Startup timing control optimization method for startup control circuit

Abstract

This application relates to the field of startup control technology, and discloses a startup timing control optimization method for a startup control circuit. Upon receiving a signal, the MCU control unit initializes a timer and simultaneously triggers the main and secondary thyristors to conduct at time T1. Real-time current data is sampled at a high frequency of 4kHz using an electrical metering chip to obtain a current waveform data sequence. Then, based on this data, the optimal engagement time of the startup winding is predicted. Simultaneously, the MCU control unit continuously records the engagement time of the startup winding and monitors the real-time current through the electrical metering chip. Finally, based on the real-time current, the engagement time of the startup winding, and the optimal engagement time, the MCU control unit adaptively cuts off the secondary thyristor, ensuring that the startup winding is accurately disconnected at a more appropriate time, thereby achieving the goals of reducing energy consumption and improving startup efficiency and stability.

Description

Technical Field

[0001] This application relates to the field of startup control technology, and more specifically, to a startup timing control optimization method for startup control circuits. Background Technology

[0002] In modern motor control systems, single-phase asynchronous motors are widely used in household appliances such as air conditioners and refrigerators, as well as compressors in industrial equipment, due to their simple structure and low cost. However, these motors cannot start on their own and must rely on external starting devices to provide the necessary starting torque. Traditionally, PTC (positive temperature coefficient) starters and counterweight starters are two commonly used solutions. However, each has significant limitations.

[0003] PTC starters rely on their own heat to reach a high resistance state to disconnect the starting winding. While this method effectively completes the starting task, its slow response speed results in an unsatisfactory starting process. Furthermore, even after starting, the PTC element remains in a high resistance state, continuously consuming energy and increasing unnecessary energy consumption. On the other hand, hammer starters disconnect the starting winding through mechanical action after the current decreases. This method is not only affected by mechanical wear, but its switching speed is also uncontrollable, affecting the accuracy and reliability of starting.

[0004] With technological advancements, electronic starting solutions are gradually becoming a new alternative to traditional methods. Compared to traditional PTC or counterweight starters, electronic starters can more precisely control the connection and disconnection times of the starting winding, avoiding starting failures caused by aging mechanical components or environmental changes. However, in practical applications, accurately determining the timing of start-up completion becomes a key aspect of optimizing this solution. If the starting winding is disconnected too early, the motor may not receive sufficient starting torque, resulting in starting failure; conversely, if it is disconnected too late, unnecessary energy waste will occur. Summary of the Invention

[0005] To address the aforementioned issues and improve energy utilization efficiency and startup performance during startup, this application proposes a startup timing control optimization method for startup control circuits.

[0006] According to one aspect of this application, a startup timing control optimization method for a startup control circuit is provided, comprising: S1: Upon receiving a startup command, the MCU control unit initializes a timer and triggers the main thyristor and the secondary thyristor to conduct at time T1; S2: After triggering the conduction of the secondary thyristor and the main thyristor, real-time current data is sampled at high frequency by an electrical metering chip to obtain a current waveform data sequence; S3: Based on the current waveform data sequence, the MCU control unit predicts the optimal engagement time of the startup winding; S4: The MCU control unit records the engagement time of the startup winding through the timer and monitors the real-time current through the electrical metering chip; S5: Based on the real-time current, the engagement time of the startup winding, and the optimal engagement time of the startup winding, the MCU control unit adaptively cuts off the secondary thyristor.

[0007] In one possible implementation, in S2, the sampling frequency of the high-frequency sampling is 4kHz.

[0008] In one possible implementation, S3 includes: extracting early key features of the current from the current waveform data sequence, the early key features of the current including the first current peak, the time to reach the first current peak, the current integral value within a specified time window, and the current rise slope; classifying the first current peak and the time to reach the first current peak to obtain current peak classification results and peak time classification results; inputting the current peak classification results, the peak time classification results, the current integral value within the specified time window, and the current rise slope into a multiple linear regression model to obtain the optimal intervention time of the starting winding.

[0009] In one possible implementation, the current peak value classification results include low, medium and high; the peak time classification results include fast, medium and full.

[0010] In one possible implementation, S5 includes: constructing a first judgment condition and a second judgment condition, wherein the first judgment condition is whether the real-time current has shown a significant decreasing trend and tends to stabilize, and the second judgment condition is whether the intervention time of the starting winding exceeds the optimal intervention time of the starting winding; when the second judgment condition is satisfied and the first judgment condition is satisfied, the MCU control unit cuts off the secondary thyristor; when the second judgment condition is not satisfied but the first judgment condition is satisfied, the MCU control unit cuts off the secondary thyristor; when the second judgment condition is satisfied but the first judgment condition is not satisfied, the MCU control unit triggers a protection mechanism.

[0011] In one possible implementation, the first judgment condition is whether multiple consecutive real-time currents are lower than a preset current threshold.

[0012] In one possible implementation, the first judgment condition further includes: when the plurality of real-time currents are all lower than a preset current threshold, determining the characterization coefficients of the plurality of real-time currents based on the transient response at a fixed time point, the transition process based on time segmentation, and the steady-state characteristics based on global time sequence, and determining whether the characterization coefficients are greater than an empirical threshold.

[0013] In one possible implementation, determining the characterization coefficients of the multiple real-time currents based on fixed timing points for transient response, based on timing segmentation for transient process, and based on global timing for steady-state characteristics includes: calculating the transient response of each real-time current; calculating the rate of change of response based on the transient response of each real-time current; calculating transient process verification coefficients for the stability of the rate of change of response under global timing; calculating the global power spectral density for the rate of change of response; and using the global power spectral density as a dynamic factor to perform global timing inertia correction on the transient process verification coefficients to obtain the characterization coefficients.

[0014] Compared with existing technologies, the startup timing control optimization method for startup control circuits provided in this application first initializes the timer after receiving the startup command and simultaneously triggers the main and auxiliary thyristors to conduct at time T1. Then, the current waveform data sequence is obtained by high-frequency sampling of real-time current data at a frequency of 4kHz using an electrical metering chip. Next, the MCU control unit predicts the optimal engagement time of the startup winding based on this data. Simultaneously, the MCU control unit continuously records the engagement time of the startup winding and monitors the real-time current through the electrical metering chip. Finally, based on the real-time current, the engagement time of the startup winding, and the optimal engagement time, the MCU control unit adaptively cuts off the auxiliary thyristor, ensuring that the startup winding is accurately disconnected at a more appropriate time, thereby reducing energy consumption, improving startup efficiency and stability, and enhancing the reliability and safety of the system. Attached Figure Description

[0015] The above and other objects, features, and advantages of this application will become more apparent from the more detailed description of the embodiments of this application in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the embodiments of this application to explain this application and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0016] Figure 1 The figure shows a schematic flowchart of a startup timing control optimization method for a startup control circuit according to an embodiment of this application.

[0017] Figure 2 The figure shows a schematic block diagram of a start-up control circuit according to an embodiment of the present application.

[0018] Figure 3 The figure shows a schematic flowchart of step S3 in the startup timing control optimization method for a startup control circuit according to an embodiment of the present application.

[0019] Figure 4 The figure shows a schematic flowchart of step S5 in the startup timing control optimization method for a startup control circuit according to an embodiment of the present application. Detailed Implementation

[0020] Hereinafter, exemplary embodiments according to this application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments of this application. It should be understood that this application is not limited to the exemplary embodiments described herein.

[0021] Figure 1 The figure shows a schematic flowchart of a startup timing control optimization method for a startup control circuit according to an embodiment of this application. Figure 2 A schematic block diagram of a startup control circuit according to an embodiment of this application is shown. Figure 1 and Figure 2 As shown, this application provides a startup timing control optimization method for a startup control circuit, comprising: S1: After receiving a startup command, the MCU control unit initializes a timer and triggers the main thyristor and the auxiliary thyristor to conduct at time T1; S2: After triggering the conduction of the auxiliary thyristor and the main thyristor, real-time current data is sampled at high frequency by an electrical metering chip to obtain a current waveform data sequence; S3: Based on the current waveform data sequence, the MCU control unit predicts the optimal engagement time of the startup winding; S4: The MCU control unit records the engagement time of the startup winding through the timer and monitors the real-time current through the electrical metering chip; S5: Based on the real-time current, the engagement time of the startup winding, and the optimal engagement time of the startup winding, the MCU control unit adaptively cuts off the auxiliary thyristor.

[0022] Specifically, in step S1, after receiving the start command, the MCU control unit initializes the timer and triggers the conduction of the main and auxiliary thyristors at time T1. It should be understood that when the system receives the start command signal, the MCU immediately completes the timer initialization operation through its internal instructions. This timer, as a hardware resource, typically uses a high-frequency crystal oscillator as a reference for the synchronous clock, and adjusts the timing frequency through an internal automatic frequency divider (e.g., dividing the main frequency of 72MHz to 1kHz) to meet the accuracy requirements of time measurement. During initialization, the MCU resets the timer's count value to zero and simultaneously sets the automatic reload register (ARR) to specify the timing target value, thereby providing a precise time trigger reference for the conduction operation of the main and auxiliary thyristors. Furthermore, the MCU is also configured with a timer interrupt function to ensure that an interrupt event is triggered when the count value reaches the ARR specification, which guarantees real-time response to subsequent specific triggering actions.

[0023] In one embodiment, the setting of time T1 is dynamically adjusted based on specific motor parameters and load conditions. For example, in some embodiments, T1 may be set between tens and hundreds of milliseconds after the start command is issued, depending on the motor type and its required starting current characteristics. In a specific embodiment, for a household refrigerator compressor application scenario, the compressor requires a high initial current to overcome static friction and reach operating speed; therefore, T1 might be set to approximately 100 milliseconds after the motor start command is issued. This timing point is chosen based on experimental data and empirical formulas, aiming to ensure that sufficient current flows through the starting winding to provide the necessary starting torque before the motor begins to rotate.

[0024] In one specific embodiment, triggering the conduction of the main and auxiliary thyristors at time T1 includes: the MCU sending a high-level signal to an external GPIO pin. This high-level signal drives the control terminals of the main and auxiliary thyristors, guiding them to conduct, thereby causing the start-up winding to begin engaging in the main circuit. This process effectively limits the impact of instantaneous current on the circuit during startup and avoids motor start-up overshoot problems caused by conduction timing deviations.

[0025] Specifically, in step S2, after the secondary and primary thyristors are triggered to conduct, real-time current data is sampled at high frequency by the electricity metering chip to obtain a current waveform data sequence. It should be understood that the purpose of high-frequency sampling is to quickly acquire the dynamic current response during startup, that is, to capture the transient changes and characteristics of the current waveform during startup, which is crucial for time-sensitive optimization control during startup.

[0026] In one specific embodiment, in step S2, the sampling frequency for high-frequency sampling is 4kHz. This frequency ensures that the sampling is dense enough to extract subtle current changes without exceeding the chip's processing capacity and wasting system resources. This design lays the foundation for capturing the rapid changes in current during the startup phase and the precise timing of current peaks.

[0027] Specifically, the dynamic changes in current during startup are characterized by high nonlinearity and rapid response. The current waveform may exhibit complex transient and transitional changes due to various factors such as load instability and fluctuations in the startup winding engagement time. Without high-frequency sampling, current waveform data can easily be lost or the sampling resolution may be insufficient, thus affecting the accuracy of subsequent analysis. For example, the first peak current and its occurrence time are crucial data for predicting the optimal engagement time of the startup winding. The current peak typically forms within a very short time after circuit startup; if the sampling frequency is insufficient, the system may not be able to accurately capture this information. Furthermore, the accurate extraction of waveform features such as the current integral value and current rise slope within a specified time window also relies on high-frequency sampling. These data provide algorithmic input for subsequent optimization models.

[0028] Specifically, in step S3, the MCU control unit predicts the optimal engagement time of the starting winding based on the current waveform data sequence. It should be understood that predicting the optimal engagement time of the starting winding is a crucial step in ensuring motor starting efficiency and optimizing energy consumption. The core of using the MCU control unit to predict based on the current waveform data sequence lies in improving the accuracy of timing control under data-driven conditions and the dynamic response capability of the starting circuit. The current waveform during the starting phase typically exhibits significant complexity and dynamic changes. Key characteristics such as the transient peak value, growth slope, and time integral of the current reflect the electrical operating state and power requirements during the starting process. These characteristics are closely related to the final engagement time of the starting winding. Therefore, if the optimal engagement time is not accurately predicted based on real-time current data, the circuit may experience insufficient starting power due to an excessively short engagement time, or energy waste and overheating due to an excessively long engagement time, leading to decreased overall operating efficiency or equipment damage.

[0029] In one embodiment, such as Figure 3As shown, step S3 includes: S31: Extracting key early current features from the current waveform data sequence, the key early current features including the first current peak, the time to reach the first current peak, the current integral value within a specified time window, and the current rise slope; S32: Classifying the first current peak and the time to reach the first current peak to obtain current peak classification results and peak time classification results; S33: Inputting the current peak classification results, peak time classification results, current integral value within a specified time window, and current rise slope into a multiple linear regression model to obtain the optimal intervention time of the starting winding.

[0030] Specifically, after the electricity metering chip collects current data at a high frequency of 4kHz, it generates a real-time current waveform data sequence, which consists of the current change trajectory from the moment of startup to the present time. After the current waveform data sequence is generated, the MCU control unit extracts early key features from it. These features include the first current peak, the time to reach the peak, the current integral value within a specified time window, and the current rise slope. The first current peak reflects the initial current supply capacity of the winding; the peak occurrence time is used to determine the speed of current response after winding intervention; and the current integral within the specified time window quantifies the total current contribution during the entire startup phase. The current rise slope serves as a key indicator of dynamic trends, reflecting the rapid degree of current change over time. By extracting these early features, the MCU control unit can comprehensively perceive the electrical behavior patterns during the startup phase.

[0031] In one specific embodiment, the MCU control unit analyzes the current waveform data sequence point by point, identifies the current peak point, and locks its corresponding time index. Then, the current peak value is obtained by scanning the maximum value in the data sequence using an algorithm, and the time point can be accurately located by multiplying the sampling number by the sampling period. Simultaneously, the integral value of the current within a specified time window is calculated by summing the current amplitude flowing through the circuit within a specified time to reflect the total power contribution during the startup phase. The current rise slope, as a dynamic characteristic, can be calculated point by point using a numerical difference method to obtain the rate of change of current over time.

[0032] After feature extraction, the first current peak value and the time to reach the first current peak value are further categorized. This categorization aims to discretize continuous data, making it more suitable for regression model training requirements and enhancing data standardization. In one embodiment, the current peak value categorization results include low, medium, and high levels; the peak value arrival time categorization results include fast, medium, and full levels. In a specific embodiment, for the current peak value, a current less than 10A can be defined as low level, 10A to 20A as medium level, and greater than 20A as high level. For the peak value arrival time, a peak value arrival time less than 50ms can be defined as low level, 50ms to 100ms as medium level, and greater than 100ms as full level. Of course, this is only an example, and the specific division can be adjusted according to the actual data. Categorization not only provides data classification capabilities but also provides a clear input range for the regression model through the setting of categorization criteria.

[0033] After the grading is completed, these grading results, along with other features, are input into a multiple linear regression model. The regression model predicts the optimal intervention time for starting the winding by calculating the weights of the variables. Specifically, the multiple linear regression model is expressed as follows: In the formula, Indicates the optimal engagement time for starting the winding. and These are the peak current range and peak time range results, respectively, assigned as scalar values. For example, the medium range is set to 2. The integral value of the current within a specified time window. The slope of the current rise, These are the regression coefficients of the model, obtained through training. Specifically, the model's parameters are estimated using the least squares method, by inputting known training samples from the experimental data into the model for fitting. Regression training can be completed using tools such as the regression fitting function of Python or MATLAB, obtaining the coefficient values ​​through error minimization.

[0034] Specifically, in step S4, the MCU control unit records the engagement time of the starting winding through the timer and monitors the real-time current through the electricity metering chip. It should be understood that the current state during startup exhibits significant dynamic changes and time sensitivity, especially after the starting winding engages, its operating time directly affects the startup effect and energy efficiency. Traditional methods, based on fixed timing control, typically cannot handle dynamic loads and nonlinear current changes in real time, easily leading to inaccurate engagement time and decreased system performance. By using the MCU control unit in conjunction with the timer to record the engagement time of the starting winding, real-time monitoring and precise control of the winding engagement time can be achieved. The timer provides high-precision time recording, utilizing an internal crystal oscillator and frequency division factor to achieve microsecond-level time recording, ensuring that the dynamic timing sequence of each startup process is completely recorded, providing data support for subsequent judgments. For example, if the starting winding engages for too long, it will lead to unnecessary energy consumption, and the timer can provide real-time alerts to the MCU to determine whether the current engagement time exceeds the optimal engagement time, helping the system to make timely judgments and adjustments.

[0035] In one specific embodiment, the MCU initializes its internal timer module and sets an interrupt timer to achieve real-time recording of the winding engagement time. For example, when using an STM32 series MCU, the timer count can be reset to zero via configuration code, while simultaneously activating the timer to achieve time recording. When the winding begins engagement, the MCU triggers the timer to start recording the time. When an interrupt is triggered, the MCU stores the current count value in an internal variable for subsequent comparison or output. The software portion of the data entry uses timer callback functions to dynamically record and transmit time, ensuring that the MCU can access the time data for logical analysis at any time.

[0036] Specifically, in step S5, based on the real-time current, the engagement time of the starting winding, and the optimal engagement time of the starting winding, the MCU control unit adaptively cuts off the secondary thyristor. It should be understood that in the starting control circuit, the adaptive cutoff of the secondary thyristor is a crucial step in optimizing motor starting efficiency and energy consumption. By combining dynamic analysis of real-time current, engagement time, and optimal engagement time, the MCU can accurately determine the timing of the secondary thyristor's cutoff, thereby avoiding energy waste or equipment performance damage that may result from premature or late winding engagement. This design establishes real-time response logic for the starting circuit while ensuring system stability and safety.

[0037] In a specific embodiment, such as Figure 4As shown, step S5 includes: S51: Constructing a first judgment condition and a second judgment condition, wherein the first judgment condition is whether the real-time current has shown a significant decreasing trend and tends to stabilize, and the second judgment condition is whether the intervention time of the starting winding exceeds the optimal intervention time of the starting winding; S52: When the second judgment condition is satisfied and the first judgment condition is satisfied, the MCU control unit cuts off the secondary thyristor; S53: When the second judgment condition is not satisfied but the first judgment condition is satisfied, the MCU control unit cuts off the secondary thyristor; S54: When the second judgment condition is satisfied but the first judgment condition is not satisfied, the MCU control unit triggers a protection mechanism.

[0038] Specifically, the core of the first judgment condition lies in capturing the dynamic process of the current gradually declining from its peak and stabilizing during the startup phase by monitoring the waveform changes of the real-time current. A significant decrease and stabilization of the current indicates that the starting winding and the overall circuit have gradually transitioned from the high-energy-consumption state of startup to normal operation. At this point, the load on the winding begins to stabilize, and disconnecting the starting winding will not cause drastic current fluctuations or system instability. Using this current fluctuation characteristic as a condition utilizes the sensitivity of the current itself to load changes and is an important basis for assessing whether the system has entered a stable phase during startup. More specifically, the downward trend and stable state of the current reflect that the mechanical load of the equipment is gradually reaching the rated operating point, and the electrical pressure is released to the normal range, objectively indicating that the auxiliary role of the starting winding is nearing its end.

[0039] The second judgment condition reflects personalized control for specific equipment and startup conditions. By comparing the intervention time of the startup winding with the optimal intervention duration, the system constrains and guarantees on a time scale. The optimal intervention duration is predicted based on current waveform characteristics using a multiple linear regression model or lookup table, possessing the ability to dynamically and in real-time adapt to different startup conditions. This means that the working time of the startup winding is not a fixed preset value, but an intelligent adjustment result supported by experimental, simulation, and actual data. If the intervention time of the startup winding exceeds this intelligently predicted duration, it indicates that, according to the current startup condition, the winding has completed most of the necessary auxiliary tasks. Continuing to extend the working time is not only meaningless but also easily leads to unnecessary energy waste and equipment burden.

[0040] By combining these two judgment conditions, the system can effectively avoid two potential risks and efficiency losses. On the one hand, if the real-time current has shown a significant decrease and tends to stabilize, but the starting winding intervention time has not exceeded the optimal intervention duration, the system can still consider disconnecting the winding to avoid unnecessary energy consumption. On the other hand, if the starting winding intervention time has exceeded the optimal intervention duration, but the current has not yet shown a stabilizing trend, it indicates that there may be an anomaly in the startup (such as sudden load changes, equipment failure, etc.). In this case, triggering the protection mechanism to ensure equipment safety becomes particularly important. The protection mechanism can promptly disconnect the circuit or issue a fault warning to avoid hardware damage, thereby improving equipment reliability. Through this dynamic judgment, not only is the intelligence level of the startup system improved, but the flexibility and accuracy of the response are also enhanced.

[0041] In one specific embodiment, the first judgment condition is whether multiple consecutive real-time currents are lower than a preset current threshold. Current data is generated by a metering chip using high-frequency sampling to analyze the stability of the current decrease phase. The preset current threshold is a reference value when the current decreases to a certain range or remains below a certain value. In one specific embodiment, the measured stable current decrease is 3A, and the system can set the threshold range to 3A to 3.5A. This threshold serves as the trigger standard for the first judgment condition. When multiple consecutive real-time current values ​​meet this threshold range, the MCU determines that the current has shown a stable decreasing trend.

[0042] Here, when all the real-time currents are below the preset current threshold, if we further consider the possible impact of the relatively complex on / off timing of the main / slave thyristors on the real-time currents, it is expected that the multiple real-time currents can have predetermined timing steady-state characteristics, including transient response based on fixed timing points, transition process based on timing segments, and steady-state characteristics based on global timing.

[0043] Therefore, in another specific embodiment, the first judgment condition further includes: when the plurality of real-time currents are all lower than a preset current threshold, determining the characterization coefficients of the plurality of real-time currents based on the transient response at a fixed time point, the transition process based on time segmentation, and the steady-state characteristics based on global time sequence, and judging whether the characterization coefficients are greater than an empirical threshold.

[0044] Specifically, determining the characterization coefficients of the multiple real-time currents based on the transient response at a fixed time point, the transient process based on time segmentation, and the steady-state characteristics based on global time sequence includes: assuming the multiple real-time currents are... In this case, the transient response of each real-time current is first calculated as follows: ;in, and They represent the first The and the first A real-time current, Indicates the first A transient response.

[0045] Furthermore, based on the transient response of each real-time current, the response change rate is calculated as follows: ;in, and Indicates that respectively represent the first The and the first A transient response, Indicates the first The rate of change of response.

[0046] That is, the real-time current value of the previous moment. As an external stimulus, the non-homogeneous projection analysis of the real-time current in each time series segment is performed by analyzing the rate of change of response.

[0047] The stability of the response rate of change under global time series is verified by calculating the transient process verification coefficient, which is expressed as: ;in, and Sets The mean and standard deviation, This represents the verification coefficient of the transition process.

[0048] Then, the global power spectral density is calculated for the response rate of change, and expressed as: ;in, This represents the global power spectral density.

[0049] Using the global power spectral density as a dynamic factor, global temporal inertial correction is performed on the transient process verification coefficients to obtain the characterization coefficients, expressed as: ;in, Represents the natural constant. Represents the characterization coefficient.

[0050] In this way, characterization coefficients can be obtained that simultaneously represent the transient response based on fixed timing points, the transient process based on timing segments, and the steady-state characteristics based on global timing of the multiple real-time currents. Then through the characterization coefficients Whether it is greater than an empirical threshold (for example, setting an empirical threshold of 0.7; of course, this is just an example, and the empirical threshold can be adjusted based on equipment characteristics and actual experimental data, and this application does not make specific limitations), can realize the steady-state determination of the system at different time interval granularities, thereby improving the accuracy of the determination of the first judgment condition, while fully considering the non-stationarity or nonlinear characteristics of the real-time current in the time interval superimposed with the time interval for determining the stable trend during the current decrease phase.

[0051] In summary, the startup timing control optimization method for startup control circuits provided in this application firstly initializes the timer upon receiving the startup command and simultaneously triggers the main and auxiliary thyristors to conduct at time T1. Subsequently, a current waveform data sequence is obtained by high-frequency sampling of real-time current data at a frequency of 4kHz using an electrical metering chip. Next, the MCU control unit predicts the optimal engagement time of the startup winding based on this data. Simultaneously, the MCU control unit continuously records the engagement time of the startup winding and monitors the real-time current through the electrical metering chip. Finally, based on the real-time current, the engagement time of the startup winding, and the optimal engagement time, the MCU control unit adaptively cuts off the auxiliary thyristor, ensuring that the startup winding is accurately disconnected at a more appropriate time, thereby reducing energy consumption, improving startup efficiency and stability, and enhancing the reliability and safety of the system.

[0052] The basic principles of this application have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this application are merely examples and not limitations, and should not be considered as essential features of each embodiment of this application. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the application to the necessity of employing the aforementioned specific details for implementation.

[0053] The flowcharts of the methods involved in this application are merely illustrative examples and are not intended to require or imply that connections, arrangements, or configurations must be made in the manner shown in the flowcharts. As those skilled in the art will recognize, these devices, apparatuses, equipment, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0054] It should also be noted that the steps in the method of this application can be decomposed and / or recombined. These decompositions and / or recombinations should be considered as equivalent solutions of this application.

[0055] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of this application. Therefore, this application is not intended to be limited to the aspects shown herein, but rather to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0056] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this application to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. A startup timing control optimization method for a startup control circuit, characterized in that, include: S1: After receiving the start command, the MCU control unit initializes the timer and triggers the main and auxiliary thyristors to conduct at time T1; S2: After the secondary and primary thyristors are triggered to conduct, the real-time current data is sampled at high frequency by the electricity metering chip to obtain the current waveform data sequence. S3: The MCU control unit predicts the optimal engagement time of the starting winding based on the current waveform data sequence; S4: The MCU control unit records the engagement time of the start-up winding through the timer and monitors the real-time current through the electricity metering chip; S5: Based on the real-time current, the engagement time of the starting winding, and the optimal engagement time of the starting winding, the MCU control unit adaptively cuts off the secondary thyristor. Wherein, S3 includes: Extract early key features of the current from the current waveform data sequence. The early key features of the current include the first current peak, the time to reach the first current peak, the current integral value within a specified time window, and the current rise slope. The first current peak value and the time to reach the first current peak value are divided into categories to obtain current peak value category results and peak value time category results. The peak current grading results, the peak time grading results, the current integral value within the specified time window, and the current rise slope are input into a multiple linear regression model to obtain the optimal intervention time of the starting winding.

2. The startup timing control optimization method for a startup control circuit according to claim 1, characterized in that, In S2, the sampling frequency of the high-frequency sampling is 4kHz.

3. The startup timing control optimization method for a startup control circuit according to claim 1, characterized in that, The current peak value classification results include low, medium and high; the peak time classification results include fast, medium and full.

4. The startup timing control optimization method for a startup control circuit according to claim 1, characterized in that, The S5 includes: Construct a first judgment condition and a second judgment condition. The first judgment condition is whether the real-time current has shown a significant decreasing trend and tends to stabilize. The second judgment condition is whether the intervention time of the starting winding exceeds the optimal intervention time of the starting winding. When both the second and first judgment conditions are met, the MCU control unit cuts off the secondary thyristor. When the second judgment condition is not met but the first judgment condition is met, the MCU control unit cuts off the secondary thyristor; When the second judgment condition is met but the first judgment condition is not met, the MCU control unit triggers the protection mechanism.

5. The startup timing control optimization method for a startup control circuit according to claim 4, characterized in that, The first judgment condition is whether multiple consecutive real-time currents are lower than a preset current threshold.

6. The startup timing control optimization method for a startup control circuit according to claim 5, characterized in that, The first judgment condition further includes: when the multiple real-time currents are all lower than the preset current threshold, determining the characterization coefficients of the multiple real-time currents based on the transient response at a fixed time point, the transition process based on time segmentation, and the steady-state characteristics based on global time sequence, and judging whether the characterization coefficients are greater than the empirical threshold.

7. The startup timing control optimization method for a startup control circuit according to claim 6, characterized in that, Determine the characterization coefficients for the transient response based on fixed time points, the transient process based on time segmentation, and the steady-state characteristics based on global time sequence of the multiple real-time currents, including: Calculate the transient response of each real-time current; Calculate the rate of change of response based on the transient response of each real-time current; The stability of the response rate of change under global time series is verified by calculating the transient process verification coefficient; Calculate the global power spectral density based on the rate of change of response; Using the global power spectral density as a dynamic factor, the transient process verification coefficients are subjected to global temporal inertial correction to obtain the characterization coefficients.

Images