A permanent magnet motor load detection method and control system

Abstract

The present application provides a permanent magnet motor load detection method and control system, which relates to the field of motor detection, and includes the following steps: S100, measuring and modeling motor parameters; S200, using a combination of an electromotive force magnetic field observer and single pulse response signal processing to obtain the position and angle information of the motor rotor, thereby calculating the motor speed. The present application uses a trained polynomial regression model to accurately sense the load weight corresponding to the motor speed, thereby improving the load detection accuracy.

Description

Technical Field

[0001] The present invention relates to the field of motor detection, and in particular to a permanent magnet motor load detection method and control system. Background Art

[0002] In existing household appliances, especially those driven by permanent magnet motors such as washing machines, accurate detection and control of motor loads are key to ensuring efficient operation of the equipment and user experience. However, traditional load detection methods often have problems such as insufficient accuracy and susceptibility to interference from external factors. For example, some methods only rely on changes in current or voltage to indirectly infer the load. This method is less accurate when the shape, size and drag coefficient of the motor body and attachments (such as washing machine drum) change. In addition, the deadlock phenomenon that the motor may encounter during startup and operation, as well as back electromotive force and high-frequency spike interference, are all important factors affecting the accuracy of load detection.

[0003] Therefore, we made improvements to this and proposed a permanent magnet motor load detection method and control system. Summary of the invention

[0004] The purpose of the present invention is to address the problems that the existing traditional load detection methods often have insufficient accuracy and are easily interfered by external factors.

[0005] In order to achieve the above-mentioned purpose of the invention, the present invention provides the following permanent magnet motor load detection method and control system to improve the above-mentioned problem.

[0006] The specific application is as follows:

[0007] A permanent magnet motor load detection method comprises the following steps:

[0008] S100, measuring and modeling motor parameters;

[0009] S200, using a combination of an electromotive force magnetic field observer and single pulse response signal processing to obtain position and angle information of the motor rotor, thereby calculating the motor speed;

[0010] S300, using a constant voltage V, measure the speed of the motor when it is unloaded and calibrate S0 to minimize the influence of the shape, size and drag coefficient of the motor body and necessary attachments on the test results;

[0011] S400, using the trained polynomial regression model to sense the current load weight of the washing machine through the motor speed, and the obtained load weight of the washing machine is used to adjust the washing mode and water volume to ensure the best efficiency and effect of the washing process;

[0012] S500, in order to accurately measure the load, different levels of constant voltage are used to measure the load, and different loads are driven by the maximum level of voltage. When the measured load is less than the lower limit of the maximum level of load, the corresponding level is reduced and re-measured, and the above method is used to establish a database;

[0013] S600, in order to suppress the increase of back electromotive force caused by charging and discharging of energy storage elements and the peak interference at high frequency, a manganese-zinc ferrite anti-interference magnetic ring is placed on the power line between the controller and the motor.

[0014] As the preferred technical solution of this application, the technical steps adopted in S100 to S600 include:

[0015] S700, when the motor is started, two phases (for example, phases AB) are selected and energized briefly. The conduction of the two phases will generate a magnetic field with a fixed direction. Due to the physical structure of the motor and the configuration of the magnetic material, this magnetic field will try to align the magnetic poles of the rotor with it. This magnetic field interacts with the magnetic field of the permanent magnet to make the motor rotor rotate to a starting position, that is, the rotor positioning is completed;

[0016] S701, when the two phases AB are powered on, the third phase (phase C) is detected by a voltage sampling circuit to monitor the electromotive force information;

[0017] S702, using a Kalman filter algorithm to calculate the specific position of the rotor according to the electromotive force information;

[0018] By first energizing one set of phases (such as AB or AC) to make the motor rotor rotate to a non-aligned position, and then switching to another set of phases (such as BC) to energize, the influence of the deadlock phenomenon is avoided. The non-aligned position means that the rotor poles are not completely aligned with any stator poles, that is, there is a certain angle offset between the rotor poles and the stator poles.

[0019] As the preferred technical solution of this application, when there is a load weight When the voltage applied is the same as when no load is applied, the elapsed time is detected and collected. The motor speed at this time is S1, where the time It is the time required for the motor to rotate one circle. Similarly, when there is a load weight time, elapsed time The motor speed is S n , and use this to build a database so that the measured motor speed is relative to the load.

[0020] As the preferred technical solution of this application, the polynomial regression model is trained and calculated using a polynomial regression algorithm, which includes:

[0021] Washing machine load weight With the measured motor speed Variation, choose the order of the polynomial , construct a polynomial regression model: ,in, The regression coefficient is obtained by minimizing the residual sum of squares: in, For the Washing machine load weight for group data, For the Motor elapsed time of group data The speed of As the preferred technical solution of the present application, S400 uses the trained polynomial regression model to sense the current washing machine load weight through the motor speed, specifically including:

[0022] S401, detection elapsed time The motor speed at

[0023] S402: input the motor speed value into a trained polynomial regression model, sense the current washing machine load weight through the motor speed, and use the obtained washing machine load weight to adjust the washing mode and water volume.

[0024] A permanent magnet motor load detection and control system, comprising:

[0025] Host computer: The host computer is the control center of the entire system, responsible for receiving sensor data, processing data, executing load sensing algorithms, and sending control commands to the motor driver;

[0026] Motor driver: The motor driver directly controls the operation of the motor, including starting, stopping, and speed regulation. The motor driver adjusts the input current of the motor according to the instructions of the host computer, thereby changing the torque and speed of the motor;

[0027] Motor body: The motor is the core power source of the washing machine and operates according to the control of the driver;

[0028] Sensor module: used to monitor the current and torque of the motor in real time. These data are fed back to the host computer for analysis and judgment of the current load of the washing machine.

[0029] As a preferred technical solution of the present application, the sensor module further includes:

[0030] At least one sensor for collecting current and voltage data of the motor;

[0031] A processor is connected to the at least one sensor, and the processor is used to perform the following steps:

[0032] The load is measured using different levels of constant voltage;

[0033] When the real-time load status changes, the motor's operating parameters are automatically adjusted;

[0034] The processor also includes a dynamic adjustment module, which dynamically adjusts the working parameters of the motor according to the change of the real-time load state.

[0035] As a preferred technical solution of the present application, the dynamic adjustment module further includes:

[0036] A load status monitoring unit, used for continuously monitoring the real-time load status;

[0037] A parameter adjustment unit, used to adjust the speed and power operating parameters of the motor when the load state monitoring unit detects a load change;

[0038] The effect evaluation unit is used to evaluate the motor performance after the parameter adjustment and feed back to the parameter adjustment unit for further optimization.

[0039] As a preferred technical solution of this application, the dynamic adjustment module also includes:

[0040] A learning unit, used to record historical data of the real-time load state and train an optimal parameter adjustment strategy based on the historical data;

[0041] The prediction unit is used to predict the future load change trend and adjust the motor's operating parameters in advance to cope with the expected load change.

[0042] As the preferred technical solution of this application, it also includes:

[0043] A user interface module, the user interface module is used to:

[0044] Display the real-time load status and its changes;

[0045] Provides manual adjustment options, allowing users to manually adjust the motor's operating parameters according to actual conditions;

[0046] The manual adjustment result is fed back to the dynamic adjustment module to optimize the dynamic adjustment strategy.

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] In the scheme of this application:

[0049] 1. By adopting the motor sensorless control system, the electromotive force magnetic field observer and the single pulse response signal processing, the position and angle information of the motor rotor can be accurately obtained, which significantly improves the accuracy of load detection;

[0050] 2. The speed of the motor when it is not loaded is measured and calibrated by constant voltage, and different levels of constant voltage are used to measure the load, which effectively eliminates the influence of the shape, size and wind resistance coefficient of the motor body and attachments on the test results;

[0051] 3. By first energizing the AC or BC phase, the motor rotates to a position perpendicular to the starting position, and then energizing the AB phase, the deadlock phenomenon is completely avoided, ensuring the normal start-up and operation of the motor;

[0052] 4. A manganese-zinc ferrite anti-interference magnetic ring is placed on the power line between the controller and the motor, which effectively suppresses the back electromotive force rise and high-frequency spike interference caused by the charging and discharging of the energy storage element, and improves the stability and reliability of the system;

[0053] 5. Through the trained polynomial regression model, the load weight corresponding to the motor speed is accurately sensed, and then the washing mode and water volume are adjusted to ensure the optimal efficiency and effect of the washing process. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 A schematic diagram of a permanent magnet motor load detection method provided in this application;

[0055] Figure 2 Schematic diagram of the permanent magnet motor load detection control system provided in this application. DETAILED DESCRIPTION

[0056] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0057] As described in the background art, traditional load detection methods often have insufficient accuracy and are easily interfered by external factors.

[0058] In order to solve this technical problem, the present invention provides a permanent magnet motor load detection method and control system, which are applied to the field of motor detection.

[0059] Specifically, please refer to Figure 1 , the permanent magnet motor load detection method comprises the following steps:

[0060] S100, measuring and modeling motor parameters;

[0061] Using a sensorless motor control system: Before using a sensorless control system to obtain the position and angle information of the permanent magnet motor rotor, the motor parameters must be measured and modeled first. This step involves accurately measuring the key electrical parameters of the motor (such as resistance, inductance, and magnetic flux), and building a motor mechanism model based on these data to describe its performance and behavior.

[0062] S200, using the combination of electromotive force magnetic field observer and single pulse response signal processing to obtain the position and angle information of the motor rotor, so as to calculate the motor speed, and using the electromotive force magnetic field observer, single pulse response signal processing and other algorithms to estimate the precise position and angle of the rotor through the motor's electrical signal without induction, thereby achieving precise control of the motor without the need for traditional physical sensors. This series of measures makes motor control more efficient, accurate and reliable. Electromotive force magnetic field observer and single pulse response signal processing belong to the induction-free control strategy, which is included in the motor induction-free control system.

[0063] S300, using a constant voltage V (such as 100V, 50V, etc.), measure the speed of the motor when it is unloaded (here unloaded refers to the motor body and necessary attachments, for example: the unloaded motor of a washing machine refers to the motor body plus the washing machine drum, and the necessary attachments refer to the washing machine drum) and calibrate S0 to minimize the influence of the shape, size and drag coefficient of the motor body and necessary attachments on the test results;

[0064] S400, using the trained polynomial regression model to sense the current load weight of the washing machine through the motor speed, and the obtained load weight of the washing machine is used to adjust the washing mode and water volume to ensure the best efficiency and effect of the washing process;

[0065] S500, in order to accurately measure the load, different levels of constant voltage are used to measure the load. Different loads use different levels of constant voltage (for example: 100V, 50V, etc. correspond to 30Kg-20Kg, 20Kg-10Kg, etc.). First, use the maximum level of voltage to drive. When the measured load is less than the lower limit of the maximum level load, reduce the corresponding level and re-measure, and use the above method to establish a database;

[0066] S600. In order to suppress the increase of back electromotive force caused by charging and discharging of energy storage elements and the peak interference at high frequency, a manganese-zinc ferrite anti-interference magnetic ring is installed on the power line between the controller and the motor to suppress the harmonics from being transmitted to the motor body and causing increased losses, and at the same time suppress the harmonics generated by the motor body from being transmitted to the controller and causing interference.

[0067] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings.

[0068] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features and technical solutions in the embodiments may be combined with each other.

[0069] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0070] Example 1, please refer to Figure 1 , a permanent magnet motor load detection method, the technical steps adopted in S100~S600 include:

[0071] S700, when the motor is started, two phases (for example, phases AB) are selected and energized briefly. The conduction of the two phases will generate a magnetic field with a fixed direction. Due to the physical structure of the motor and the configuration of the magnetic material, this magnetic field will try to align the magnetic poles of the rotor with it. This magnetic field interacts with the magnetic field of the permanent magnet to make the motor rotor rotate to a starting position, that is, the rotor positioning is completed;

[0072] S701, when the two phases AB are powered on, the third phase (phase C) is detected by a voltage sampling circuit to monitor the electromotive force information;

[0073] S702, using a Kalman filter algorithm to calculate the specific position of the rotor according to the electromotive force information;

[0074] By first energizing one set of phases (such as AB or AC) to make the motor rotor rotate to a non-aligned position, and then switching to another set of phases (such as BC) to energize, the influence of the deadlock phenomenon is avoided. The non-aligned position means that the rotor poles are not completely aligned with any stator poles, that is, there is a certain angle offset between the rotor poles and the stator poles.

[0075] In S300, when there is load weight When the voltage applied is the same as when no load is applied, the elapsed time is detected and collected. The motor speed at this time is S1, where the time It is the time required for the motor to rotate one circle. Similarly, when there is a load weight time, elapsed time The motor speed is S n , and use this to build a database so that the measured motor speed corresponds to the load. The purpose of measuring the motor speed at time t0 is to ensure that the measured speed value can more truly reflect the performance of the motor under actual load conditions.

[0076] In S400, the polynomial regression model is trained and calculated using a polynomial regression algorithm, which includes:

[0077] Washing machine load weight With the measured motor speed Variation, choose the order of the polynomial , construct a polynomial regression model: ,in, The regression coefficient is obtained by minimizing the residual sum of squares: in, For the Washing machine load weight for group data, For the Motor elapsed time of group data The speed of is the number of data.

[0078] S400 uses the trained polynomial regression model to sense the current washing machine load weight through the motor speed, including:

[0079] S401, detection elapsed time The motor speed at

[0080] S402. Input the motor speed value into the trained polynomial regression model, and sense the current washing machine load weight through the motor speed. The obtained washing machine load weight is used to adjust the washing mode and water volume to ensure the best efficiency and effect of the washing process.

[0081] Example 2, please refer to Figure 2 , a permanent magnet motor load detection and control system, comprising:

[0082] Host computer: The host computer is the control center of the entire system, responsible for receiving sensor data, processing data, executing load sensing algorithms, and sending control commands to the motor driver;

[0083] Motor driver: The motor driver directly controls the operation of the motor, including starting, stopping, and speed regulation. The motor driver adjusts the input current of the motor according to the instructions of the host computer, thereby changing the torque and speed of the motor;

[0084] Motor body: The motor is the core power source of the washing machine and operates according to the control of the driver;

[0085] Sensor module: used to monitor the current and torque of the motor in real time. These data are fed back to the host computer for analysis and judgment of the current load of the washing machine.

[0086] The sensor module includes:

[0087] At least one sensor for collecting current and voltage data of the motor;

[0088] A processor is connected to the at least one sensor, and the processor is used to perform the following steps:

[0089] The load is measured using different levels of constant voltage;

[0090] When the real-time load status changes, the motor's operating parameters are automatically adjusted;

[0091] The processor also includes a dynamic adjustment module, which dynamically adjusts the working parameters of the motor according to the change of the real-time load state to optimize the washing efficiency and energy consumption.

[0092] The dynamic adjustment module further comprises:

[0093] A load status monitoring unit, used for continuously monitoring the real-time load status;

[0094] A parameter adjustment unit, used to adjust the speed and power operating parameters of the motor when the load state monitoring unit detects a load change;

[0095] The effect evaluation unit is used to evaluate the motor performance after the parameter adjustment and feed back to the parameter adjustment unit for further optimization.

[0096] The dynamic adjustment module also includes:

[0097] A learning unit, used to record historical data of the real-time load state and train an optimal parameter adjustment strategy based on the historical data;

[0098] The prediction unit is used to predict the future load change trend and adjust the motor's operating parameters in advance to cope with the expected load change.

[0099] Also includes:

[0100] A user interface module, the user interface module is used to:

[0101] Display the real-time load status and its changes;

[0102] Provides manual adjustment options, allowing users to manually adjust the motor's operating parameters according to actual conditions;

[0103] The manual adjustment result is fed back to the dynamic adjustment module to optimize the dynamic adjustment strategy.

[0104] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral one; it can be a mechanical connection, an electrical connection, or communication with each other; it can be a direct connection, or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements, unless otherwise clearly defined. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0105] Obviously, the embodiments described above are only some embodiments of the present invention, rather than all embodiments. The preferred embodiments of the present invention are given in the accompanying drawings, but they do not limit the patent scope of the present invention. The present invention can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present invention more thorough and comprehensive. Although the present invention has been described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions recorded in the aforementioned specific embodiments, or to perform equivalent replacements for some of the technical features therein. Any equivalent structure made using the contents of the specification and drawings of the present invention, directly or indirectly used in other related technical fields, is also within the scope of patent protection of the present invention.

Claims

1. A permanent magnet motor load detection method, characterized in that: The following steps are involved: S100, measuring and modeling motor parameters; S200, using a combination of an electromotive force magnetic field observer and single pulse response signal processing to obtain position and angle information of the motor rotor, thereby calculating the motor speed; S300, using a constant voltage V, measure the speed of the motor when it is unloaded and calibrate S0; S400, using the trained polynomial regression model to sense the current load weight of the washing machine through the motor speed, and obtaining the load weight of the washing machine; S500, using constant voltages of different levels to measure the load, using constant voltages of different levels for different loads, first using the maximum level of voltage to drive, when the measured load is less than the lower limit of the maximum level of load, reducing the corresponding level and re-measuring, and using the above method to establish a database; S600, in order to suppress the rise of back electromotive force and high-frequency spike interference caused by charging and discharging of energy storage elements, a manganese-zinc ferrite anti-interference magnetic ring is placed on the power line between the controller and the motor; In S400, the polynomial regression model is trained and calculated using a polynomial regression algorithm, which includes: Washing machine load weight With the measured motor speed Variation, choose the order of the polynomial , construct a polynomial regression model: ,in, The regression coefficient is obtained by minimizing the residual sum of squares: in, For the Washing machine load weight for group data, For the Motor elapsed time of group data The speed of is the number of data.

2. A permanent magnet motor load detection method according to claim 1, characterized in that: The technical steps used in S100~S600 include: S700, when the motor starts, two phases are selected and energized briefly. The conduction of the two phases will generate a magnetic field with a fixed direction. This magnetic field interacts with the magnetic field of the permanent magnet to make the motor rotor rotate to a starting position, that is, the rotor positioning is completed; S701, when the two phases AB are powered on, the third phase monitoring electromotive force information is detected by a voltage sampling circuit; S702: Calculate the specific position of the rotor using a Kalman filter algorithm based on the electromotive force information.

3. A permanent magnet motor load detection method according to claim 2, characterized in that: In S300, when there is load weight When the voltage applied is the same as when no load is applied, the elapsed time is detected and collected. The motor speed at this time is S1, where the time It is the time required for the motor to rotate one circle. Similarly, when there is a load weight time, elapsed time The motor speed is S n , and use this to establish a database so that the measured motor speed corresponds to the load.

4. A permanent magnet motor load detection method according to claim 3, characterized in that: S400 uses the trained polynomial regression model to sense the current washing machine load weight through the motor speed, including: S401, detection elapsed time The motor speed at S402: input the motor speed value into a trained polynomial regression model, sense the current washing machine load weight through the motor speed, and use the obtained washing machine load weight to adjust the washing mode and water volume.

5. A permanent magnet motor load detection control system, using the permanent magnet motor load detection method according to claim 4, characterized in that: include: Host computer: The host computer is the control center of the entire system, responsible for receiving sensor data, processing data, executing load sensing algorithms, and sending control commands to the motor driver; Motor driver: The motor driver directly controls the operation of the motor, including starting, stopping, and speed regulation. The motor driver adjusts the input current of the motor according to the instructions of the host computer, thereby changing the torque and speed of the motor; Motor body: The motor is the core power source of the washing machine and operates according to the control of the driver; Sensor module: used to monitor the current and torque of the motor in real time. These data are fed back to the host computer for analysis and judgment of the current load of the washing machine.

6. A permanent magnet motor load detection and control system according to claim 5, characterized in that: The sensor module includes: At least one sensor for collecting current and voltage data of the motor; A processor is connected to the at least one sensor, and the processor is used to perform the following steps: The load is measured using different levels of constant voltage; When the real-time load status changes, the motor's operating parameters are automatically adjusted; The processor also includes a dynamic adjustment module, which dynamically adjusts the working parameters of the motor according to the change of the real-time load state.

7. A permanent magnet motor load detection and control system according to claim 6, characterized in that: The dynamic adjustment module further comprises: A load status monitoring unit, used for continuously monitoring the real-time load status; A parameter adjustment unit, used to adjust the speed and power operating parameters of the motor when the load state monitoring unit detects a load change; The effect evaluation unit is used to evaluate the motor performance after the parameter adjustment and feed back to the parameter adjustment unit for further optimization.

8. A permanent magnet motor load detection and control system according to claim 7, characterized in that: The dynamic adjustment module also includes: A learning unit, used to record historical data of the real-time load state and train an optimal parameter adjustment strategy based on the historical data; The prediction unit is used to predict the future load change trend and adjust the motor's operating parameters in advance to cope with the expected load change.

9. A permanent magnet motor load detection and control system according to claim 8, characterized in that: Also includes: A user interface module, the user interface module is used to: Display the real-time load status and its changes; Provides manual adjustment options, allowing users to manually adjust the motor's operating parameters according to actual conditions; The manual adjustment result is fed back to the dynamic adjustment module to optimize the dynamic adjustment strategy.

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