Frequency converter dynamic response optimization method and system and medium

Abstract

The invention provides a frequency converter dynamic response optimization method and system and a medium, and relates to the technical field of frequency converters, and the method comprises the steps: receiving a motor drive signal, driving a first signal processing threshold to carry out sparse representation, determining a time-varying transfer sequence, and carrying out the amplification processing of the time-varying transfer sequence based on local phase expansion; the second frequency modulation node identifies the time-varying transfer sequence so as to determine a frequency conversion signal based on a Markov decision of directional frequency modulation in the first state space and the second action space; and triggering a third pulse optimization node, performing pulse coding on the frequency conversion signal, determining an optimized frequency conversion signal through relative equilibrium optimization based on switching loss and harmonic suppression, and performing control driving on the frequency converter. According to the invention, the technical problem of unstable control performance caused by insufficient response to dynamic characteristics of the motor due to the fact that a frequency converter control method in the prior art generally depends on preset fixed control parameters and simple frequency adjustment is solved.

Description

Technical Field

[0001] This invention relates to the field of frequency converter technology, and more specifically to a method, system, and medium for optimizing the dynamic response of frequency converters. Background Technology

[0002] Inverters are widely used in motor drive systems, primarily controlling motor speed and torque by adjusting output frequency and voltage. The core function of an inverter is to regulate motor power output to achieve energy savings, improve efficiency, and extend motor life. However, under high-speed operation and complex conditions, the inverter's dynamic response and control accuracy are crucial to the overall system performance. Traditional inverter control methods typically rely on preset fixed control parameters and simple frequency adjustment. This method is insufficient for responding to the dynamic characteristics of the motor, especially when the load changes drastically. Inverters often require a long time to adjust the output signal. When the motor load changes significantly, the inverter's slow response can lead to unstable speed and torque, resulting in motor overload or low efficiency, and potentially even equipment damage or shutdown. Summary of the Invention

[0003] This application provides a method, system, and medium for optimizing the dynamic response of frequency converters, aiming to solve the technical problem that existing frequency converter control methods typically rely on preset fixed control parameters and simple frequency adjustment, resulting in insufficient response to the dynamic characteristics of the motor and unstable control performance.

[0004] The first aspect disclosed in this application provides a method for optimizing the dynamic response of a frequency converter. The method includes: receiving a motor drive signal, driving a first signal processing threshold, performing sparse representation on the motor drive signal, and determining a time-varying transfer sequence, wherein the time-varying transfer sequence undergoes amplification processing based on local phase expansion; a second frequency modulation node identifies the time-varying transfer sequence and determines a frequency converter signal based on Markov decision-making for directional frequency modulation under a first state space and a second action space, wherein the first state space is defined by motor drive state characteristics and the second action space is defined by PID parameters; triggering a third pulse optimization node to perform pulse coding on the frequency converter signal, and determining an optimized frequency converter signal through relative balance optimization based on switching losses and harmonic suppression, and controlling and driving the frequency converter.

[0005] The second aspect of this application discloses a variable frequency drive (VFD) dynamic response optimization system. The system is used in the aforementioned VFD dynamic response optimization method. The system includes: a time-varying transfer sequence determination module, used to receive a motor drive signal, drive a first signal processing threshold, perform sparse representation on the motor drive signal, and determine a time-varying transfer sequence, wherein the time-varying transfer sequence undergoes amplification processing based on local phase expansion; a variable frequency signal determination module, used by a second frequency modulation node to identify the time-varying transfer sequence and determine the variable frequency signal based on Markov decision-making for directional frequency modulation under a first state space and a second action space, wherein the first state space is defined by motor drive state characteristics, and the second action space is defined by PID parameters; and a control drive module, used to trigger a third pulse optimization node to perform pulse coding on the variable frequency signal, determine an optimized variable frequency signal through relative balance optimization based on switching losses and harmonic suppression, and control and drive the VFD.

[0006] The third aspect disclosed in this application provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the inverter dynamic response optimization method in the first aspect.

[0007] One or more technical solutions provided in this application have at least the following beneficial effects: By employing sparse representation and local phase expansion processing of the motor drive signal, the instantaneous changes and dynamic characteristics in the motor response can be accurately captured. Local amplification improves sensitivity to rapidly changing signals, ensuring that the frequency converter responds more quickly and accurately to changes in the motor drive signal, avoiding excessive lag or oscillation. This allows the system to precisely capture the interaction process between the motor and the frequency converter, thereby optimizing the frequency converter's response speed and improving its stability and sensitivity. Furthermore, by performing Markov decision-making for directional frequency modulation in the first state space and the second action space, intelligent decision support can be provided for frequency converter signal generation. Markov decision-making helps to address the issue of motor-driven frequency modulation. The optimal frequency converter signal is determined by the pre-state and PID parameters. This method can intelligently adjust the frequency and amplitude of the signal to ensure that the motor maintains optimal control under different loads or operating conditions. By pulse coding the frequency converter signal and optimizing the processing of switching losses and harmonic suppression, the switching losses and harmonics can be effectively reduced while maintaining the motor control accuracy, thereby improving the overall efficiency of the motor drive system. By optimizing the frequency converter signal to control the frequency converter to drive the motor, a more precise control effect can be achieved. The optimized signal not only improves the smoothness of motor startup but also prevents instability phenomena such as overshoot and oscillation, thereby enhancing the stability and reliability of the system.

[0008] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0009] Figure 1 This is a schematic diagram of the inverter dynamic response optimization method provided in an embodiment of this application.

[0010] Figure 2 This is a schematic diagram of the inverter dynamic response optimization system structure provided in an embodiment of this application.

[0011] Explanation of reference numerals in the attached diagram: Time-varying transmission sequence determination module 10, frequency conversion signal determination module 20, control drive module 30. Detailed Implementation

[0012] This application provides a method, system, and medium for optimizing the dynamic response of frequency converters, which solves the technical problem that existing frequency converter control methods typically rely on preset fixed control parameters and simple frequency adjustment, resulting in insufficient response to the dynamic characteristics of the motor and unstable control performance.

[0013] After introducing the basic principles of this application, various non-limiting embodiments of this application will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.

[0014] Example 1, as Figure 1 As shown in the embodiment of this application, a method for optimizing the dynamic response of a frequency converter is provided, the method comprising: The system receives a motor drive signal, drives a first signal processing threshold, performs sparse representation on the motor drive signal, and determines a time-varying transfer sequence, wherein the time-varying transfer sequence undergoes amplification processing based on local phase expansion.

[0015] The system receives motor drive signals, which contain instructions for motor operation. A first signal processing threshold is a threshold setting used to preprocess the received motor drive signal. Setting an appropriate first signal processing threshold helps filter out important parts of the signal, such as noise filtering, ensuring that the data processed subsequently is more representative. Sparse representation is a signal processing technique that selectively retains important information, i.e., key features, from the motor drive signal based on the first signal processing threshold, while removing redundant data. This means that only the most useful components are retained in the motor drive signal, reducing unnecessary computation and data storage. A time-varying transfer sequence is a sequence of transfer functions of the motor drive system that varies over time, reflecting the dynamic relationship between the motor state and the motor drive signal. Local phase expansion refers to amplifying the phase of a local region of the motor drive signal, thereby increasing the influence of important parts of the signal. This process helps strengthen the effective information in the signal and reduce the influence of interference signals.

[0016] The second frequency modulation node identifies the time-varying transmission sequence and determines the frequency conversion signal based on the Markov decision of directional frequency modulation under the first state space and the second action space. The first state space is defined by the motor drive state characteristics, and the second action space is defined by PID parameters.

[0017] The task of the second frequency modulation node is to identify dynamically changing patterns from the time-varying transmission sequence, pinpoint unstable or adjustable components, and make corresponding adjustments to the frequency converter signal. The first state space describes the motor drive state characteristics, such as motor speed, load current, and vibration; these characteristics reflect the operating state of the motor system. The second action space is related to PID parameters (proportional, integral, and derivative coefficients). Based on the current state of the motor, the frequency converter signal is controlled by adjusting the PID parameters to make the motor run more smoothly. By combining the first state space and the second action space, a Markov decision process is used to directionally adjust the frequency. The Markov decision process is an optimization method that selects the optimal action sequence (in this case, PID parameter adjustment) based on the transition probabilities of states and actions. In this way, the frequency converter signal can be dynamically adjusted according to the motor's operating state to achieve optimal performance. Based on the Markov decision process, the output frequency converter signal is determined.

[0018] The third pulse optimization node is triggered to perform pulse coding on the frequency conversion signal. Through relative balance optimization based on switching loss and harmonic suppression, the optimized frequency conversion signal is determined, and the frequency converter is controlled and driven.

[0019] The third pulse optimization node optimizes the output pulse sequence of the frequency converter signal. The purpose of pulse optimization is to reduce unnecessary losses in the system and improve system efficiency. Pulse coding of the frequency converter signal converts the analog signal into a series of digital control signals, allowing the frequency converter to precisely adjust the motor based on these signals. Each switching operation during frequency conversion incurs losses. One goal of the optimization process is to reduce these switching losses. The switching action of the frequency converter introduces harmonics, which can pollute the power grid. Optimization requires balancing switching losses and harmonic suppression to ensure stable motor operation and minimize the impact on the power grid. Through a relatively balanced optimization based on switching losses and harmonic suppression, the generated optimized frequency converter signal minimizes energy waste and harmonic interference, improving overall system efficiency. The final optimized frequency converter signal is input to the frequency converter, which controls the motor's operation based on the optimized signal. By optimizing the frequency converter signal, the motor can operate under optimal conditions, achieving higher efficiency and a longer service life.

[0020] Furthermore, a peripheral data interface is segmented in the interaction thread between the motor and the frequency converter; a frequency conversion decision plugin is deployed in the data interface, wherein the frequency conversion decision plugin includes a first signal processing threshold, a second frequency modulation node, and a third pulse optimization node.

[0021] Interaction thread refers to the data communication and control logic execution process between the motor and the frequency converter. It describes how the frequency converter interacts with the motor in real time, including how to obtain status information from the motor and how to control the operation of the motor. Peripheral data interface refers to the connection point between the motor and the frequency converter and external devices.

[0022] The frequency conversion decision module is deployed at the data interface between the motor and the frequency converter. It is responsible for determining the frequency converter's adjustment strategy based on the received motor status data. The module's task is to analyze the input signal, make corresponding decisions, and control the motor through the frequency converter. The first signal processing threshold is used to preprocess the motor drive signal and set a threshold value. This threshold value can be dynamically adjusted based on the motor's load, current, or other key parameters. By controlling the first signal processing threshold, the frequency conversion decision module can filter out valid signals and remove useless information, ensuring the signal quality of subsequent processing. The second frequency modulation node is used to analyze the time-varying transmission sequence and make frequency modulation decisions based on the analysis results. The frequency modulation decision relies on a Markov decision process, combining the motor's state space and action space to dynamically adjust the frequency conversion signal. The third pulse optimization node is used to optimize the output pulse sequence of the frequency conversion signal. It mainly uses pulse coding and optimization techniques to reduce switching losses and harmonic interference. By adjusting the pulse width and frequency distribution, the motor can operate efficiently, avoiding energy waste and electromagnetic interference.

[0023] Furthermore, sparsely representing the motor drive signal to determine the time-varying propagation sequence includes: A threshold condition is determined, wherein the condition is set by a signal step of preset amplitude and a change in pulse direction; the frequency conversion decision plug-in receives the motor drive signal, performs signal point filtering based on the threshold condition according to the first signal processing threshold, and determines a sparse signal sequence; the sparse signal sequence is subjected to phase expansion of fuzzy signal segments to determine the time-varying transmission sequence.

[0024] Threshold conditions are used to filter signals. A signal step refers to a sudden jump in the signal's time domain; for example, a sudden change in motor load will cause a jump in the drive signal. Step points are used to identify significant changes in the signal. Pulse direction changes refer to changes in the pulse width or frequency direction within the signal; for example, periodic changes or frequency variations in control signals can reflect the system's adjustment requirements. By setting thresholds for signal amplitude, step and pulse change points in the signal are filtered out. The aim is to focus only on those changes that significantly affect motor operation during subsequent processing, removing other irrelevant or minor fluctuations.

[0025] The variable frequency drive (VFD) decision module receives motor drive signals. Signal point filtering involves selecting important signal points from the motor drive signals and removing unnecessary interference. This process is based on set threshold conditions. If the signal variation (such as amplitude or frequency) exceeds the set threshold value, these signal points are considered important and retained; conversely, if the signal fluctuation is less than the threshold value, it is ignored or filtered out. Sparse representation is a signal processing method that reduces computation and storage by retaining only the important components of the signal. The filtered signal points form a sparse signal sequence, meaning that only those key parts that affect motor control are retained, reducing redundant data.

[0026] In sparse signal sequences, some signal segments are ambiguous, meaning their amplitude, frequency, and other variations are unclear. The existence of ambiguous signal segments implies that signal changes are complex or incompletely determined. Phase spreading is a signal processing technique used to adjust and amplify the phase of a signal to better adapt to system requirements. Here, by applying phase spreading to ambiguous signal segments, the signal becomes more precise in time or frequency, better reflecting the dynamic changes of the motor. For motor drive signals, phase spreading helps to effectively control and adjust the fluctuating parts of the signal, thereby improving the control accuracy of the frequency converter. Through phase spreading, the time-varying transfer sequence is determined, which describes the time-varying control response between the motor and the frequency converter.

[0027] Furthermore, the time-varying transport sequence undergoes amplification processing based on local phase expansion, including: The sparse signal sequence is traversed to identify ambiguous signal segments, wherein a preset density and wave dynamics of signal points are used as the identification criteria; for the ambiguous signal segments, the expansion ratio is determined; and based on the expansion ratio, phase expansion processing is performed on the ambiguous signal segments in the sparse signal sequence.

[0028] The purpose of traversing a sparse signal sequence is to examine each data point in the sequence one by one, identifying those parts with unclear or uncertain signal characteristics. These parts are characterized by frequent signal fluctuations or complex change patterns. These parts are marked as fuzzy signal segments. For example, the rapid changes in motor load lead to complex changes in the control signal, making it difficult to capture accurately. In fuzzy signal segments, signal changes may not be a single linear trend but rather exhibit frequent high-frequency fluctuations, making the signal itself unclear. Fluctuation density refers to parts of the signal that change very frequently, possibly due to sudden load changes or rapid changes in motor status. Wave dynamics refers to how the signal changes over time, such as how frequency and amplitude change. By analyzing wave dynamics, the complexity of the signal can be identified. By setting preset density and wave dynamics as identification criteria, fuzzy signal segments can be quickly located. These fuzzy signal segments are irregular or chaotic parts of the signal that require further processing and optimization.

[0029] After identifying the ambiguous signal segment, the expansion ratio is determined, which is how to amplify the range of signal variation in the time or frequency domain. The goal of expansion is to make the signal in the ambiguous segment clearer and more distinct, thus making it easier for subsequent processing steps to identify and analyze. Determining the expansion ratio usually involves mathematical calculations, such as dynamically adjusting the expansion ratio based on the signal's amplitude and rate of change. This ratio must ensure that the important features of the signal are amplified without distorting the overall trend of the signal.

[0030] After determining the phase expansion ratio, phase expansion processing is performed. Phase expansion adjusts the ambiguous parts of the signal by amplifying the phase, making the signal details clearer. This is done by increasing the phase difference of the signal to enhance its variability, thus aiding in subsequent signal analysis and optimization. For example, in signals with dense fluctuations and varied amplitude changes, phase expansion amplifies the signal details within a certain time or frequency range to ensure rapid identification. For instance, in a signal fluctuation range of 0-π, phase expansion changes the signal phase to 0-π / 2-π, thus more clearly showing the signal changes within this range. In this way, more precise capture of signal detail changes can be achieved, especially in those densely fluctuating and ambiguous signal segments. This process is part of local amplification processing. By locally amplifying important segments of the signal, it makes the signal details more prominent, which is crucial for the dynamic response of the signal, especially in motor control, where any subtle changes can affect the motor's operational stability and efficiency.

[0031] Furthermore, the frequency conversion signal is determined using Markov decision-making based on the first state space and the second action space for directional frequency modulation, including: By identifying the time-varying transmission sequence, a first state deviation group is determined based on the first state space within the second frequency modulation node; based on the first state deviation group, a first state group and a second state group are determined, and a Markov decision based on state change is executed to determine a third control group based on the second action space, wherein the third control group includes at least PID parameters; based on the third control group, a frequency conversion signal is generated.

[0032] The first state space describes the characteristics of the motor drive state, such as the motor speed, load current, vibration, etc. These characteristics reflect the operating state of the motor system. The deviation of the motor state is analyzed, that is, the difference between the actual operating state and the expected target value (such as the set speed, current, etc.). The first state deviation group is an error set composed of multi-dimensional features, such as: current error, indicating the difference between the current current and the target current; speed deviation, indicating the difference between the current speed and the set speed.

[0033] The first and second state groups represent the system's state sets under different conditions. The first state group contains the motor's current state information, such as speed and current. The second state group represents the desired or target state set, such as the set target speed or current. These two state groups are determined based on the first state deviation group (i.e., the actual error of the motor). A Markov decision process (MDP) is used to select the optimal action strategy given the current state. It assumes that the current decision depends only on the current state and is independent of past states and decisions. State change refers to making decisions based on the changing trend of the current state. This is achieved by modeling the state and using the MDP to calculate the evolution of possible future states, thereby determining the optimal control strategy. Here, the MDP selects the most suitable control measures based on the current motor state (such as speed and current) and the target state (desired speed, target current, etc.).

[0034] The second action space is related to the PID parameters (proportional, integral, and derivative coefficients). Based on the current state of the motor, the PID parameters are adjusted to control the frequency converter signal, making the motor run more smoothly. The third control group is a set of control strategies based on the Markov decision mentioned above. It includes a series of control parameters, with the PID parameters being the most important part. The PID parameters are used to adjust the system input so that the system output is as close as possible to the desired target. Here, the optimization of the PID parameters is used to adjust the output signal of the frequency converter, including the proportional gain, integral time, and derivative time.

[0035] The final frequency conversion signal is generated based on the third control group. The frequency conversion signal is directly used to control the operation of the frequency converter to achieve precise speed regulation and stable operation of the motor.

[0036] Furthermore, determining the optimal frequency conversion signal includes: Based on the frequency conversion signal, a PWM pulse sequence is determined; guided by switching loss and harmonic suppression, the pulse distribution of the PWM pulse sequence is optimized to determine an optimized frequency conversion signal.

[0037] PWM (Pulse Width Modulation) is a common method for controlling motor speed and output power. The basic principle of PWM is to regulate voltage by adjusting the pulse width (i.e., duty cycle), thereby controlling the motor's power. In this step, the frequency converter signal is converted into a PWM signal. The frequency converter signal represents the frequency and amplitude information required for motor drive, while the PWM signal is the actual control signal that achieves these requirements. This process involves converting the time-domain signal of the frequency converter signal to ensure that the motor operates at a stable frequency. The generated PWM pulse sequence controls the motor's power output by changing the pulse width.

[0038] With the goal of reducing switching losses and harmonic generation, the pulse distribution of the PWM pulse sequence is optimized. In this process, encoding technology is used to process the PWM signal, so that the optimization problem can be represented and solved algebraically. Specifically, encoding is used to convert different pulse points, pulse widths and other related characteristics of the PWM signal into a symbol sequence. For the encoded symbol sequence, the pulse distribution is further fine-tuned so that the final PWM signal can achieve the best possible effect while satisfying the requirements of switching losses and harmonic suppression. This fine-tuning ensures that the frequency converter can operate with minimal losses and interference.

[0039] Furthermore, guided by switching losses and harmonic suppression, the pulse distribution of the PWM pulse sequence is optimized, including: The PWM pulse sequence is pulse-state encoded to determine the pulse symbol sequence, wherein each pulse node is represented by a first symbol based on switching loss and a second symbol based on harmonic suppression; a preset iteration method is introduced, wherein iterative processing is performed based on the location of the target pulse point and the relative balance of the first and second symbols, and the target pulse point is a pre-optimized pulse point; according to the preset iteration method, the pulse symbol sequence is iteratively optimized to determine the optimized pulse distribution; in the PWM pulse sequence, the optimized pulse distribution is replaced to determine the optimized frequency conversion signal.

[0040] Pulse-state encoding of PWM pulse sequences means converting the PWM signal into a sequence of symbols by encoding different characteristics of each pulse, such as pulse width, frequency, and timing. The pulse symbol sequence is determined by encoding the different characteristics of each pulse, such as pulse width, frequency, and timing. The first symbol is related to switching losses, which typically occur during each switching action. Encoding the timing and amplitude of the switching helps assess the contribution of each pulse to power loss. The second symbol is related to harmonic suppression. Harmonics are caused by irregular PWM waveforms; therefore, the encoding process needs to consider the effect of each pulse on harmonic reduction, thereby optimizing the signal's spectral characteristics.

[0041] A preset iterative approach is introduced. Specifically, this iterative approach involves continuously optimizing the distribution of PWM pulses through multiple steps, ensuring that the final PWM signal simultaneously meets the requirements for reducing switching losses and suppressing harmonics. Target pulse points are the critical pulses that need to be adjusted during the optimization process. By selecting target pulse points, efforts can be focused on adjusting the timing and width of these pulses, thus significantly impacting the overall PWM signal performance. During the iteration process, the pulse distribution is adjusted based on the relative balance between the first and second symbols. The weights of switching losses and harmonic suppression need to be balanced through the iteration process; that is, reducing the impact of switching losses at certain pulse positions while simultaneously increasing the optimization of harmonic suppression. This is accomplished by gradually adjusting characteristics such as pulse width and frequency.

[0042] According to a preset iterative method, the pulse symbol sequence is progressively optimized. In this process, the optimization effect of the pulse is calculated at each step, including switching losses and harmonic suppression, and adjustments are made according to the optimization target. This is a multi-round adjustment process. After each adjustment, the pulse distribution is evaluated. If the effect is better, the adjustment result is maintained; if the effect is not ideal, adjustments continue until the predetermined optimization target is met. Finally, through these iterative steps, the optimized pulse distribution is obtained.

[0043] The original PWM pulse sequence is replaced with an optimized pulse distribution. Specifically, the original pulse timing and width are replaced with the adjusted result, so that the new PWM signal has lower switching losses and better harmonic suppression performance during operation, and finally an optimized frequency conversion signal is obtained.

[0044] Furthermore, the variable frequency decision plugin outputs the optimized variable frequency signal, transmits it according to the interaction thread, responds to the variable frequency drive, and executes variable frequency control drive; wherein, after executing the variable frequency control drive, the process includes: tracking the variable frequency drive response, wherein the variable frequency drive response includes the operating data of the variable frequency drive and the motor; performing variable frequency optimization evaluation based on the variable frequency drive response to determine the variable frequency control coefficient; adding the variable frequency control coefficient to a temporary database, performing variable frequency error location and source tracing for a preset period, and performing feedback response optimization management.

[0045] The optimized frequency conversion signal is transmitted to the frequency converter through an interactive thread. During this process, the frequency converter responds and begins to perform control operations according to the optimized frequency conversion signal. This transmission mechanism ensures the accurate transmission of the optimized frequency conversion signal from the frequency conversion decision module to the frequency converter.

[0046] After the variable frequency drive is executed, the next task is to track the variable frequency drive response. This means that the operating status of the inverter and the motor will be monitored in real time. The tracked content includes the operating data of the inverter and the motor, such as current, voltage, speed, temperature, etc. This data is used to judge the control effect of the inverter, including whether the inverter works according to the predetermined goal and whether the motor runs in the expected working state.

[0047] Based on the variable frequency drive response, the effectiveness of the variable frequency control drive is evaluated, including whether it can effectively avoid abrupt, large-scale control jumps. For example, when starting the drive system, especially when the motor load is large or the start-up moment is sensitive, a slow, step-by-step starting strategy is usually adopted. This starting method is smoother than abrupt starting (significantly changing voltage and frequency), reducing motor shock and vibration and improving system stability. By monitoring the drive response data, it is determined whether the drive effect matches expectations. If the motor can start smoothly and reach the predetermined speed, and there is no excessive jitter or overload during operation, the optimized variable frequency signal is effective. Conversely, if obvious control anomalies occur, such as overshoot, oscillation, or instability, the variable frequency control parameters need to be re-evaluated and adjusted. Based on the optimization evaluation results, the variable frequency control coefficient is determined. The variable frequency control coefficient is an indicator for adjusting PID parameters, used to finely adjust the inverter output to further optimize the control effect.

[0048] Storing variable frequency control coefficients in a temporary database ensures that all optimized parameters and control data are recorded for easy review and adjustment. Regularly performing variable frequency error localization and tracing allows for rapid identification of the root cause of any abnormal or unexpected responses. By analyzing historical data, variable frequency control coefficients, and variable frequency signals, the cause of faults or performance degradation can be effectively located. Based on the results of error localization and tracing analysis, feedback response optimization is performed. This means readjusting the variable frequency control strategy and optimized parameters based on feedback obtained during actual operation, ensuring that the performance of the variable frequency drive and motor remains at its optimal level during long-term operation.

[0049] In summary, the inverter dynamic response optimization method provided in this application has the following technical effects: By employing sparse representation and local phase expansion processing of the motor drive signal, the instantaneous changes and dynamic characteristics in the motor response can be accurately captured. Local amplification improves sensitivity to rapidly changing signals, ensuring that the frequency converter responds more quickly and accurately to changes in the motor drive signal, avoiding excessive lag or oscillation. This allows the system to precisely capture the interaction process between the motor and the frequency converter, thereby optimizing the frequency converter's response speed and improving its stability and sensitivity. Furthermore, by performing Markov decision-making for directional frequency modulation in the first state space and the second action space, intelligent decision support can be provided for frequency converter signal generation. Markov decision-making helps to address the issue of motor-driven frequency modulation. The optimal frequency converter signal is determined by the pre-state and PID parameters. This method can intelligently adjust the frequency and amplitude of the signal to ensure that the motor maintains optimal control under different loads or operating conditions. By pulse coding the frequency converter signal and optimizing the processing of switching losses and harmonic suppression, the switching losses and harmonics can be effectively reduced while maintaining the motor control accuracy, thereby improving the overall efficiency of the motor drive system. By optimizing the frequency converter signal to control the frequency converter to drive the motor, a more precise control effect can be achieved. The optimized signal not only improves the smoothness of motor startup but also prevents instability phenomena such as overshoot and oscillation, thereby enhancing the stability and reliability of the system.

[0050] Example 2, based on the same inventive concept as the inverter dynamic response optimization method in the foregoing examples, such as... Figure 2 As shown in the figure, this application embodiment provides a frequency converter dynamic response optimization system, the system comprising: The time-varying transfer sequence determination module 10 is used to receive the motor drive signal, drive the first signal processing threshold, perform sparse representation on the motor drive signal, and determine the time-varying transfer sequence, wherein the time-varying transfer sequence undergoes amplification processing based on local phase expansion; the frequency conversion signal determination module 20 is used for the second frequency modulation node to identify the time-varying transfer sequence, and determine the frequency conversion signal based on the Markov decision of directional frequency modulation under the first state space and the second action space, wherein the first state space is defined by the motor drive state characteristics, and the second action space is defined by PID parameters; the control drive module 30 is used to trigger the third pulse optimization node, perform pulse coding on the frequency conversion signal, determine the optimized frequency conversion signal through relative balance optimization based on switching loss and harmonic suppression, and control and drive the frequency converter.

[0051] Furthermore, a peripheral data interface is segmented in the interaction thread between the motor and the frequency converter; a frequency conversion decision plugin is deployed in the data interface, wherein the frequency conversion decision plugin includes a first signal processing threshold, a second frequency modulation node, and a third pulse optimization node.

[0052] Furthermore, the time-varying transit sequence determination module 10 is used to perform the following operation steps: A threshold condition is determined, wherein the condition is set by a signal step of preset amplitude and a change in pulse direction; the frequency conversion decision plug-in receives the motor drive signal, performs signal point filtering based on the threshold condition according to the first signal processing threshold, and determines a sparse signal sequence; the sparse signal sequence is subjected to phase expansion of fuzzy signal segments to determine the time-varying transmission sequence.

[0053] Furthermore, the time-varying transit sequence determination module 10 is used to perform the following operation steps: The sparse signal sequence is traversed to identify ambiguous signal segments, wherein a preset density and wave dynamics of signal points are used as the identification criteria; for the ambiguous signal segments, the expansion ratio is determined; and based on the expansion ratio, phase expansion processing is performed on the ambiguous signal segments in the sparse signal sequence.

[0054] Furthermore, the frequency conversion signal determination module 20 is used to perform the following operation steps: By identifying the time-varying transmission sequence, a first state deviation group is determined based on the first state space within the second frequency modulation node; based on the first state deviation group, a first state group and a second state group are determined, and a Markov decision based on state change is executed to determine a third control group based on the second action space, wherein the third control group includes at least PID parameters; based on the third control group, a frequency conversion signal is generated.

[0055] Furthermore, the control drive module 30 is used to perform the following operation steps: Based on the frequency conversion signal, a PWM pulse sequence is determined; guided by switching loss and harmonic suppression, the pulse distribution of the PWM pulse sequence is optimized to determine an optimized frequency conversion signal.

[0056] Furthermore, the control drive module 30 is used to perform the following operation steps: The PWM pulse sequence is pulse-state encoded to determine the pulse symbol sequence, wherein each pulse node is represented by a first symbol based on switching loss and a second symbol based on harmonic suppression; a preset iteration method is introduced, wherein iterative processing is performed based on the location of the target pulse point and the relative balance of the first and second symbols, and the target pulse point is a pre-optimized pulse point; according to the preset iteration method, the pulse symbol sequence is iteratively optimized to determine the optimized pulse distribution; in the PWM pulse sequence, the optimized pulse distribution is replaced to determine the optimized frequency conversion signal.

[0057] Furthermore, the variable frequency decision plugin outputs the optimized variable frequency signal, transmits it according to the interaction thread, responds to the variable frequency drive, and executes variable frequency control drive; wherein, after executing the variable frequency control drive, the process includes: tracking the variable frequency drive response, wherein the variable frequency drive response includes the operating data of the variable frequency drive and the motor; performing variable frequency optimization evaluation based on the variable frequency drive response to determine the variable frequency control coefficient; adding the variable frequency control coefficient to a temporary database, performing variable frequency error location and source tracing for a preset period, and performing feedback response optimization management.

[0058] Through the foregoing detailed description of the inverter dynamic response optimization method, those skilled in the art can clearly understand the inverter dynamic response optimization system in this embodiment. Since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and relevant parts can be referred to the method section.

[0059] Example 3 provides a storage medium on which a computer program is stored, which, when executed by a processor, implements any step of Example 1.

[0060] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0061] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for optimizing the dynamic response of a frequency converter, characterized by The method comprises: receiving a motor driving signal, driving a first signal processing threshold, performing sparse representation on the motor driving signal, determining a time-varying transfer sequence, wherein the time-varying transfer sequence exists amplification processing based on local phase expansion; a second frequency modulation node identifies the time-varying transfer sequence to determine a variable frequency signal based on Markov decision of directional frequency modulation under a first state space and a second action space, wherein the first state space is defined by motor driving state characteristics, and the second action space is defined by PID parameters; a third pulse optimization node is triggered to perform pulse coding on the variable frequency signal, and an optimized variable frequency signal is determined by relatively balanced optimization based on switching loss and harmonic suppression to control and drive the frequency converter.

2. The method of claim 1, wherein the frequency converter dynamic response optimization is performed by: an external data interface is arranged in the interaction thread of the motor and the frequency converter; a variable frequency decision plug-in is arranged in the data interface, wherein the variable frequency decision plug-in comprises the first signal processing threshold, the second frequency modulation node and the third pulse optimization node.

3. The method of claim 2, wherein the step of determining the frequency of the variable frequency drive is performed by the variable frequency drive. The method comprises: determining a threshold condition, wherein the condition is set by signal step and pulse direction change with a preset amplitude; the variable frequency decision plug-in receives the motor driving signal, performs signal point screening based on the threshold condition according to the first signal processing threshold, and determines a sparse signal sequence; phase expansion is performed on the fuzzy signal section of the sparse signal sequence to determine the time-varying transfer sequence.

4. The method of claim 3, wherein the frequency converter dynamic response optimization is performed by: The time-varying transfer sequence exists amplification processing based on local phase expansion, comprising: traverse the sparse signal sequence to identify fuzzy signal sections, wherein the preset density of signal points and fluctuation state are used as identification basis; determine the phase expansion ratio for the fuzzy signal section; perform phase expansion processing on the fuzzy signal section in the sparse signal sequence according to the phase expansion ratio.

5. The method of claim 1, wherein the frequency converter dynamic response optimization is performed by a computer. The method comprises: determine a first state deviation group according to the first state space in the second frequency modulation node by identifying the time-varying transfer sequence; determine a first state group and a second state group according to the first state deviation group, perform Markov decision of state trend change, and determine a third regulation group based on the second action space, wherein the third regulation group at least comprises PID parameters; generate a variable frequency signal according to the third regulation group.

6. The method of claim 1, wherein the frequency converter dynamic response optimization is performed by a computer. The method comprises: determine a PWM pulse sequence according to the variable frequency signal; perform pulse distribution optimization on the PWM pulse sequence to determine an optimized variable frequency signal, which is guided by switching loss and harmonic suppression.

7. The method of claim 6, wherein the step of determining the frequency of the variable frequency drive is performed by: determining a frequency of the variable frequency drive based on the frequency of the motor and the speed of the motor. The method comprises: perform pulse state coding on the PWM pulse sequence to determine a pulse code sequence, wherein each pulse node is represented by a first code based on switching loss and a second code based on harmonic suppression; introduce a preset iteration method, wherein the positioning of a target pulse point and the relative balance based on the first code and the second code are iteratively processed, and the target pulse point is a pre-optimized pulse point. According to the preset iteration mode, the pulse symbol sequence is iteratively optimized to determine an optimized pulse distribution; In the PWM pulse sequence, the optimized pulse distribution is replaced to determine the optimized variable frequency signal.

8. The method of claim 2, wherein the frequency converter dynamic response optimization is performed by a computer. The variable frequency decision plug-in outputs the optimized variable frequency signal, transmits according to the interaction thread, and performs variable frequency control driving in response to the frequency converter; After performing the variable frequency control driving, the following steps are included: Tracking the variable frequency driving response, wherein the variable frequency driving response contains the operation data of the frequency converter and the motor; According to the variable frequency driving response, a variable frequency optimization evaluation is performed to determine a variable frequency control coefficient; The variable frequency control coefficient is added to a temporary database to perform variable frequency error positioning and tracing for a preset period, and feedback response optimization management is performed.

9. A variable frequency drive dynamic response optimization system, characterized by, The system for implementing the variable frequency converter dynamic response optimization method of any one of claims 1-8, the system comprising: A time-varying transfer sequence determination module (10) for receiving a motor driving signal, driving a first signal processing threshold, performing sparse representation on the motor driving signal, and determining a time-varying transfer sequence, wherein the time-varying transfer sequence has amplification processing based on local phase expansion; A variable frequency signal determination module (20) for identifying the time-varying transfer sequence at a second frequency modulation node to determine a variable frequency signal based on Markov decision of directional frequency modulation under a first state space and a second action space, wherein the first state space is defined by motor driving state characteristics and the second action space is defined by PID parameters; A control driving module (30) for triggering a third pulse optimization node to perform pulse coding on the variable frequency signal, determining an optimized variable frequency signal through relative balanced optimization based on switching loss and harmonic suppression, and performing control driving on the variable frequency converter.

10. A storage medium having stored thereon a computer program, characterized in that The computer program is executed by the processor to realize the steps of the variable frequency converter dynamic response optimization method of any one of claims 1 to 8.

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