An Adaptive Startup Method and System Based on Voltage and Current Monitoring

Abstract

This application relates to the field of adaptive starting technology, and discloses an adaptive starting method and system based on voltage and current monitoring. The method includes: an MCU control unit triggers the secondary and primary thyristors to start the motor through preset starting logic, and samples current and voltage data in real time at high frequency. Once a starting failure is detected, the system automatically captures and stores short-term voltage and current sequences before and after the failure point. Then, key features are extracted from these sequences, and the cause of the failure is determined through decision tree model analysis. Based on this analysis result and the current number of retries, the MCU control unit outputs a corresponding retry strategy to ensure that the motor can successfully start or enter a safe mode. This not only improves the reliability of motor starting but also supports subsequent fault diagnosis through detailed data recording, greatly enhancing the maintainability and safety of the system.

Description

Technical Field

[0001] This application relates to the field of adaptive startup technology, and more specifically, to an adaptive startup method and system based on voltage and current monitoring. Background Technology

[0002] In modern industry and household appliances, motors play a crucial role as key components. However, traditional motor starting methods, such as PTC starters and counterweight starters, have revealed many limitations and defects in practical applications. These shortcomings directly affect the reliability and safety of the motor, while also increasing maintenance costs and technical complexity.

[0003] Traditional mechanical starters are designed primarily based on simple physical mechanisms, resulting in limited protection functions and passive responses. For example, a hammer starter relies on the electromagnetic force generated by the large current at startup to overcome gravity and engage the contacts, then disconnects by gravity once the current decreases; a PTC starter utilizes the increased resistance of materials at specific temperatures to disconnect the starting winding. Neither of these methods possesses the ability to actively monitor the real-time operating status of the compressor, providing only indirect, situation-specific protection. This design philosophy means they cannot provide real-time monitoring and comprehensive protection for multiple key parameters such as voltage, power, and temperature, thus limiting their ability to handle complex electrical faults. Furthermore, due to the lack of intelligent analysis capabilities, traditional starters often fail to respond effectively to undervoltage, overvoltage, or abnormal power conditions, and may even cause more serious damage due to erroneous actions. Summary of the Invention

[0004] To address the shortcomings of traditional motor starters in terms of protection mechanisms and fault diagnosis, this application proposes an adaptive starting method and system based on voltage and current monitoring. This method utilizes an MCU control unit combined with an electrical metering chip to achieve precise control and intelligent monitoring of the motor starting process.

[0005] According to one aspect of this application, an adaptive startup method and system based on voltage and current monitoring is provided, comprising: an MCU control unit triggering a secondary thyristor and a primary thyristor to start a motor through preset startup logic, while simultaneously sampling current and voltage at high frequency through an electrical metering chip; a startup failure determination logic of the MCU control unit, and once a startup failure is determined, capturing and storing short-term voltage and current sequences before and after the failure point through the electrical metering chip; the MCU control unit extracting key current features and key voltage features from the voltage and current sequences, and determining the cause of failure based on the key current features and key voltage features; the MCU control unit outputting a retry strategy based on the cause of failure and the current number of retries; and the MCU control unit executing the retry strategy.

[0006] In one possible implementation, the MCU control unit triggers the secondary and primary thyristors to start the motor by pre-setting startup logic, including: the MCU control unit first triggers the secondary thyristor to conduct to connect the motor's starting winding; the MCU control unit then triggers the primary thyristor to conduct to connect the motor's primary winding.

[0007] In one possible implementation, the failure determination logic includes the current failing to reach the expected threshold within a specified time, the current failing to fall back after rising, and the overcurrent / overvoltage protection being triggered.

[0008] In one possible implementation, the key current characteristics include peak current, current rise rate, time to reach peak current, current before failure, and current fluctuation characteristics, and the key voltage characteristics include average voltage before failure, minimum voltage during startup, and voltage drop magnitude.

[0009] In one possible implementation, determining the cause of failure based on key current features and key voltage features includes: inputting the key current features and key voltage features into a decision tree model to obtain the cause of failure.

[0010] In one possible implementation, the MCU control unit outputs a retry strategy based on the failure reason and the current number of retries, including: the MCU control unit queries a preset multi-level retry strategy table to obtain the retry strategy based on the failure reason and the current number of retries, the retry strategy including strategy type, waiting seconds and parameter configuration identifier for the next startup, the strategy type including retry and locking.

[0011] In one possible implementation, inputting the key current features and the key voltage features into a decision tree model to obtain the failure cause includes: associating the peak current time, the current before failure, and the current fluctuation features through a current fluctuation function to obtain a current correlation expression, the current correlation expression including a fluctuation function adjustment factor; associating the current fluctuation features, the average voltage before failure, the minimum voltage during startup, and the voltage drop amplitude through a system voltage stability function to obtain a voltage correlation expression, the voltage correlation expression including a stability risk adjustment factor; minimizing the current correlation expression while maximizing the voltage correlation expression by adjusting the fluctuation function adjustment factor and the stability risk adjustment factor; substituting the determined fluctuation function adjustment factor and the stability risk adjustment factor into the current correlation expression and the voltage correlation expression, and inputting the current correlation expression, the voltage correlation expression, the key current features, and the key voltage features into a decision tree model to obtain the failure cause.

[0012] According to another aspect of this application, an adaptive startup system based on voltage and current monitoring is also provided, the adaptive startup system based on voltage and current monitoring including a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor, when executing the computer program, implements the steps of the adaptive startup method based on voltage and current monitoring as described above.

[0013] Compared with existing technologies, the adaptive starting method and system based on voltage and current monitoring provided in this application includes: an MCU control unit triggers the secondary and primary thyristors to start the motor through preset starting logic, and samples current and voltage data at high frequency in real time. Once a starting failure is detected, the system automatically captures and stores short-term voltage and current sequences before and after the failure point. Then, key features are extracted from these sequences, and the cause of the failure is determined through decision tree model analysis. Based on this analysis result and the current number of retries, the MCU control unit outputs a corresponding retry strategy to ensure that the motor can successfully start or enter a safe mode. This not only improves the reliability of motor starting but also supports subsequent fault diagnosis through detailed data recording, greatly enhancing the maintainability and safety of the system. Attached Figure Description

[0014] 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.

[0015] Figure 1 The illustration shows a schematic flowchart of an adaptive startup method based on voltage and current monitoring according to an embodiment of this application.

[0016] Figure 2 The figure shows a schematic block diagram of a start-up control circuit according to an embodiment of the present application.

[0017] Figure 3 The illustration shows a schematic flowchart of an adaptive start-up method and system based on voltage and current monitoring according to an embodiment of this application, in which the MCU control unit triggers the secondary and primary thyristors to start the motor through preset start-up logic.

[0018] Figure 4 The figure shows a schematic structural diagram of an adaptive start-up system based on voltage and current monitoring according to an embodiment of the present application. Detailed Implementation

[0019] 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.

[0020] Figure 1 The illustration shows a schematic flowchart of an adaptive startup method based on voltage and current monitoring 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 an adaptive startup method based on voltage and current monitoring, including: S1: The MCU control unit triggers the secondary and primary thyristors to start the motor through preset startup logic, and simultaneously samples the current and voltage at high frequency through an electrical metering chip; S2: The MCU control unit has startup failure determination logic, and once startup failure is determined, it captures and stores the short-term voltage and current sequences before and after the failure point through the electrical metering chip; S3: The MCU control unit extracts key current features and key voltage features from the voltage and current sequences, and determines the cause of failure based on the key current features and key voltage features; S4: The MCU control unit outputs a retry strategy based on the cause of failure and the current number of retries; S5: The MCU control unit executes the retry strategy.

[0021] For example, in step S1, the MCU control unit triggers the secondary and primary thyristors to start the motor through preset startup logic, while simultaneously sampling the current and voltage at high frequency using an electrical metering chip. It should be understood that one of the main challenges in the motor startup process is ensuring sufficient starting torque and avoiding unnecessary burden on the motor and the power grid. Traditionally, direct starting methods can lead to instantaneous large current surges, which may not only damage the motor itself but also affect the stability of the entire power grid. Therefore, by using thyristor technology, the motor startup process can be controlled more precisely, thereby reducing such surges. In one embodiment, such as... Figure 3 As shown, the MCU control unit triggers the secondary thyristor and the primary thyristor to start the motor through preset start-up logic, including: S11: The MCU control unit first triggers the secondary thyristor to conduct to connect the motor's start-up winding; S12: The MCU control unit then triggers the primary thyristor to conduct to connect the motor's primary winding.

[0022] Specifically, at the start of the startup process, the MCU control unit first triggers the secondary thyristor to conduct, thereby activating the motor's starting winding. This step is crucial for providing initial torque, as the starting winding is designed to generate a large electromagnetic torque at low speeds, helping the motor overcome static friction and other resistances, enabling the motor to transition from a standstill to a rotating state. During this process, the electrical metering chip samples current and voltage at high frequency, monitors the motor's status in real time, and feeds the data back to the MCU control unit.

[0023] As the motor speed gradually increases and the starting conditions are met, such as whether the motor's starting current has dropped to near or within the expected operating current range, the MCU control unit triggers the main thyristor to connect the compressor's main circuit and enter normal operation. During this process, the MCU control unit is not only responsible for controlling the triggering timing of the thyristor, but also continuously monitors the motor's operating parameters through the electrical metering chip.

[0024] For example, in step S2, the MCU control unit initiates the failure determination logic, and once a startup failure is determined, it captures and stores the short-term voltage and current sequences before and after the failure point through the electricity metering chip. It should be understood that motors are highly susceptible to multiple factors during startup, such as power supply quality, load fluctuations, and mechanical structure, leading to frequent startup failures. Therefore, during startup, the MCU control unit initiates the failure determination logic, enabling the device to not only detect and determine startup failure immediately but also to completely save the electrical signals at critical moments, providing a data foundation for subsequent analysis and maintenance.

[0025] Specifically, the startup failure determination refers to the MCU control unit integrated within the control system, relying on high-speed communication with the electricity metering chip to receive and analyze the current and voltage waveforms generated during the compressor startup process in real time. Throughout the startup window period, the MCU control unit performs algorithmic analysis on the data stream, promptly providing a startup success or failure conclusion based on set criteria, and executing corresponding data saving and protection actions. In one embodiment, the failure determination logic includes situations where the current fails to reach the expected threshold within a specified time, the current rises but does not fall back, and overcurrent / overvoltage protection is triggered.

[0026] Specifically, during the initial startup phase of a motor, the motor accelerates from a standstill and needs to overcome significant inertial resistance, resulting in a relatively high starting current, often referred to as stall current or peak current. If, within a preset time window (e.g., 1 second), the measured current fails to reach or exceed a pre-set expected threshold, the startup is considered a failure. This expected threshold can be determined based on the motor's rated power, design parameters, and startup characteristics. In a specific embodiment, for a household refrigerant compressor with a rated operating current of 3.5A and an actual startup current ranging from 8A to 12A, an expected threshold of 7A can be set. Once the 1-second cycle has elapsed, analysis of the sampling points shows that if the highest current value recorded is consistently lower than this predetermined threshold, the startup is considered unsuccessful. Common causes of such failures include excessive external load, capacitor degradation or open circuit, and insufficient power supply (undervoltage).

[0027] Next, considering the physical process of motor startup, the current curve goes through several stages: startup rise, peak value, and stabilization. Under normal circumstances, in the initial startup phase, the current is limited only by the short-circuit impedance of the windings. The current surges rapidly after energization, and as the speed increases and the back EMF strengthens, the current will inevitably drop until it stabilizes. If the motor rotor is stalled, or the load is stuck, the peak current will remain high for a long time without a significant downward trend. This not only increases the risk of equipment damage but also often reflects mechanical failures, coil short circuits, or severe stalling. Therefore, the MCU control unit continuously monitors the subsequent current waveform after detecting the peak current. For example, within 0.7 to 1.2 seconds after the peak current appears, the current drop should reach more than 25%, meaning the current must drop to 0.75 times the peak value; otherwise, it is considered that the current has not dropped. Of course, this is just an example and can be adjusted according to specific circumstances.

[0028] Then, considering that in actual operation, under certain special circumstances, the compressor's input voltage may suddenly rise abnormally or the current may momentarily exceed the allowable limit, causing damage to high-risk components or even igniting the circuit, the MCU control unit continuously reads samples from the power metering chip, closely monitoring the highest current and voltage extreme values. Once the current value exceeds the safe allowable limit threshold (e.g., 12A), or the voltage exceeds the design allowable range (e.g., 265V), regardless of the operating state, overcurrent / overvoltage protection will be executed, shutting down all main and auxiliary thyristors, and determining that the startup has completely failed.

[0029] For example, in step S3, the MCU control unit extracts key current and voltage features from the voltage and current sequences, and determines the cause of failure based on these features. It should be understood that motor starting failure can be caused by various factors, such as power supply problems, mechanical resistance, electrical faults, etc. Relying solely on simple threshold comparisons (e.g., whether the current reaches the expected level) cannot provide sufficient information to accurately identify the specific problem. Therefore, by extracting and analyzing key current and voltage features, and accurately determining the cause of failure based on these features, the MCU control unit can effectively address the issue.

[0030] In one embodiment, the current key characteristics include peak current, current rise rate, time to reach peak current, current before failure, and current fluctuation characteristics, and the voltage key characteristics include average voltage before failure, minimum voltage during startup, and voltage drop magnitude.

[0031] Specifically, peak current extraction involves the MCU control unit acquiring real-time current values ​​at high frequency, traversing the entire startup process (typically the time window range for startup task determination), and taking the maximum value from the time-series sampled data as the peak current. For example, in the initial stage of compressor power-on, the current rises sharply due to the small stator back EMF. As the speed increases and the back EMF strengthens, the current falls back and eventually stabilizes at the operating value. Within the MCU sampling window, the program continuously records the maximum current sampling point, which is defined as the peak current. The current rise rate is calculated by dividing the difference between the current at each moment and the current sampling value at the previous moment by the sampling interval (which can be approximated as the differential), extracting the maximum slope to reflect the startup impact intensity. The extraction of the time to reach the peak current is done by taking the sampling start moment as zero and finding the time point corresponding to the peak current sampling. This time is the time to reach the peak current, providing direct feedback on whether the equipment startup efficiency is abnormal. The determination of the current before failure is generally based on the N milliseconds (e.g., 100ms) before the moment when the startup failure is determined by criteria, or the average current of a specified sampling point, or the current of the last sampling point, as the current before failure, to reflect the dynamic characteristic changes of the system before the failure. Current fluctuation characteristics focus more on describing the amplitude and pattern of current curve fluctuations during startup. Specifically, this can be achieved by taking a segment within the judgment period and calculating statistical features such as slope, first / second difference, variance, and standard deviation, thereby revealing abnormal phenomena such as unstable startup, violent oscillations, or current jitter. For example, if the current sampled 200 milliseconds before failure is set as I(t), then the root mean square deviation var(I(t)) is calculated to characterize the current activity and abnormal fluctuations.

[0032] The average voltage before failure refers to the arithmetic mean of all voltage samples within a short window (e.g., 100ms) before a failure is determined. This characteristic accurately reflects whether the voltage at the power supply end or line end drops significantly when the starting load increases sharply. The minimum voltage during startup represents the lowest point in the entire startup range and is often used to detect hidden circuit problems such as abnormal power quality and instantaneous voltage drops. The voltage drop amplitude refers to the difference between the voltage before startup and the lowest voltage during startup. This indicator visually depicts the impact of the motor starting and loading on the entire power grid, and also indirectly indicates the quality of the line, power supply, or overall electrical matching.

[0033] Next, considering that diagnosing fault causes solely based on independent parameters is far from sufficient, in complex electrical systems, various faults such as poor starting, stalled rotor damage, and undervoltage failure often exhibit cross-coupled and highly variable current and voltage characteristics. While a single feature can reflect part of the phenomenon, it is difficult to accurately distinguish based on only one. Therefore, in one embodiment, determining the cause of failure based on key current and voltage features includes: inputting the key current and voltage features into a decision tree model to obtain the cause of failure. The decision tree model is preferred for this scenario primarily because of its inherent hierarchical conditional flow advantage, which allows it to weave multiple feature variables into a discrimination path combining traditional empirical criteria and data-driven approaches, rather than passively stacking individual boundary thresholds. Higher-order decision trees can also better reflect the nonlinear mapping relationships between parameters, possessing interpretability and ease of maintenance, facilitating continuous optimization and expansion based on actual engineering operation and maintenance experience.

[0034] In a specific embodiment, the parallel extraction of all the aforementioned feature parameters is first implemented in the MCU control unit. Then, according to a predetermined format, such as a vector {peak current, current rise rate, time to reach peak current, current before failure, current fluctuation characteristics, average voltage before failure, minimum voltage during startup, voltage drop amplitude}, it is input into the decision tree algorithm module. The modeling and training process of the decision tree can be completed offline using a large amount of fault data during the experimental phase, and the final criteria and paths are then embedded into the MCU control unit. Taking compressor stall as an example, it is generally characterized by a huge peak current that does not significantly decrease over a long period, and a significant drop in the minimum voltage during startup, even below the limit threshold. For failures caused by poor power quality, the characteristics are a peak current far below the nominal value and a sharp instantaneous voltage drop during startup. The decision tree model will prioritize classification of the root node based on the most sensitive and common anomaly features, and then branch to fine-grained parameter comparisons to achieve multi-level, progressive failure type screening. For example, the first-level criterion may determine whether the peak current exceeds the limit. If so, it is determined that the stall protection or the motor is stuck. If not, it will switch to analyzing the ratio of the current rise rate to the time to reach the peak current to determine whether the load is abnormal or the starting capacitor has failed. The next step is to determine the power supply side fault based on the voltage drop amplitude and the average voltage drop before the failure. In this layered nesting, the final output is a clear and semantically clear failure reason code, which is convenient for subsequent maintenance, analysis and optimization.

[0035] When the key current features and the key voltage features are input into the decision tree model, if the multidimensional coupling characteristics between the key current features and the key voltage features are not considered, that is, the coupling characteristics between current parameters, between voltage parameters, and between current / voltage parameters, the decision tree model may fail in multiple joint decisions, affecting the training / inference efficiency of the decision tree model. Therefore, it is preferable to first perform decision stability correction on the key current features and the key voltage features based on multidimensional coupling modeling.

[0036] Based on this, in another embodiment, inputting the key current features and the key voltage features into a decision tree model to obtain the failure cause includes: first, associating the peak current time, the current before failure, and the current fluctuation features through a current fluctuation function to obtain a current correlation expression, wherein the current correlation expression includes a fluctuation function adjustment factor, expressed as: ;in, Adjustment factor for the fluctuation function, Indicates the current before failure. Indicates the time to reach peak current. Indicates current fluctuation characteristics, Represents the natural constant. This indicates the expression of current correlation.

[0037] Then, the current fluctuation characteristics, the average voltage before failure, the minimum voltage during startup, and the voltage drop amplitude are correlated using a system voltage stability function to obtain a voltage correlation expression. This voltage correlation expression includes a stability risk adjustment factor, which takes into account the current fluctuation characteristics. and voltage drop To model the system voltage stability function by considering the stochastic stability fluctuation diffusion effect on the overall system voltage stability, i.e., the stability diffusion characteristic with inconsistent fluctuation directions, we can express it as follows: ;in, To adjust for stability risks, This represents the average voltage before failure. Indicates the minimum voltage during startup. Indicates the voltage drop magnitude. Indicates current fluctuation characteristics, This indicates a voltage correlation expression.

[0038] Next, joint decision constraints are applied, that is, by adjusting the adjustment factor of the fluctuation function. and the stability risk adjustment factor Minimize the current correlation expression Simultaneously maximize the voltage correlation expression , that is, maximize In order to determine the factors and .

[0039] Then, the determined fluctuation function adjustment factor is... and the stability risk adjustment factor Substitute the current correlation expression and the voltage correlation expression into the decision tree model; then input the current correlation expression, the voltage correlation expression, the key current features, and the key voltage features into the decision tree model to obtain the failure reason. Using this mechanism, the decision stability of the key current features and the key voltage features can be improved based on multi-dimensional coupling modeling, thereby enhancing the training / inference efficiency of the decision tree model.

[0040] For example, in step S4, the MCU control unit outputs a retry strategy based on the cause of failure and the current number of retries. It should be understood that ordinary startup failures are highly likely due to occasional anomalies, such as temporary power grid fluctuations, changes in the operating environment, or occasional minor faults in the equipment itself. Completely prohibiting subsequent operations based on a single failure not only fails to tolerate some recoverable field disturbances but may also create unnecessary service risks. Conversely, if retries are allowed without restriction for all failure causes, it may lead to repeated equipment overload, excessive wear and tear, or even repeated operation with inherently irreparable problems, creating serious safety hazards. Therefore, the MCU control unit outputs a retry strategy based on the cause of failure and the current number of retries. This retry strategy is not a simple restart upon failure but rather a customized decision made after systematic analysis, depending on the type of startup failure, the environmental context in which it occurred, and the historical number of retries.

[0041] In one embodiment, the MCU control unit outputs a retry strategy based on the failure reason and the current number of retries. This includes: the MCU control unit queries a preset multi-level retry strategy table to obtain the retry strategy based on the failure reason and the current number of retries. The retry strategy includes a strategy type, a waiting time in seconds, and a parameter configuration identifier for the next startup. The strategy type includes retry and locking. It should be understood that the multi-level retry strategy table is a pre-defined strategy rule database, which arranges different responses according to various potential failure reasons and plans the combined actions to be taken for each next step based on the current number of retries completed. The table not only records the retry category markers corresponding to common failures such as undervoltage, overvoltage, stall, damaged startup capacitor, abnormal initial startup data, failure of main and auxiliary thyristors to conduct correctly, and sensor signal loss, but also provides a tiered progression based on the number of retries—for example, the mildest waiting and retry is used after the first failure, the waiting time is appropriately extended or parameters are slightly adjusted for the second, and a complete lockout is selected for manual reset for the third, thus preventing the device from falling into a vicious cycle of frequent self-resets.

[0042] In one embodiment, the MCU control unit's program structure needs to construct a two-dimensional lookup table indexed by the failure reason code and the number of retries. In this embodiment, the table can be divided into the following columns: reason number (e.g., code 001 for undervoltage, 002 for overvoltage, 003 for stall, 004 for short-term overcurrent, 005 for signal interference), current retry round (round 1, round 2, round 3, etc.), retry strategy type (retry / lock), waiting time (in seconds, such as 10s, 30s, 60s, etc.), and parameter configuration identifier (used to indicate whether to change the parameter scheme or strengthen protection measures for the next startup, such as whether to use amplitude compensation or to disconnect the startup winding in advance). Once startup fails, the MCU criterion module outputs the reason code and the accumulated number of retries, and the strategy lookup module can then index the optimal strategy scheme from the table.

[0043] In a specific embodiment, the corresponding strategy table is filled in as follows: For undervoltage (code 001), after the first failure, the MCU control unit automatically waits 15 seconds before retrying. If it fails the second time, it waits 30 seconds. If it fails again, the system is locked and can only be reset by manual on-site inspection. For overvoltage (code 002), after the first failure, the MCU control unit automatically enters protection shutdown and starts the timer module, delaying for 30 seconds before attempting the first restart. If it is still determined to be an overvoltage fault, it enters shutdown protection again, with the waiting time extended to 60 seconds. If overvoltage is still detected on the third retry, the MCU control unit locks the system, preventing automatic restart and illuminating the fault indicator light. The event is stored in the non-volatile memory area, which can only be restored after the source of the fault is eliminated manually and a manual reset is performed on-site. For stall (code 003), due to the dual electrical and mechanical risks, only one retry is allowed. If it still fails, it is immediately locked. Even if there is a subsequent start / stop requirement, unless manually forcibly reset, the device remains in the protection lock state, preventing further damage to the protection windings and semiconductor devices. A critical event flag is written to the memory area. For example, a short-term overcurrent (code 004) can be identified as an effect of intermittent power grid fluctuations, and a maximum of three retries are allowed. The retry interval can be gradually increased from the initial 10 seconds to 60 seconds to reduce the impact on the system. As for suspected soft faults such as signal interference (code 005) or sensor loss, temporary degradation start-up parameters can be added to extend the disturbance observation period and provide an extra chance. If two or three consecutive failures occur, the system will be immediately locked to prevent accidental continuous operation.

[0044] Here, the parameter configuration flag is a crucial part of the retry strategy, giving it stronger adaptability. For example, if retry fails continuously due to excessive load, the parameter configuration flag can prompt adjustments to the on / off timing of the secondary thyristor, appropriately advance the conduction of the primary thyristor, lower the startup criteria, or switch to soft protection current limiting mode upon restarting, in order to compensate for compatibility issues under special operating environments. Conversely, for pure line voltage anomalies, there is no need to modify local parameters; instead, the original configuration should be maintained, focusing on extending the waiting time or completely locking the circuit.

[0045] This rigorous, multi-level retry strategy balances the self-recovery capability from occasional environmental disturbances during actual equipment use with the complete prevention of fundamental systemic failures, ensuring that automation features do not lead to serious safety hazards. It effectively achieves a system balance across multiple objectives (high availability, automation, long lifespan, security, and traceability) for various household, commercial, and industrial products. Finally, in step S5, the MCU control unit executes the retry strategy output in step S4.

[0046] In summary, the adaptive starting method and system based on voltage and current monitoring provided in this application include: an MCU control unit triggers the secondary and primary thyristors to start the motor through preset starting logic, and samples current and voltage data at high frequency in real time. Once a starting failure is detected, the system automatically captures and stores short-term voltage and current sequences before and after the failure point. Then, key features are extracted from these sequences, and the cause of the failure is determined through decision tree model analysis. Based on this analysis result and the current number of retries, the MCU control unit outputs a corresponding retry strategy to ensure that the motor can successfully start or enter a safe mode. This not only improves the reliability of motor starting but also supports subsequent fault diagnosis through detailed data recording, greatly enhancing the maintainability and safety of the system.

[0047] This application also provides an adaptive start-up system based on voltage and current monitoring, such as... Figure 4 As shown, the adaptive startup system 400 based on voltage and current monitoring includes a memory 401, a processor 402, and a computer program 403 stored in the memory and executable on the processor. The processor 402 executes the computer program 403 to implement the steps of the adaptive startup method based on voltage and current monitoring as described above.

[0048] 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.

[0049] The block diagrams of devices, apparatuses, devices, and systems involved in this application are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, 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.

[0050] It should also be noted that in the apparatus, equipment, and methods of this application, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered as equivalent solutions of this application.

[0051] 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.

[0052] 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. An adaptive startup method based on voltage and current monitoring, characterized in that, include: The MCU control unit triggers the secondary and primary thyristors to start the motor through preset startup logic, while simultaneously sampling current and voltage at high frequency through an electrical metering chip. The MCU control unit employs startup failure determination logic, and once a startup failure is determined, it captures and stores short-term voltage and current sequences before and after the failure point through the electrical metering chip. The MCU control unit extracts key current and voltage features from the voltage and current sequences. The key current features include peak current, current rise rate, time to reach peak current, current before failure, and current fluctuation characteristics. The key voltage features include average voltage before failure, minimum voltage during startup, and voltage drop amplitude. The MCU control unit inputs the key current and voltage features into a decision tree model to obtain the cause of failure. Based on the cause of failure and the current number of retries, the MCU control unit queries a preset multi-level retry strategy table to obtain a retry strategy. The MCU control unit then executes the retry strategy.

2. The adaptive startup method based on voltage and current monitoring according to claim 1, characterized in that, The MCU control unit triggers the secondary and primary thyristors to start the motor through preset startup logic, including: the MCU control unit first triggers the secondary thyristor to conduct to connect the motor's starting winding; the MCU control unit then triggers the primary thyristor to conduct to connect the motor's primary winding.

3. The adaptive startup method based on voltage and current monitoring according to claim 1, characterized in that, The failure determination logic includes situations where the current does not reach the expected threshold within a specified time, the current rises but does not fall back, and overcurrent / overvoltage protection is triggered.

4. The adaptive startup method based on voltage and current monitoring according to claim 1, characterized in that, The retry strategy includes a strategy type, a waiting time in seconds, and a parameter configuration identifier for the next startup. The strategy type includes retry and locking.

5. The adaptive startup method based on voltage and current monitoring according to claim 4, characterized in that, The process of inputting the key current features and the key voltage features into a decision tree model to obtain the failure cause includes: correlating the peak current time, the current before failure, and the current fluctuation features through a current fluctuation function to obtain a current correlation expression, which includes a fluctuation function adjustment factor; correlating the current fluctuation features, the average voltage before failure, the minimum voltage during startup, and the voltage drop amplitude through a system voltage stability function to obtain a voltage correlation expression, which includes a stability risk adjustment factor; minimizing the current correlation expression while maximizing the voltage correlation expression by adjusting the fluctuation function adjustment factor and the stability risk adjustment factor; substituting the determined fluctuation function adjustment factor and the stability risk adjustment factor into the current correlation expression and the voltage correlation expression, and inputting the current correlation expression, the voltage correlation expression, the key current features, and the key voltage features into the decision tree model to obtain the failure cause.

6. An adaptive start-up system based on voltage and current monitoring, the adaptive start-up system based on voltage and current monitoring comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the adaptive startup method based on voltage and current monitoring as described in any one of claims 1-5.

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