Vector frequency conversion control method and system based on dynamic response

Abstract

The invention relates to the technical field of vector frequency conversion control, in particular to a vector frequency conversion control method and system based on dynamic response. The method comprises the following steps: acquiring real-time state information, constructing a precise state observation model, and outputting a disturbance compensation signal according to state deviation; acquiring signal characteristics, designing a multi-stage filtering architecture, and outputting filtered stable signals according to noise characteristics; dynamic operation parameters are obtained, an intelligent demand recognition model is constructed, an adaptive control mode is output according to working condition demands, the response speed and stability are balanced, and real-time feedback and adaptive compensation are performed through a dynamic response optimization mechanism; through real-time disturbance compensation based on state observation, the influence of disturbance on system operation parameters can be effectively reduced, the motor can operate more stably when being disturbed, and the disturbance suppression capability of the system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of vector frequency conversion control, and in particular to a vector frequency conversion control method and system based on dynamic response. Background Art

[0002] Vector variable frequency control technology is widely used in motor drive, industrial automation and other fields. By decomposing the stator current of the AC motor into excitation current and torque current, it can achieve independent control of the motor torque and flux, significantly improving the control performance of the motor.

[0003] However, the system will be subject to external disturbances (such as sudden load changes, grid voltage fluctuations, etc.), causing fluctuations in the motor's operating parameters such as speed and torque, affecting the system's control accuracy and stability. Summary of the Invention

[0004] The purpose of the present invention is to provide a vector frequency conversion control method and system based on dynamic response, aiming to solve the technical problem that the existing system is subject to external disturbances, resulting in fluctuations in the operating parameters such as the speed and torque of the motor, affecting the control accuracy and stability of the system.

[0005] To achieve the above object, the present invention adopts a vector frequency conversion control method based on dynamic response, which includes the following steps:

[0006] Acquire real-time state information, build an accurate state observation model, and output disturbance compensation signals based on state deviations;

[0007] Acquire signal characteristics and design a multi-stage filtering architecture to output a filtered and stable signal based on noise characteristics;

[0008] Acquire dynamic operating parameters and build an intelligent demand identification model to output adaptive control mode according to working conditions, balance response speed and stability, and provide real-time feedback and adaptive compensation through dynamic response optimization mechanism.

[0009] Among them, in the steps of obtaining real-time state information, building an accurate state observation model, and outputting a disturbance compensation signal according to the state deviation:

[0010] According to the key state parameters of the vector frequency conversion control system, select appropriate sensors and install them in the corresponding positions of the motor and control system to ensure accurate collection of the required real-time state information;

[0011] Based on the mathematical model of the motor and combined with the real-time collected state information, a state estimation algorithm is used to build an accurate state observation model;

[0012] The deviation between the actual state and the estimated state is calculated, and according to the deviation size and change trend, a disturbance compensation signal is generated through the sliding mode control algorithm.

[0013] Among them, according to the key state parameters of the vector frequency conversion control system, select appropriate sensors and reasonably install the sensors at the corresponding positions of the motor and control system to ensure accurate collection of the required real-time state information:

[0014] State parameters include motor speed, rotor flux and current.

[0015] Among them, in the steps of obtaining signal characteristics, designing a multi-stage filtering architecture, and outputting a filtered stable signal according to noise characteristics:

[0016] Perform spectrum analysis and time domain analysis on the collected system signals to obtain the signal's frequency distribution, amplitude variation, and noise type characteristics. By analyzing the signal characteristics, determine the main frequency range and characteristics of the noise.

[0017] Design a multi-stage filtering architecture based on noise characteristics and use different types of filter combinations;

[0018] The collected system signal is input into a multi-stage filtering architecture, and after filtering step by step, a filtered stable signal is output.

[0019] Among them, in the step of designing a multi-stage filtering architecture and using different types of filter combinations based on noise characteristics:

[0020] The filter combination includes a low-pass filter and a band-stop filter. The low-pass filter is used to suppress high-frequency noise, and the band-stop filter is used to eliminate interference signals of specific frequencies.

[0021] Among them, in the steps of obtaining dynamic operating parameters, building an intelligent demand identification model, outputting an adaptive control mode according to working conditions, balancing response speed and stability, and implementing real-time feedback and adaptive compensation through a dynamic response optimization mechanism:

[0022] Real-time collection of system dynamic operating parameters to understand the system's operating status and working condition changes, providing a basis for the selection of control mode;

[0023] Build an intelligent demand identification model, collect a large amount of historical data, train and learn the system's dynamic operating parameters and corresponding working condition requirements, and establish a mapping relationship between the two;

[0024] According to the identified working condition requirements, select the appropriate control mode from the preset control mode library;

[0025] Through the dynamic response optimization mechanism, the system's operating status and control effect are fed back in real time, and the control mode is adaptively compensated and adjusted based on the feedback information.

[0026] Among them, in the step of selecting an adaptive control mode from a preset control mode library according to the identified working condition requirements:

[0027] The fast response mode enables the system to quickly track the set value by improving the gain and response speed of the control system. The stable operation mode improves the stability and anti-interference ability of the system by optimizing the control parameters. The energy-saving mode reduces the energy consumption of the system by adjusting the operating parameters of the motor.

[0028] The present invention also provides a vector frequency conversion control system based on dynamic response, comprising a dynamic response module, a high-frequency noise suppression module and a multi-mode control module; wherein:

[0029] The dynamic response module is used to obtain real-time state information, build an accurate state observation model, and output a disturbance compensation signal according to the state deviation;

[0030] The high-frequency noise suppression module is used to obtain signal characteristics and design a multi-stage filtering architecture to output a filtered stable signal according to the noise characteristics;

[0031] The multimodal control module is used to obtain dynamic operating parameters and build an intelligent demand recognition model, output an adaptive control mode according to working condition requirements, balance response speed and stability, and provide real-time feedback and adaptive compensation through a dynamic response optimization mechanism.

[0032] The present invention provides a vector variable frequency control method and system based on dynamic response, which obtains real-time state information and builds a precise state observation model. It outputs a disturbance compensation signal according to the state deviation, obtains signal characteristics, designs a multi-stage filtering architecture, outputs a filtered stable signal according to the noise characteristics, obtains dynamic operating parameters, builds an intelligent demand identification model, outputs an adaptive control mode according to the working condition requirements, balances response speed and stability, and uses a dynamic response optimization mechanism for real-time feedback and adaptive compensation. Through real-time disturbance compensation based on state observation, it is possible to effectively reduce the impact of disturbances on system operating parameters, enable the motor to run more smoothly when disturbed, and improve the system's ability to suppress disturbances. In the above manner, through real-time disturbance compensation based on state observation, it is possible to effectively reduce the impact of disturbances on system operating parameters, enable the motor to run more smoothly when disturbed, and improve the system's ability to suppress disturbances. In addition, multiple high-frequency noise suppression technologies are used to effectively filter out high-frequency noise interference in the system, enhance the stability of the system, and flexibly switch the control mode according to the dynamic requirements of the system, so that a good balance between response speed and stability can be achieved under different working conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0034] Figure 1 It is a flowchart of the steps of the vector frequency conversion control method based on dynamic response of the present invention.

[0035] Figure 2 It is a step flow chart of S100 of the present invention.

[0036] Figure 3 It is a step flow chart of S200 of the present invention.

[0037] Figure 4 It is a step flow chart of S300 of the present invention.

[0038] Figure 5 It is a structural principle diagram of the vector frequency conversion control system based on dynamic response of the present invention.

[0039] 401-dynamic response module, 402-high frequency noise suppression module, 403-multi-modal control module. DETAILED DESCRIPTION

[0040] Exemplary embodiments are described in detail herein, with examples illustrated in the accompanying drawings. When the following description refers to the drawings, identical numerals in different drawings represent identical or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with this application.

[0041] The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. As used in this application and the appended claims, the singular forms "a," "an," "the," and "the" are intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.

[0042] It should be understood that although the terms first, second, third, etc. may be used in this application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from each other. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0043] See also Figures 1 to 4 The present invention provides a vector frequency conversion control method based on dynamic response, comprising the following steps:

[0044] S100: Acquire real-time state information, build an accurate state observation model, and output a disturbance compensation signal based on the state deviation.

[0045] In this embodiment, real-time state information is obtained, and an accurate state observation model is constructed. A disturbance compensation signal is output according to the state deviation. The specific process is as follows:

[0046] S101: Based on the key state parameters of the vector frequency conversion control system, select appropriate sensors and install them in appropriate locations on the motor and control system to ensure accurate acquisition of the required real-time state information. State parameters include motor speed, rotor flux, and current.

[0047] S102: Based on the mathematical model of the motor and combined with the real-time collected state information, a state estimation algorithm is used to construct an accurate state observation model;

[0048] S103: Calculate the deviation between the actual state and the estimated state, and generate a disturbance compensation signal through a sliding mode control algorithm according to the deviation size and change trend.

[0049] In the above process, first, based on the key state parameters of the vector frequency conversion control system, appropriate sensors are selected and reasonably installed at the corresponding positions of the motor and control system to ensure that the required real-time state information is accurately collected. The state parameters include motor speed, rotor flux and current. Then, based on the mathematical model of the motor and combined with the real-time collected state information, a state estimation algorithm is used to construct an accurate state observation model, and the deviation between the actual state and the estimated state is calculated. According to the size of the deviation and the change trend, a disturbance compensation signal is generated through the sliding mode control algorithm.

[0050] S200: Acquire signal characteristics and design a multi-stage filtering architecture to output a filtered stable signal based on noise characteristics.

[0051] In this embodiment, the signal characteristics are obtained and a multi-stage filtering architecture is designed to output a filtered stable signal according to the noise characteristics. The specific process is as follows:

[0052] S201: Perform spectrum analysis and time domain analysis on the collected system signal to obtain the signal's frequency distribution, amplitude variation, and noise type characteristics, and determine the main frequency range and characteristics of the noise by analyzing the signal characteristics;

[0053] S202: Design a multi-stage filtering architecture based on noise characteristics, using different types of filter combinations. The filter combinations include low-pass filters and band-stop filters. The low-pass filters are used to suppress high-frequency noise, and the band-stop filters are used to eliminate interference signals of specific frequencies.

[0054] S203: Input the collected system signal into a multi-stage filtering architecture, and after filtering step by step, output a filtered stable signal.

[0055] In the above process, the collected system signal is first subjected to spectrum analysis and time domain analysis to obtain the signal's frequency distribution, amplitude change and noise type characteristics. By analyzing the signal characteristics, the main frequency range and characteristics of the noise are determined. According to the noise characteristics, a multi-stage filtering architecture is designed, and different types of filter combinations are used. The filter combination includes a low-pass filter and a band-stop filter. The low-pass filter is used to suppress high-frequency noise, and the band-stop filter is used to eliminate interference signals of specific frequencies. The collected system signal is then input into the multi-stage filtering architecture. After stage-by-stage filtering processing, the filtered stable signal is output.

[0056] S300: Acquires dynamic operating parameters and builds an intelligent demand identification model to output adaptive control modes based on operating conditions, balancing response speed and stability. It also provides real-time feedback and adaptive compensation through a dynamic response optimization mechanism.

[0057] In this implementation, dynamic operating parameters are obtained and an intelligent demand identification model is constructed to output an adaptive control mode according to the working condition requirements, balance response speed and stability, and provide real-time feedback and adaptive compensation through a dynamic response optimization mechanism. The specific process is as follows:

[0058] S301: Real-time acquisition of system dynamic operating parameters to understand the system's operating status and operating condition changes, providing a basis for the selection of control modes;

[0059] S302: Build an intelligent demand recognition model, collect a large amount of historical data, train and learn the system's dynamic operating parameters and corresponding working condition requirements, and establish a mapping relationship between the two;

[0060] S303: Based on the identified working condition requirements, an adaptive control mode is selected from a preset control mode library. Different control modes have different control parameters and strategies. The fast response mode enables the system to quickly track the set value by improving the control system gain and response speed. The stable operation mode improves the system stability and anti-interference ability by optimizing the control parameters. The energy-saving mode reduces the system energy consumption by adjusting the motor operating parameters.

[0061] S304: Through the dynamic response optimization mechanism, the operating status and control effect of the system are fed back in real time, and the control mode is adaptively compensated and adjusted based on the feedback information.

[0062] In the above process, the dynamic operating parameters of the system are collected in real time to understand the operating status and working condition changes of the system, provide a basis for the selection of the control mode, and build an intelligent demand identification model to collect a large amount of historical data. The dynamic operating parameters of the system and the corresponding working condition requirements are trained and learned, and a mapping relationship between the two is established. Then, according to the identified working condition requirements, an adaptive control mode is selected from the preset control mode library. Different control modes have different control parameters and strategies. The fast response mode enables the system to quickly track the set value by improving the gain and response speed of the control system. The stable operation mode improves the stability and anti-interference ability of the system by optimizing the control parameters. The energy-saving mode reduces the energy consumption of the system by adjusting the operating parameters of the motor. Finally, through the dynamic response optimization mechanism, the operating status and control effect of the system are fed back in real time, and the control mode is adaptively compensated and adjusted according to the feedback information.

[0063] See also Figure 5 The present invention also provides a vector frequency conversion control system based on dynamic response, including a dynamic response module 401, a high-frequency noise suppression module 402 and a multi-mode control module 403; wherein:

[0064] The dynamic response module 401 is used to obtain real-time state information, build an accurate state observation model, and output a disturbance compensation signal according to the state deviation;

[0065] The high-frequency noise suppression module 402 is used to obtain signal characteristics and design a multi-stage filtering architecture to output a filtered stable signal according to the noise characteristics;

[0066] The multimodal control module 403 is used to obtain dynamic operating parameters and build an intelligent demand recognition model, output an adaptive control mode according to working condition requirements, balance response speed and stability, and provide real-time feedback and adaptive compensation through a dynamic response optimization mechanism.

[0067] In this embodiment, the dynamic response module 401 is used to obtain real-time state information, and build an accurate state observation model, and output a disturbance compensation signal according to the state deviation. The high-frequency noise suppression module 402 is used to obtain signal characteristics and design a multi-stage filtering architecture to output a filtered stable signal according to the noise characteristics. The multi-modal control module 403 is used to obtain dynamic operating parameters and build an intelligent demand identification model to output an adaptive control mode according to the working condition requirements, balance the response speed and stability, and through the dynamic response optimization mechanism, real-time feedback and adaptive compensation; in the above method, the three modules are mutually In collaboration, the high-frequency noise suppression module 402 serves as the "front end" of signal processing, providing clean signals for the dynamic response module 401 and the multi-modal control module 403. The dynamic response module 401 focuses on real-time observation and disturbance compensation of the system status, and feeds back the status information to the multi-modal control module 403. The multi-modal control module 403 generates a control signal according to the system status and working condition requirements, and acts on the system together with the compensation signal of the dynamic response module 401, forming a complete vector variable frequency control system with good dynamic response and anti-interference ability.

[0068] Those skilled in the art will readily conceive of other embodiments of the present application after considering the specification and practicing the contents disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present application that follow the general principles of this application and include common knowledge or customary techniques in the art that are not disclosed in this application.

[0069] It will be understood that the present application is not limited to the exact construction that has been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof.

Claims

1. A vector frequency conversion control method based on dynamic response, characterized in that: The steps include: Acquire real-time state information, build an accurate state observation model, and output disturbance compensation signals based on state deviations; Acquire signal characteristics and design a multi-stage filtering architecture to output a filtered and stable signal based on noise characteristics; Acquire dynamic operating parameters and build an intelligent demand identification model to output adaptive control mode according to working conditions, balance response speed and stability, and provide real-time feedback and adaptive compensation through dynamic response optimization mechanism.

2. The vector frequency conversion control method based on dynamic response according to claim 1, characterized in that: In the steps of obtaining real-time state information, building an accurate state observation model, and outputting a disturbance compensation signal based on the state deviation: According to the key state parameters of the vector frequency conversion control system, select appropriate sensors and install them in the corresponding positions of the motor and control system to ensure accurate collection of the required real-time state information; Based on the mathematical model of the motor and combined with the real-time collected state information, a state estimation algorithm is used to build an accurate state observation model; The deviation between the actual state and the estimated state is calculated, and according to the deviation size and change trend, a disturbance compensation signal is generated through the sliding mode control algorithm.

3. The vector frequency conversion control method based on dynamic response according to claim 2, characterized in that: According to the key state parameters of the vector frequency conversion control system, select appropriate sensors and reasonably install the sensors at the corresponding positions of the motor and control system to ensure accurate collection of the required real-time state information: State parameters include motor speed, rotor flux and current.

4. The vector frequency conversion control method based on dynamic response according to claim 1, characterized in that: In the steps of obtaining signal characteristics, designing a multi-stage filtering architecture, and outputting a filtered and stabilized signal based on noise characteristics: Perform spectrum analysis and time domain analysis on the collected system signals to obtain the signal's frequency distribution, amplitude variation, and noise type characteristics. By analyzing the signal characteristics, determine the main frequency range and characteristics of the noise. Design a multi-stage filtering architecture based on noise characteristics and use different types of filter combinations; The collected system signal is input into a multi-stage filtering architecture, and after filtering step by step, a filtered stable signal is output.

5. The vector frequency conversion control method based on dynamic response according to claim 4, characterized in that: In the steps of designing a multi-stage filtering architecture and using different types of filter combinations based on noise characteristics: The filter combination includes a low-pass filter and a band-stop filter. The low-pass filter is used to suppress high-frequency noise, and the band-stop filter is used to eliminate interference signals of specific frequencies.

6. The vector frequency conversion control method based on dynamic response according to claim 1, characterized in that: In the steps of obtaining dynamic operating parameters, building an intelligent demand identification model, outputting adaptive control modes according to working conditions, balancing response speed and stability, and implementing real-time feedback and adaptive compensation through dynamic response optimization mechanisms: Real-time collection of system dynamic operating parameters to understand the system's operating status and working condition changes, providing a basis for the selection of control mode; Build an intelligent demand identification model, collect a large amount of historical data, train and learn the system's dynamic operating parameters and corresponding working condition requirements, and establish a mapping relationship between the two; According to the identified working condition requirements, select the appropriate control mode from the preset control mode library; Through the dynamic response optimization mechanism, the system's operating status and control effect are fed back in real time, and the control mode is adaptively compensated and adjusted based on the feedback information.

7. The vector frequency conversion control method based on dynamic response according to claim 6, characterized in that: In the step of selecting an adapted control mode from a preset control mode library according to the identified working condition requirements: The fast response mode enables the system to quickly track the set value by improving the gain and response speed of the control system. The stable operation mode improves the stability and anti-interference ability of the system by optimizing the control parameters. The energy-saving mode reduces the energy consumption of the system by adjusting the operating parameters of the motor.

8. A vector frequency conversion control system based on dynamic response, applied to the vector frequency conversion control method based on dynamic response as claimed in claim 1, characterized in that: It includes a dynamic response module, a high-frequency noise suppression module and a multi-modal control module; wherein: The dynamic response module is used to obtain real-time state information, build an accurate state observation model, and output a disturbance compensation signal according to the state deviation; The high-frequency noise suppression module is used to obtain signal characteristics and design a multi-stage filtering architecture to output a filtered stable signal according to the noise characteristics; The multimodal control module is used to obtain dynamic operating parameters and build an intelligent demand identification model, output an adaptive control mode according to working condition requirements, balance response speed and stability, and provide real-time feedback and adaptive compensation through a dynamic response optimization mechanism.

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