to provide data support for subsequent switching execution.

[0110] Preferably, step S224 includes the following steps:

[0111] Step S2241: collecting the current rated current of the corresponding current transformer and buffer transformer to obtain the first current transformer rated current and the first buffer transformer rated current; pre-planning the switching path of the corresponding current transformer and buffer transformer based on the critical parallel buffering strategy of the current transformer to generate a pre-planned switching path;

[0112] Step S2242: updating the data of the first current transformer rated current and the first buffer transformer rated current according to the pre-planned switching path to generate the second current transformer rated current and the second buffer transformer rated current;

[0113] Step S2243: When the current ratio of the rated current of the second current transformer to the rated current of the first current transformer is greater than 90% or the current ratio of the rated current of the second buffer transformer to the rated current of the first buffer transformer is greater than 95%, the corresponding current transformer and buffer transformer are switched at zero crossing points to generate critical current transformer switching data; otherwise, the pre-planned switching path is adjusted.

[0114] In an embodiment of the present invention, the rated current value of the main current transformer to be switched is collected and recorded as the rated current of the first current transformer. The rated current value of the buffer transformer involved in the switching is collected and recorded as the rated current of the first buffer transformer. Ensure that the collected data is accurate, and consider the rated current variation range of the equipment under different environments or load conditions. According to the critical parallel buffering strategy of the current transformer, analyze the connection status between the current transformer and the buffer transformer, and determine the logical order of switching.

[0115] Consider the following factors for path planning: Prioritize switching points that minimize voltage fluctuations and system shocks. Ensure that the main transformer gradually unloads the load and the buffer transformer gradually takes over the load, and generate a pre-planned switching path, including switching nodes, switching sequence, and expected current distribution. Based on the pre-planned switching path, dynamically update the rated current of the first current transformer and the rated current of the first buffer transformer: adjust the current distribution ratio of the main transformer and the buffer transformer according to the load distribution strategy of the switching path, and calculate the transition current values ​​of the main transformer and the buffer transformer during the switching process. Output the updated rated current data: the rated current of the second current transformer (the rated current value of the main transformer after adjustment), the rated current of the second buffer transformer (the rated current value of the buffer transformer after adjustment). Compare the ratio of the rated current of the second current transformer to the rated current of the first current transformer, and the ratio of the rated current of the second buffer transformer to the rated current of the first buffer transformer: if the second current transformer rated current / the first current transformer rated current>90%, or the second buffer transformer rated current / the first buffer transformer rated current>95%, then the current switching condition is considered to be met. When the switching condition is met, select the zero crossing point of the current waveform as the switching time: avoid arcing and system instability caused by instantaneous current mutation. The control system instructs to start the switching action, switch part or all of the load of the main transformer to the buffer transformer, and generate critical current transformer switching data. If the switching condition is not met, the pre-planned switching path needs to be adjusted: re-optimize the switching node position, current distribution ratio or switching order, calculate the rated current data again according to the adjusted path and verify the switching condition, and repeat this process until the switching condition is met.

[0116] Preferably, step S23 includes the following steps:

[0117] Step S231: when the classification result of the current transformer operation state is the current transformer abnormal state segment, outputting a transformer signal to the corresponding current transformer to generate a current transformer abnormal state segment output signal; performing signal waveform analysis on the current transformer abnormal state output signal to generate a current transformer abnormal state segment waveform;

[0118] Step S232: performing waveform strength analysis on the waveform of the abnormal state segment of the current transformer to generate the waveform strength of the abnormal state segment of the current transformer; performing light load state identification on the waveform of the abnormal state segment of the current transformer according to the waveform strength of the abnormal state segment of the current transformer to generate light load abnormal state data of the current transformer;

[0119] Step S233: calculating the waveform amplitude of the abnormal state segment waveform of the current transformer to obtain the waveform amplitude of the abnormal state segment of the current transformer; identifying the overload state of the abnormal state segment waveform of the current transformer according to the waveform amplitude of the abnormal state segment of the current transformer to generate the overload abnormal state data of the current transformer;

[0120] Step S234: performing waveform distortion detection on the waveform of the abnormal state segment of the current transformer to obtain waveform distortion data of the abnormal state segment of the current transformer; performing saturation state identification on the waveform of the abnormal state segment of the current transformer according to the waveform distortion data of the abnormal state segment of the current transformer to generate saturation abnormal state data of the current transformer;

[0121] Step S235: performing light load error compensation on the corresponding current transformer based on the abnormal state segment waveform of the current transformer, and generating a light load abnormality response strategy; performing current limiting protection on the corresponding current transformer based on the abnormal overload state data of the current transformer, and generating an overload abnormality response strategy; performing excitation current adjustment on the corresponding current transformer based on the abnormal saturation state data of the current transformer, and generating a saturation abnormality response strategy;

[0122] Step S236: dynamically switching the abnormal state of the corresponding current transformer using the light load abnormality coping strategy, the overload abnormality coping strategy and the saturation abnormality coping strategy to generate abnormal current transformer switching data.

[0123] In the embodiment of the present invention, when the classification result of the operation state of the current transformer shows that it has entered the abnormal state segment, the signal output system is immediately started. The current transformer outputs the current signal in its current state to the system. This signal contains real-time information about the abnormality of the current transformer, such as current waveform, frequency, amplitude and other characteristics. The output abnormal state segment signal of the current transformer is analyzed to extract various characteristics of the signal waveform: check whether the signal waveform has periodic abnormal fluctuations, identify the morphological changes of the signal waveform, such as the sudden rise or fall of the instantaneous current, and generate the abnormal state segment waveform of the current transformer for subsequent more in-depth analysis and processing. The intensity characteristics of the abnormal state segment waveform of the current transformer are analyzed by Fourier transform or other signal processing technology. The purpose of the intensity analysis is to understand the amplitude change of the current waveform, especially whether there is too low or unstable current fluctuation. According to the waveform intensity analysis results, it is judged whether the current transformer is in a light load state. In the light load state, the output waveform amplitude of the current transformer is low, and the waveform frequency or fluctuation amplitude shows certain typical characteristics, and the light load abnormal state data of the current transformer is generated to provide a basis for the next response strategy. Accurately calculate the amplitude of the abnormal state waveform of the current transformer. The amplitude calculation extracts and evaluates the signal peak to determine the instantaneous maximum amplitude of the current. Ensure that the calculation covers the changes of the entire waveform, not just the local peak. According to the calculation results of the waveform amplitude, determine whether the current transformer is in an overload state. In the overload state, the waveform amplitude will increase significantly, and the current intensity exceeds the rated capacity of the equipment, generating the current transformer overload abnormal state data for subsequent protection measures and strategy formulation. Use high-precision signal processing algorithms (such as wavelet transform or other time-frequency analysis methods) to perform distortion detection on the abnormal state waveform of the current transformer. Distortion detection focuses on the distortion characteristics of the signal, such as distortion of the signal shape or interference of high-frequency noise, which are usually caused by equipment saturation or failure. According to the distortion detection results, determine whether the current transformer is in a saturated state. The saturated state is usually manifested as severe distortion of the waveform or a saturated flat area, which makes the current sensor unable to accurately capture the fluctuations of the signal, and generates the current transformer saturation abnormal state data to provide support for subsequent adjustment and compensation measures. Based on the abnormal state data of the current transformer under light load, the instability of the current waveform under light load is reduced through the compensation algorithm of the current transformer signal, and a light load abnormality response strategy is generated, such as improving the sensitivity of current detection or automatically adjusting the current threshold. Based on the abnormal state data of the current transformer under overload, the current limiting protection mechanism is started, which includes setting the current limiting threshold to prevent the equipment from being damaged by the current overload, and generating an overload abnormal response strategy, including disconnecting the current transformer in a short time or switching to the backup transformer to protect the equipment from overload damage.Based on the abnormal saturation state data of the current transformer, adjust the excitation current of the current transformer to avoid the influence of saturation on signal accuracy, and generate saturation abnormality response strategies, such as adjusting the power supply voltage or adding compensation equipment to ensure that the current transformer can return to normal working state. Using the generated light load abnormality response strategy, overload abnormality response strategy and saturation abnormality response strategy, the current transformer is automatically controlled to enter the corresponding protection or adjustment mode. According to the real-time monitoring data, the working state of the current transformer is dynamically adjusted, such as: starting error compensation under light load conditions, activating current limiting protection under overload conditions, and adjusting the excitation current under saturation conditions. Generate abnormal current transformer switching data and record key parameters and states during the switching process for subsequent analysis and maintenance.

[0124] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes:

[0125] Step S31: performing switching timing analysis on the current transformer through the current transformer switching execution data to generate current transformer switching timing data; performing adaptive switching simulation on the current transformer according to the current transformer switching timing data to generate current transformer switching simulation data;

[0126] Step S32: performing a switching timing error evaluation on the current transformer switching simulation data according to the current transformer switching timing data to generate switching timing error data; performing a switching timing prediction on the switching timing error data to generate a current transformer switching timing prediction model;

[0127] Step S33: using the current transformer switching timing prediction model to predict the switching timing of the current transformer switching execution data, and generating current transformer switching timing prediction data; based on the current transformer switching timing prediction data, adaptively controlling the switching timing of the current transformer switching execution data, and generating current transformer switching timing control data.

[0128] In an embodiment of the present invention, the switching timing process is analyzed according to the current transformer switching execution data. This data contains key information such as the current state of the current transformer, the target state to be switched, the starting point and the end point of the switching. Analyze whether the switching timing meets the predetermined requirements, and check each link in the switching process, such as the start time, switching delay and stability. Through timing analysis, detailed switching timing data is obtained, including the timestamps of each stage, the operation steps and the execution time of each step. This data can be used for subsequent simulation and optimization. The current transformer is adaptively switched by switching timing data to simulate various changes and influencing factors in the switching process, such as current fluctuations, load changes, equipment response, etc. Use power system simulation software (such as MATLAB / Simulink) to perform timing simulation, input the simulation system according to the switching timing data, and generate the dynamic response of the current transformer switching. Through simulation, the current waveform, switching time, system stability after switching and other information in the switching process are obtained, and these simulation data provide a basis for optimizing the switching process. Compare the current transformer switching timing data with the simulation data to identify the timing error that occurs during the switching process. Timing errors can be manifested as problems such as early or late switching time, delayed execution of actions, and untimely signal response. Based on the difference between the actual switching timing and the simulated timing, the switching timing error data is generated. The error data includes time delays, deviations, and abnormal fluctuations in the switching process. Based on the switching timing error data, a current transformer switching timing prediction model is constructed. The model can predict the error trend of the switching timing through historical error data, thereby providing accurate timing prediction for future switching decisions. The prediction model is trained using machine learning algorithms (such as regression analysis, time series analysis, neural networks, etc.). By inputting the switching timing error data, the model can learn the relationship between the timing deviation and the system state and make error predictions. In the process of building the model, the model parameters are continuously optimized to improve its prediction accuracy. For example, methods such as cross-validation are used to test the generalization ability of the model, and the accuracy of the model is enhanced by adjusting the training data set. Finally, a timing prediction model with high prediction accuracy is obtained. Based on the generated current transformer switching timing prediction model, the switching execution data is predicted. By inputting the current switching execution data (such as current, load and other status information), the prediction model will output the switching timing and error range in the future, and generate the current transformer switching timing prediction data, which includes the deviation in the switching process, the expected switching time and the control parameters that need to be adjusted. According to the switching timing prediction data, the switching timing of the current transformer is adjusted in real time through the adaptive control system. The control system will dynamically optimize the various parameters in the switching process (such as time delay, response speed, etc.) according to the timing information provided by the prediction model to ensure that the switching process is as smooth and accurate as possible.The feedback control mechanism is used to fine-tune the timing of the current transformer. For example, when a switching delay is predicted, the system can start the switching operation in advance; if the switching time is predicted to be advanced, the switching time is adjusted accordingly to avoid premature start. The current transformer switching timing control data is generated through adaptive control. This data includes the adjusted switching timing, execution parameters, and optimization solutions for device response.

[0129] Preferably, step S4 comprises the following steps:

[0130] Step S41: performing adaptive current transformer closed-loop control on current transformer switching execution data based on current transformer switching timing control data to generate a current transformer closed-loop control result;

[0131] Step S42: Visualizing the closed-loop control result of the current transformer to perform adaptive switching control of the current transformer.

[0132] In an embodiment of the present invention, the switching process of the current transformer is adjusted by an adaptive closed-loop control system based on the switching timing control data of the current transformer (such as switching timing prediction data, error data, etc.). This process mainly dynamically adjusts the control strategy through a real-time feedback system to ensure the accuracy and stability of the switching timing and reduce the risk of system instability. The core principle of adaptive closed-loop control is to make dynamic adjustments based on real-time feedback information. When a deviation occurs during the switching of the current transformer, the control system can detect it in real time and compensate for the switching process to ensure the switching accuracy. The switching timing control data is input into the closed-loop control system. The control system monitors the switching execution data of the current transformer (such as current load, current waveform, switching response, etc.) in real time, and obtains the deviation between the current switching state and the target switching state through error calculation. According to the error data, the system adjusts the control parameters (such as time delay, start threshold, etc.) in real time, and optimizes the switching process through dynamic adjustment. For example, if it is predicted that the switching delay is too long, the system can adjust to start in advance; if the switch is too early, the system will delay the start. Adaptive PID (proportional-integral-differential) control algorithms, fuzzy control or other algorithms suitable for timing control are used to self-adjust and optimize the control strategy based on real-time data. These algorithms can effectively adjust the control strategy based on errors and real-time feedback information to ensure a smooth transition of the switching process. Through closed-loop control, the system finally generates the closed-loop control results of the current transformer. The closed-loop control results will include data such as the adjusted switching timing, control parameters and actual execution time, providing a basis for subsequent operations. Convert the closed-loop control results of the current transformer into a visual chart or dashboard to help operators understand the execution status, performance indicators and any potential problems of the switching control in real time. Through clear visualization, the monitoring and adjustment efficiency of the control process is improved. Display the timing data of the current transformer switching, including the time changes before and after the switching, the operation delay, etc., which helps to monitor whether the switching operation meets the expected time window. Display the current waveform of the current transformer, the load changes and any abnormal fluctuations during the switching process. Through the waveform diagram, the operator can evaluate in real time whether there are overload, interference or fluctuation problems during the switching process. Show how the adaptive control system adjusts the control parameters such as switching threshold, delay time, etc. according to real-time feedback. Through this chart, operators can understand how the control system responds to changes and optimize the switching process. Display the stability indicators of the current transformer, such as the stability of the current transformer after switching, the error correction effect, etc., to ensure stable operation of the equipment during the switching process. Use data visualization platforms (such as Power BI, Tableau, or customized HMI / SCADA systems) to display control results. Through these platforms, closed-loop control results, error data, timing data, waveform data, etc. are integrated into easy-to-understand charts and dynamic visualization elements. The platform supports real-time updates of control results, and operators can monitor various indicators through the dashboard and take necessary adjustments.If an abnormality occurs during the switching process, the system will prompt the operator through a warning or early warning mechanism.

[0133] In this specification, an adaptive switching system of a current transformer is provided, which is used to execute the adaptive switching method of the current transformer mentioned above, and the adaptive switching system of the current transformer includes:

[0134] The state segmentation module is used to collect the primary side parameters and the secondary side parameters of the current transformer to obtain the primary side collection parameters and the secondary side collection parameters of the current transformer; the current transformer state segmentation initialization is performed according to the primary side collection parameters and the secondary side collection parameters of the current transformer, so as to generate the current transformer operation state classification result;

[0135] A switching execution module is used to judge the switching result of the current transformer according to the classification result of the current transformer operation state. When the classification result of the current transformer operation state is the critical state segment of the current transformer, the corresponding current transformer is buffered in parallel to generate critical current transformer switching data; when the classification result of the current transformer operation state is the abnormal state segment of the current transformer, the corresponding current transformer is dynamically switched to the abnormal state to generate abnormal current transformer switching data; based on the critical current transformer switching data and the abnormal current transformer switching data, the current transformer is switched and executed to generate current transformer switching execution data;

[0136] A switching timing control module is used to perform switching timing analysis on the current transformer through the current transformer switching execution data to generate current transformer switching timing data; perform adaptive switching simulation on the current transformer according to the current transformer switching timing data to generate current transformer switching simulation data; perform adaptive switching timing control on the current transformer using the current transformer switching simulation data and the current transformer switching timing data to generate current transformer switching timing control data;

[0137] The switching visualization module is used to perform switching display visualization on the current transformer switching execution data based on the current transformer switching timing control data, so as to perform the adaptive switching control operation of the current transformer.

[0138] The beneficial effect of the present invention is that by collecting the parameters of the primary and secondary sides of the current transformer and performing state segmentation initialization based on these data, the working state of the current transformer can be accurately judged. The clear division between different states (such as normal, critical, abnormal, etc.) enables the current transformer to make reasonable switching decisions according to different working environments and conditions, thereby improving the response speed and adaptability of the system. Switching discrimination is performed based on the state classification result of the current transformer, and parallel buffering can be performed when the current transformer is in a critical state to avoid damage to the equipment due to overload; in an abnormal state, dynamic abnormal state switching can be triggered to effectively protect the current transformer and ensure safe operation of the system. By generating specific switching data, the switching execution process of the current transformer in different states is ensured to be efficient and accurate, and the risk of failure and the spread of system failures are effectively reduced. The timing analysis and adaptive simulation of the switching execution data can optimize the timing in the switching process of the current transformer to avoid current shock or system instability caused by the switching operation. Through adaptive switching timing control, the switching strategy can be automatically adjusted according to different current states and external conditions to ensure a smoother switching process, reduce the need for human intervention, and improve the intelligence level of the system. By visually displaying the switching timing control data and execution data, the switching process of the current transformer is intuitively visible, and the operator can monitor the switching status and adjust the parameters in real time, and promptly discover and deal with potential problems. The visualization function improves the convenience and transparency of operation, enhances the controllability of the current transformer switching process, and helps operators make timely and accurate decisions, especially in complex environments, to ensure the safe operation of equipment and systems. Therefore, the present invention improves the sensitivity and stability of current transformer switching through precise state classification, dynamic switching mechanism, timing optimization and visual display.

[0139] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0140] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. An adaptive switching method for a current transformer, characterized in that: The following steps are involved: Step S1: collecting primary side parameters and secondary side parameters of the current transformer to obtain primary side collected parameters of the current transformer and secondary side collected parameters of the current transformer; The current transformer state is initialized in segments according to the current transformer primary side acquisition parameters and the current transformer secondary side acquisition parameters, thereby generating a current transformer operation state classification result; Step S2: according to the classification result of the current transformer operation state, the switching result of the current transformer is judged. When the classification result of the current transformer operation state is the critical state segment of the current transformer, the corresponding current transformer is buffered in parallel to generate critical current transformer switching data; when the classification result of the current transformer operation state is the abnormal state segment of the current transformer, the corresponding current transformer is dynamically switched to the abnormal state to generate abnormal current transformer switching data; Performing switching execution on the current transformer based on the critical current transformer switching data and the abnormal current transformer switching data, and generating current transformer switching execution data; Step S3: performing a switching timing analysis on the current transformer through the current transformer switching execution data to generate current transformer switching timing data; Performing adaptive switching simulation on the current transformer according to the current transformer switching timing data to generate current transformer switching simulation data; Using the current transformer switching simulation data and the current transformer switching timing data to perform adaptive switching timing control on the current transformer, and generate current transformer switching timing control data; Step S4: Switching and displaying the current transformer switching execution data based on the current transformer switching timing control data to visualize the current transformer switching execution data, so as to perform the adaptive switching control operation of the current transformer.

2. The adaptive switching method of current transformer according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: collecting primary side parameters of the current transformer in the intelligent manufacturing equipment to obtain primary side collection parameters of the current transformer, wherein the primary side parameter collection includes working current collection, temperature collection and saturation state collection; Step S12: collecting secondary side parameters of the current transformer in the intelligent manufacturing equipment based on the primary side collection parameters of the current transformer to obtain the secondary side collection parameters of the current transformer, wherein the secondary side parameter collection includes voltage collection, current collection and load power factor collection; Step S13: performing data preprocessing on the primary side acquisition parameters of the current transformer and the secondary side acquisition parameters of the current transformer to generate the primary side acquisition parameters of the standard current transformer and the secondary side acquisition parameters of the standard current transformer; Step S14: Initialize the current transformer status in sections according to the primary side acquisition parameters of the standard current transformer and the secondary side acquisition parameters of the standard current transformer, thereby generating a classification result of the current transformer operation status.

3. The adaptive switching method of current transformer according to claim 2, characterized in that: Step S14 includes the following steps: Step S141: setting a state segmentation rule according to the primary side acquisition parameters of the current transformer and the secondary side acquisition parameters of the current transformer to generate a current transformer state segmentation rule; Step S142: performing a comparison analysis on the primary side acquisition parameters of the current transformer and the secondary side acquisition parameters of the current transformer one by one according to the current transformer state segmentation rule, and obtaining current transformer difference comparison analysis data; Step S143: classifying the operating status of the current transformer difference comparison analysis data based on the preset difference comparison threshold value, when the current transformer difference comparison analysis data is greater than the preset difference comparison threshold value, generating a current transformer abnormal status segment; when the current transformer difference comparison analysis data is equal to the preset difference comparison threshold value, generating a current transformer critical status segment; Step S144: when the current transformer difference comparison analysis data is less than the preset difference comparison threshold, a current transformer normal state segment is generated; the current transformer abnormal state segment, the current transformer critical state segment and the current transformer normal state segment are integrated to generate current transformer state segment result data; Step S145: Initialize the current transformer state segmentation for the current transformer primary side acquisition parameters and the current transformer secondary side acquisition parameters according to the current transformer state segmentation result data, thereby generating a current transformer operation state classification result.

4. The adaptive switching method of current transformer according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: the current transformer operation state classification result is used to determine the current transformer switching result. When the current transformer operation state classification result is the current transformer normal state segment, the corresponding current transformer is not switched. Step S22: when the classification result of the current transformer operation state is the current transformer critical state segment, parallel buffering is performed on the corresponding current transformer to generate a current transformer critical parallel buffering strategy; based on the current transformer critical parallel buffering strategy, the corresponding current transformer is pre-switched to generate critical current transformer switching data; Step S23: when the classification result of the current transformer operation state is the current transformer abnormal state segment, dynamically switching the corresponding current transformer to the abnormal state, and generating abnormal current transformer switching data; Step S24: Switching the current transformer is performed based on the critical current transformer switching data and the abnormal current transformer switching data to generate current transformer switching execution data.

5. The adaptive switching method of current transformer according to claim 4, characterized in that: Step S22 includes the following steps: Step S221: when the classification result of the current transformer operation state is the critical state section of the current transformer, impedance matching demand calculation is performed on the corresponding current transformer primary side acquisition parameters and current transformer secondary side acquisition parameters to obtain critical buffer demand evaluation data; Step S222: selecting a buffer transformer from a preset backup transformer pool based on the critical buffer demand assessment data, and generating buffer transformer matching data; configuring the parallel parameters of the current transformer using the buffer transformer matching data and the critical buffer demand assessment data, and generating parallel buffer configuration data; Step S223: performing virtual access simulation on the parallel buffer configuration data to generate parallel connection test data; adjusting the buffer connection of the parallel buffer configuration data through the parallel connection test data, thereby generating a critical parallel buffer strategy for the current transformer; Step S224: pre-switching the corresponding current transformer and buffer transformer based on the current transformer critical parallel buffering strategy to generate critical current transformer switching data.

6. The adaptive switching method of current transformer according to claim 5, characterized in that: Step S224 includes the following steps: Step S2241: collecting the current rated current of the corresponding current transformer and buffer transformer to obtain the first current transformer rated current and the first buffer transformer rated current; pre-planning the switching path of the corresponding current transformer and buffer transformer based on the critical parallel buffering strategy of the current transformer to generate a pre-planned switching path; Step S2242: updating the data of the first current transformer rated current and the first buffer transformer rated current according to the pre-planned switching path to generate the second current transformer rated current and the second buffer transformer rated current; Step S2243: When the current ratio of the rated current of the second current transformer to the rated current of the first current transformer is greater than 90% or the current ratio of the rated current of the second buffer transformer to the rated current of the first buffer transformer is greater than 95%, the corresponding current transformer and buffer transformer are switched at zero crossing points to generate critical current transformer switching data; otherwise, the pre-planned switching path is adjusted.

7. The adaptive switching method of current transformer according to claim 6, characterized in that: Step S23 includes the following steps: Step S231: when the classification result of the current transformer operation state is the current transformer abnormal state segment, outputting a transformer signal to the corresponding current transformer to generate a current transformer abnormal state segment output signal; performing signal waveform analysis on the current transformer abnormal state output signal to generate a current transformer abnormal state segment waveform; Step S232: performing waveform strength analysis on the waveform of the abnormal state segment of the current transformer to generate the waveform strength of the abnormal state segment of the current transformer; performing light load state identification on the waveform of the abnormal state segment of the current transformer according to the waveform strength of the abnormal state segment of the current transformer to generate light load abnormal state data of the current transformer; Step S233: calculating the waveform amplitude of the abnormal state segment waveform of the current transformer to obtain the waveform amplitude of the abnormal state segment of the current transformer; identifying the overload state of the abnormal state segment waveform of the current transformer according to the waveform amplitude of the abnormal state segment of the current transformer to generate the overload abnormal state data of the current transformer; Step S234: performing waveform distortion detection on the waveform of the abnormal state segment of the current transformer to obtain waveform distortion data of the abnormal state segment of the current transformer; performing saturation state identification on the waveform of the abnormal state segment of the current transformer according to the waveform distortion data of the abnormal state segment of the current transformer to generate saturation abnormal state data of the current transformer; Step S235: performing light load error compensation on the corresponding current transformer based on the abnormal state segment waveform of the current transformer, and generating a light load abnormality response strategy; performing current limiting protection on the corresponding current transformer based on the abnormal overload state data of the current transformer, and generating an overload abnormality response strategy; performing excitation current adjustment on the corresponding current transformer based on the abnormal saturation state data of the current transformer, and generating a saturation abnormality response strategy; Step S236: dynamically switching the abnormal state of the corresponding current transformer using the light load abnormality coping strategy, the overload abnormality coping strategy and the saturation abnormality coping strategy to generate abnormal current transformer switching data.

8. The adaptive switching method of current transformer according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: performing switching timing analysis on the current transformer through the current transformer switching execution data to generate current transformer switching timing data; performing adaptive switching simulation on the current transformer according to the current transformer switching timing data to generate current transformer switching simulation data; Step S32: performing a switching timing error evaluation on the current transformer switching simulation data according to the current transformer switching timing data to generate switching timing error data; performing a switching timing prediction on the switching timing error data to generate a current transformer switching timing prediction model; Step S33: using the current transformer switching timing prediction model to predict the switching timing of the current transformer switching execution data, and generating current transformer switching timing prediction data; based on the current transformer switching timing prediction data, adaptively controlling the switching timing of the current transformer switching execution data, and generating current transformer switching timing control data.

9. The adaptive switching method of current transformer according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: performing adaptive current transformer closed-loop control on current transformer switching execution data based on current transformer switching timing control data to generate a current transformer closed-loop control result; Step S42: Visualizing the closed-loop control result of the current transformer to perform adaptive switching control of the current transformer.

10. An adaptive switching system for a current transformer, characterized in that: For executing the adaptive switching method of the current transformer as claimed in claim 1, the adaptive switching system of the current transformer comprises: The state segmentation module is used to collect the primary side parameters and the secondary side parameters of the current transformer to obtain the primary side collection parameters and the secondary side collection parameters of the current transformer; the current transformer state segmentation initialization is performed according to the primary side collection parameters and the secondary side collection parameters of the current transformer, so as to generate the current transformer operation state classification result; A switching execution module is used to judge the switching result of the current transformer according to the classification result of the current transformer operation state. When the classification result of the current transformer operation state is the critical state segment of the current transformer, the corresponding current transformer is buffered in parallel to generate critical current transformer switching data; when the classification result of the current transformer operation state is the abnormal state segment of the current transformer, the corresponding current transformer is dynamically switched to the abnormal state to generate abnormal current transformer switching data; based on the critical current transformer switching data and the abnormal current transformer switching data, the current transformer is switched and executed to generate current transformer switching execution data; A switching timing control module is used to perform switching timing analysis on the current transformer through the current transformer switching execution data to generate current transformer switching timing data; perform adaptive switching simulation on the current transformer according to the current transformer switching timing data to generate current transformer switching simulation data; perform adaptive switching timing control on the current transformer using the current transformer switching simulation data and the current transformer switching timing data to generate current transformer switching timing control data; The switching visualization module is used to switch and display the current transformer switching execution data based on the current transformer switching timing control data to perform adaptive switching control operations of the current transformer.

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