Closing control method and system of primary fusion switch and electronic equipment

Abstract

The invention provides a closing control method and system of a primary fusion switch and electronic equipment, and relates to the technical field of automatic control of a power system, and the method comprises the steps: receiving a closing instruction, and calculating an expected time difference between a current moment and a next voltage zero crossing moment; the expected time difference is adjusted according to the inherent closing time parameter of the permanent magnetic mechanism, and a closing delay instruction is generated; a closing delay instruction is received, and closing operation is triggered to be completed in the target time window; monitoring real-time frequency fluctuation data of the target power grid based on an adaptive threshold adjustment mechanism, and dynamically adjusting a voltage zero crossing point prediction threshold according to the real-time frequency fluctuation data; on the basis of an online learning calibration mechanism, the inherent closing time parameter is automatically updated according to the historical closing data of the target power grid, so that accurate closing can be realized at the voltage zero crossing point, transient current and inrush current phenomena generated during closing are remarkably reduced, and the stability and safety of a power system are greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of automatic control of power systems, and in particular to a closing control method, system and electronic equipment for a primary fusion switch. Background Art

[0002] With the widespread access to new energy sources such as wind and solar energy, the power system faces more complex and changeable operating conditions.

[0003] Conventional power switchgear employs spring mechanisms, which exhibit significant time discreteness during the closing process. This makes precise control of the closing moment difficult, especially when closing is random within a voltage cycle. Closing cannot be guaranteed to occur at the voltage zero crossing point, potentially leading to excessive closing transient currents and increasing inrush current risks. This can also cause grid oscillations and transient overvoltages, reducing grid safety and reliability. For example, the closing time discreteness of primary devices with traditional spring-driven mechanisms is high, making it impossible to accurately predict and control closing timing, especially in renewable energy grid-connected applications. This increases grid safety risks.

[0004] Therefore, it is necessary to provide a closing control method, system and electronic equipment for a primary fusion switch to solve the above technical problems. Summary of the Invention

[0005] To solve the above technical problems, the present invention provides a closing control method, system and electronic equipment for a primary fusion switch, which is used to solve the problem that primary equipment with traditional spring mechanisms cannot achieve precise closing at the voltage zero crossing point, resulting in excessively high closing transient current, increased inrush current risk, and affected grid stability. It is also unable to effectively reduce the grid impact caused by closing, resulting in low grid connection efficiency.

[0006] The present invention provides a closing control method for a primary fusion switch, the closing control method comprising: Receive the closing command and calculate the expected time difference between the current moment and the next voltage zero crossing moment; The expected time difference is adjusted according to the inherent closing time parameter of the permanent magnet mechanism to generate a corresponding closing delay instruction; receiving the closing delay instruction and triggering the closing operation to be completed within the target time window; Based on the adaptive threshold adjustment mechanism, the real-time frequency fluctuation data of the target power grid is monitored, and the voltage zero-crossing prediction threshold corresponding to the target time window is dynamically adjusted according to the real-time frequency fluctuation data; Based on an online learning calibration mechanism, the inherent closing time parameter is automatically updated according to the historical closing data of the target power grid.

[0007] Preferably, the receiving of the closing instruction and the calculation of the expected time difference between the current moment and the next voltage zero-crossing moment specifically include: monitoring a communication channel from a remote dispatch center or a local control unit and receiving a closing command in the communication channel; Verifying the legitimacy of the closing instruction and performing content analysis on the closing instruction that has passed the verification to obtain closing instruction operation information; Based on the closing instruction operation information, the current moment is obtained, and according to the voltage waveform data and voltage frequency information of the target power grid, the next voltage zero-crossing moment is predicted, and the expected time difference between the current moment and the next voltage zero-crossing moment is calculated.

[0008] Preferably, the calculation process of the next voltage zero-crossing moment and the expected time difference is as follows: The expression for setting the voltage at any moment is as follows: Where, represents the voltage at any time t; Indicates the peak voltage; represents the angular frequency; Indicates the initial phase, that is, the offset of the voltage waveform relative to the time axis; when When the target grid has the next voltage zero crossing, that is, ,get: Where, Indicates the time when the next voltage zero crossing occurs in the target power grid; represents the angular frequency; Indicates the initial phase, that is, the offset of the voltage waveform relative to the time axis; Indicates the sequence number of the next voltage zero crossing point; The expected time difference between the current moment and the next voltage zero-crossing moment is calculated as follows: Where, Indicates the expected time difference between the current moment and the next voltage zero-crossing moment; Indicates the time when the next voltage zero crossing occurs in the target power grid; Indicates the current moment.

[0009] Preferably, the adjusting the expected time difference according to the inherent closing time parameter of the permanent magnet mechanism to generate a corresponding closing delay instruction specifically includes: The inherent closing time parameter includes an average inherent closing time and a standard deviation; Based on the normal distribution algorithm, the expected time difference is adjusted according to the average inherent closing time and the standard deviation. The corresponding calculation formula is as follows: Where, represents the adjusted expected time difference; Indicates the expected time difference between the current moment and the next voltage zero-crossing moment; Indicates the average inherent closing time; Indicates the Z score corresponding to the confidence level of the normal distribution algorithm; represents the standard deviation; The closing delay instruction is generated according to the adjusted expected time difference, and the closing delay instruction includes the closing delay time and closing actuator information.

[0010] Preferably, a target closing actuator is determined according to the closing actuator information, and the target closing actuator starts timing until the closing delay time ends; Within the target time window, when it is monitored that the voltage of the target power grid meets the preset closing condition, that is, the voltage of the target power grid is less than or equal to the voltage zero-crossing prediction threshold, the target closing actuator is triggered and performs the closing operation.

[0011] Preferably, the adaptive threshold adjustment mechanism is based on monitoring the real-time frequency fluctuation data of the target power grid, and dynamically adjusting the voltage zero-crossing prediction threshold corresponding to the target time window according to the real-time frequency fluctuation data, specifically including: monitoring real-time frequency fluctuation data of the target power grid based on the adaptive threshold adjustment mechanism; According to the real-time frequency fluctuation data, the current cycle length of the target power grid is determined, and the predicted voltage zero-crossing time is determined according to the current cycle length. The corresponding calculation formula is as follows: Where T represents the current cycle length of the target power grid; f represents the frequency of the target power grid; Indicates the current moment; Indicates the predicted voltage zero-crossing time; the time when the next voltage zero-crossing occurs in the target grid That is, the actual voltage zero-crossing time, based on the predicted voltage zero-crossing time and the actual voltage zero crossing moment Adjust the voltage zero-crossing prediction threshold, and the corresponding calculation formula is as follows: Where, Indicates the adjusted current time The voltage zero-crossing prediction threshold; Represents the proportional gain of the PID controller; Indicates the current time The predicted voltage zero-crossing time and the actual voltage zero crossing moment The error between Represents the integral gain of the PID controller; Indicates the current time The predicted voltage zero-crossing time and the actual voltage zero crossing moment The cumulative error between Represents the differential gain of the PID controller.

[0012] Preferably, the automatic updating of the inherent closing time parameter based on the online learning calibration mechanism according to the historical closing data of the target power grid specifically includes: When extracting the historical closing features corresponding to the historical closing data and classifying the historical closing features based on the support vector machine, the corresponding optimization problem is as follows: Where, represents the weight vector of the i-th support vector machine, which is used to determine the direction of the classification hyperplane; represents the bias term of the i-th support vector machine, which is used to determine the distance between the classification hyperplane and the origin; represents the slack variable when the j-th historical closing feature is linearly inseparable in the i-th support vector machine; C represents the regularization parameter, which is used to control the trade-off between classification interval and classification error; R represents the total number of historical closing features; min represents the minimum value operation; Represents the summation symbol.

[0013] Preferably, the constraints corresponding to the optimization problem are as follows: Where, Indicates the feature category label corresponding to the j-th historical closing feature; represents the jth historical closing feature; represents the weight vector of the i-th support vector machine, which is used to determine the direction of the classification hyperplane; represents the bias term of the i-th support vector machine, which is used to determine the distance between the classification hyperplane and the origin; represents the slack variable when the j-th historical closing feature is linearly inseparable in the i-th support vector machine; N represents the total number of historical closing features; Based on the feature classification results of the historical closing features, the closing time interval category corresponding to the historical closing data is predicted, and the inherent closing time parameter is adjusted and updated according to the prediction result.

[0014] A closing control system for a primary fusion switch, the closing control system comprising: A closing command receiving module is used to receive a closing command and calculate the expected time difference between the current moment and the next voltage zero-crossing moment; A delay instruction generating module is used to adjust the expected time difference according to the inherent closing time parameter of the permanent magnet mechanism and generate a corresponding closing delay instruction; A closing operation execution module, configured to receive the closing delay instruction and trigger the closing operation to be completed within a target time window; An adaptive threshold adjustment module is used to monitor the real-time frequency fluctuation data of the target power grid based on the adaptive threshold adjustment mechanism, and dynamically adjust the voltage zero-crossing prediction threshold corresponding to the target time window according to the real-time frequency fluctuation data; An online learning calibration module is used to automatically update the inherent closing time parameter according to the historical closing data of the target power grid based on an online learning calibration mechanism.

[0015] An electronic device includes a memory and a processor, wherein the memory stores a computer program. When the processor runs the computer program stored in the memory, the processor executes the closing control method of a primary fusion switch as described in any one of the above items.

[0016] Compared with related technologies, the closing control method, system, and electronic device for a single-phase fusion switch provided by the present invention have the following beneficial effects: The present invention receives a closing instruction and calculates the expected time difference between the current moment and the next voltage zero-crossing moment; adjusts the expected time difference according to the inherent closing time parameter of the permanent magnet mechanism to generate a corresponding closing delay instruction; receives the closing delay instruction and triggers the closing operation to be completed within the target time window; based on an adaptive threshold adjustment mechanism, monitors the real-time frequency fluctuation data of the target power grid, and dynamically adjusts the voltage zero-crossing prediction threshold corresponding to the target time window according to the real-time frequency fluctuation data; based on an online learning calibration mechanism, automatically updates the inherent closing time parameter according to the historical closing data of the target power grid, so that accurate closing can be achieved at the voltage zero-crossing point, significantly reducing the transient current and inrush current phenomena generated during closing, greatly improving the stability and safety of the power system, and can maintain high-precision closing control in a power grid frequency fluctuation environment through the adaptive threshold adjustment mechanism and the online learning calibration mechanism, thereby enhancing the anti-interference ability and reliability of the entire system.

[0017] The present invention adopts a fast and accurate closing control method based on the characteristics of a permanent magnet mechanism, which can achieve accurate closing at the voltage zero point, significantly reducing the transient current and inrush current phenomena generated during closing, and greatly improving the stability and safety of the power system; the present invention adopts an adaptive threshold adjustment mechanism, which can maintain high-precision closing control in an environment of power grid frequency fluctuations, thereby enhancing the anti-interference ability and reliability of the entire system; the present invention effectively integrates an online learning and calibration function, which can correct the inherent closing time changes caused by mechanism aging, ensure the accuracy of the inherent closing time under long-term operation, extend the maintenance cycle of the power system, reduce operation and maintenance costs, and improve the overall performance indicators of power grid equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A flow chart of a closing control method for a primary fusion switch provided in an embodiment of the present invention; Figure 2 A system block diagram of a closing control system for a primary fusion switch provided in an embodiment of the present invention; Figure 3 A schematic diagram of the hardware structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only 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 ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0020] like Figure 1 FIG. 1 is a flow chart of a closing control method of a primary fusion switch provided by an embodiment of the present invention. Figure 1 The execution subject of the method shown may be a software and / or hardware device. The execution subject of the present application may include but is not limited to at least one of the following: user equipment, network equipment, etc. Among them, user equipment may include but is not limited to computers, smart phones, personal digital assistants (PDAs) and the electronic devices mentioned above. Network equipment may include but is not limited to a single network server, a server group consisting of multiple network servers, or a cloud based on cloud computing consisting of a large number of computers or network servers, wherein cloud computing is a type of distributed computing, a super virtual computer composed of a group of loosely coupled computers. This embodiment does not limit this. It includes steps S1 to S5, as follows: S1, receives the closing command and calculates the expected time difference between the current moment and the next voltage zero crossing moment; Among them, when the closing command is received, the current voltage waveform of the power grid can be monitored and analyzed in real time, and the voltage periodicity can be accurately predicted, and then the expected time difference from the current moment to the next voltage waveform zero crossing point can be calculated.

[0021] It should be noted that the voltage zero crossing point refers to the moment when the grid voltage waveform crosses the zero line from negative to positive or from positive to negative. It can effectively reduce the arc generation at the moment of closing the circuit breaker and protect the equipment from impact.

[0022] S2, adjusting the expected time difference according to the inherent closing time parameter of the permanent magnet mechanism, and generating a corresponding closing delay instruction; Among them, the expected time difference can be adjusted accordingly according to the pre-set inherent closing time parameters of the permanent magnet mechanism to generate an accurate closing delay instruction, thereby ensuring that the closing action occurs within the optimal time window near the voltage zero crossing point, achieving impact-free or minimal impact closing.

[0023] In addition, as the switch actuator, the permanent magnet mechanism has an inherent closing time, that is, the time from receiving the instruction to actually completing the closing action, which directly affects the accuracy of the closing operation.

[0024] S3, receiving the closing delay instruction, triggering the closing operation to be completed within the target time window; It is understood that upon receiving the closing delay command, the corresponding closing operation logic can be triggered to ensure that the closing action can be successfully completed within the target time window. This step requires not only a high degree of timeliness but also the reliability of the closing operation to avoid closing failures or equipment damage caused by timing errors.

[0025] S4, based on the adaptive threshold adjustment mechanism, monitoring the real-time frequency fluctuation data of the target power grid, and dynamically adjusting the voltage zero-crossing prediction threshold corresponding to the target time window according to the real-time frequency fluctuation data; In practical applications, to further improve the accuracy and adaptability of closing operations, an adaptive threshold adjustment mechanism can be introduced. This mechanism can monitor the frequency fluctuation data of the target power grid in real time, which reflects the dynamic changes in the power grid's operating status.

[0026] Based on the real-time frequency fluctuation data of the target power grid, the voltage zero-crossing prediction threshold corresponding to the target time window can be dynamically adjusted to adapt to slight changes in the grid frequency and ensure that the synchronization of the closing operation is not affected by grid fluctuations.

[0027] S5, based on an online learning calibration mechanism, automatically updating the inherent closing time parameter according to the historical closing data of the target power grid.

[0028] Among them, in order to continuously optimize the performance of the closing operation, an online learning calibration mechanism can be adopted to automatically update the inherent closing time parameters of the permanent magnet mechanism by analyzing the historical closing data of the target power grid, including closing time, voltage waveform characteristics, etc.

[0029] Through the above approach, the predictive capability of the closing control system for future closing operations can be improved, so that the entire closing control system can be adaptively adjusted according to the actual operating conditions of the power grid, achieving efficient and accurate power grid switch control.

[0030] In a specific implementation process, the receiving of the closing instruction and the calculation of the expected time difference between the current moment and the next voltage zero-crossing moment specifically include: monitoring a communication channel from a remote dispatch center or a local control unit and receiving a closing command in the communication channel; Verifying the legitimacy of the closing instruction and performing content analysis on the closing instruction that has passed the verification to obtain closing instruction operation information; Based on the closing instruction operation information, the current moment is obtained, and according to the voltage waveform data and voltage frequency information of the target power grid, the next voltage zero-crossing moment is predicted, and the expected time difference between the current moment and the next voltage zero-crossing moment is calculated.

[0031] First, the status of the communication channels from the remote dispatch center or local automation control unit can be continuously monitored to ensure that the information transmission path is unobstructed. Then, communication technologies such as fiber optic communication or wireless communication networks can be used to receive the closing instructions transmitted via these communication channels in real time.

[0032] Next, to ensure safe and stable grid operation, received closing commands can be rigorously verified for legitimacy. This includes multiple security mechanisms, including digital signature verification, permission verification, and command format checks, to ensure their authenticity and validity. For verified closing commands, their content can be parsed to extract specific information about the closing operation, including the target circuit breaker number, closing time requirements, and additional conditions.

[0033] Furthermore, the current system timestamp can be accurately obtained based on the parsed closing command operation information, serving as a reference point for time calculation. Simultaneously, the target grid's real-time voltage database can be accessed, which stores waveform data of grid voltage changes over time, as well as real-time monitoring information of voltage and frequency.

[0034] Finally, the precise moment of the next voltage zero crossing can be predicted based on historical voltage waveform data and current voltage frequency information. This allows the expected time difference between the current moment and the predicted next voltage zero crossing to be calculated. Based on this time difference, the circuit breaker can be closed at the optimal phase of the voltage waveform, reducing grid surges and harmonics, ensuring safe and stable grid operation.

[0035] The calculation process of the next voltage zero-crossing moment and the expected time difference is as follows: The expression for setting the voltage at any moment is as follows: Where, represents the voltage at any time t; Indicates the peak voltage; represents the angular frequency; Indicates the initial phase, that is, the offset of the voltage waveform relative to the time axis; when When the target grid has the next voltage zero crossing, that is, ,get: Where, Indicates the time when the next voltage zero crossing occurs in the target power grid; represents the angular frequency; Indicates the initial phase, that is, the offset of the voltage waveform relative to the time axis; Indicates the sequence number of the next voltage zero crossing point; The expected time difference between the current moment and the next voltage zero-crossing moment is calculated as follows: Where, Indicates the expected time difference between the current moment and the next voltage zero-crossing moment; Indicates the time when the next voltage zero crossing occurs in the target power grid; Indicates the current moment.

[0036] It is understandable that the voltage at any moment can be set first .when When , the voltage waveform in the target grid will reach the next zero-crossing point, i.e. ,at this time Solving the above equation, we can get , where k is an integer representing the sequence number of the next voltage zero crossing point.

[0037] Finally, the expected time difference from the current moment to the next voltage zero-crossing point can be accurately predicted, which helps to ensure the stable operation of the power system and fault prediction.

[0038] The adjusting the expected time difference according to the inherent closing time parameter of the permanent magnet mechanism to generate a corresponding closing delay instruction specifically includes: The inherent closing time parameter includes an average inherent closing time and a standard deviation; Based on the normal distribution algorithm, the expected time difference is adjusted according to the average inherent closing time and the standard deviation. The corresponding calculation formula is as follows: Where, represents the adjusted expected time difference; Indicates the expected time difference between the current moment and the next voltage zero-crossing moment; Indicates the average inherent closing time; Indicates the Z score corresponding to the confidence level of the normal distribution algorithm; represents the standard deviation; The closing delay instruction is generated according to the adjusted expected time difference, and the closing delay instruction includes the closing delay time and closing actuator information.

[0039] It should be noted that the inherent closing time parameters include the average inherent closing time and the standard deviation. The average inherent closing time reflects the average time of the permanent magnet mechanism during multiple closing operations, while the standard deviation measures the degree of dispersion of these time values relative to the average.

[0040] Furthermore, based on the normal distribution algorithm in statistics, the expected time difference between the current moment and the next voltage zero crossing point can be adjusted using the average inherent closing time and standard deviation, so that the expected time difference can be compensated according to the inherent characteristics of the permanent magnet mechanism, thereby improving the accuracy and reliability of the closing operation.

[0041] Finally, a specific closing delay control instruction can be generated based on the adjusted expected time difference. This instruction includes the closing delay time and information about the closing actuator, such as the actuator type, number, and any specific operating parameters that may be required. This helps ensure the precise execution of the closing operation and improves the stability and security of the entire power system.

[0042] Determine a target closing actuator according to the closing actuator information, and the target closing actuator starts timing until the closing delay time ends; Within the target time window, when it is monitored that the voltage of the target power grid meets the preset closing condition, that is, the voltage of the target power grid is less than or equal to the voltage zero-crossing prediction threshold, the target closing actuator is triggered and performs the closing operation.

[0043] In practical applications, the target closing actuator can be accurately identified and selected based on the closing actuator information. Once the target closing actuator is selected, its built-in timing module is activated, and the timing process will continue until the preset closing delay time is completely over.

[0044] During this period, the target grid's voltage dynamics can be monitored in real time to determine whether it meets pre-set closing conditions. Specifically, if the target grid's real-time voltage is detected to be lower than or equal to a preset voltage zero-crossing prediction threshold within a specific target time window, the closing condition is met.

[0045] Furthermore, the target closing actuator will receive the corresponding trigger signal, and then start and execute the closing action, so that the power grid status can be accurately judged and quickly responded to, ensuring the safe, stable and efficient operation of the power grid and realizing the automated control of the power system.

[0046] The adaptive threshold adjustment mechanism is based on monitoring the real-time frequency fluctuation data of the target power grid and dynamically adjusting the voltage zero-crossing prediction threshold corresponding to the target time window according to the real-time frequency fluctuation data, specifically including: monitoring real-time frequency fluctuation data of the target power grid based on the adaptive threshold adjustment mechanism; According to the real-time frequency fluctuation data, the current cycle length of the target power grid is determined, and the predicted voltage zero-crossing time is determined according to the current cycle length. The corresponding calculation formula is as follows: Where T represents the current cycle length of the target power grid; f represents the frequency of the target power grid; Indicates the current moment; Indicates the predicted voltage zero-crossing moment; The moment when the next voltage zero crossing occurs in the target power grid That is, the actual voltage zero-crossing time, based on the predicted voltage zero-crossing time and the actual voltage zero crossing moment Adjust the voltage zero-crossing prediction threshold, and the corresponding calculation formula is as follows: Where, Indicates the adjusted current time The voltage zero-crossing prediction threshold; Represents the proportional gain of the PID controller; Indicates the current time The predicted voltage zero-crossing time and the actual voltage zero crossing moment The error between Represents the integral gain of the PID controller; Indicates the current time The predicted voltage zero-crossing time and the actual voltage zero crossing moment The cumulative error between Represents the differential gain of the PID controller.

[0047] Among them, first of all, high-precision sensors and signal processing technology can be used to continuously monitor the real-time frequency fluctuation data of the target power grid based on an adaptive threshold adjustment mechanism.

[0048] Then, the current cycle length of the target power grid can be calculated based on the collected real-time frequency fluctuation data, and the predicted voltage zero-crossing time can be determined based on the current cycle length. The voltage zero-crossing prediction threshold can be adjusted in combination with the actual voltage zero-crossing time, so that the voltage zero-crossing prediction threshold can be accurately controlled to improve the stability and reliability of the power grid operation.

[0049] The method of automatically updating the inherent closing time parameter based on the historical closing data of the target power grid based on the online learning calibration mechanism specifically includes: When extracting the historical closing features corresponding to the historical closing data and classifying the historical closing features based on the support vector machine, the corresponding optimization problem is as follows: Where, represents the weight vector of the i-th support vector machine, which is used to determine the direction of the classification hyperplane; represents the bias term of the i-th support vector machine, which is used to determine the distance between the classification hyperplane and the origin; represents the slack variable when the j-th historical closing feature is linearly inseparable in the i-th support vector machine; C represents the regularization parameter, which is used to control the trade-off between classification interval and classification error; R represents the total number of historical closing features; min represents the minimum value operation; Represents the summation symbol.

[0050] The constraints corresponding to the optimization problem are as follows: Where, Indicates the feature category label corresponding to the j-th historical closing feature; represents the jth historical closing feature; represents the weight vector of the i-th support vector machine, which is used to determine the direction of the classification hyperplane; represents the bias term of the i-th support vector machine, which is used to determine the distance between the classification hyperplane and the origin; represents the slack variable when the j-th historical closing feature is linearly inseparable in the i-th support vector machine; N represents the total number of historical closing features; Based on the feature classification results of the historical closing features, the closing time interval category corresponding to the historical closing data is predicted, and the inherent closing time parameter is adjusted and updated according to the prediction result.

[0051] In practical applications, based on the online learning calibration mechanism, the inherent closing time parameters can be dynamically adjusted and optimized according to the historical closing data accumulated over a long period of time in the target power grid.

[0052] Specifically, we first conduct an in-depth analysis of historical closing data to extract representative and discriminative historical closing features. We then use the support vector machine (SVM) algorithm to construct an optimization problem and constraints to classify these historical closing features.

[0053] Finally, based on the results of the above feature classification, the closing time interval categories corresponding to the historical closing data can be predicted, and combined with the actual operating conditions, the inherent closing time parameters can be accurately adjusted and updated to further improve the closing efficiency and stability of the power grid.

[0054] like Figure 2 FIG. 1 is a system block diagram of a closing control system for a primary fusion switch according to an embodiment of the present invention. The closing control system includes: A closing command receiving module is used to receive a closing command and calculate the expected time difference between the current moment and the next voltage zero-crossing moment; A delay instruction generating module is used to adjust the expected time difference according to the inherent closing time parameter of the permanent magnet mechanism and generate a corresponding closing delay instruction; A closing operation execution module, configured to receive the closing delay instruction and trigger the closing operation to be completed within a target time window; An adaptive threshold adjustment module is used to monitor the real-time frequency fluctuation data of the target power grid based on the adaptive threshold adjustment mechanism, and dynamically adjust the voltage zero-crossing prediction threshold corresponding to the target time window according to the real-time frequency fluctuation data; An online learning calibration module is used to automatically update the inherent closing time parameter according to the historical closing data of the target power grid based on an online learning calibration mechanism.

[0055] Figure 2 The apparatus of the embodiment shown can be used to perform Figure 1 The implementation principles and technical effects of the steps in the method embodiment shown are similar and will not be repeated here.

[0056] An electronic device includes a memory and a processor, wherein the memory stores a computer program. When the processor runs the computer program stored in the memory, the processor executes the closing control method of a primary fusion switch as described in any one of the above items.

[0057] like Figure 3 FIG. 1 is a schematic diagram of the hardware structure of an electronic device provided by an embodiment of the present invention. The electronic device 30 includes: a processor 31, a memory 32 and a computer program; The memory 32 is used to store the computer program, which may also be a flash memory. The computer program is, for example, an application program or a functional module for implementing the above method.

[0058] The processor 31 is configured to execute the computer program stored in the memory to implement the various steps performed by the device in the above method. For details, please refer to the relevant description in the above method embodiment.

[0059] Optionally, the memory 32 may be independent or integrated with the processor 31 .

[0060] When the memory 32 is a device independent of the processor 31, the device may further include: The bus 33 is used to connect the memory 32 and the processor 31 .

[0061] The present invention also provides a readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, it is used to implement the methods provided in the various embodiments described above.

[0062] The readable storage medium may be a computer storage medium or a communication medium. Communication media include any medium that facilitates the transfer of computer programs from one location to another. Computer storage media may be any available medium that can be accessed by a general-purpose or special-purpose computer. For example, a readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium may also be an integral part of the processor. The processor and the readable storage medium may be located in an application-specific integrated circuit (ASIC). In addition, the ASIC may be located in a user device. Of course, the processor and the readable storage medium may also exist as discrete components in a communication device. The readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.

[0063] The present invention also provides a program product, which includes execution instructions stored in a readable storage medium. At least one processor of a device can read the execution instructions from the readable storage medium, and at least one processor executes the execution instructions so that the device implements the methods provided in the various embodiments described above.

[0064] In the embodiments of the above-mentioned devices, it should be understood that the processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASICs), etc. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the present invention may be directly executed by a hardware processor or by a combination of hardware and software modules within the processor.

[0065] Through the introduction of the above embodiments, the present invention adopts a closing control method, system and electronic equipment of a single fusion switch, which receives a closing instruction and calculates the expected time difference between the current moment and the next voltage zero crossing moment; adjusts the expected time difference according to the inherent closing time parameter of the permanent magnet mechanism to generate a corresponding closing delay instruction; receives the closing delay instruction and triggers the closing operation to be completed within the target time window; based on the adaptive threshold adjustment mechanism, monitors the real-time frequency fluctuation data of the target power grid, and dynamically adjusts the voltage zero crossing prediction threshold corresponding to the target time window according to the real-time frequency fluctuation data; based on the online learning calibration mechanism, automatically updates the inherent closing time parameters according to the historical closing data of the target power grid, so that accurate closing can be achieved at the voltage zero crossing point, significantly reducing the transient current and inrush current phenomena generated during closing, greatly improving the stability and safety of the power system, and can maintain high-precision closing control in a power grid frequency fluctuation environment through the adaptive threshold adjustment mechanism and the online learning calibration mechanism, thereby enhancing the anti-interference ability and reliability of the entire system.

[0066] The present invention adopts a fast and accurate closing control method based on the characteristics of a permanent magnet mechanism, which can achieve accurate closing at the voltage zero point, significantly reducing the transient current and inrush current phenomena generated during closing, and greatly improving the stability and safety of the power system; the present invention adopts an adaptive threshold adjustment mechanism, which can maintain high-precision closing control in an environment of power grid frequency fluctuations, thereby enhancing the anti-interference ability and reliability of the entire system; the present invention effectively integrates an online learning and calibration function, which can correct the inherent closing time changes caused by mechanism aging, ensure the accuracy of the inherent closing time under long-term operation, extend the maintenance cycle of the power system, reduce operation and maintenance costs, and improve the overall performance indicators of power grid equipment.

[0067] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A closing control method for a primary fusion switch, characterized in that: The closing control method includes: Receive the closing command and calculate the expected time difference between the current moment and the next voltage zero-crossing moment; The expected time difference is adjusted according to the inherent closing time parameter of the permanent magnet mechanism to generate a corresponding closing delay instruction; receiving the closing delay instruction and triggering the closing operation to be completed within the target time window; Based on the adaptive threshold adjustment mechanism, the real-time frequency fluctuation data of the target power grid is monitored, and the voltage zero-crossing prediction threshold corresponding to the target time window is dynamically adjusted according to the real-time frequency fluctuation data; Based on an online learning calibration mechanism, the inherent closing time parameter is automatically updated according to the historical closing data of the target power grid.

2. The closing control method of a primary fusion switch according to claim 1, characterized in that: The receiving of the closing instruction and calculating the expected time difference between the current moment and the next voltage zero-crossing moment specifically include: monitoring a communication channel from a remote dispatch center or a local control unit and receiving a closing command in the communication channel; Verifying the legitimacy of the closing instruction and performing content analysis on the closing instruction that has passed the verification to obtain closing instruction operation information; Based on the closing instruction operation information, the current moment is obtained, and according to the voltage waveform data and voltage frequency information of the target power grid, the next voltage zero-crossing moment is predicted, and the expected time difference between the current moment and the next voltage zero-crossing moment is calculated.

3. The closing control method of a primary fusion switch according to claim 2, characterized in that: The calculation process of the next voltage zero-crossing moment and the expected time difference is as follows: The expression for setting the voltage at any moment is as follows: Where, represents the voltage at any time t; Indicates the peak voltage; represents the angular frequency; Indicates the initial phase, that is, the offset of the voltage waveform relative to the time axis; when When the target grid has the next voltage zero crossing, that is, ,get: Where, Indicates the time when the next voltage zero crossing occurs in the target power grid; represents the angular frequency; Indicates the initial phase, that is, the offset of the voltage waveform relative to the time axis; Indicates the sequence number of the next voltage zero crossing point; The expected time difference between the current moment and the next voltage zero-crossing moment is calculated as follows: Where, Indicates the expected time difference between the current moment and the next voltage zero-crossing moment; Indicates the time when the next voltage zero crossing occurs in the target power grid; Indicates the current moment.

4. The closing control method of a primary fusion switch according to claim 1, characterized in that: The adjusting the expected time difference according to the inherent closing time parameter of the permanent magnet mechanism to generate a corresponding closing delay instruction specifically includes: The inherent closing time parameter includes an average inherent closing time and a standard deviation; Based on the normal distribution algorithm, the expected time difference is adjusted according to the average inherent closing time and the standard deviation. The corresponding calculation formula is as follows: Where, represents the adjusted expected time difference; Indicates the expected time difference between the current moment and the next voltage zero-crossing moment; Indicates the average inherent closing time; Indicates the Z score corresponding to the confidence level of the normal distribution algorithm; represents the standard deviation; The closing delay instruction is generated according to the adjusted expected time difference, and the closing delay instruction includes the closing delay time and closing actuator information.

5. The closing control method of a primary fusion switch according to claim 4, characterized in that: Determine a target closing actuator according to the closing actuator information, and the target closing actuator starts timing until the closing delay time ends; Within the target time window, when it is monitored that the voltage of the target power grid meets the preset closing condition, that is, the voltage of the target power grid is less than or equal to the voltage zero-crossing prediction threshold, the target closing actuator is triggered and performs the closing operation.

6. The closing control method of a primary fusion switch according to claim 1, characterized in that: The adaptive threshold adjustment mechanism is based on monitoring the real-time frequency fluctuation data of the target power grid and dynamically adjusting the voltage zero-crossing prediction threshold corresponding to the target time window according to the real-time frequency fluctuation data, specifically including: monitoring real-time frequency fluctuation data of the target power grid based on the adaptive threshold adjustment mechanism; According to the real-time frequency fluctuation data, the current cycle length of the target power grid is determined, and the predicted voltage zero-crossing time is determined according to the current cycle length. The corresponding calculation formula is as follows: Where T represents the current cycle length of the target power grid; f represents the frequency of the target power grid; Indicates the current moment; Indicates the predicted voltage zero-crossing time; the time when the next voltage zero-crossing occurs in the target grid That is, the actual voltage zero-crossing time, based on the predicted voltage zero-crossing time and the actual voltage zero crossing moment Adjust the voltage zero-crossing prediction threshold, and the corresponding calculation formula is as follows: Where, Indicates the adjusted current time The voltage zero-crossing prediction threshold; Represents the proportional gain of the PID controller; Indicates the current time The predicted voltage zero-crossing time and the actual voltage zero crossing moment The error between Represents the integral gain of the PID controller; Indicates the current time The predicted voltage zero-crossing time and the actual voltage zero crossing moment The cumulative error between Represents the differential gain of the PID controller.

7. The closing control method of a primary fusion switch according to claim 1, characterized in that: The method of automatically updating the inherent closing time parameter based on the historical closing data of the target power grid based on the online learning calibration mechanism specifically includes: When extracting the historical closing features corresponding to the historical closing data and classifying the historical closing features based on the support vector machine, the corresponding optimization problem is as follows: Where, represents the weight vector of the i-th support vector machine, which is used to determine the direction of the classification hyperplane; represents the bias term of the i-th support vector machine, which is used to determine the distance between the classification hyperplane and the origin; represents the slack variable when the j-th historical closing feature is linearly inseparable in the i-th support vector machine; C represents the regularization parameter, which is used to control the trade-off between classification interval and classification error; R represents the total number of historical closing features; min represents the minimum value operation; Represents the summation symbol.

8. The closing control method of a primary fusion switch according to claim 7, characterized in that: The constraints corresponding to the optimization problem are as follows: Where, Indicates the feature category label corresponding to the j-th historical closing feature; represents the jth historical closing feature; represents the weight vector of the i-th support vector machine, which is used to determine the direction of the classification hyperplane; represents the bias term of the i-th support vector machine, which is used to determine the distance between the classification hyperplane and the origin; represents the slack variable when the j-th historical closing feature is linearly inseparable in the i-th support vector machine; N represents the total number of historical closing features; Based on the feature classification results of the historical closing features, the closing time interval category corresponding to the historical closing data is predicted, and the inherent closing time parameter is adjusted and updated according to the prediction result.

9. A closing control system for a primary fusion switch, applied to the closing control method for a primary fusion switch according to any one of claims 1 to 8, the closing control system comprising: A closing command receiving module is used to receive a closing command and calculate the expected time difference between the current moment and the next voltage zero-crossing moment; A delay instruction generating module, configured to adjust the expected time difference according to the inherent closing time parameter of the permanent magnet mechanism and generate a corresponding closing delay instruction; A closing operation execution module, configured to receive the closing delay instruction and trigger the closing operation to be completed within a target time window; An adaptive threshold adjustment module is used to monitor the real-time frequency fluctuation data of the target power grid based on the adaptive threshold adjustment mechanism, and dynamically adjust the voltage zero-crossing prediction threshold corresponding to the target time window according to the real-time frequency fluctuation data; An online learning calibration module is used to automatically update the inherent closing time parameter according to the historical closing data of the target power grid based on an online learning calibration mechanism.

10. An electronic device, characterized in that: The method comprises a memory and a processor, wherein a computer program is stored in the memory, and when the processor runs the computer program stored in the memory, the processor executes the closing control method of a primary fusion switch according to any one of claims 1 to 8.

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