A switching method, device, medium, and program product for a primary and secondary fusion switch.

Abstract

A switching method, apparatus, medium, and program product for a primary and secondary integrated switch are disclosed, relating to the field of intelligent power dispatching. The method includes: acquiring real-time and historical electrical parameters of the operating power source; determining the system disturbance level based on the rate of change of the real-time electrical parameters relative to the historical electrical parameters, and determining the system sampling period based on the system disturbance level; collecting target electrical parameters of the power source to be switched in within the system sampling period, and calculating a switching evaluation index based on the real-time electrical parameters and the target electrical parameters; determining the dynamic control parameters of the power source to be switched in based on the switching evaluation index and the system disturbance level; generating a switching enable signal when the adjustment rate of the dynamic control parameters is greater than a first preset threshold and the switching evaluation index is less than a second preset threshold; and controlling the primary and secondary integrated switch to perform a switching operation based on the switching enable signal. Implementing this application can improve the transient response performance during power switching.

Description

Technical Field

[0001] This application relates to the field of intelligent power dispatching, and in particular to a switching method, device, medium, and program product for a primary and secondary integrated switch. Background Technology

[0002] With the intelligent development of power systems, critical load locations such as data centers and hospitals are placing higher demands on power supply reliability. These locations typically have multiple power sources, including mains power, generators, and energy storage systems, requiring switching between these sources to ensure power continuity. During the switching process, due to differences in frequency and phase between different power sources, and the extremely high power quality requirements of the loads, optimizing the transient characteristics during power switching has become a crucial technical challenge.

[0003] Power switching schemes in related technologies typically employ synchronous detection technology to switch between different power sources. This scheme first uses a phase-locked loop (PLL) circuit to detect the phase difference between the power source to be switched in and the operating power source. When the phase difference reaches a set range, a switching operation is triggered. During the switching process, a PID controller adjusts the output parameters of the power source to be switched in, and an LC filter network is used to suppress voltage fluctuations during the switching process. Simultaneously, a buffer circuit is used to store the energy at the moment of switching to compensate for power fluctuations during the switching process.

[0004] However, in practical applications, the linear characteristics of PID controllers result in a slow response speed under large disturbance conditions, making it difficult to accurately track rapid changes in load. Summary of the Invention

[0005] This application provides a switching method, apparatus, medium, and program product for a primary and secondary integrated switch, which is used to improve the transient response performance during power switching.

[0006] In a first aspect, this application provides a switching method for a primary and secondary integrated switch, applied to a switchgear. The method includes: acquiring real-time electrical parameters and historical electrical parameters of the operating power supply; determining the system disturbance level based on the rate of change of the real-time electrical parameters relative to the historical electrical parameters, and determining the system sampling period according to the system disturbance level; the system sampling period is inversely proportional to the system disturbance level; acquiring the target electrical parameters of the power supply to be switched in within the system sampling period, and calculating a switching evaluation index based on the real-time electrical parameters and the target electrical parameters; the switching evaluation index includes a voltage amplitude deviation coefficient, a frequency stability coefficient, and a phase matching coefficient; determining the dynamic control parameters of the power supply to be switched in based on the switching evaluation index and the system disturbance level; generating a switching enable signal when the adjustment rate of the dynamic control parameters is greater than a first preset threshold and the switching evaluation index is less than a second preset threshold; and controlling the primary and secondary integrated switch to perform a switching operation according to the switching enable signal.

[0007] In the above embodiments, the switching device acquires the operating power parameters in real time and dynamically adjusts the system sampling period, and performs switching control in combination with the switching evaluation index, so that the switching process can flexibly adjust the control strategy according to the system disturbance level; at the same time, it improves the response speed by shortening the sampling period when the disturbance is large, and appropriately extends the sampling period when the disturbance is small to reduce system overhead.

[0008] In conjunction with some embodiments of the first aspect, in some embodiments, the steps of determining the system disturbance level based on the rate of change of real-time electrical parameters relative to historical electrical parameters, and determining the system sampling period based on the system disturbance level, specifically include: acquiring multiple sets of historical electrical parameter samples within a preset time window; performing moving average filtering on the multiple sets of historical electrical parameter samples to obtain reference electrical parameters; calculating the voltage change rate, frequency change rate, and phase change rate of the real-time electrical parameters relative to the reference electrical parameters; comparing the voltage change rate, frequency change rate, and phase change rate with the corresponding disturbance level judgment thresholds to obtain numerical comparison results; determining the highest disturbance level from multiple preset disturbance levels as the system disturbance level based on the numerical comparison results; the disturbance level judgment threshold increasing with the disturbance level; and determining the system sampling period based on the system disturbance level.

[0009] In the above embodiments, the switch switching device obtains benchmark parameters by performing a moving average filter on historical data, calculates multi-dimensional change rates by combining real-time parameters, and uses progressive disturbance level judgment thresholds to achieve accurate classification of system disturbance levels. This can effectively suppress the influence of data noise and improve the accuracy of disturbance level judgment.

[0010] In conjunction with some embodiments of the first aspect, in some embodiments, the step of collecting the target electrical parameters of the power supply to be switched in during the system sampling period and calculating the switching evaluation index based on the real-time electrical parameters and the target electrical parameters specifically includes: collecting the target electrical parameters of the power supply to be switched in during the system sampling period; extracting the effective value of the operating voltage, operating frequency, and operating phase angle from the real-time electrical parameters, and extracting the effective value of the target voltage, target frequency, and target phase angle from the target electrical parameters; calculating the voltage amplitude deviation coefficient based on the effective value of the operating voltage and the effective value of the target voltage, calculating the frequency stability coefficient based on the operating frequency and the target frequency, and calculating the phase matching coefficient based on the operating phase angle and the target phase angle; determining the weighting coefficients of the voltage amplitude deviation coefficient, the frequency stability coefficient, and the phase matching coefficient respectively, and calculating the switching evaluation index by weighting.

[0011] In the above embodiments, the switching device extracts feature parameters in three dimensions: voltage, frequency, and phase, and uses a weighted calculation method to comprehensively evaluate the switching conditions. The flexible configuration of the weight coefficients allows the system to adjust the importance of each parameter according to the actual application scenario, thereby improving the adaptability of the switching evaluation.

[0012] In conjunction with some embodiments of the first aspect, in some embodiments, before determining the dynamic control parameters of the power supply to be switched in based on the switching evaluation index and the system disturbance level, the method further includes: selecting initial control parameters from a preset set of control parameter templates based on the system disturbance level; calculating correction coefficients for each parameter in the initial control parameters based on the switching evaluation index; determining the dynamic control parameters of the power supply to be switched in based on the initial control parameters and the corresponding correction coefficients; the control parameters include voltage regulation coefficients, frequency regulation coefficients, and phase regulation coefficients; the correction coefficients and the switching evaluation index satisfy an exponential function relationship, and the correction coefficients decrease exponentially as the switching evaluation index increases.

[0013] In the above embodiments, the switching device selects the initial control parameters based on the system disturbance level, and realizes the dynamic adjustment of the parameters through the correction coefficient. The exponential function relationship between the correction coefficient and the evaluation index ensures the smooth transition of the control parameters with the state change, effectively avoiding system oscillation caused by parameter mutation.

[0014] In conjunction with some embodiments of the first aspect, in some embodiments, the step of determining the dynamic control parameters of the power supply to be switched in based on the initial control parameters and the corresponding correction coefficients specifically includes: using the system disturbance level, switching evaluation index, and historical switching data including overshoot, settling time, and steady-state error as training samples, and training a correction coefficient model using a polynomial regression method; the order of the regression polynomial in the polynomial regression method does not exceed the third order; determining the reference value matrix of the correction coefficients based on the correction coefficient model; the reference value matrix includes voltage correction coefficient, frequency correction coefficient, and phase correction coefficient; calculating the statistical distribution characteristics of each correction coefficient under different disturbance levels based on the reference value matrix, and constructing a piecewise linear regression model based on the statistical distribution characteristics; the statistical distribution characteristics include mean, variance, and skewness; inputting the system disturbance level and switching evaluation index into the piecewise linear regression model to obtain the real-time correction coefficients; and calculating the dynamic control parameters of the power supply to be switched in based on the real-time correction coefficients and the initial control parameters.

[0015] In the above embodiments, the switch switching device uses a multinomial regression to train the correction coefficient model and combines it with piecewise linear regression to construct a dynamic parameter adjustment mechanism.

[0016] In conjunction with some embodiments of the first aspect, in some embodiments, after controlling the primary and secondary fusion switches to perform a switching operation according to the switching enable signal, the method further includes: real-time monitoring of the transient response waveform of the primary and secondary fusion switches during the switching process; extracting characteristic parameters of the transient response waveform; the characteristic parameters include overshoot, settling time, and steady-state error; and adjusting dynamic control parameters when the characteristic parameters exceed the preset performance index range.

[0017] In the above embodiments, the switching device monitors the transient response characteristics during the switching process in real time and adjusts the control parameters in a timely manner when the performance indicators exceed the limits, ensuring that the system can continuously optimize the control effect in actual operation and improving the robustness of the system.

[0018] In conjunction with some embodiments of the first aspect, in some embodiments, the step of adjusting the dynamic control parameters when the characteristic parameters exceed the preset performance index range specifically includes: calculating the deviation values ​​of the overshoot, settling time, and steady-state error of the transient response waveform with the corresponding performance index; determining the initial adjustment direction of the voltage regulation coefficient, frequency regulation coefficient, and phase regulation coefficient according to the sign of each deviation value; iteratively adjusting the dynamic control parameters according to a preset step size, and recording the new transient response characteristics after each adjustment; if the adjusted characteristic parameters are better than before the adjustment, keeping the current adjustment direction unchanged and increasing the step size by a preset growth coefficient; if the adjusted characteristic parameters are worse than before the adjustment, reversing the adjustment direction and decreasing the step size by a preset reduction coefficient; statistically analyzing the adjustment effect of a preset number of times, and recording the corresponding adjusted dynamic control parameters when the adjustment direction changes continuously or the step size is less than the minimum threshold.

[0019] In the above embodiments, the switching device adopts an iterative optimization strategy based on performance index deviation. By dynamically adjusting the step size and direction, it achieves precise optimization of control parameters, which can ensure convergence efficiency while avoiding oscillations during parameter adjustment, thus realizing stable optimization of control parameters.

[0020] In a second aspect, embodiments of this application provide a switch switching device, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, and the memory is used to store computer program code, the computer program code including computer instructions, and the one or more processors call the computer instructions to cause the switch switching device to perform the method described in the first aspect and any possible implementation thereof.

[0021] Thirdly, embodiments of this application provide a computer program product containing instructions that, when the computer program product is run on a switch switching device, cause the switch switching device to perform the method described in the first aspect and any possible implementation thereof.

[0022] Fourthly, embodiments of this application provide a computer-readable storage medium including instructions that, when executed on a switch switching device, cause the switch switching device to perform the method described in the first aspect and any possible implementation thereof.

[0023] Understandably, the switch-changing device provided in the second aspect, the computer program product provided in the third aspect, and the computer storage medium provided in the fourth aspect are all used to execute the method provided in the embodiments of this application. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects in the corresponding methods, and will not be repeated here.

[0024] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0025] 1. By employing a dynamic sampling mechanism based on system disturbance levels and a multi-dimensional switching evaluation system, the disturbance level is determined by real-time calculation of the rate of change of electrical parameters, and the sampling period is dynamically adjusted accordingly. This allows the system to maintain an appropriate response speed under different disturbance intensities. A comprehensive evaluation using voltage, frequency, and phase dimensions ensures the comprehensiveness of switching conditions, and real-time adjustment of dynamic control parameters improves system adaptability. Therefore, it can improve response speed while ensuring switching stability. This effectively solves the problem of response lag or resource waste caused by fixed sampling periods in existing technologies, achieving efficient and stable control of the power switching process.

[0026] 2. By employing a parameter template selection mechanism based on disturbance level and an exponentially decaying correction coefficient adjustment method, a baseline control strategy is established by selecting initial control parameters from a preset template. Dynamic adjustment is achieved by calculating the correction coefficients of each parameter based on the switching evaluation index, allowing control parameters to change smoothly with the system state. This effectively solves the system oscillation problem caused by fixed or drastic control parameters in existing technologies, as well as the poor convergence during parameter optimization. It achieves adaptive optimization of control parameters and improves the dynamic control performance of the system.

[0027] 3. By employing real-time transient response monitoring and a performance-based parameter adjustment mechanism, the system continuously optimizes its control performance by monitoring characteristic parameters such as overshoot, settling time, and steady-state error during the switching process and adjusting control parameters promptly when limits are exceeded. The closed-loop feedback parameter adjustment strategy ensures real-time correction of the control effect, thus adapting to various operating conditions. This effectively solves the problems of control performance degradation caused by the lack of a real-time optimization mechanism in existing technologies, as well as the instability of long-term system performance, enhancing the system's adaptability and reliability. Attached Figure Description

[0028] Figure 1 This is a flowchart illustrating a switching method for a primary and secondary fusion switch in an embodiment of this application;

[0029] Figure 2 This is another flowchart illustrating the switching method of the primary and secondary fusion switches in the embodiments of this application;

[0030] Figure 3 This is a schematic diagram of the physical device structure of a switch switching device in the embodiments of this application. Detailed Implementation

[0031] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification of this application, the singular expressions “a,” “an,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to any or all possible combinations including one or more of the listed items.

[0032] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0033] To facilitate understanding, the application scenarios of the embodiments of this application are described below.

[0034] In the power supply system of a large data center, multiple backup power sources, including mains power, diesel generators, and UPS power supplies, have been deployed due to continuous business expansion. During system operation, grid fluctuations frequently occur: sometimes minor voltage fluctuations caused by power supply bureau maintenance, sometimes frequency jitter caused by the start-up and shutdown of large loads, and in severe cases, phase abrupt changes caused by lightning strikes. The existing fixed-parameter switching scheme performs poorly in the face of this complex and variable power supply environment: it is too sensitive to minor disturbances, leading to frequent false switching and affecting system stability; and its response is slow under severe disturbances, failing to switch to backup power in a timely manner.

[0035] In related technologies, power switching control can be achieved using a fixed-parameter switching scheme employing phase-locked loop (PLL) detection and PID control. Specifically, a PLL circuit detects the phase difference between the power supply to be switched in and the operating power supply. When the phase difference is less than a set threshold, switching is triggered. Simultaneously, a PID controller adjusts the output parameters to suppress voltage fluctuations during the switching process. The following describes a scenario using the primary and secondary fusion switch switching method from related technologies.

[0036] An industrial park uses a commercially available PLC-controlled automatic switching device. This device uses a phase-locked loop circuit to detect phase and adjusts output parameters through a PID controller. Its switching logic is as follows: when the main power supply voltage drops below 85% of its rated value for 100ms, the switching process is triggered. However, in actual operation, frequent starts and stops of large motors within the park cause drastic changes in the power grid disturbance. Fixed judgment thresholds and PID parameters are inadequate: when set conservatively, the response is too slow under sudden voltage drops, causing sensitive equipment to trip; when set aggressively, even slight power grid fluctuations lead to frequent malfunctions, increasing switch lifespan. Furthermore, due to the linear characteristics of the PID controller, the adjustment process is slow under large disturbance conditions, and voltage fluctuations during switching often exceed 20%, causing protective shutdowns of the load equipment.

[0037] The switching method of the primary and secondary integrated switch in this embodiment of the application determines the system disturbance level by calculating the rate of change of electrical parameters in real time, and dynamically selects the sampling period and control parameter template based on the disturbance level, thereby improving the timeliness and accuracy of switching. The following describes a scenario in which the switching method of the primary and secondary integrated switch in this application is used.

[0038] A semiconductor manufacturing plant has adopted the dynamic adaptive switching scheme proposed in this application. This scheme dynamically classifies system disturbances into three levels—minor, moderate, and severe—by real-time calculation of the rate of change in three dimensions: voltage, frequency, and phase. When a short-circuit fault is detected in the power supply line, the system immediately classifies it as a severe disturbance, automatically shortens the sampling period to 100μs, and simultaneously activates a preset fast switching parameter template. Based on this, control parameters are adjusted in real-time according to the switching evaluation index, making the switching process smooth and controllable. Transient fluctuations during the switching process are controlled within 5%, and the entire process takes less than 20ms, far below the equipment's sensitivity time.

[0039] As can be seen, the switching method of the primary and secondary integrated switch in the embodiments of this application can not only realize the power switching function, but also effectively solve the problem that the fixed parameter scheme cannot adapt to the complex power supply environment. Through multi-dimensional evaluation and adaptive control, the intelligent and precise switching control is realized.

[0040] To facilitate understanding, the method provided in this implementation will be described in detail below, using the above scenario as an example. Please refer to [link / reference]. Figure 1 This is a flowchart illustrating a switching method for a primary and secondary fusion switch in an embodiment of this application.

[0041] S101. Obtain the real-time and historical electrical parameters of the operating power supply.

[0042] Among them, the operating power supply refers to the power supply currently supplying power to the load, which is used to provide a continuous and stable power supply to the load; real-time electrical parameters refer to the electrical characteristic data such as voltage, current, frequency, and phase of the operating power supply at the current moment, which are used to reflect the real-time operating status of the power supply; historical electrical parameters refer to the sequence of electrical characteristic data of the operating power supply recorded over a period of time in history, which are used as a reference benchmark for evaluating the stability of the power supply.

[0043] When a switching device needs to switch power supplies, it first needs to assess the operating status of the current power supply. Specifically, the switching device uses a sampling circuit to collect real-time electrical parameters such as the effective voltage value, frequency, and phase angle of the operating power supply, while simultaneously reading historical electrical parameters from a preset time window (e.g., the most recent 5 minutes) from the data storage unit. The sampling frequency of the sampling circuit is typically no less than 10kHz to ensure accurate capture of rapid changes in electrical parameters.

[0044] In some embodiments, electrical parameters can be acquired and processed in various ways: Optionally, the switchgear can use high-precision voltage and current transformers to collect raw signals, amplify and filter them through signal conditioning circuits, convert the analog signals into digital quantities through a 16-bit or 24-bit analog-to-digital converter, and finally use a DSP for digital signal processing to obtain the required parameters; Optionally, the switchgear can also directly use a digital energy meter chip for sampling and read the processed electrical parameter data through an SPI or I2C interface. It is understood that other types of sensors and signal processing methods can also be used to acquire electrical parameters, and this is not limited here.

[0045] S102. Determine the system disturbance level based on the rate of change of real-time electrical parameters relative to historical electrical parameters, and determine the system sampling period based on the system disturbance level.

[0046] Among them, the rate of change refers to the ratio of the change amplitude of real-time electrical parameters relative to historical electrical parameters to time, which is used to characterize the dynamic change characteristics of electrical parameters; the system disturbance level indicates the severity of external interference to the power system, which is usually divided into several levels such as minor disturbance, moderate disturbance and severe disturbance; the system sampling period refers to the time interval for the switching device to collect the parameters of the power source to be switched in, which is used to balance sampling accuracy and system overhead; the system sampling period is inversely proportional to the system disturbance level.

[0047] After acquiring electrical parameters, the switching device needs to assess the system's disturbance state to determine an appropriate sampling strategy. Specifically, it first calculates the rate of change of real-time voltage, frequency, and phase relative to their historical averages. These rates of change are then compared with preset multi-level disturbance thresholds, and the highest disturbance level is selected as the system disturbance level. Subsequently, the sampling period is dynamically adjusted according to the disturbance level; the higher the disturbance level, the shorter the sampling period, in order to improve the system's response speed to disturbances.

[0048] In some embodiments, the determination of disturbance level and sampling period can be achieved in multiple ways: Optionally, the switch switching device uses a moving average algorithm to filter historical data to obtain a baseline value, calculates the difference between the real-time value and the baseline value, divides it by the time interval to obtain the rate of change, compares the rate of change with the classification thresholds {0.1, 0.3, 0.5, 0.7, 0.9} to determine the disturbance level, and then calculates the sampling period using the mapping function T=Tbase / (1+k*level), where Tbase is the baseline sampling period and k is the adjustment coefficient; Optionally, the switch switching device can also use wavelet transform to detect signal abrupt change characteristics and determine the disturbance level based on the amplitude of the wavelet coefficients. It is understood that other mathematical models and algorithms can also be used to evaluate the disturbance level, which are not limited here.

[0049] S103. Collect the target electrical parameters of the power supply to be switched in during the system sampling period, and calculate the switching evaluation index based on the real-time electrical parameters and the target electrical parameters.

[0050] Among them, the target electrical parameters represent the characteristic parameters of the power supply to be switched in, such as voltage, frequency, and phase, and are used to evaluate its matching degree with the operating power supply; the switching evaluation index includes voltage amplitude deviation coefficient, frequency stability coefficient, and phase matching coefficient; the voltage amplitude deviation coefficient refers to the relative deviation between the target voltage and the operating voltage, and is used to measure the voltage matching degree; the frequency stability coefficient represents the ability of the target frequency to follow the operating frequency, reflecting frequency synchronization; the phase matching coefficient is used to characterize the phase angle difference between the two power supplies, reflecting the phase synchronization degree.

[0051] After determining the sampling period, the switching device needs to collect and evaluate the parameters of the power supply to be switched in. Specifically, the switching device continuously samples the power supply to be switched in according to the system sampling period to obtain parameters such as its effective voltage value, frequency, and phase angle. Then, it calculates the voltage amplitude deviation coefficient kv=|V2-V1| / V1, the frequency stability coefficient kf=|f2-f1| / f1, and the phase matching coefficient kp=|φ2-φ1| / π, and performs a weighted summation according to preset weighting coefficients to obtain a comprehensive switching evaluation index.

[0052] In some embodiments, the handover evaluation index can be calculated in several ways: Optionally, the switching device can employ an adaptive weighting algorithm to dynamically adjust the weights of each coefficient according to the current operating conditions, and determine the final evaluation index through fuzzy inference, with the weight coefficients automatically adjusted according to the system disturbance level and load characteristics; Optionally, the switching device can also employ a machine learning model based on historical handover data, establishing a mapping relationship between parameter features and handover success rate through a neural network, and outputting the handover evaluation index. It is understood that other evaluation methods can also be used to determine the degree to which handover conditions are met, and these are not limited here.

[0053] S104. Determine the dynamic control parameters of the power supply to be switched in based on the switching evaluation index and the system disturbance level.

[0054] Among them, dynamic control parameters refer to the control quantities used to adjust the output characteristics of the power supply to be switched in, including voltage regulation coefficient, frequency regulation coefficient and phase regulation coefficient, which are used to achieve dynamic matching of power supply characteristics; the dynamic characteristics of control parameters represent their ability to adaptively adjust as the system state changes.

[0055] The switching device needs to determine an appropriate control strategy based on the current system state. Specifically, it first selects benchmark control parameters that match the current disturbance level from a preset parameter template library, and then calculates correction coefficients based on the switching evaluation index to dynamically adjust the benchmark parameters. The correction coefficients decrease exponentially with the switching evaluation index, ensuring a smooth transition of control parameters and avoiding system oscillations caused by sudden changes.

[0056] In some embodiments, dynamic adjustment of control parameters can be achieved in several ways: Optionally, the switching device employs a model predictive control algorithm to establish the system state equation and solve for the optimal control sequence online, dynamically updating the control parameters based on the predictive model, with the adjustment of control parameters based on system constraints and performance indicators; Optionally, the switching device may also employ an iterative learning control strategy, continuously optimizing the control parameters using experience accumulated during each switching process. It is understood that other control algorithms can also be used to achieve dynamic parameter optimization, which is not limited here.

[0057] S105. When the adjustment rate of the dynamic control parameter is greater than the first preset threshold and the switching evaluation index is less than the second preset threshold, a switching enable signal is generated.

[0058] Among them, the adjustment rate represents the speed of change of dynamic control parameters and is used to measure the response capability of the control system; the first preset threshold is the minimum requirement for the adjustment rate of control parameters to ensure that the system has sufficient adjustment capability; the second preset threshold is the upper limit constraint of the switching evaluation index to ensure that the switching conditions are fully met; the switching enable signal is the control command that triggers the switching action.

[0059] When the control parameters are adjusted to the appropriate level and the switching conditions are met, the switching device needs to generate a switching control signal. Specifically, the adjustment rate is obtained by calculating the time derivative of the control parameters in real time. The adjustment rate is compared with a first preset threshold to confirm that the control system has reached a stable state. At the same time, it is checked whether the switching evaluation index is lower than a second preset threshold to ensure switching safety. When both conditions are met, a switching enable signal is generated.

[0060] In some embodiments, the determination of switching conditions can be achieved in multiple ways: Optionally, the switch switching device uses a sliding time window to calculate the average rate of change of control parameters, combines fuzzy decision rules to comprehensively evaluate the switching conditions, and triggers a switching signal when multiple safety constraints are met; Optionally, the switch switching device can also use a state machine to implement switching logic control, and ensure the reliability of the switching process through multiple state transitions. It is understood that other decision-making methods can also be used to determine the timing of switching, which are not limited here.

[0061] S106. Control the primary and secondary fusion switches to perform switching operations according to the switching enable signal.

[0062] Among them, the primary and secondary integrated switch is an intelligent switching device that integrates primary switching equipment and secondary measurement and control unit, which can realize functions such as power switching and status monitoring; the switching operation includes the complete process of disconnecting the running power supply and connecting the power supply to be switched in, and it is necessary to ensure the continuity and safety of the switching process.

[0063] Upon receiving an enable signal, the switchgear needs to execute the actual switchgear action. Specifically, it first checks the mechanical and electrical condition of the switch body. After confirming that the switching conditions are met, it performs open and close operations according to a preset timing control strategy. During the switching process, the switch status and electrical parameters are monitored in real time to ensure the switching process is safe and reliable.

[0064] In some embodiments, switch switching control can be implemented in multiple ways: Optionally, the switch switching device adopts a timing diagram-based control strategy to precisely control the timing of the actions of each actuator, ensure the reliability of the switching process through hardware interlocking and software interlocking, and record various state variables during the switching process in real time; Optionally, the switch switching device can also adopt a distributed control architecture, decomposing the switching control task into multiple sub-modules to complete collaboratively, thereby improving the reliability and flexibility of the system. It is understood that other control methods can also be used to implement switch switching operations, which are not limited here.

[0065] The above embodiments mainly introduce parameter optimization schemes based on disturbance levels and handover evaluation indices. In practical applications, artificial intelligence algorithms, big data analysis, and other technologies can be combined to further improve the system's predictive capabilities and optimization effects. The following section supplements the scenario described in this embodiment.

[0066] A smart grid demonstration zone further optimized the application of this solution. They integrated the switching device into the regional energy management system and established a self-learning model based on historical data. The system can predict potential grid disturbances in advance by analyzing weather data, load curves, and other information, and proactively adjust the switching strategy. For example, when an impending thunderstorm is detected, the system automatically lowers the disturbance judgment threshold and increases the sampling frequency to prepare for possible grid fluctuations. Simultaneously, by analyzing the transient response characteristics of each switch, the system continuously optimizes the correction coefficient model, making the switching process increasingly accurate. Data after one year shows that the system's average switching time has been reduced by 40%, transient fluctuations have decreased by 60%, and power supply reliability has increased to over 99.8%.

[0067] In light of the above scenarios, the following is a more detailed description of the process provided in this implementation. Please refer to [link / reference]. Figure 2 This is another flowchart illustrating the switching method of the primary and secondary fusion switches in this application embodiment.

[0068] S201. Obtain the real-time and historical electrical parameters of the operating power supply.

[0069] Referring to step S101, the switch switching device will acquire real-time electrical parameters and historical electrical parameters.

[0070] S202. Determine the system disturbance level based on the rate of change of real-time electrical parameters relative to historical electrical parameters, and determine the system sampling period based on the system disturbance level.

[0071] Referring to step S102, the switching device will determine the system disturbance level and the system sampling period.

[0072] In some embodiments, the switching device acquires multiple sets of historical electrical parameter samples within a preset time window; performs a moving average filter on the multiple sets of historical electrical parameter samples to obtain reference electrical parameters; calculates the voltage change rate, frequency change rate, and phase change rate of the real-time electrical parameters relative to the reference electrical parameters; compares the voltage change rate, frequency change rate, and phase change rate with the corresponding disturbance level judgment thresholds to obtain numerical comparison results; determines the highest disturbance level from multiple preset disturbance levels as the system disturbance level based on the numerical comparison results; the disturbance level judgment threshold increases with the disturbance level; and determines the system sampling period based on the system disturbance level.

[0073] Among them, the preset time window represents the time range of historical data collection, which is used to determine the benchmark interval for parameter analysis; the benchmark electrical parameter refers to the reference standard value obtained through data processing, which is used to evaluate the degree of change of real-time parameters; the rate of change represents the dynamic change speed of electrical parameters, including three dimensions: voltage, frequency and phase; the disturbance level judgment threshold is the boundary value used to divide different disturbance levels, which increases with the increase of the disturbance level; the system sampling period represents the time interval for subsequent parameter collection.

[0074] Switching devices need to assess system disturbance states based on historical and real-time data. Specifically, the switching device first collects multiple sets of historical electrical parameter samples within a preset time window, eliminates random fluctuations through moving average filtering, and obtains stable reference electrical parameters. Then, it calculates the voltage, frequency, and phase change rates of the real-time electrical parameters relative to the reference electrical parameters. These three rates of change are compared with corresponding level judgment thresholds, and the highest disturbance level is selected as the current system state. Finally, based on the determined disturbance level, a matching sampling time interval is selected from a preset sampling period configuration table.

[0075] It should be noted that a weighted comprehensive evaluation mechanism should be used when calculating the rate of change of voltage, frequency, and phase. First, the rate of change of each dimension is normalized and mapped to the interval [0, 1]. Then, weighting coefficients are set according to the load's sensitivity to each parameter: voltage rate of change weights 0.4, frequency rate of change weights 0.35, and phase rate of change weights 0.25. When the rate of change of multiple parameters simultaneously approaches different level judgment thresholds, a weighted summation method is used to obtain the comprehensive disturbance index. The disturbance level judgment thresholds are divided into {0.2, 0.4, 0.6, 0.8}, corresponding to four disturbance levels: slight, mild, moderate, and severe. The time window length is determined based on the system response characteristics; 5 minutes is used for conventional power distribution systems, and it can be shortened to 1 minute for special loads sensitive to disturbances. The choice of window length must ensure that it can completely cover the rise and recovery phases of a disturbance process.

[0076] In some embodiments, disturbance state assessment can be achieved in multiple ways: Optionally, the switching device employs a fuzzy comprehensive evaluation method, using the three rates of change of voltage, frequency, and phase as fuzzy variables as inputs. By establishing a fuzzy rule base, membership functions for different disturbance levels are defined, and the system disturbance level is determined using the maximum membership principle. Then, a suitable sampling period is obtained by looking up the table based on the disturbance level. Optionally, the switching device can also employ an evaluation method based on statistical characteristics, calculating the mean, variance, and other statistics of historical data to establish a normal distribution model. The deviation of real-time parameters is converted into probability values, and the disturbance level is determined by setting a confidence interval, thereby selecting a sampling strategy. It is understood that other evaluation methods can also be used to determine the system disturbance state; this is not limited here.

[0077] S203. Collect the target electrical parameters of the power supply to be switched in during the system sampling period, and calculate the switching evaluation index based on the real-time electrical parameters and the target electrical parameters.

[0078] Referring to step S103, the switch switching device will calculate the switching evaluation index.

[0079] In some embodiments, the switching device collects the target electrical parameters of the power supply to be switched in during the system sampling period; extracts the effective value of the operating voltage, operating frequency, and operating phase angle from the real-time electrical parameters, and extracts the effective value of the target voltage, target frequency, and target phase angle from the target electrical parameters; calculates the voltage amplitude deviation coefficient based on the effective value of the operating voltage and the effective value of the target voltage, calculates the frequency stability coefficient based on the operating frequency and the target frequency, and calculates the phase matching coefficient based on the operating phase angle and the target phase angle; determines the weighting coefficients of the voltage amplitude deviation coefficient, frequency stability coefficient, and phase matching coefficient respectively, and calculates the switching evaluation index by weighting.

[0080] Among them, the target electrical parameters represent the parameter values ​​of the power supply to be switched in, which are used to establish the target benchmark for switching control; the amplitude deviation coefficient represents the quantitative index of the voltage matching degree, reflecting the difficulty of voltage control; the frequency stability coefficient is the normalized characterization of frequency difference, used to assess the possibility of frequency synchronization; the phase matching coefficient represents the quantitative value of phase angle deviation, reflecting the basis for selecting the switching time; and the weighting coefficient refers to the importance of each parameter in the comprehensive evaluation.

[0081] After determining the sampling period, the switching device needs to evaluate the suitability of the switching conditions. Specifically, the switching device first acquires the voltage, frequency, and phase parameters of the power supply to be switched in with high precision within the sampling period, while simultaneously obtaining the real-time parameter values ​​of the operating power supply. Then, it calculates the voltage amplitude deviation coefficient, frequency stability coefficient, and phase matching coefficient, all of which are normalized to ensure that the calculation results are within the [0, 1] interval. Finally, based on the characteristics of the current operating conditions, the weight values ​​of each coefficient are determined, and a comprehensive switching evaluation index is obtained through weighted summation.

[0082] It should be noted that the weighting coefficients of the handover assessment index employ an adaptive dynamic adjustment mechanism, calculated in real-time based on load characteristics and system operating status. The weighting calculation is based on three factors: the severity of parameter deviations, the load's sensitivity to each parameter, and the correlation between each parameter and the handover success rate in historical handover data. Specifically, each parameter is first standardized into a relative deviation value, and the min-max standardization method is used to map the values ​​to the [0, 1] interval. Then, the deviation value is converted into a score using the exponential function e^(-k*x), where k is the sensitivity coefficient, determined by the load characteristics. The weighting coefficients of each parameter are determined by multiplying the normalized score value by the historical correlation coefficient. The final effective value range of the handover assessment index is [0, 1]. A smaller value indicates more ideal handover conditions; a value of 0.3 is recommended as the allowable handover threshold.

[0083] In some embodiments, the switching evaluation index can be calculated in several ways: Optionally, the switching device employs an adaptive weighting method, dynamically adjusting the weight coefficients of each parameter according to the real-time system status, and determining the importance of parameters by introducing a fuzzy evaluation matrix to achieve accurate evaluation of switching conditions; alternatively, the switching device can also employ a neural network evaluation method, using voltage, frequency, and phase parameters as network inputs, and learning the optimal weight configuration through training to output a reasonable evaluation index. It is understood that other evaluation methods can also be used to achieve a comprehensive evaluation of switching conditions, and this is not limited here.

[0084] S204. Select initial control parameters from a set of preset control parameter templates based on the system disturbance level.

[0085] Among them, the control parameter template represents a set of pre-configured parameter schemes for different disturbance levels, which are used to provide a baseline control strategy; the initial control parameters refer to the baseline adjustment parameters selected from the template, including voltage adjustment coefficient, frequency adjustment coefficient and phase adjustment coefficient, which are used to establish the initial control baseline; the parameter template library is a collection of multiple parameter configuration schemes stored hierarchically according to the disturbance level, and each scheme contains a complete set of control parameters.

[0086] After determining the system disturbance level, the switching device needs to establish an initial control strategy that matches the current operating conditions. Specifically, the switching device first accesses the parameter template library and extracts the parameter configuration scheme corresponding to the current disturbance level. Then, based on the specific value of the disturbance level, interpolation is used to calculate more accurate initial control parameters. The parameter template configuration scheme is adjusted as the disturbance level increases; higher disturbance levels correspond to more aggressive control parameters to improve system response speed.

[0087] In some embodiments, the initial control parameters can be selected in several ways: Optionally, the switching device employs a fuzzy rule reasoning method, using the disturbance level as an input variable, defining the mapping relationship between the disturbance level and parameter templates by establishing a fuzzy rule base, and using the centroid method to defuzzify and obtain the optimal initial parameter set; Optionally, the switching device can also adopt a parameter optimization strategy based on historical data, statistically analyzing the parameter distribution characteristics of successful switching cases under different disturbance levels, and selecting the parameter set with the best switching effect as the initial parameters. It is understood that other parameter selection methods can also be used to determine the initial control parameters, and this is not limited here.

[0088] S205. Calculate the correction coefficients for each parameter in the initial control parameters based on the switching evaluation index.

[0089] Among them, the correction coefficient represents the adjustment ratio factor for the initial control parameters, which is used to achieve dynamic optimization of the parameters; parameter correction refers to the process of adjusting the initial parameters based on the switching evaluation index, which is used to improve the matching degree between the control parameters and the actual operating conditions; the adjustment direction represents the increasing or decreasing trend of the correction coefficient, reflecting the trend of parameter optimization.

[0090] After acquiring the initial control parameters, the switching device needs to adjust the parameters according to the actual switching conditions. Specifically, the switching device first analyzes the deviation characteristics of the three components—voltage, frequency, and phase—in the switching evaluation index and establishes correction models for each parameter. Then, it calculates the correction coefficient corresponding to each control parameter. The correction coefficient decreases exponentially with the switching evaluation index to ensure the smoothness and stability of parameter adjustment.

[0091] In some embodiments, the correction coefficient can be calculated in several ways: Optionally, the switching device employs an adaptive weighting algorithm to determine the calculation formula for the corresponding correction coefficient based on the weights of each component of the switching evaluation index, and dynamically adjusts the correction intensity through parameter sensitivity analysis to achieve precise correction of the control parameters; Optionally, the switching device can also employ a model prediction method to establish a mathematical model of the switching process and obtain the correction coefficient that optimizes the performance indicators through online optimization calculation. It is understood that other calculation methods can also be used to determine the correction coefficient, and this is not limited here.

[0092] S206. Determine the dynamic control parameters of the power supply to be switched on based on the initial control parameters and the corresponding correction coefficients.

[0093] Among them, dynamic control parameters refer to the actual control quantities after correction, which are used to adjust the output characteristics of the power supply to be switched on; parameter tuning refers to the process of combining the initial control parameters with the correction coefficients to generate the final control strategy; parameter constraints refer to the limitation of the range of control parameters to ensure the rationality of parameter adjustment.

[0094] After obtaining the correction coefficients, the switching device needs to determine the dynamic control parameters for practical application. Specifically, the switching device multiplies the initial control parameters by the corresponding correction coefficients to obtain preliminary dynamic control parameter values. Then, it performs amplitude limiting processing according to preset parameter constraints to ensure that all parameters are within a reasonable range. Finally, it performs parameter smoothing to avoid system instability caused by sudden parameter changes.

[0095] In some embodiments, the dynamic control parameters can be determined in several ways: Optionally, the switching device employs a parameter optimization algorithm, using the corrected parameters as the optimization objective, and iteratively calculates the optimal control parameters that satisfy all constraints based on multiple constraints; alternatively, the switching device may also employ a fuzzy control strategy, utilizing expert experience to establish fuzzy rules, and determining the final dynamic control parameters through inference operations. It is understood that other optimization methods can also be used to determine the dynamic control parameters, and this is not limited here.

[0096] In some embodiments, the switching device uses system disturbance level, switching evaluation index, and historical switching data including overshoot, settling time, and steady-state error as training samples to train and generate a correction coefficient model using a polynomial regression method; the order of the regression polynomial in the polynomial regression method does not exceed third order; based on the correction coefficient model, a reference value matrix of correction coefficients is determined; the reference value matrix includes voltage correction coefficient, frequency correction coefficient, and phase correction coefficient; according to the reference value matrix, the statistical distribution characteristics of each correction coefficient under different disturbance levels are calculated, and a piecewise linear regression model is constructed based on the statistical distribution characteristics; the statistical distribution characteristics include mean, variance, and skewness; the system disturbance level and switching evaluation index are input into the piecewise linear regression model to obtain real-time correction coefficients; and the dynamic control parameters of the power supply to be switched in are calculated based on the real-time correction coefficients and initial control parameters.

[0097] Among them, the training samples refer to the historical data set used to build the correction model, which includes the correspondence between system state and switching effect; the correction coefficient model represents the mathematical description of parameter correction and is used to predict the optimal correction value; the benchmark value matrix refers to the set of reference values ​​of correction coefficients under different conditions and is used to establish the correction law; the statistical distribution characteristics represent the numerical law of correction coefficients and reflect the general trend of parameter correction.

[0098] After obtaining the switching evaluation index, the switching device needs to establish a scientific parameter correction mechanism. Specifically, the switching device first trains a correction coefficient model based on historical switching data, and uses a polynomial regression method of no more than order three to fit the relationship between the system state and the correction effect. Then, the baseline value matrix is ​​calculated through the model, and the distribution characteristics of the correction coefficients under different disturbance levels are analyzed. Finally, a piecewise linear regression model is constructed to map the current system state to specific correction coefficients, realizing dynamic adjustment of parameters.

[0099] It should be noted that the correction coefficient model is constructed using a multinomial regression method. During the training phase, the system disturbance level and the handover assessment index are used as input features, and historical handover data including overshoot, settling time, and steady-state error are used as training labels. The model parameters are optimized by minimizing the mean square error between the predicted values ​​and the actual correction coefficients, and the model order does not exceed three to avoid overfitting. When using the model, the current system disturbance level and the handover assessment index are input, and the output is a matrix of baseline values ​​including voltage correction coefficients, frequency correction coefficients, and phase correction coefficients.

[0100] The piecewise linear regression model is built upon the output of the correction coefficient model. During training, it utilizes the statistical distribution characteristics (mean, variance, skewness) of the correction coefficients under different disturbance levels in the baseline matrix to determine the linear function parameters for each interval by minimizing the piecewise linear fitting error. The model's piecewise structure better adapts to parameter adjustment needs under different disturbance levels. In practical applications, this model receives the system disturbance level and the handover evaluation index as input and outputs real-time correction coefficients for dynamic adjustment of control parameters. The two models work together to achieve a mapping from historical data to real-time parameter adjustments, ensuring adaptive optimization capabilities during the handover process.

[0101] In some embodiments, the correction coefficient can be calculated in several ways: Optionally, the switch switching device uses support vector regression to map historical samples to a high-dimensional feature space and constructs a nonlinear regression model through a kernel function to achieve accurate prediction of the correction coefficient; alternatively, the switch switching device can also use a random forest algorithm to integrate the prediction results of multiple decision trees to improve the generalization ability and robustness of the correction model. It is understood that other modeling methods can also be used to determine the correction coefficient, and this is not limited here.

[0102] S207. Determine the dynamic control parameters of the power supply to be switched in based on the switching evaluation index and the system disturbance level.

[0103] Referring to step S104, the switch switching device will determine the dynamic control parameters.

[0104] S208. When the adjustment rate of the dynamic control parameter is greater than the first preset threshold and the switching evaluation index is less than the second preset threshold, a switching enable signal is generated.

[0105] Referring to step S105, the switching device will generate a switching enable signal.

[0106] S209. Control the primary and secondary fusion switches to perform switching operations according to the switching enable signal.

[0107] Referring to step S106, the switch switching device will control the switch to perform a switching operation.

[0108] S210. Real-time monitoring of the transient response waveform of the primary and secondary fusion switch during the switching process.

[0109] Among them, the transient response waveform represents the dynamic change curve of electrical parameters during the switching process and is used to evaluate the switching effect; the monitoring point refers to the key moment of waveform sampling and is used to capture characteristic changes; the sampling accuracy represents the time and amplitude resolution of waveform acquisition and affects the accuracy of the monitoring results.

[0110] While performing switching operations, the switching device needs to monitor the switching process in real time. Specifically, the switching device uses a high-speed sampling circuit to collect instantaneous voltage and current values ​​during the switching process, forming a continuous transient response waveform. The sampling frequency is no less than 10kHz to ensure accurate capture of rapidly changing transient characteristics. Simultaneously, the waveform characteristics at key time points are recorded to provide a basis for subsequent analysis.

[0111] In some embodiments, transient response monitoring can be achieved in multiple ways: Optionally, the switchgear employs digital oscilloscope technology, sets multiple trigger conditions and sampling channels, and records the complete switching process waveform through a buffering mechanism to achieve accurate capture of transient characteristics; alternatively, the switchgear may also employ a distributed measurement architecture, simultaneously sampling at multiple key points, and reconstructing the complete transient process through data fusion. It is understood that other monitoring methods can also be used to record transient responses, and these are not limited here.

[0112] S211. Extract the characteristic parameters of the transient response waveform.

[0113] The characteristic parameters include overshoot, settling time, and steady-state error.

[0114] Among them, overshoot represents the maximum deviation of the waveform from the steady-state value, reflecting the impact of the switching process; settling time refers to the time required for the waveform to stabilize from the start of switching, used to measure the speed of the switching process; steady-state error represents the residual deviation when the system finally stabilizes, reflecting the switching accuracy; characteristic parameters are key indicators characterizing the quality of transient response.

[0115] After obtaining the transient response waveform, the switching device needs to extract characteristic parameters reflecting the switching performance. Specifically, the switching device first preprocesses the waveform data, including filtering, noise reduction, and reference correction. Then, it calculates the maximum deviation of the waveform to obtain the overshoot, calculates the time required for the waveform to enter the stable range to obtain the settling time, and calculates the difference between the final steady-state value and the target value to obtain the steady-state error.

[0116] In addition, the transient response waveform sampling adopts an adaptive sampling rate strategy, with the basic sampling frequency set at 20kHz, automatically increasing to 50kHz when rapid changes are detected. The sampled data undergoes noise reduction processing using wavelet transform, employing a 3-level decomposition using the db4 wavelet basis function to retain effective signals while suppressing high-frequency noise. Feature extraction uses a sliding window method, with a window length of 1 / 10 of the switching time and a step size of 50 times the sampling period. For the data within each window, statistical features such as mean, standard deviation, and peak factor are calculated. The time-series changes of these features are used to identify transient characteristics such as overshoot and oscillation. Simultaneously, Hilbert transform is used to calculate instantaneous frequency and phase features, achieving a complete characterization of the dynamic characteristics of the switching process. To improve the reliability of feature extraction, median filtering is used to smooth the extraction results, with the filtering window size set to 5 sampling points.

[0117] In some embodiments, feature parameters can be extracted in various ways: Optionally, the switching device employs wavelet transform to decompose the waveform into multiple scales, extracting feature parameters from different frequency bands, and reconstructing them to obtain accurate performance indicators; alternatively, the switching device may also employ pattern recognition technology to establish a waveform feature template library, identify key feature points through a matching algorithm, and calculate performance parameters. It is understood that other signal processing methods can also be used to extract feature parameters, and this is not limited here.

[0118] S212. When the characteristic parameters exceed the preset performance index range, adjust the dynamic control parameters.

[0119] Among them, the preset performance indicators are the performance requirements that need to be met during the switching process, including the maximum allowable overshoot, the longest settling time, and the maximum steady-state error; parameter adjustment refers to the process of optimizing control parameters based on performance deviations; and adjustment strategy represents the specific methods and steps for parameter optimization.

[0120] After obtaining the characteristic parameters, the switching device needs to evaluate its switching performance and perform necessary optimizations. Specifically, the switching device compares the extracted characteristic parameters with preset performance indicators. When any parameter exceeds the allowable range, the degree and direction of the exceedance are analyzed. Then, according to pre-set adjustment rules, the dynamic control parameters are modified in a targeted manner to gradually improve the switching performance.

[0121] In some embodiments, parameter adjustment can be achieved in multiple ways: Optionally, the switching device employs an iterative learning control strategy, utilizing experience accumulated during multiple switching processes to establish a correlation model between feature parameters and control parameters, and continuously optimizing parameter configuration through gradient descent; alternatively, the switching device may also employ a neural network adaptive control method, learning the dynamic characteristics of the switching process online and adjusting control parameters in real time to improve system performance. It is understood that other optimization methods can also be used to adjust control parameters, which are not limited here.

[0122] In some embodiments, the switching device calculates deviation values ​​between the overshoot, settling time, and steady-state error of the transient response waveform and the corresponding performance indicators; determines the initial adjustment direction of the voltage regulation coefficient, frequency regulation coefficient, and phase regulation coefficient based on the sign of each deviation value; iteratively adjusts the dynamic control parameters according to a preset step size, recording new transient response characteristics after each adjustment; if the adjusted characteristic parameters are better than before the adjustment, the current adjustment direction is maintained and the step size is increased by a preset growth coefficient; if the adjusted characteristic parameters are worse than before the adjustment, the adjustment direction is reversed and the step size is decreased by a preset reduction coefficient; the adjustment effect of a preset number of adjustments is statistically analyzed, and when the adjustment direction changes continuously or the step size is less than the minimum threshold, the corresponding adjusted dynamic control parameters are recorded.

[0123] Among them, the deviation value indicates the degree to which the characteristic parameter exceeds the target range and is used to determine the adjustment direction; the adjustment direction refers to the increasing or decreasing trend of parameter modification, which guides the optimization process; the preset step size indicates the magnitude of each adjustment and affects the convergence speed of the optimization process; the growth coefficient and the reduction coefficient represent the proportional factors of the step size adjustment, respectively, and are used to adaptively control the optimization process.

[0124] After the switching device detects that the characteristic parameters exceed the limits, it needs to optimize the dynamic control parameters. Specifically, the switching device first calculates the deviation value of the characteristic parameters and determines the initial adjustment direction based on the sign of the deviation. Then, it adopts an iterative optimization strategy with a variable step size. When the adjustment effect is good, the step size is increased to accelerate convergence; when the adjustment effect deteriorates, the step size is decreased and the direction is changed. Finally, the final parameter configuration is determined by termination conditions such as continuous changes in adjustment direction or excessively small step size.

[0125] It should be noted that the parameter optimization employs an iterative algorithm with an adaptive step size. The initial step size is set to 5% of the normal operating range of the corresponding parameter. The step size adjustment uses an exponential decay mechanism, with an increase coefficient of 1.2 and a decrease coefficient of 0.8. This ensures rapid convergence in the initial stages of optimization while allowing for fine-tuning as the solution approaches the optimum. After each parameter adjustment, the improvement is assessed by calculating the switching evaluation index. To ensure the convergence and real-time performance of the optimization process, a maximum of 20 iterations is set. Iteration is terminated early if the adjustment direction changes for three consecutive iterations or the step size falls below 1% of the initial value. The entire optimization process balances optimization speed and stability through dynamic coefficient adjustment, making it suitable for various switching scenarios.

[0126] In some embodiments, parameter optimization can be achieved in multiple ways: Optionally, the switching device employs a simulated annealing algorithm to perform a random search in the parameter space, controlling the probability of the temperature control parameter accepting a suboptimal solution to avoid getting trapped in local optima; alternatively, the switching device can also employ a particle swarm optimization algorithm to search for the optimal parameter combination in parallel, improving optimization efficiency through swarm intelligence. It is understood that other optimization algorithms can also be used to adjust the control parameters, and this is not limited here.

[0127] In this embodiment, a dynamic adaptive control strategy based on historical data and real-time monitoring is adopted. This establishes a complete control system encompassing disturbance level determination, parameter template selection, correction coefficient calculation, and characteristic parameter optimization. Combined with a comprehensive evaluation mechanism across voltage, frequency, and phase dimensions, the sampling period and control parameters can be adjusted in real-time according to the power grid status, achieving intelligent optimization of the switching strategy. This effectively solves the problems of poor reliability and weak adaptability of traditional fixed-parameter schemes in complex power supply environments, as well as the slow response of PID controllers under large disturbance conditions. It achieves efficient and stable control of the switching process, significantly improving the reliability of the power supply system.

[0128] The switching device in the embodiments of this invention is described below from the perspective of hardware processing. Please refer to [link / reference]. Figure 3 This is a schematic diagram of the physical structure of a switch switching device in an embodiment of this application.

[0129] It should be noted that, Figure 3 The structure of the switch switching device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0130] like Figure 3As shown, the switching device includes a Central Processing Unit (CPU) 301, which can perform various appropriate actions and processes based on a program stored in Read-Only Memory (ROM) 302 or a program loaded from storage section 308 into Random Access Memory (RAM) 303, such as performing the methods described in the above embodiments. The RAM 303 also stores various programs and data required for system operation. The CPU 301, ROM 302, and RAM 303 are interconnected via a bus 304. An Input / Output (I / O) interface 305 is also connected to the bus 304.

[0131] The following components are connected to I / O interface 305: input section 306 including audio input devices, push-button switches, etc.; output section 307 including a liquid crystal display (LCD) and audio output devices, indicator lights, etc.; storage section 308 including a hard disk, etc.; and communication section 309 including a network interface card such as a LAN (Local Area Network) card, modem, etc. Communication section 309 performs communication processing via a network such as the Internet. Drive 310 is also connected to I / O interface 305 as needed. Removable media 311, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 310 as needed so that computer programs read from them can be installed into storage section 308 as needed.

[0132] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing computer programs for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 309, and / or installed from removable medium 311. When the computer program is executed by central processing unit (CPU) 301, it performs the various functions defined in the present invention.

[0133] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0134] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. Each block in a flowchart or block diagram may represent a module, program segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those shown in the drawings.

[0135] Specifically, the switch switching device in this embodiment includes a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, it implements the switching method of the primary and secondary fusion switch provided in the above embodiment.

[0136] In another aspect, the present invention also provides a computer-readable storage medium, which may be included in the switch-changing device described in the above embodiments; or it may exist independently and not assembled into the switch-changing device. The storage medium carries one or more computer programs that, when executed by a processor of the switch-changing device, cause the switch-changing device to implement the primary and secondary fusion switch switching method provided in the above embodiments.

[0137] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0138] As used in the above embodiments, depending on the context, the term "when..." can be interpreted as meaning "if...", "after...", "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if (the stated condition or event) is interpreted as meaning "if determining...", "in response to determining...", "when (the stated condition or event) is detected", or "in response to detecting (the stated condition or event)".

[0139] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A switching method for a primary and secondary fusion switch, characterized in that, Applied to a switchgear, the method includes: Obtain real-time and historical electrical parameters of the operating power supply; The system disturbance level is determined based on the rate of change of the real-time electrical parameters relative to the historical electrical parameters, and the system sampling period is determined based on the system disturbance level; the system sampling period is inversely proportional to the system disturbance level. The system collects the target electrical parameters of the power supply to be switched in during the system sampling period, and calculates the switching evaluation index based on the real-time electrical parameters and the target electrical parameters; the switching evaluation index includes voltage amplitude deviation coefficient, frequency stability coefficient and phase matching coefficient; Based on the switching evaluation index and the system disturbance level, determine the dynamic control parameters of the power supply to be switched in; When the adjustment rate of the dynamic control parameter is greater than a first preset threshold and the switching evaluation index is less than a second preset threshold, a switching enable signal is generated. The switching enable signal controls the primary and secondary fusion switches to perform a switching operation.

2. The method according to claim 1, characterized in that, The steps of determining the system disturbance level based on the rate of change of the real-time electrical parameters relative to the historical electrical parameters, and determining the system sampling period based on the system disturbance level, specifically include: Obtain multiple sets of historical electrical parameter samples within a preset time window; The reference electrical parameters are obtained by performing a moving average filter on the multiple sets of historical electrical parameter samples. Calculate the rate of change of voltage, rate of change of frequency, and rate of change of phase of the real-time electrical parameters relative to the reference electrical parameters; The voltage change rate, frequency change rate, and phase change rate are compared with the corresponding disturbance level judgment thresholds to obtain numerical comparison results. Based on the numerical comparison results, the highest disturbance level is determined from a set of preset disturbance levels as the system disturbance level; the threshold for determining the disturbance level increases as the disturbance level increases. The system sampling period is determined based on the system disturbance level.

3. The method according to claim 1, characterized in that, The step of acquiring the target electrical parameters of the power supply to be switched in during the system sampling period, and calculating the switching evaluation index based on the real-time electrical parameters and the target electrical parameters, specifically includes: The target electrical parameters of the power supply to be switched in are collected during the system sampling period. Extract the effective value of the operating voltage, operating frequency, and operating phase angle from the real-time electrical parameters, and extract the effective value of the target voltage, target frequency, and target phase angle from the target electrical parameters; The voltage amplitude deviation coefficient is calculated based on the operating voltage RMS value and the target voltage RMS value; the frequency stability coefficient is calculated based on the operating frequency and the target frequency; and the phase matching coefficient is calculated based on the operating phase angle and the target phase angle. The weighting coefficients of the voltage amplitude deviation coefficient, frequency stability coefficient, and phase matching coefficient are determined, and the switching evaluation index is obtained by weighted calculation.

4. The method according to claim 1, characterized in that, Before the step of determining the dynamic control parameters of the power supply to be switched in based on the switching evaluation index and the system disturbance level, the method further includes: Initial control parameters are selected from a set of preset control parameter templates based on the system disturbance level. The correction coefficients for each parameter in the initial control parameters are calculated based on the switching evaluation index. The dynamic control parameters of the power supply to be switched in are determined based on the initial control parameters and the corresponding correction coefficients. The initial control parameters include voltage regulation coefficient, frequency regulation coefficient, and phase regulation coefficient. The correction coefficients and the switching evaluation index satisfy an exponential function relationship, and the correction coefficients decrease exponentially as the switching evaluation index increases.

5. The method according to claim 4, characterized in that, The step of determining the dynamic control parameters of the power supply to be switched on based on the initial control parameters and the corresponding correction coefficients specifically includes: The system disturbance level, the switching evaluation index, and historical switching data including overshoot, settling time, and steady-state error are used as training samples to train a correction coefficient model using a polynomial regression method; the order of the regression polynomial in the polynomial regression method does not exceed the third order. Based on the correction coefficient model, a reference value matrix for the correction coefficients is determined; the reference value matrix includes voltage correction coefficients, frequency correction coefficients, and phase correction coefficients. Based on the benchmark matrix, the statistical distribution characteristics of each correction coefficient under different disturbance levels are calculated, and a piecewise linear regression model is constructed based on the statistical distribution characteristics; the statistical distribution characteristics include mean, variance, and skewness. The system disturbance level and the handover evaluation index are input into the piecewise linear regression model to obtain real-time correction coefficients; The dynamic control parameters of the power supply to be switched in are calculated based on the real-time correction coefficient and the initial control parameters.

6. The method according to claim 1, characterized in that, After the step of controlling the primary and secondary fusion switches to perform a switching operation according to the switching enable signal, the method further includes: Real-time monitoring of the transient response waveform of the primary and secondary fusion switch during the switching process; Extract the characteristic parameters of the transient response waveform; the characteristic parameters include overshoot, settling time, and steady-state error. When the characteristic parameter exceeds the preset performance index range, the dynamic control parameter is adjusted.

7. The method according to claim 6, characterized in that, The step of adjusting the dynamic control parameters when the characteristic parameters exceed the preset performance index range specifically includes: The overshoot, settling time, and steady-state error of the transient response waveform are respectively compared with the corresponding performance indicators to calculate the deviation values. Based on the sign of each deviation value, determine the initial adjustment direction of the voltage regulation coefficient, frequency regulation coefficient, and phase regulation coefficient; The dynamic control parameters are iteratively adjusted according to a preset step size, and the new transient response characteristics are recorded after each adjustment. If the adjusted feature parameters are better than before, the current adjustment direction remains unchanged and the step size is increased by the preset growth coefficient; if the adjusted feature parameters are worse than before, the adjustment direction is reversed and the step size is decreased by the preset reduction coefficient. The adjustment effect of the preset number of parameters is statistically analyzed. When the adjustment direction changes continuously or the step size is less than the minimum threshold, the corresponding dynamic control parameters after the adjustment are recorded.

8. A switch switching device, characterized in that, The switch switching device includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code including computer instructions, and the one or more processors call the computer instructions to cause the switch switching device to perform the method as described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that, When the instruction is executed on the switchgear, the switchgear performs the method as described in any one of claims 1-7.

10. A computer program product, characterized in that, When the computer program product is run on the switchgear, it causes the switchgear to perform the method as described in any one of claims 1-7.

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