Permanent magnet synchronous motor speed sensorless control method based on parameter online identification

Abstract

The invention relates to the technical field of speed sensorless control, in particular to a permanent magnet synchronous motor speed sensorless control method based on parameter online identification, which comprises the following steps of: collecting current and voltage data of a permanent magnet synchronous motor before starting, and recording an initial position of a rotor in a static state; data are classified through peak value and waveform analysis of current and voltage, and a data set before starting is obtained. According to the invention, through the comparative analysis of the dynamic prediction data and the real-time data, the control strategy is effectively adjusted, the starting performance and the operation efficiency of the motor are optimized, especially in the operation process of the motor, the real-time operation efficiency and the stability of the motor are monitored through continuous feedback, the operation flexibility of the motor and the overall reliability of the system are guaranteed, and the reliability of the system is improved. According to the method for updating the parameters of the motor in real time, the motor can be accurately controlled under different operation conditions, torque fluctuation and reverse rotation risks are reduced, and adaptability and operation safety are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of speed sensorless control, and in particular to a speed sensorless control method of a permanent magnet synchronous motor based on online parameter identification. Background Art

[0002] The field of speed sensorless control technology mainly involves how to estimate the speed and position through the electrical signals of the motor itself without using a physical speed sensor. This technology is widely used in the control system of permanent magnet synchronous motor (PMSM), especially in those applications with high cost, space and environmental requirements. The speed sensorless control method estimates the real-time speed and position information of the motor by using advanced algorithms to process the current and voltage data of the motor windings. This technology can not only reduce the complexity and cost of the system, but also improve the reliability and robustness of the system. The main challenge is how to ensure the control accuracy and stability under various working conditions and dynamic changes.

[0003] Among them, the speed sensorless control method of permanent magnet synchronous motor with online parameter identification refers to the use of real-time online methods to identify motor parameters and achieve precise control of permanent magnet synchronous motors without relying on external speed sensors. The main uses of this control method include various industrial and commercial applications, such as electric vehicles, robots, wind power generation, and automation equipment. Through online parameter identification, the control system can adjust and optimize the control strategy in real time, effectively improving the performance and efficiency of the motor without a physical speed sensor. This method helps to further reduce the cost and complexity of the system, while improving the flexibility of operation and the overall reliability of the system.

[0004] Existing speed sensorless control technology has some obvious deficiencies in actual operation. For example, in the absence of a physical speed sensor, it is difficult to accurately predict and compensate for parameter changes in a dynamic environment by relying on traditional control algorithms, resulting in insufficient motor control accuracy and affecting the stability and reliability of motor performance. Especially in industrial and commercial applications, motors need to frequently cope with a variety of complex or changing operating conditions. Traditional methods fail to adjust control strategies in real time, resulting in reduced operating efficiency and even safety hazards. For example, in electric vehicles or automated equipment, the requirements for motor control are extremely stringent, and any slight delay or error will lead to a decline in the performance of the entire system. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings in the prior art and to propose a speed sensorless control method for a permanent magnet synchronous motor based on online parameter identification.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: a speed sensorless control method of a permanent magnet synchronous motor based on online parameter identification, comprising the following steps:

[0007] S1: Collect the current and voltage data of the permanent magnet synchronous motor before starting, record the initial position of the rotor in a stationary state, classify the data through the peak and waveform analysis of the current and voltage, and obtain the pre-startup data set;

[0008] S2: Calculate the resistance and inductance through the pre-startup data set, perform initialization state estimation based on online parameter identification, set initial filter parameters, and generate an estimated value of the motor initial state;

[0009] S3: simulating the behavior of the motor when it starts according to the estimated value of the initial state of the motor, and generating motor dynamic prediction data by comparing the predicted and real-time current and voltage data and recording the deviation;

[0010] S4: comparing the motor dynamic prediction data with the real-time measurement data, adjusting the state parameters of the filter by updating the difference, extracting key motor parameters, including resistance and inductance, and obtaining a motor parameter data set;

[0011] S5: According to the motor parameter data set, adjust the speed sensorless settings, match the differentiated operating conditions, optimize the starting performance of the motor, avoid torque fluctuation and reversal risks, and generate an optimized control strategy;

[0012] S6: Based on the optimization control strategy, control the speed sensorless, monitor the real-time operating efficiency and stability of the permanent magnet synchronous motor, optimize the operating speed and efficiency through continuous feedback adjustment, and obtain the operating stability feedback result.

[0013] As a further solution of the present invention, the pre-startup data set includes current peak, trough, voltage cycle waveform, and initial motor rotor position; the motor initial state estimation value includes a value calculated by resistance and inductance, and preset filtering parameters; the motor dynamic prediction data includes a simulated startup current waveform, voltage waveform, and real-time and predicted deviation values; the motor parameter data set includes adjusted state parameters of resistance, inductance, and filter; the optimization control strategy includes an adjusted speed sensorless setting, matched differentiated operating conditions, optimized motor starting performance, and avoided torque fluctuations and reversal risks; the operating stability feedback result includes the motor operating speed and efficiency obtained through continuous feedback optimization, and real-time monitored operating efficiency and stability data.

[0014] As a further solution of the present invention, the current and voltage data of the permanent magnet synchronous motor before starting are collected, the initial position of the rotor in a stationary state is recorded, and the data are classified by analyzing the peak and waveform of the current and voltage. The steps for obtaining the data set before starting are specifically as follows:

[0015] S101: Install current and voltage sensors on the permanent magnet synchronous motor, configure data collection parameters to measure current and voltage, record the position of the rotor when it is stationary, and collect initial current and voltage values ​​to generate an initial current and voltage data set;

[0016] S102: continuously monitoring the real-time waveforms of current and voltage during the startup of the permanent magnet synchronous motor according to the initial current and voltage data set, recording the duration and interval of the data waveforms, marking the peak points in the current and voltage waveforms, and obtaining current and voltage waveform data;

[0017] S103: Analyze the abnormal waveform points in the current and voltage waveform data, identify the characteristic period and shape of the waveform, classify the abnormal points and the standard waveform, and generate a pre-startup data set.

[0018] As a further solution of the present invention, the steps of calculating the resistance and inductance through the pre-startup data set, performing initialization state estimation according to online parameter identification, setting initial filter parameters, and generating the motor initial state estimation value are specifically as follows:

[0019] S201: extracting peak data of current and voltage from the pre-startup data set, calculating average current and voltage in each cycle, calculating resistance and inductance of the permanent magnet synchronous motor, and generating resistance and inductance calculation results;

[0020] S202: using the resistance and inductance calculation results, according to known permanent magnet synchronous motor model parameters and measured data, performing online parameter identification, updating the resistance and inductance values ​​of the permanent magnet synchronous motor model, estimating the initial working state of the permanent magnet synchronous motor, and generating motor parameter identification results;

[0021] S203: Based on the motor parameter identification result, a Kalman filter algorithm is used to perform state estimation and dynamic adjustment of filter parameters under real-time working conditions to generate an estimated value of the motor initial state.

[0022] As a further solution of the present invention, the formula of the Kalman filter algorithm is as follows:

[0023]

[0024] in, represents the posterior state estimate at time step k, represents the prior state estimate at time step k, K k represents the Kalman gain at time step k, zk represents the actual measurement value at time step k, H k Represents the observation model matrix at time step k.

[0025] As a further solution of the present invention, according to the estimated value of the initial state of the motor, the behavior of the motor at startup is simulated, and the steps of generating the motor dynamic prediction data are specifically as follows by comparing the predicted and real-time current and voltage data and recording the deviation:

[0026] S301: using the estimated value of the initial state of the motor as an input parameter, setting the starting conditions of the permanent magnet synchronous motor simulator, simulating the behavior of the permanent magnet synchronous motor when starting, including the speed increase process and current change, and generating a permanent magnet synchronous motor starting simulation result;

[0027] S302: comparing the predicted current and voltage data in the permanent magnet synchronous motor startup simulation result with the motor startup data collected in real time, recording the deviation between the two, analyzing the source of the deviation, adjusting the simulation parameters to reflect the motor behavior, and generating a deviation analysis result;

[0028] S303: Based on the deviation analysis results, the accuracy of the simulation model is optimized, the prediction error is avoided by repeated testing, the consistency between the simulation results and the real-time operating status is checked, and the motor dynamic prediction data is generated.

[0029] As a further solution of the present invention, the motor dynamic prediction data is compared with the real-time measurement data, and the state parameters of the difference adjustment filter are updated to extract key motor parameters, including resistance and inductance, and the steps of obtaining the motor parameter data set are specifically as follows:

[0030] S401: Compare the motor dynamic prediction data with the on-site real-time measurement data, identify the difference between the two, analyze the cause of the difference, and generate a prediction and measurement comparison result;

[0031] S402: According to the comparison result between the prediction and the actual measurement, the filter parameters are adjusted, the state parameters are updated to match the real-time measurement data, the prediction error is avoided, and the filtering effect is optimized, and the filter parameter adjustment result is generated;

[0032] S403: Utilizing the filter parameter adjustment result, extracting key parameters of the motor, including resistance and inductance, verifying the reliability and consistency of parameter extraction, and generating a motor parameter data set.

[0033] As a further solution of the present invention, according to the motor parameter data set, the setting of the speed sensorless is adjusted to match the differentiated operating conditions, optimize the starting performance of the motor, avoid torque fluctuation and reversal risks, and generate the optimized control strategy in the following steps:

[0034] S501: Analyze resistance and inductance parameters in the motor parameter data set, adjust the speed sensorless configuration according to the parameters, verify that the speed sensorless setting matches the target operating condition of the permanent magnet synchronous motor, and generate a sensor configuration adjustment result;

[0035] S502: Based on the sensor configuration adjustment result, evaluate the performance of the permanent magnet synchronous motor under differentiated starting conditions, adjust the motor control strategy according to the differentiated operating conditions, avoid torque fluctuation during starting, and generate a starting performance optimization result;

[0036] S503: Using the startup performance optimization result, adjusting parameters to avoid the risk of reverse rotation, checking the smoothness and safety of the motor startup process, and generating an optimization control strategy.

[0037] As a further solution of the present invention, based on the optimization control strategy, the speed sensor is controlled to monitor the real-time operating efficiency and stability of the permanent magnet synchronous motor, and the operating speed and efficiency are optimized through continuous feedback adjustment, and the steps of obtaining the operating stability feedback result are specifically as follows:

[0038] S601: Control the speed sensorless motor according to the optimization control strategy, verify that the speed sensorless motor works normally according to the preset strategy, monitor the efficiency and stability of the permanent magnet synchronous motor during startup and operation, and generate a sensor control monitoring result;

[0039] S602: extracting data related to the efficiency and operation stability of the permanent magnet synchronous motor from the sensor control monitoring result, adjusting the operation speed and efficiency parameters through continuous feedback, optimizing the overall performance of the permanent magnet synchronous motor, and generating a continuous feedback adjustment result;

[0040] S603: utilizing the continuous feedback adjustment result and adopting a genetic algorithm according to the characteristics of the permanent magnet synchronous motor to iteratively optimize the operating parameters and generate an operating stability feedback result.

[0041] As a further solution of the present invention, the formula of the genetic algorithm is as follows:

[0042]

[0043] Among them, P new Represents the new operating parameters, P old is the current operating parameter, α is the weight coefficient of the adjustment step, ΔP represents the adjustment amplitude of the parameter, E target is the target operating efficiency, E current is the current operating efficiency, |E target -E current | 1 / 2 It is the square root of the difference between the target and current efficiency.

[0044] Compared with the prior art, the advantages and positive effects of the present invention are:

[0045] In the present invention, by collecting the current and voltage data of the permanent magnet synchronous motor before starting and recording the initial position of the rotor in a stationary state, an accurate prediction of the motor state is achieved. Not only is the initial accuracy of the motor control improved, but also the control strategy is effectively adjusted through comparative analysis of dynamic prediction data and real-time data to optimize the starting performance and operating efficiency of the motor. In particular, during the operation of the motor, the real-time operating efficiency and stability of the motor are monitored through continuous feedback to ensure the flexibility of the motor operation and the overall reliability of the system. This method of real-time updating of motor parameters can accurately control the motor under different operating conditions, reduce torque fluctuations and reversal risks, and significantly improve adaptability and operational safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a schematic diagram of the main steps of the present invention;

[0047] Figure 2 It is a schematic diagram of the refinement of S1 of the present invention;

[0048] Figure 3 It is a schematic diagram of the refinement of S2 of the present invention;

[0049] Figure 4 It is a schematic diagram of the refinement of S3 of the present invention;

[0050] Figure 5 It is a schematic diagram of the refinement of S4 of the present invention;

[0051] Figure 6 It is a detailed schematic diagram of S5 of the present invention;

[0052] Figure 7 It is a detailed schematic diagram of S6 of the present invention. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0054] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0055] See also Figure 1 The present invention provides a technical solution, a speed sensorless control method for a permanent magnet synchronous motor based on online parameter identification, comprising the following steps:

[0056] S1: Collect the current and voltage data of the permanent magnet synchronous motor before starting, and simultaneously record the initial position of the rotor in the static state. Classify the data through the peak and waveform analysis of the current and voltage, mark the data attributes, and obtain the pre-startup data set;

[0057] S2: Calculate the resistance and inductance through the pre-startup data set, perform initialization state estimation based on online parameter identification, set initial filter parameters, perform correction based on the basic characteristics of the motor, and generate an estimate of the motor's initial state;

[0058] S3: Based on the estimated value of the motor's initial state, simulate the motor's behavior at startup, collect and analyze real-time current and voltage data, and generate motor dynamic prediction data by comparing the predicted and real-time data records.

[0059] S4: Compare the motor dynamic prediction data with the real-time measurement data, adjust the state parameters of the filter by updating the difference, extract key motor parameters from the update process, including the current measurement values ​​of resistance and inductance, and obtain the motor parameter data set;

[0060] S5: According to the motor parameter data set, adjust the speed sensorless settings, adjust the starting strategy according to the real-time measurement parameters, match the differentiated operating conditions of the permanent magnet synchronous motor, optimize the starting performance of the motor, avoid torque fluctuations and reversal risks, and generate an optimized control strategy;

[0061] S6: Based on the optimization control strategy, implement speed sensorless control, monitor the real-time operating efficiency and stability of the permanent magnet synchronous motor, optimize the operating speed and efficiency through continuous feedback adjustment, check the continuity of operation, and obtain feedback results on operating stability.

[0062] The pre-startup data set includes the current peak, trough, voltage cycle waveform, and initial motor rotor position. The motor initial state estimation value includes the value calculated by resistance and inductance, and the preset filter parameters. The motor dynamic prediction data includes the simulated startup current waveform, voltage waveform, and real-time and predicted deviation values. The motor parameter data set includes the adjusted state parameters of resistance, inductance, and filter. The optimized control strategy includes adjusted speed sensorless settings, matched differentiated operating conditions, optimized motor starting performance, and avoided torque fluctuations and reversal risks. The operating stability feedback results include the motor operating speed and efficiency obtained through continuous feedback optimization, and real-time monitored operating efficiency and stability data.

[0063] See also Figure 2 , collect the current and voltage data of the permanent magnet synchronous motor before starting, record the initial position of the rotor in the static state, and classify the data through the peak and waveform analysis of the current and voltage. The specific steps for obtaining the pre-startup data set are:

[0064] S101: Install current and voltage sensors on the permanent magnet synchronous motor, configure data collection parameters to measure current and voltage, record the position of the rotor when it is stationary, and collect initial current and voltage values. The execution process of generating an initial current and voltage data set is as follows;

[0065] Install current and voltage sensors on the permanent magnet synchronous motor. According to the start and stop operating status of the permanent magnet synchronous motor, select appropriate sensors and installation locations to optimize the accuracy and reliability of the data. The initial data points collected need to be sufficient to ensure the accuracy of subsequent analysis. These initial data points are crucial to understanding the performance of the motor under static and dynamic conditions. By configuring the data collection parameters, you can set the data collection frequency and the type of data collected, such as the current amplitude, current waveform, voltage size and voltage waveform, etc. Recording the position of the rotor when it is stationary is the key to determining the internal state of the motor, because the position of the rotor directly affects the magnetic field distribution and starting characteristics of the motor. At the same time, collect the initial current and voltage values ​​to provide baseline data before the motor starts. Comparative analysis of data changes before and after starting helps to identify problems that occur during the motor startup process, provide an important basis for subsequent fault diagnosis and performance evaluation, and generate an initial current and voltage data set.

[0066] S102: According to the initial current and voltage data set, the real-time waveforms of current and voltage during the startup of the permanent magnet synchronous motor are continuously monitored, the duration and interval of the data waveforms are recorded, and the peak points in the current and voltage waveforms are marked. The execution process of obtaining the current and voltage waveform data is as follows;

[0067] The duration and interval of the recorded data waveform are calculated according to the formula Calculate the total duration of the data waveform. Where T represents the total duration, ti and t i+1 Represents the timestamps of two consecutive data points. Formula explanation and formula calculation derivation process: Set the data point timestamps to 1 second, 2 seconds, 3 seconds, to the nth second, the data point interval is 1 second, and add the time of each interval to get the total duration. If n=10, then T=9 seconds, indicating that the recording of current and voltage waveforms lasted for 9 seconds in total.

[0068] S103: Analyze the abnormal waveform points in the current and voltage waveform data, identify the characteristic period and shape of the waveform, classify the abnormal points and the standard waveform, and generate the pre-startup data set. The execution process is as follows;

[0069] Analyze the abnormal waveform points in the current and voltage waveform data, and identify the characteristic period and shape of the waveform. The identification of abnormal points is based on the continuous monitoring of the current and voltage data waveforms. By analyzing the deviation between the data points and the average value, the waveform points that exceed the standard deviation are found. These points are marked as abnormal. The characteristic period of the waveform is obtained by calculating the period average of a series of continuous waveforms. The shape analysis is defined based on the peak and valley values ​​of the waveform and the spacing between these points. The process involves not only simple numerical calculations, but also complex graphic recognition and pattern analysis techniques to classify abnormal points and standard waveforms. Through classification, more accurate state analysis and fault prediction of the motor can be performed. It contains all the key current and voltage information before the motor starts, provides a detailed reference for the maintenance and fault diagnosis of the motor, and generates a pre-startup data set.

[0070] See also Figure 3 ,The steps of calculating the resistance and inductance through the pre-startup data set, performing initialization state estimation based on parameter online identification, setting initial filter parameters, and generating the motor initial state estimation value are as follows:

[0071] S201: extracting the peak data of current and voltage from the pre-startup data set, calculating the average current and voltage in each cycle, calculating the resistance and inductance of the permanent magnet synchronous motor, and generating the resistance and inductance calculation results. The execution process is as follows;

[0072] Calculate the resistance and inductance of the permanent magnet synchronous motor according to the formula and Calculate the resistance and inductance. In the formula, R represents the resistance, V represents the peak voltage, and I avg Represents the average current; L represents inductance, ∫Vdt represents the integral of voltage over time, and ∫Idt represents the integral of current over time. Formula explanation and formula calculation derivation process: Considering that the voltage peak extracted from the data set is 120V and the average current is 10A, the calculated resistance is The integrals of voltage and current are set to 600 V·s and 50 A·s respectively, and the inductance is calculated as This calculation method provides a quick estimate of the electrical characteristics of the motor and can be used for further model validation and tuning.

[0073] S202: Using the resistance and inductance calculation results, according to the known permanent magnet synchronous motor model parameters and measured data, perform online parameter identification, update the resistance and inductance values ​​of the permanent magnet synchronous motor model, estimate the initial working state of the permanent magnet synchronous motor, and generate the motor parameter identification result. The execution process is as follows;

[0074] Using the resistance and inductance calculation results, online identification is performed based on the known permanent magnet synchronous motor model parameters and measured data. By comparing the calculated resistance and inductance with the existing model parameters of the motor, necessary adjustments are made to update the model. The online identification method not only enhances the accuracy of the model, but also can perform self-correction based on real-time data, improve the response speed and accuracy of the motor control system, and estimate the initial working state of the permanent magnet synchronous motor. It involves the evaluation of external factors such as the initial load and temperature conditions of the motor to ensure the optimal performance of the motor under various operating conditions, providing a scientific basis for the long-term operation and maintenance of the motor, enabling the maintenance team to adjust and optimize the working state of the motor in a timely manner, ensure the reliability and stability of the motor performance, and generate motor parameter identification results.

[0075] S203: Based on the motor parameter identification result, the Kalman filter algorithm is used to perform state estimation and dynamic adjustment of filter parameters under real-time working conditions, and the execution process of generating the motor initial state estimation value is as follows;

[0076] The formula of the Kalman filter algorithm is as follows:

[0077]

[0078] in, represents the posterior state estimate at time step k, represents the prior state estimate at time step k, K k represents the Kalman gain at time step k, z k represents the actual measurement value at time step k, H k Represents the observation model matrix at time step k.

[0079] Parameter acquisition and setting:

[0080] It is the prior state estimate, which is the predicted state of the motor before time k. The previous estimated value is set to 3000 rpm. This value is obtained from the motor through the sensor and smoothed through data processing.

[0081] z kis the actual measured value at the current time step k, which is set to 3050 rpm and is directly measured by a high-precision speed sensor.

[0082] H k is the observation model matrix and is set to 1, which means that the state estimation is directly reflected as the velocity output.

[0083] K k is the Kalman gain, which adjusts the weight of measurement noise and prediction error. If the standard deviation of the prediction error is 20rpm and the standard deviation of the measurement noise is 5rpm, the gain K k The calculation method is have to

[0084] Formula operation: Prior state estimation The speed is 3000rpm, and the measured value is z k The observed model H k is 1, Kalman gain K k It is 0.941.

[0085] The specific calculation steps of the formula are as follows:

[0086] Calculate the prediction error:

[0087] Adjust the Kalman gain: 0.941×50≈47.05rpm.

[0088] Update the posterior state estimate:

[0089] The results show that after considering the comprehensive information of the actual measurement value and the prior prediction, the speed state of the motor is updated, and the new estimated value is close to the actual measurement value, which shows the effectiveness of the filtering algorithm in dynamically adjusting and accurately reflecting the real-time state of the motor. In this way, it can ensure that the control system accurately responds to the actual operating conditions of the motor and optimizes performance and efficiency.

[0090] See also Figure 4 , based on the estimated value of the motor's initial state, simulate the motor's behavior at startup, and generate the motor's dynamic prediction data by comparing the predicted and real-time current and voltage data and recording the deviation. The specific steps are as follows:

[0091] S301: using the estimated value of the initial state of the motor as an input parameter, setting the starting conditions of the permanent magnet synchronous motor simulator, simulating the behavior of the permanent magnet synchronous motor when starting, including the speed increase process and current change, and generating the execution process of the permanent magnet synchronous motor starting simulation result is as follows;

[0092] Using the estimated value of the motor's initial state as the input parameter and setting the starting conditions of the permanent magnet synchronous motor simulator is the key to simulating the motor's starting behavior and the basis for ensuring that the simulator accurately reflects the motor's actual working state. The input parameters include the estimated motor resistance, inductance, and initial temperature, etc. These parameters directly affect the accuracy and reliability of the simulation. The simulation of the permanent magnet synchronous motor's starting behavior, including the speed increase process and current change, requires precise control of the simulator's various parameters to ensure the authenticity of the results. The speed increase and current change are key indicators for evaluating the motor's starting performance. Through these simulation results, the various performance parameters during the motor starting process can be intuitively displayed, generating permanent magnet synchronous motor starting simulation results.

[0093] S302: Compare the predicted current and voltage data in the permanent magnet synchronous motor startup simulation result with the motor startup data collected in real time, record the deviation between the two, analyze the source of the deviation, adjust the simulation parameters to reflect the motor behavior, and generate the execution process of the deviation analysis result as follows;

[0094] The predicted current and voltage data in the permanent magnet synchronous motor startup simulation results are compared with the motor startup data collected in real time. The comparison process is an important part of evaluating the simulation accuracy. The deviation between the two is recorded. These deviation data are the basis for analyzing the difference between simulation accuracy and actual performance. The source of the deviation is analyzed, focusing on the accuracy of the simulation parameter settings and the changing trend of real-time data. The simulation parameters are adjusted to reflect the motor behavior. By adjusting the parameters, the simulator can more accurately reflect the actual operating status of the motor, help technicians understand the specific reasons for the simulation deviation, and guide the optimization of future simulations to generate deviation analysis results.

[0095] S303: Based on the deviation analysis results, the accuracy of the simulation model is optimized, the prediction error is avoided by repeated testing, the consistency between the simulation results and the real-time operating status is checked, and the execution process of generating the motor dynamic prediction data is as follows;

[0096] Based on the deviation analysis results, the accuracy of the simulation model is optimized according to the formula Calculate the prediction deviation. In the formula, Δ represents the total prediction deviation, P predicted represents the simulated predicted value, P actual Represents the actual measured value. Detailed explanation of the formula and the derivation process of the formula calculation: If in a simulation, the predicted current value is 100A, and the actual measured current value is 105A, and the voltage is predicted to be 220V, and the actual measured value is 230V, then the deviation calculation is Through this calculation, the prediction error of the model can be quantified and the simulation parameters can be further adjusted to reduce the error and enhance the reliability and accuracy of the simulation model.

[0097] See also Figure 5, compare the motor dynamic prediction data with the real-time measurement data, adjust the state parameters of the filter by updating the difference, extract the key motor parameters, including resistance and inductance, and obtain the motor parameter data set in the following steps:

[0098] S401: Compare the motor dynamic prediction data with the on-site real-time measurement data, identify the difference between the two, analyze the cause of the difference, and generate the prediction and actual measurement comparison results. The execution process is as follows;

[0099] Comparing the motor dynamic prediction data with the real-time measurement data on site is a key step in evaluating the accuracy of the prediction model. By identifying the differences between the two, the performance of the prediction model in actual applications can be directly observed. The identification of the differences helps to understand the limitations of the model under specific operating conditions and analyze the reasons for the differences, mainly focusing on the inconsistency between the model settings and the actual operating environment, such as load changes, unstable power supply, etc. Through this analysis, the model can be further adjusted to improve its applicability and accuracy in actual applications, provide a basis for model optimization, ensure that the model can provide more reliable predictions in future applications, and generate prediction and measurement comparison results.

[0100] S402: According to the comparison results between the prediction and the actual measurement, the parameters of the filter are adjusted, the state parameters are updated to match the real-time measurement data, the prediction error is avoided, and the filtering effect is optimized. The execution process of generating the filter parameter adjustment result is as follows;

[0101] Adjust the filter parameters according to the formula P new =P old +α×(P measured -P predicted ), update the state parameters. Where, P new represents the new filter parameter, P old represents the old filter parameters, α represents the adjustment coefficient, P measured represents the measured data, P predicted Represents the predicted data. Formula explanation and formula calculation derivation process: Consider an example where the old filter parameter is 0.8, the adjustment coefficient is set to 0.05, the measured data is 0.75, and the predicted data is 0.70. The new filter parameter is calculated as P new =0.8+0.05×(0.75-0.70)=0.8025. This shows that by fine-tuning the filter parameters, the real-time measurement data can be matched more accurately, the prediction error can be reduced, the filtering effect can be optimized, and the model can be more adapted to the actual operating conditions.

[0102] S403: Using the filter parameter adjustment result, extracting the key parameters of the motor, including resistance and inductance, verifying the reliability and consistency of the parameter extraction, and generating the motor parameter data set. The execution process is as follows;

[0103] The key parameters of the motor, including resistance and inductance, are extracted using the filter parameter adjustment results. This step is an important process to ensure that the motor model accurately reflects the actual motor performance. The extraction of key parameters is based on the updated filter parameters. The accurate extraction of these parameters is crucial for the optimization of the motor control system. The reliability and consistency of the parameter extraction are verified. By comparing with the known motor parameters, the accuracy and reliability of the extracted parameters are confirmed. The verification process ensures the practical application value and technical credibility of the parameters, provides detailed data support for further research and application of the motor, and generates a motor parameter data set.

[0104] See also Figure 6 ,According to the motor parameter data set, the speed sensorless settings are adjusted to match the differentiated operating conditions, optimize the motor's starting performance, avoid torque fluctuations and reversal risks, and generate the optimized control strategy in the following steps:

[0105] S501: Analyze the resistance and inductance parameters in the motor parameter data set, adjust the speed sensorless configuration according to the parameters, verify that the speed sensorless setting matches the target operating condition of the permanent magnet synchronous motor, and generate the sensor configuration adjustment result. The execution process is as follows;

[0106] Adjust the speed sensorless configuration according to the resistance and inductance parameters in the motor parameter data set, according to the formula Verify the speed sensorless settings. In the formula, C represents the configuration parameters of the speed sensorless, R represents the resistance, L represents the inductance, and K represents the adjustment coefficient. Formula explanation and formula calculation derivation process: Set the resistance R to 10Ω, the inductance L to 5H, and the adjustment coefficient K to 0.2, then the speed sensorless configuration parameters are calculated as By adjusting K, the target operating conditions of the permanent magnet synchronous motor can be accurately matched, ensuring that the sensor configuration matches the motor performance and optimizing the motor's operating efficiency and response speed.

[0107] S502: Based on the sensor configuration adjustment result, the performance of the permanent magnet synchronous motor under differentiated starting conditions is evaluated, and the motor control strategy is adjusted according to the differentiated operating conditions to avoid torque fluctuations during starting. The execution process of generating the starting performance optimization result is as follows;

[0108] Based on the sensor configuration adjustment results, the performance of the permanent magnet synchronous motor under differentiated starting conditions is evaluated. The evaluation is a practical application link to test the effect of the sensor configuration. Different starting conditions involve factors such as temperature changes and load fluctuations. The motor control strategy is adjusted according to the conditions. The purpose of the adjustment is to avoid the torque fluctuations that occur during startup. Torque fluctuations cause unstable motor operation. These problems can be effectively avoided through precise control strategy adjustment. This shows that after the control strategy is adjusted, the motor can maintain better performance and higher operating efficiency under different starting conditions, generating starting performance optimization results.

[0109] S503: Using the startup performance optimization result, adjusting parameters to avoid the risk of reverse rotation, checking the stability and safety of the motor startup process, and generating the execution process of the optimization control strategy are as follows;

[0110] Using the starting performance optimization results, the risk of reversal is avoided by adjusting parameters. Reversal is a risk encountered during the motor starting process, which may cause equipment damage or safety accidents. The parameter adjustment is aimed at ensuring the smoothness and safety of the motor during the starting process, including adjusting the motor's torque control, current limit and other parameters. These adjustments help ensure that the motor will not reverse at startup due to load or power supply problems, and check the smoothness and safety of the motor starting process. The inspection process is completed through actual starting tests to ensure that the motor can operate safely and reliably under all operating conditions, provide strong technical support for the long-term operation of the motor, and generate optimized control strategies.

[0111] See also Figure 7 , based on the optimization control strategy, control the speed sensorless, monitor the real-time operating efficiency and stability of the permanent magnet synchronous motor, optimize the operating speed and efficiency through continuous feedback adjustment, and obtain the operating stability feedback results in the following steps:

[0112] S601: Control the speed sensorless motor according to the optimized control strategy, verify that the speed sensorless motor works normally according to the preset strategy, monitor the efficiency and stability of the permanent magnet synchronous motor during startup and operation, and generate the execution process of the sensor control monitoring result as follows;

[0113] Verify that the speed sensor works normally according to the preset strategy, according to the formula Calculate the efficiency of the permanent magnet synchronous motor. In the formula, E represents the motor efficiency, Output represents the motor output power, and Input represents the motor input power. Formula explanation and formula calculation derivation process: Set the input power of the permanent magnet synchronous motor during startup and operation to 5000W and the output power to 4850W, then the efficiency calculation is The calculation results show that the motor operates efficiently and the speed sensor works effectively according to the preset control strategy, maintaining the motor efficiency at a high level.

[0114] S602: extracting data related to the efficiency and operation stability of the permanent magnet synchronous motor from the sensor control monitoring results, adjusting the operation speed and efficiency parameters through continuous feedback, optimizing the overall performance of the permanent magnet synchronous motor, and generating the continuous feedback adjustment results. The execution process is as follows;

[0115] Extract data related to the efficiency and operating stability of the permanent magnet synchronous motor from the sensor control monitoring results. The step is to conduct an in-depth analysis of the effect of speed sensorless operation. The operating speed and efficiency parameters are adjusted through continuous feedback. This adjustment is based on real-time monitoring data. The purpose is to further optimize the overall performance of the motor. The continuous feedback mechanism ensures that the motor can maintain optimal performance under changing operating conditions. By adjusting the speed and efficiency parameters, energy consumption can be reduced and the response speed can be improved. Through real-time adjustment and optimization, the permanent magnet synchronous motor can achieve higher efficiency and better operating stability, generating continuous feedback adjustment results.

[0116] S603: Using the continuous feedback adjustment result, according to the characteristics of the permanent magnet synchronous motor, using a genetic algorithm, iteratively optimizing the operating parameters, and generating the operating stability feedback result. The execution process is as follows;

[0117] The formula of genetic algorithm is as follows:

[0118]

[0119] Among them, P new Represents the new operating parameters, P old is the current operating parameter, α is the weight coefficient of the adjustment step, ΔP represents the adjustment amplitude of the parameter, E target is the target operating efficiency,

[0120] E current is the current operating efficiency, |E target -E current | 1 / 2 It is the square root of the difference between the target and current efficiency.

[0121] Detailed explanation of the formula and the process of formula calculation and derivation:

[0122] This formula is used to dynamically adjust the operating parameters of the permanent magnet synchronous motor according to the difference between the target efficiency and the current efficiency. The following is a detailed explanation of the parameters and the calculation process:

[0123] P old It is the current operating parameter of the motor, such as current or voltage. The actual value needs to be obtained through the motor sensor monitoring. At a certain moment, the actual operating current of the motor is monitored to be 10A.

[0124] E targetIt is the set target efficiency, the value is set based on performance requirements and obtained through energy efficiency evaluation, for example, it is set to 95%.

[0125] E current is the current efficiency of the motor, which is monitored in real time by sensors and obtained through data analysis. The current efficiency is set to 92%.

[0126] ΔP represents the parameter adjustment amount, which is obtained by analyzing past operating data. For example, by analyzing the average efficiency improvement demand of the motor's past 100 starts, the adjustment amount is calculated to be 0.5A.

[0127] α is the weight coefficient for adjusting the step size. The optimal value is determined through experiments to adjust the adjustment speed and accuracy. The setting value is 0.1. The value is obtained through multiple experimental adjustments to ensure that the motor adjustment will not become unstable due to too fast changes.

[0128] In the actual calculation process, the square root of the absolute value of the calculation efficiency difference is:

[0129]

[0130] Calculate the ratio of the adjustment to the square root of the efficiency difference:

[0131]

[0132] Calculate the new operating parameter P new :

[0133] P new =P old +α·adjustment=10+0.1·2.887≈10.289

[0134] The result shows that by adjusting the algorithm, the new operating current of the motor is 10.289A. The change will help the motor approach the set efficiency target, reflecting the improvement of efficiency brought by the precise adjustment of the motor operating parameters. Through such an iterative process, the operating efficiency of the motor can be gradually optimized until it reaches or approaches the target efficiency, achieving the optimization of performance and stability.

[0135] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A speed sensorless control method for a permanent magnet synchronous motor based on online parameter identification, characterized in that: The following steps are involved: Collect the current and voltage data of the permanent magnet synchronous motor before starting, record the initial position of the rotor in the static state, classify the data through the peak and waveform analysis of the current and voltage, and obtain the pre-startup data set; The resistance and inductance are calculated by using the pre-startup data set, initialization state estimation is performed according to online parameter identification, initial filter parameters are set, and an initial state estimation value of the motor is generated; Simulating the behavior of the motor at startup based on the estimated value of the motor initial state, and generating motor dynamic prediction data by comparing the predicted and real-time current and voltage data and recording the deviation; Comparing the motor dynamic prediction data with the real-time measurement data, and extracting key motor parameters, including resistance and inductance, by updating the state parameters of the difference adjustment filter to obtain a motor parameter data set; According to the motor parameter data set, adjusting the speed sensorless settings, matching differentiated operating conditions, optimizing the motor starting performance, avoiding torque fluctuations and reversal risks, and generating an optimized control strategy; Based on the optimization control strategy, the speed sensor is controlled to monitor the real-time operating efficiency and stability of the permanent magnet synchronous motor, the operating speed and efficiency are optimized through continuous feedback adjustment, and the operating stability feedback result is obtained.

2. The permanent magnet synchronous motor speed sensorless control method based on online parameter identification according to claim 1 is characterized in that: The pre-startup data set includes the peak value, trough value, voltage cycle waveform, and initial motor rotor position of the current; the motor initial state estimation value includes the value calculated by resistance and inductance, and preset filter parameters; the motor dynamic prediction data includes the simulated startup current waveform, voltage waveform, and real-time and predicted deviation values; the motor parameter data set includes the adjusted state parameters of resistance, inductance, and filter; the optimization control strategy includes adjusted speed sensorless settings, matched differentiated operating conditions, optimized motor starting performance, and avoided torque fluctuations and reversal risks; the operating stability feedback results include the motor operating speed and efficiency obtained through continuous feedback optimization, and real-time monitored operating efficiency and stability data.

3. The permanent magnet synchronous motor speed sensorless control method based on online parameter identification according to claim 1 is characterized in that: Collect the current and voltage data of the permanent magnet synchronous motor before starting, record the initial position of the rotor in a stationary state, and classify the data through the peak and waveform analysis of the current and voltage. The specific steps to obtain the pre-startup data set are as follows: Install current and voltage sensors on the permanent magnet synchronous motor, configure data collection parameters to measure current and voltage, record the position of the rotor when it is stationary, and collect initial current and voltage values ​​to generate an initial current and voltage data set; According to the initial current and voltage data set, continuously monitor the real-time waveforms of current and voltage during the startup process of the permanent magnet synchronous motor, record the duration and interval of the data waveforms, mark the peak points in the current and voltage waveforms, and obtain current and voltage waveform data; The abnormal waveform points in the current and voltage waveform data are analyzed, the characteristic period and shape of the waveform are identified, the abnormal points and the standard waveform are classified, and a pre-startup data set is generated.

4. The permanent magnet synchronous motor speed sensorless control method based on online parameter identification according to claim 1 is characterized in that: The steps of calculating the resistance and inductance by using the pre-startup data set, performing initialization state estimation according to online parameter identification, setting initial filter parameters, and generating the motor initial state estimation value are as follows: Extracting peak current and voltage data from the pre-startup data set, calculating average current and voltage in each cycle, calculating resistance and inductance of the permanent magnet synchronous motor, and generating resistance and inductance calculation results; Using the resistance and inductance calculation results, according to known permanent magnet synchronous motor model parameters and measured data, online parameter identification is performed to update the resistance and inductance values ​​of the permanent magnet synchronous motor model, estimate the initial working state of the permanent magnet synchronous motor, and generate motor parameter identification results; According to the motor parameter identification result, a Kalman filter algorithm is used to perform state estimation and dynamic adjustment of filter parameters under real-time working conditions to generate an estimated value of the motor initial state.

5. The permanent magnet synchronous motor speed sensorless control method based on online parameter identification according to claim 4 is characterized in that: The formula of the Kalman filter algorithm is as follows: in, represents the posterior state estimate at time step k, represents the prior state estimate at time step k, K k represents the Kalman gain at time step k, z k represents the actual measurement value at time step k, H k Represents the observation model matrix at time step k.

6. The permanent magnet synchronous motor speed sensorless control method based on online parameter identification according to claim 1 is characterized in that: The steps of simulating the motor's behavior at startup based on the estimated value of the motor's initial state and generating the motor's dynamic prediction data by comparing the predicted and real-time current and voltage data and recording the deviation are as follows: Using the estimated value of the initial state of the motor as an input parameter, setting the starting conditions of the permanent magnet synchronous motor simulator, simulating the behavior of the permanent magnet synchronous motor when starting, including the speed increase process and current change, and generating a permanent magnet synchronous motor starting simulation result; Comparing the predicted current and voltage data in the permanent magnet synchronous motor startup simulation result with the motor startup data collected in real time, recording the deviation between the two, analyzing the source of the deviation, adjusting the simulation parameters to reflect the motor behavior, and generating the deviation analysis result; Based on the deviation analysis results, the accuracy of the simulation model is optimized, repeated tests are performed to avoid prediction errors, the consistency of the simulation results with the real-time operating status is checked, and the motor dynamic prediction data is generated.

7. The permanent magnet synchronous motor speed sensorless control method based on online parameter identification according to claim 1 is characterized in that: The motor dynamic prediction data is compared with the real-time measurement data, and the state parameters of the filter are adjusted by updating the difference to extract key motor parameters, including resistance and inductance, to obtain the motor parameter data set. Specifically, the steps are as follows: Compare the motor dynamic prediction data with the on-site real-time measurement data, identify the differences between the two, analyze the causes of the differences, and generate prediction and measurement comparison results; According to the comparison results of the prediction and actual measurement, the parameters of the filter are adjusted, the state parameters are updated to match the real-time measurement data, the prediction error is avoided, and the filtering effect is optimized to generate the filter parameter adjustment result; The filter parameter adjustment result is used to extract key parameters of the motor, including resistance and inductance, verify the reliability and consistency of parameter extraction, and generate a motor parameter data set.

8. The permanent magnet synchronous motor speed sensorless control method based on online parameter identification according to claim 1 is characterized in that: According to the motor parameter data set, the speed sensorless settings are adjusted to match differentiated operating conditions, the starting performance of the motor is optimized, and the torque fluctuation and reversal risks are avoided. The steps of generating the optimized control strategy are as follows: Analyzing resistance and inductance parameters in the motor parameter data set, adjusting the speed sensorless configuration according to the parameters, verifying that the speed sensorless setting matches the target operating condition of the permanent magnet synchronous motor, and generating a sensor configuration adjustment result; Based on the sensor configuration adjustment result, the performance of the permanent magnet synchronous motor under differentiated starting conditions is evaluated, the motor control strategy is adjusted according to the differentiated operating conditions, the torque fluctuation during starting is avoided, and the starting performance optimization result is generated; By using the starting performance optimization results, the risk of reversal is avoided by adjusting parameters, the smoothness and safety of the motor starting process are checked, and an optimization control strategy is generated.

9. The permanent magnet synchronous motor speed sensorless control method based on online parameter identification according to claim 1 is characterized in that: Based on the optimization control strategy, the speed sensor is controlled to monitor the real-time operating efficiency and stability of the permanent magnet synchronous motor, and the operating speed and efficiency are optimized through continuous feedback adjustment to obtain the operating stability feedback results. Specifically, the steps are as follows: Control the speedless sensor according to the optimization control strategy, verify that the speedless sensor works normally according to the preset strategy, monitor the efficiency and stability of the permanent magnet synchronous motor during startup and operation, and generate sensor control monitoring results; Extracting data related to the efficiency and operating stability of the permanent magnet synchronous motor from the sensor control monitoring results, adjusting the operating speed and efficiency parameters through continuous feedback, optimizing the overall performance of the permanent magnet synchronous motor, and generating continuous feedback adjustment results; The continuous feedback adjustment result is utilized, and according to the characteristics of the permanent magnet synchronous motor, a genetic algorithm is adopted to iteratively optimize the operating parameters to generate an operating stability feedback result.

10. The permanent magnet synchronous motor speed sensorless control method based on online parameter identification according to claim 1, characterized in that: The formula of the genetic algorithm is as follows: Among them, P new Represents the new operating parameters, P old is the current operating parameter, α is the weight coefficient of the adjustment step, ΔP represents the adjustment amplitude of the parameter, E target is the target operating efficiency, E current is the current operating efficiency, |E target -E current | 1 / 2 It is the square root of the difference between the target and current efficiency.

Images