SOGI parameter self-tuning SiC motor speed-sensorless control method

Abstract

The invention discloses an SOGI parameter self-tuning SiC motor speed-sensorless control method, and aims to solve the problems of fixed injection frequency and SOGI parameter mismatch in the prior art. The method comprises five links of working condition identification and injection frequency dynamic switching, SOGI parameter adaptive tuning, dead zone time cooperative compensation, rotor position estimation and error feedback and system integration optimization. Working conditions are divided through two-dimensional fuzzy clustering, differential injection frequencies are adapted, SOGI parameter self-tuning is achieved by combining fuzzy control and PI adjustment, a dead-time voltage distortion model is established, cooperative compensation is carried out, and rotor position estimation precision is improved by adopting a weighted fusion algorithm. According to the method, the sixth harmonic suppression rate can be increased to 95% or above, the rotating speed estimation error is reduced to be within 10 r / min, the full-working-condition adaptability and stability of the system are enhanced, engineering practicability is achieved, and the method is suitable for the fields of industrial transmission, new energy power generation and the like.

Description

Technical Field

[0001] This invention relates to a speed sensorless control method for SiC motors with SOGI parameter self-tuning. Background Technology

[0002] In fields such as industrial drives and new energy power generation, sensorless vector control systems for induction motors are widely used due to their advantages of eliminating mechanical speed sensors, reducing system costs, and lowering failure rates. Accurate estimation of rotor position and speed is the core of achieving high-performance control in this system, and the high-frequency pulse injection method is the mainstream technology for sensorless control in the zero and low-speed domains.

[0003] In recent years, silicon carbide (SiC) wide-bandgap semiconductor devices have been rapidly applied in the field of motor drives due to their high switching frequency and other characteristics. However, existing high-frequency pulsation injection control schemes based on SiC inverters still have key technical problems: First, the injection frequency uses a fixed value, which does not consider the influence of changes in motor speed, load, and other operating conditions. High-frequency injection increases energy loss at low speeds and light loads, and easily introduces additional harmonic interference at medium speeds. Second, SOGI uses a fixed parameter design, and the natural frequency is prone to mismatch under dynamic operating conditions, resulting in a decrease in the suppression effect of the 6th harmonic and an increase in rotor position estimation error. Existing technologies mostly focus on single-dimensional optimization and fail to solve the above-mentioned comprehensive problems at the system level, resulting in significant room for improvement in rotor position estimation accuracy and system stability under all operating conditions of induction motors. Summary of the Invention

[0004] This invention discloses a speed sensorless control method for SiC motors with self-tuning SOGI parameters, which solves the problems of fixed injection frequency and SOGI parameter mismatch in the prior art.

[0005] To solve the above problems, the technical solution of the present invention is as follows: A sensorless speed sensor control method for SiC motors with SOGI parameter self-tuning includes step 1: operating condition identification and dynamic switching of injection frequency, building a motor operating condition monitoring module, and real-time acquisition of the motor speed. The stator current signal is converted into α-β axis currents using Clark transform, and then the dq axis current components are obtained using Park transform. The effective value of the stator current is then calculated. Introducing load factor metric A two-dimensional fuzzy clustering algorithm is used to divide the operating conditions into three categories: low-speed heavy load, low-speed light load, and medium speed. Differentiated high-frequency injection frequencies are configured for different categories: 1500Hz~1700Hz for low-speed heavy load, 900Hz~1100Hz for low-speed light load and no load, and 400Hz~600Hz for medium speed. A hysteresis threshold is set for each operating condition, and the injection frequency is adaptively switched based on the operating condition range and the hysteresis threshold.

[0006] Furthermore, in step 1, the stator current distortion rate is introduced as the basis for frequency correction. When the current distortion rate exceeds a preset threshold, the base injection frequency is lowered according to a preset correction coefficient, with each adjustment step being 40Hz~60Hz, until the current distortion rate drops below the preset threshold. When the motor is in dynamic speed regulation, the injection frequency is temporarily increased by 80Hz~120Hz. When the trigger conditions for increasing and decreasing the injection frequency are met simultaneously, the instruction to increase the injection frequency is executed first to ensure the observability of the rotor position during the dynamic process. After the motor speed change rate |dn / dt| recovers to within the preset steady-state threshold, the frequency adjustment strategy based on the current distortion rate is executed.

[0007] Furthermore, step 2 includes: adaptive tuning of SOGI parameters, constructing a harmonic detection module based on fast Fourier transform, and real-time acquisition of q-axis high-frequency current amplitude. And extract the amplitude of multiple harmonics. With frequency Calculate frequency deviation Using the 6th harmonic amplitude as input, the SOGI natural frequency correction is output through a fuzzy controller. and gain correction amount Dynamically update SOGI's inherent frequency and gain .

[0008] Furthermore, in step 2, a two-level trigger threshold system is established. When the amplitude of multiple harmonics exceeds 1.1 to 1.3 times the corresponding operating condition threshold, the trigger gain is increased. The tuning; when When the frequency exceeds 0.4Hz to 0.6Hz, the inherent frequency is triggered. The de-tuning threshold for amplitude tuning is 0.7 to 0.9 times the corresponding operating condition threshold, and the de-tuning threshold for frequency deviation tuning is ±0.2 Hz to ±0.4 Hz.

[0009] Furthermore, in step 2, the gain is... The tuning employs fuzzy control, using multiple harmonic amplitudes. and its rate of change For input variables, Divided into multiple fuzzy subsets; for inherent frequencies The tuning uses a PI control algorithm, and the transfer function is... The proportionality coefficient The value ranges from 0.08 to 0.12, and the integral coefficient is... The value ranges from 0.01 to 0.03.

[0010] Furthermore, in step 2, the harmonic suppression rate is calculated after tuning. ,like When the value is below 65%~75%, the parameter self-optimization program is activated, based on the current value. and Centered on the parameter, parameter scanning is performed within the ranges of ±0.15 to ±0.25 and ±2π×0.4 rad / s to ±2π×0.6 rad / s with step sizes of 0.04 to 0.06 and 2π×0.08 rad / s to 2π×0.12 rad / s, respectively.

[0011] Furthermore, step 3 is included: dead time collaborative compensation, establishing a mathematical model of dead time voltage distortion in SiC inverters. ,in The dq-axis voltage distortion components are obtained through Clark-Park transform. and Calculate the d / q axis compensation voltage , The compensation voltage is fed forward to the SVPWM modulation stage, and a graded dead time is set in combination with the high switching speed characteristics of SiC devices.

[0012] Furthermore, in step 3, the graded dead time is set as follows: dynamic speed regulation stage ( (Exceeding the preset value) settings Steady-state operation phase settings Idle / unload phase settings And temporarily near the current zero crossing point Increase by 0.1 ~0.3 .

[0013] Furthermore, in step 3, the compensation voltage is superimposed on the SVPWM reference voltage command. and , , ,when From 0.8 ~1.2 Decreased to 0.4 ~0.6 hour, It automatically adjusts from 0.9~1.1 to 0.2~0.4.

[0014] Furthermore, steps 4 and 5 are also included; Step 4: Rotor position estimation and error feedback. A weighted fusion algorithm is used to obtain the rotor position estimate. ,in The weighting coefficients are dynamically adjusted based on the rotational speed; the estimation error is calculated. The estimation error is decomposed into static error. Dynamic error Harmonic error Three components are used to generate differentiated adjustment commands based on each error component, and the error suppression rate is calculated. ; Step 5: System integration, optimization, and dynamic adjustment; establishing an integrated control architecture for operating condition identification, frequency switching, parameter tuning, and dead zone compensation; and real-time monitoring of rotor position estimation error. Motor speed With load current The parameters are used to adjust the working status of each module in conjunction with the monitoring data.

[0015] The beneficial effects of this invention are as follows: 1. Improved adaptability to all operating conditions: The dynamic injection frequency switching strategy takes into account the salient pole enhancement effect and energy loss under different speeds and loads, and solves the performance shortcomings of fixed frequency under light load / medium speed conditions, so that the induction motor can maintain good saturated salient pole characteristics under all operating conditions.

[0016] 2. Improve rotor position estimation accuracy: SOGI parameter adaptive tuning eliminates parameter mismatch problems under dynamic operating conditions. Combined with dead time collaborative compensation, the 6th harmonic suppression rate is increased to over 95%, and the speed estimation error is reduced to within 10 r / min, which greatly improves the accuracy of rotor position estimation.

[0017] 3. Enhanced system stability: The integrated control architecture enables the coordinated adjustment of each module, which can quickly respond to changes in motor operating conditions, effectively reduce current distortion and speed fluctuations, and enhance the steady-state and dynamic performance of the SiC inverter-driven sensorless control system for induction motors.

[0018] 4. It also has engineering practicality: While retaining the core advantages of high-frequency pulse injection in SiC inverters, the performance is improved through algorithm optimization rather than hardware modification. The control strategy is simple and easy to implement, reducing the system modification cost and the difficulty of engineering application. Attached Figure Description

[0019] The invention will be further described below with reference to the accompanying drawings: Figure 1 This is a flowchart illustrating a specific implementation method of a speed sensorless control method for SiC motors based on SOGI parameter self-tuning, as described in this patent. Figure 2 This patent describes the fundamental frequency standard wave and error waveform under different K values ​​with no ω deviation. Figure 3 This patent describes the fundamental frequency standard wave and error waveforms under different ω values ​​when K=0.1. Figure 4This patent describes the fundamental frequency standard wave and error waveform under different ω values ​​when K=0.5.

[0020] Figure 5 This patent shows the fundamental frequency standard wave and error waveform under different ω values ​​when K=1.0. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] like Figure 1 As shown, the SOGI parameter self-tuning SiC motor sensorless control method includes step 1: operating condition identification and dynamic switching of injection frequency. A multi-sensor fusion-based operating condition data acquisition unit is built to collect four types of parameters of the induction motor in real time: stator three-phase current, rotor estimated speed, stator voltage, and load torque. The sampling frequency is consistent with the PWM switching frequency of the SiC inverter. The collected raw data is preprocessed, and the stator three-phase current is converted to... The dq-axis current is obtained by Park transformation, and the effective value of the stator current is calculated. (in This is the effective value of the stator current. The d-axis stator current component. (This refers to the q-axis stator current component), which is obtained by combining the motor parameters (stator resistance) with the torque observer. Rotor resistance Mutual induction Calculate real-time load torque A first-order low-pass filter is applied to the speed signal to eliminate high-frequency noise, resulting in a smooth rotor speed. (Unit: r / min), introduce load factor metric ( The rated torque of the motor. (for load rate), speed and load rate As the core feature quantity for operating condition identification.

[0023] Two-dimensional fuzzy clustering algorithm is used to analyze rotational speed. and load rate The rotational speed is divided into two ranges: a low-speed range (0-50 r / min) and a medium-speed range (50-500 r / min). The low-speed range is further divided according to the load rate. Classified as low-speed heavy-load ( ), low speed and light load ( ) and low-speed no-load ( The medium speed range is uniformly classified into a single working condition range, and a working condition hysteresis threshold is set (the load rate dividing point between low speed heavy load and low speed light load is 70% in the positive direction and 65% in the negative direction; the speed dividing point between medium speed and low speed is 50r / min in the positive direction and 45r / min in the negative direction).

[0024] Different high-frequency injection frequencies are configured for different speed ranges. 1600Hz is used for low-speed, heavy-load conditions to maximize saturated saliency; 1000Hz is used for low-speed, light-load conditions to balance saliency enhancement and energy loss; and 500Hz is used for medium-speed conditions to reduce high-frequency harmonic interference. Stator current distortion rate (THDi) is introduced as a frequency correction criterion. When THDi > 5%, a correction factor is applied. The base injection frequency is lowered in 50Hz increments until THDi ≤ 5%, while the motor is in dynamic speed regulation (speed change rate). The injection frequency will be temporarily increased by 100Hz.

[0025] After switching the injection frequency, the q-axis high-frequency current amplitude was collected. Calculate the dominant salient pole component (2) in its spectrum. , If the amplitude of the primary salient pole is lower than the threshold (20mA for low-speed heavy load and 17mA for low-speed light load), the frequency self-optimization program is started. Centered on the current frequency, the frequency is scanned in 50Hz steps within a range of ±200Hz. The frequency with the largest primary salient pole amplitude is selected as the new injection frequency. At the same time, the frequency switching result is fed back to the subsequent SOGI parameter tuning stage. Through dynamic matching between the operating conditions and the injection frequency, the technical problems of increased energy consumption at low speed and light load and aggravated harmonic interference at medium speed caused by fixed injection frequency in the existing technology are solved. By utilizing the high switching frequency advantage of SiC devices, the injection frequency is adaptively switched through accurate identification of operating conditions, which enhances the stability of saturated salient pole under different operating conditions. In order to maintain good saturated salient pole characteristics of the induction motor under all operating conditions, the high-frequency injection energy loss and harmonic interference are reduced.

[0026] Furthermore, step 2 is included: SOGI parameter adaptive tuning. A high-frequency current synchronous acquisition unit based on the SiC inverter control board is built. A combination of high-speed analog-to-digital converter (ADC) chip and FPGA logic control is used to realize multi-channel synchronous acquisition of shaft high-frequency current, stator three-phase current and inverter bus voltage. The sampling frequency is set to 128kHz. At the hardware level, the induction motor stator three-phase current is acquired through a current sensor (LA55-P). After signal conditioning circuit (including filtering, amplification and isolation), it is converted into a voltage signal of 0-3.3V and input to the ADC chip. At the software level, an FPGA is used to generate a synchronous trigger signal, which controls the ADC chip to synchronously sample the signals of each channel. The collected stator three-phase current is then transformed using Clark-Park coordinates to calculate the estimated q-axis high-frequency current in the synchronous rotating coordinate system in real time. ; For q-axis high-frequency current signals Multi-stage preprocessing is performed, using a finite impulse response (FIR) bandpass filter (passband frequency is injection frequency ±100Hz) for filtering, calculating the average value of the signal within one sampling period and performing subtraction to eliminate DC bias, and using a moving average filter with 16 sampling points as the sliding window. A hybrid algorithm combining Fast Fourier Transform (FFT) and Hilbert-Huang Transform (HHT) was employed to extract the 6th harmonic features. The FFT used 2048 points, and a Hanning window was used to reduce spectral leakage. HHT analysis was performed on the 6th harmonic frequency band signals identified by FFT. Empirical Mode Decomposition (EMD) was used to decompose the signal into multiple Intrinsic Mode Functions (IMFs), and the IMF components corresponding to the 6th harmonic were extracted. The instantaneous amplitude and phase of these components were calculated using Hilbert Transform, and the precise characteristic parameters (amplitude) of the 6th harmonic were obtained by fusing them. ,frequency and phase ); A dynamic threshold determination module is constructed. Based on the operating conditions of the induction motor, a reference threshold for the amplitude of the 6th harmonic (2mA for low speed heavy load, 1.5mA for low speed light load, and 1mA for medium speed) and a frequency deviation threshold of ±0.5Hz are preset to verify the validity of the detection results. Valid detection results are used for subsequent parameter tuning, while invalid detection results are replaced by the valid harmonic characteristic parameters of the previous sampling period and trigger resampling. Using the characteristic parameters of the 6th harmonic (amplitude) Frequency deviation As the core basis for tuning triggering, a two-level triggering threshold system is established. When the amplitude of the 6th harmonic is... When the value exceeds 1.2 times the corresponding operating condition threshold, the gain is triggered. The tuning, when When the natural frequency is triggered The tuning is configured with a tuning hysteresis range (the release threshold for amplitude tuning is 0.8 times the corresponding operating condition threshold, and the release threshold for frequency deviation tuning is ±0.3Hz). Construct a fusion decision-making model combining fuzzy control and PI regulation, such as... Figures 2-5 As shown, for gain The tuning employs fuzzy control, with the 6th harmonic amplitude as the reference value. and its rate of change For input variables, Correction amount Divide the data into seven fuzzy subsets: negative large, negative medium, negative small, zero, positive small, positive medium, and positive large. Establish fuzzy rules for each subset. large and At that time, the output is positive. ,when Small and When the output is negative, ; For natural frequency The tuning uses a PI control algorithm, and the transfer function is... The proportionality coefficient Integral coefficient ; Establish a real-time update link for SOGI parameters to receive the output of the fuzzy controller. Perform defuzzification processing, combined with the initial gain (Low speed heavy load) Low speed and light load medium speed ), calculate real-time gain ,limit The value range is 0.05 to 1.5; The output of the PI controller Superimposed to the initial natural frequency To obtain the real-time natural frequency A first-order low-pass filter (filter time constant 0.01s) is used for processing; The parameter update period is consistent with the high-frequency current sampling period (1 / 128kHz). A tuning effect verification module was constructed, which acquired the high-frequency q-axis current signal after tuning and then extracted the amplitude of the 6th harmonic using FFT. Calculate the harmonic suppression rate ,like Maintain the current parameters, if Fine-tune the PI coefficient and fuzzy rules, if Start the parameter self-optimization program, based on the current... and Centered on the target, parameters are scanned in steps of 0.05 and 2π×0.1 rad / s within the ranges of ±0.2 and ±2π×0.5 rad / s, respectively. The parameter combination with the highest harmonic suppression rate is selected and updated accordingly. The characteristics of the 6th harmonic are obtained through harmonic detection. Based on fuzzy control and PI adjustment, dynamic matching of SOGI parameters is achieved, which solves the technical problem of reduced 6th harmonic suppression effect caused by SOGI parameter mismatch under dynamic operating conditions. This significantly improves the accuracy of rotor position estimation, thereby increasing the 6th harmonic suppression rate to over 95%.

[0027] Step 3 is also included: Dead time collaborative compensation. Based on the switching characteristics of the SiC inverter, a refined model of dead time voltage distortion considering the turn-on / turn-off delay of the switching transistor and the voltage drop of the transistor is established. ;( , Dead time, For PWM switching period, For bus voltage, For stator phase current, For the turn-on / turn-off delay of SiCMOSFET, (This refers to the dead-zone compensation gain coefficient), and the SiC device was calibrated experimentally. (20ns) After substituting the (30ns) parameter into the model, the three-phase voltage distortion is converted into voltage distortion components in the synchronously rotating coordinate system of the dq axis using Clark-Park coordinate transformation. and The 6kth harmonic in the distortion component was extracted by Fourier decomposition. Based on the dq-axis voltage distortion model, a real-time compensation voltage feedforward algorithm is designed, which is based on the current motor speed. With stator current , Calculate the dq axis compensation voltage , ( As a compensation coefficient, under heavy load When lightly loaded The compensation voltage is superimposed on the SVPWM reference voltage command. , The compensation voltage is modulated in real time using an FPGA, and an overmodulation suppression strategy is added. Combining the high switching speed characteristics of SiC devices, a graded dead-time configuration strategy is designed, with dynamic speed regulation stage ( )set up Steady-state operation phase settings Idle phase settings Establish the linkage between dead time and compensation voltage (when When decreasing from 2 μs to 0.5 μs, Automatically adjusted from 1 to 0.3), the stator current commutation moment is detected in real time by a Hall current sensor, and the current is temporarily adjusted near the zero-crossing point. Increase by 0.2 ; A collaborative correction mechanism between SOGI and the compensation stage is constructed to reduce the compensated q-axis high-frequency current. The input is fed into the FFT harmonic detection module to extract the amplitude of the residual 6th harmonic. With frequency ,like Start dynamic adjustment of SOGI parameters (track the natural frequency of SOGI to...) Gain The residual harmonic amplitude increases adaptively. Simultaneously, the harmonic suppression results of SOGI are fed back to the compensation voltage calculation module to correct the compensation coefficients in the distortion model. Through a triple mechanism of "passively shortening dead time + actively compensating for voltage distortion + cross-module collaborative correction", the 6kth harmonic caused by dead time is suppressed, further reducing harmonic interference, reducing voltage distortion and current fluctuations, and improving the steady-state operation performance of the system.

[0028] The process also includes step 4: rotor position estimation and error feedback. A multi-source position estimation fusion unit is built based on the high-frequency current signal after SOGI harmonic suppression and the motor fundamental wave model. The rotor position error signal is extracted from the SOGI-processed q-axis high-frequency current response. Rotor position estimation based on the high-frequency pulse injection method is achieved through a phase-locked loop (PLL). The estimation results are smoothed using a first-order low-pass filter (the filter time constant is dynamically adjusted according to the motor speed). Based on the fundamental voltage equation of the induction motor, the fundamental flux linkage of the motor is estimated using a flux linkage observer. , The rotor position estimate of the fundamental wave model method is obtained by combining stator current calculation. ; A weighted fusion algorithm is used to fuse the two estimation results, with fusion weight coefficients. Adjust in real time according to motor speed ( hour When 50 r / min ≤ n < 500 r / min It decreased linearly from 0.9 to 0.1. hour Finally, the merged rotor position estimate is obtained. ; An error hierarchical analysis module is constructed to analyze the fused rotor position estimation error. ( The actual rotor position of the motor (acquired by an encoder) is decomposed into static error. Dynamic error Harmonic error The three components are used to extract the static error through time-domain averaging. By calculating the rate of change of the error signal Combined with the motor speed change rate Separate dynamic error The 6kth harmonic error component was extracted by frequency domain analysis of the error signal using FFT. An error threshold screening mechanism is introduced (static error threshold is 0.01 rad, dynamic error threshold is 0.005 rad / s, and 6th harmonic error threshold is 0.008 rad). Based on the results of error hierarchical analysis, a hierarchical feedback instruction generation strategy is designed to address static errors. Generate motor parameter correction instructions (correction amount) , The value range is 0.001~0.01), for dynamic error. Generate a temporary injection frequency adjustment command (when) At that time, the current injection frequency is increased by 100~200Hz to reduce the 6th harmonic error. Generate SOGI parameter tuning trigger command and dead time compensation voltage correction command, and set the command execution hysteresis interval; A cross-module linkage correction mechanism for feedback results is constructed, which synchronously feeds back the error analysis results to the operating condition identification, injection frequency switching, and dead time compensation modules. A feedback adjustment effect verification unit is built to calculate the error suppression rate. ,like Start the global parameter self-optimization program; An integrated control architecture for operating condition identification, frequency switching, parameter tuning, and dead-zone compensation is established to monitor rotor position estimation errors in real time. Motor speed With load current Key parameters such as speed estimation and position estimation are monitored and adjusted in conjunction with monitoring data. The estimation accuracy is improved by multi-source position estimation fusion. Targeted feedback adjustment is achieved based on error hierarchical analysis. The integrated architecture enables collaborative optimization of each module. This reduces the speed estimation error to within 10 r / min and enhances the system's dynamic response capability and adaptability to all operating conditions.

[0029] In step 1, the sampling frequency of the operating condition data acquisition unit is kept consistent with the PWM switching frequency of the SiC inverter, which is 16kHz, to ensure that the operating condition data is synchronized with the inverter control signal and to improve the identification of operating conditions.

[0030] In step 2, the voltage signal output by the signal conditioning circuit is in the range of 0-3.3V, and the frequency resolution of the FFT can reach 0.0625Hz, which is compatible with the sampling range of the ADC chip and improves the accuracy of harmonic frequency identification, in order to ensure the accuracy of the extraction of the 6th harmonic feature parameters.

[0031] In step 2, the fuzzy controller... The fuzzy subset division includes seven categories: negative large, negative medium, negative small, zero, positive small, positive medium, and positive large. The defuzzification process adopts the centroid method to achieve nonlinear and accurate decision-making of the gain correction amount, thereby improving the adaptability and stability of SOGI gain tuning.

[0032] In step 3, the overmodulation suppression strategy of SVPWM modulation adopts a combination of amplitude clamping and phase correction to avoid the compensation voltage from exceeding the modulation range and causing new harmonics, so as to ensure the rationality and sinusoidal nature of the inverter output voltage.

[0033] In step 3, the amplitude of the 6th harmonic accounts for more than 80% of the total harmonic distortion in the phase voltage distortion component caused by the dead time, making it the main source of interference. Targeted suppression of the dominant harmonic interference is necessary to improve harmonic suppression efficiency and reduce its impact on rotor position estimation.

[0034] In step 4, the global parameter self-optimization program uses the error suppression rate as the objective function to optimize the core parameters of each module within a preset parameter range. The number of optimization iterations does not exceed 50. The working principle is to achieve optimal matching of system parameters through global optimization. The technical effect is to complete parameter optimization within 100ms, further improving the overall control performance of the system.

[0035] The embodiments described in this specification are merely examples of implementations of the inventive concept. The scope of protection of this invention should not be considered as limited to the specific forms stated in the embodiments. The scope of protection of this invention also extends to equivalent technical means that can be conceived by those skilled in the art based on the inventive concept.

Claims

1. A sensorless speed sensor control method for SiC motors with SOGI parameter self-tuning, characterized in that, This includes step 1: operating condition identification and dynamic switching of injection frequency, including building a motor operating condition monitoring module to collect the speed of the induction motor in real time. The stator current signal is converted into a Clark transform by means of the stator three-phase current signal. The dq-axis current is obtained by Park transformation, and the effective value of the stator current is calculated. Introducing load factor metrics A two-dimensional fuzzy clustering algorithm is used to divide the operating conditions into three categories: low-speed heavy load, low-speed light load, and medium speed. Differentiated high-frequency injection frequencies are configured for different categories: 1500Hz~1700Hz for low-speed heavy load, 900Hz~1100Hz for low-speed light load and no load, and 400Hz~600Hz for medium speed. A hysteresis threshold is set for each operating condition, and the injection frequency is adaptively switched based on the operating condition range and the hysteresis threshold.

2. The SOGI parameter self-tuning SiC motor sensorless control method according to claim 1, characterized in that, In step 1, the stator current distortion rate is used as the basis for frequency correction. When the current distortion rate exceeds a preset threshold, the base injection frequency is lowered according to a preset correction coefficient, with each adjustment step being 40Hz~60Hz, until the current distortion rate drops below the preset threshold. When the motor is in dynamic speed regulation, the injection frequency is temporarily increased by 80Hz~120Hz. When the triggering conditions for increasing and decreasing the injection frequency are met simultaneously, the command to increase the injection frequency is executed first to ensure the observability of the rotor position during the dynamic process. Once the motor speed change rate |dn / dt| recovers to within the preset steady-state threshold, the frequency adjustment strategy based on the current distortion rate is executed.

3. The SOGI parameter self-tuning SiC motor sensorless control method according to claim 1, characterized in that, The process also includes step 2: adaptive adjustment of SOGI parameters to construct a harmonic detection module based on Fast Fourier Transform, and real-time acquisition of the q-axis high-frequency current amplitude. And extract the amplitude of multiple harmonics. With frequency Calculate frequency deviation Using the 6th harmonic amplitude as input, the SOGI natural frequency correction is output through a fuzzy controller. and gain correction amount Dynamically update SOGI's inherent frequency and gain .

4. The SOGI parameter self-tuning SiC motor sensorless control method according to claim 3, characterized in that, In step 2, a two-level trigger threshold system is established. When the amplitude of multiple harmonics exceeds 1.1 to 1.3 times the corresponding operating condition threshold, the trigger gain is increased. The tuning; when When the frequency exceeds 0.4Hz to 0.6Hz, the inherent frequency is triggered. The de-tuning threshold for amplitude tuning is 0.7 to 0.9 times the corresponding operating condition threshold, and the de-tuning threshold for frequency deviation tuning is ±0.2 Hz to ±0.4 Hz.

5. The SOGI parameter self-tuning SiC motor sensorless control method according to claim 3, characterized in that, In step 2, the gain The tuning employs fuzzy control, using multiple harmonic amplitudes. and its rate of change For input variables, Divided into multiple fuzzy subsets; for inherent frequencies The tuning uses a PI control algorithm, and the transfer function is... The proportionality coefficient The value ranges from 0.08 to 0.12, and the integral coefficient is... The value ranges from 0.01 to 0.

03.

6. The SOGI parameter self-tuning SiC motor sensorless control method according to claim 3, characterized in that, In step 2, the harmonic suppression rate is calculated after tuning. ,in To tune the amplitude of the first 6 harmonics, The amplitude of the 6th harmonic after tuning, if When the value is below 65%~75%, the parameter self-optimization program is activated, based on the current value. and Centered on the parameter, parameter scanning is performed within the ranges of ±0.15 to ±0.25 and ±2π×0.4 rad / s to ±2π×0.6 rad / s with step sizes of 0.04 to 0.06 and 2π×0.08 rad / s to 2π×0.12 rad / s, respectively.

7. The SOGI parameter self-tuning SiC motor sensorless control method according to claim 1, characterized in that, It also includes step 3: dead time collaborative compensation, establishing a mathematical model of dead time voltage distortion in SiC inverters. ,in The dq-axis voltage distortion components are obtained through Clark-Park transform. and Calculate the d / q axis compensation voltage , The compensation voltage is fed forward to the SVPWM modulation stage, and a graded dead time is set in combination with the high switching speed characteristics of SiC devices.

8. The SOGI parameter self-tuning SiC motor sensorless control method according to claim 7, characterized in that, In step 3, the graded dead time is set as follows: dynamic speed regulation stage ( (Exceeding the preset value) settings Steady-state operation phase settings Idle / unload phase settings And temporarily near the current zero crossing point Increase by 0.1 ~0.3 .

9. The SOGI parameter self-tuning SiC motor sensorless control method according to claim 7, characterized in that, In step 3, the compensation voltage is superimposed on the SVPWM reference voltage command. and , , ,when When the dead-zone compensation gain coefficient decreases from 0.8μs~1.2μs to 0.4μs~0.6μs It automatically adjusts from 0.9~1.1 to 0.2~0.

4.

10. The SOGI parameter self-tuning SiC motor sensorless control method according to claim 9, characterized in that, It also includes steps 4 and 5; Step 4: Rotor position estimation and error feedback. A weighted fusion algorithm is used to obtain the rotor position estimate. ,in The weighting coefficients are dynamically adjusted based on the rotational speed; the estimation error is calculated. The estimation error is decomposed into static error. Dynamic error Harmonic error Three components are used to generate differentiated adjustment commands based on each error component, and the error suppression rate is calculated. ; Step 5: System integration, optimization, and dynamic adjustment; establishing an integrated control architecture for operating condition identification, frequency switching, parameter tuning, and dead zone compensation; and real-time monitoring of rotor position estimation error. Motor speed With load current The parameters are used to adjust the working status of each module in conjunction with the monitoring data.

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