Permanent magnet variable pitch drive for a wind turbine generator and method thereof

Abstract

The embodiment of the application provides a kind of permanent magnet variable pitch driver of wind turbine and its method, it is by using model reference adaptive system to the key parameter, such as stator resistance and rotor flux linkage, of variable pitch drive motor, carries out online identification, to obtain its accurate estimated value in real time, and the identified real-time parameter is seamlessly fed back to FOC controller, for dynamically adjusting the core control gain of current loop and speed loop, form the closed-loop intelligent adjustment mechanism of " identification-tuning", so that control model can always accurately match motor physical actual. In this way, it can ensure that the variable pitch system always maintains high-precision, high-dynamic-response control performance under complex and changeable working conditions, thereby improving the operation stability and power generation efficiency of wind turbine.

Description

Technical Field

[0001] This invention relates to the field of intelligent drive technology, specifically to a permanent magnet pitch driver for wind turbines and its method. Background Technology

[0002] Wind power, as an important clean and renewable energy source, occupies a core position in the global energy structure. To maximize wind energy utilization efficiency and ensure the safe and stable operation of wind turbines under varying conditions, the pitch system of wind turbine generators plays a crucial role. This system optimizes aerodynamic performance by precisely adjusting the blade pitch angle, achieving constant power output above rated wind speed and safe braking in emergencies. In pitch drive solutions, permanent magnet synchronous motors (PMSMs) are widely used due to their high power density, high efficiency, and excellent speed regulation performance. Constructing a high-performance, high-reliability PMSM pitch drive solution is key to improving the overall performance of wind turbine generators.

[0003] To achieve high-performance control of permanent magnet synchronous motors, existing technologies typically employ field-oriented control (FOC) strategies. This strategy relies on a precise mathematical model of the motor, decoupling the excitation and torque components to achieve excellent control performance similar to that of a DC motor. However, wind turbines operate outdoors for extended periods, enduring harsh environments such as drastic temperature changes, high humidity, salt spray corrosion, and continuous vibration. This results in key motor parameters not remaining constant. Specifically, the stator resistance of the motor windings changes significantly with operating temperature; the flux linkage of the permanent magnets is not only affected by temperature but also undergoes irreversible aging and decay over long-term operation; simultaneously, under high load conditions, the magnetic saturation effect of the motor core also causes changes in the dq-axis inductance. Traditional FOC controllers mostly use fixed parameters tuned offline. When the actual motor parameters do not match the parameters set in the controller, control performance will severely degrade. This parameter mismatch disrupts the decoupling effect and dynamic characteristics of the current loop, causing unstable torque output and hysteresis, which in turn reduces the speed and accuracy of pitch angle tracking, ultimately affecting the power generation efficiency and operational reliability of the wind turbine.

[0004] Therefore, an optimized permanent magnet pitch drive scheme for wind turbines is needed. Summary of the Invention

[0005] The present invention aims to at least solve one of the technical problems existing in the prior art, and provides a permanent magnet pitch driver for wind turbines and its method.

[0006] In a first aspect, embodiments of the present invention provide a method for a permanent magnet pitch driver for a wind turbine, comprising: Obtain the three-phase stator current, DC bus voltage, and mechanical rotor angle; Based on the mechanical rotor angle, the electrical angular velocity and electrical angle are calculated, and the coordinate transformation of the three-phase stator current is performed based on the electrical angle to obtain the q-axis current and d-axis current. For the q-axis current and d-axis current, perform online identification of motor parameters based on MRAS to obtain the estimated stator resistance and the estimated rotor flux. Adaptive FOC controller parameter tuning is performed based on the estimated stator resistance and estimated rotor flux to obtain new current loop gain and new speed loop gain. Based on the new current loop gain and the new speed loop gain, FOC torque and voltage commands are generated to obtain dq axis voltage commands. The gate drive signal is obtained by performing inverse Park transformation and space vector pulse width modulation on the dq axis voltage command.

[0007] In some possible embodiments, the electrical angular velocity and electrical angle are calculated based on the mechanical rotor angle, including: Decode and calibrate the mechanical rotor angle to obtain the calibrated mechanical angle; The mechanical angular velocity difference is calculated and filtered after calibration to obtain the filtered mechanical angular velocity. The calibrated mechanical angle and the filtered mechanical angular velocity are linearly transformed to obtain the electrical angular velocity and the electrical angle.

[0008] In some possible embodiments, coordinate transformation of the three-phase stator currents based on electrical angles is performed to obtain the q-axis current and d-axis current, including: The Clarke transformation is performed on the three-phase stator currents to obtain the q-axis and d-axis currents in a two-phase stationary coordinate system. Based on the electrical angle, the Park transformation is applied to the q-axis current and the d-axis current in the two-phase stationary coordinate system to obtain the q-axis current and the d-axis current.

[0009] In some possible embodiments, online identification of motor parameters based on MRAS is performed on the q-axis current and d-axis current to obtain estimated stator resistance and estimated rotor flux linkage, including: The q-axis current is estimated for the current cycle based on an adjustable model. The difference between the q-axis current and the estimated q-axis current for the current cycle is calculated as the resistance error signal; The estimated stator resistance is obtained by adjusting the estimated resistance value based on the resistance error signal and using a PI controller.

[0010] In some possible embodiments, adaptive FOC controller parameter tuning is performed based on the estimated stator resistance and estimated rotor flux linkage to obtain new current loop gain and new speed loop gain, including: The new current loop gain is obtained by self-tuning the current loop PI gain based on the estimated stator resistance. The new speed loop gain is obtained by self-tuning the speed loop PI gain based on the estimated rotor flux linkage.

[0011] In some possible embodiments, current loop PI gain self-tuning is performed based on the estimated stator resistance to obtain the new current loop gain, including: The new current loop gain is obtained by self-tuning the PI gain of the current loop based on the estimated stator resistance using the following formula:

[0012]

[0013] in, For the desired current loop bandwidth, For q-axis inductance, This is the estimated stator resistance.

[0014] In some possible embodiments, the new speed loop gain is obtained by self-tuning the speed loop PI gain based on the estimated rotor flux linkage, including: The new speed loop gain is obtained by self-tuning the speed loop PI gain based on the estimated rotor flux linkage using the following formula:

[0015]

[0016] in, For the damping ratio, For the desired speed loop natural frequency, Let the system's rotational inertia be... For the estimated rotor flux, This represents the number of pole pairs of the motor.

[0017] In a second aspect, embodiments of the present invention provide a permanent magnet pitch driver for a wind turbine, comprising: The data acquisition module is used to acquire three-phase stator current, DC bus voltage, and mechanical rotor angle. The coordinate transformation module is used to calculate the electrical angular velocity and electrical angle based on the mechanical rotor angle, and to perform coordinate transformation on the three-phase stator current based on the electrical angle to obtain the q-axis current and d-axis current. The online motor parameter identification module is used to perform MRAS-based online identification of q-axis current and d-axis current to obtain estimated stator resistance and estimated rotor flux. An adaptive FOC controller parameter tuning module is used to adaptively tune the FOC controller parameters based on the estimated stator resistance and the estimated rotor flux linkage to obtain new current loop gain and new speed loop gain. The instruction generation module is used to generate FOC torque and voltage instructions based on the new current loop gain and the new speed loop gain to obtain the dq axis voltage instruction. The gate drive signal generation module is used to perform inverse Park transformation and space vector pulse width modulation on the dq axis voltage command to obtain the gate drive signal.

[0018] In some possible embodiments, the coordinate transformation module is further configured to: Decode and calibrate the mechanical rotor angle to obtain the calibrated mechanical angle; The mechanical angular velocity difference is calculated and filtered after calibration to obtain the filtered mechanical angular velocity. The calibrated mechanical angle and the filtered mechanical angular velocity are linearly transformed to obtain the electrical angular velocity and the electrical angle.

[0019] In some possible embodiments, the coordinate transformation module is further configured to: The Clarke transformation is performed on the three-phase stator currents to obtain the q-axis and d-axis currents in a two-phase stationary coordinate system. Based on the electrical angle, the Park transformation is applied to the q-axis current and the d-axis current in the two-phase stationary coordinate system to obtain the q-axis current and the d-axis current.

[0020] Compared with existing technologies, this invention provides a permanent magnet pitch drive and method for wind turbines. It utilizes a Model Reference Adaptive System (MRAS) to identify key parameters of the pitch drive motor, such as stator resistance and rotor flux linkage, online to obtain accurate estimates in real time. These identified real-time parameters are seamlessly fed back to the FOC controller for dynamic adjustment of the core control gains in the current and speed loops, forming a closed-loop intelligent adjustment mechanism of "identification-tuning." This ensures that the control model always accurately matches the actual physical state of the motor. In this way, the pitch system maintains high-precision, high-dynamic-response control performance under complex and changing operating conditions, thereby improving the operational stability and power generation efficiency of the wind turbine. Attached Figure Description

[0021] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0022] Figure 1 A flowchart of a method for a permanent magnet pitch driver for a wind turbine according to an embodiment of the present invention; Figure 2 This is a data flow diagram of a method for a permanent magnet pitch driver for a wind turbine according to an embodiment of the present invention. Figure 3 This is a block diagram of a permanent magnet pitch driver for a wind turbine according to an embodiment of the present invention. Detailed Implementation

[0023] To enable those skilled in the art to better understand the technical solutions of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0024] Unless otherwise specifically stated, the technical or scientific terms used in the embodiments of this invention should be understood in their ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains. The terms "comprising" or "including," as used in the embodiments of this invention, do not limit the shapes, numbers, steps, actions, operations, components, elements, and / or groups thereof mentioned, nor do they exclude the appearance or addition of one or more other different shapes, numbers, steps, actions, operations, components, elements, and / or groups thereof, or the inclusion of these.

[0025] Unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps described in these embodiments do not limit the scope of the invention. It should also be understood that, for ease of description, the dimensions of the various parts shown in the drawings are not drawn to actual scale, and techniques, methods, and apparatus known to those skilled in the art may not be discussed in detail; however, where appropriate, the illustrated techniques, methods, and apparatus should be considered part of the specification. In all the examples shown and discussed herein, any other specific example may have different values. It should be noted that similar symbols and letters in the following figures denote similar items; therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0026] In the description of the embodiments of the present invention, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In the embodiments of the present invention, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in a suitable manner in any one or more embodiments or examples. Furthermore, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in the embodiments of the present invention, as well as the features of different embodiments or examples.

[0027] Hereinafter, exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of the present invention, and not all embodiments of the present invention; it should be understood that the present invention is not limited to the exemplary embodiments described herein.

[0028] The present invention proposes a method for a permanent magnet pitch driver for wind turbine units. Figure 1 A flowchart illustrating a method for a permanent magnet pitch driver for a wind turbine according to an embodiment of the present invention. Figure 2 This is a system architecture diagram of a method for a permanent magnet pitch drive for a wind turbine according to an embodiment of the present invention. Figure 1 and Figure 2 As shown, a method for a permanent magnet pitch drive for a wind turbine according to an embodiment of the present invention includes the following steps: S1, acquiring three-phase stator current, DC bus voltage, and mechanical rotor angle; S2, calculating electrical angle and electric angle based on the mechanical rotor angle, and performing coordinate transformation on the three-phase stator current based on the electrical angle to obtain q-axis current and d-axis current; S3, performing online identification of motor parameters based on MRAS on the q-axis current and d-axis current to obtain estimated stator resistance and estimated rotor flux linkage; S4, performing adaptive FOC controller parameter tuning based on the estimated stator resistance and estimated rotor flux linkage to obtain new current loop gain and new speed loop gain; S5, generating FOC torque and voltage commands based on the new current loop gain and new speed loop gain to obtain dq-axis voltage command; S6, performing inverse Park transformation and space vector pulse width modulation on the dq-axis voltage command to obtain gate drive signal.

[0029] Specifically, S1 involves acquiring the three-phase stator current, DC bus voltage, and mechanical rotor angle. The three-phase stator current refers to the instantaneous current flowing through the U, V, and W phase windings of the permanent magnet synchronous motor. It is the direct physical source of the motor's rotating magnetic field and, consequently, electromagnetic torque. Since the control algorithm needs to transform these currents from a three-phase stationary coordinate system to a two-phase rotating coordinate system to achieve decoupled control of torque and flux linkage, accurate measurement of the three-phase stator current is a fundamental prerequisite for achieving field-oriented control (FOC). The DC bus voltage is the voltage value of the DC link supplying power to the inverter in the driver. It determines the maximum AC voltage amplitude that the inverter can synthesize. During control, this voltage value is crucial for the Space Vector Pulse Width Modulation (SVPWM) algorithm to synthesize and output voltage vectors, ensuring the motor receives the desired voltage and preventing control instability or overmodulation caused by bus voltage fluctuations. The mechanical rotor angle refers to the real-time angular position of the motor rotor in physical space. This angle is the sole basis for determining the direction of the rotor permanent magnet's magnetic field. In field-oriented control, this angle must be used for coordinate transformation (Park transformation) to ensure a precise spatial relationship between the stator current vector and the rotor flux linkage vector. This is crucial for achieving high-performance torque response and speed regulation. In the technical solution of this invention, by acquiring the three-phase stator current, DC bus voltage, and mechanical rotor angle, the electrical state (stator current) and mechanical state (rotor angle) of the pitch motor, as well as the energy state (bus voltage) of the driver, are converted into information that can be processed by a digital controller. This enables accurate identification of motor parameters and adaptive adjustment of controller parameters, thereby improving the operational stability and energy capture efficiency of the wind turbine.

[0030] In practice, this can be accomplished through the collaboration of hardware sensors and a microcontroller. Specifically, the three-phase stator current can be measured using current sensors (such as Hall effect sensors or sampling resistors) installed on the motor phase lines; the DC bus voltage can be sampled using voltage sensors or a resistor divider network; and the mechanical rotor angle can be captured by position sensors (such as rotary transformers or absolute encoders) installed on the motor shaft. These sensors convert physical quantities into analog or digital electrical signals, which are then acquired and digitized by the analog-to-digital converter (ADC) within the controller or through a dedicated interface.

[0031] Specifically, S2 calculates the electrical angular velocity and electrical angle based on the mechanical rotor angle, and performs coordinate transformation on the three-phase stator current based on the electrical angle to obtain the q-axis current and d-axis current. In practice, firstly, the electrical angular velocity and electrical angle are calculated based on the mechanical rotor angle. The electrical angle describes the direction of the rotor magnetic field induced by the stator windings, and it has a relationship with the mechanical angle determined by the physical structure of the motor. The electrical angular velocity is the rate of change of the electrical angle, corresponding to the angular frequency of the rotation of the electromagnetic field inside the motor. These two electrical parameters are the basis for coordinate system transformation and speed loop control in the FOC algorithm. It should be understood that the essence of field-oriented control is performed in a dq coordinate system that rotates synchronously with the rotor flux linkage. All control algorithms, including PI regulation of the current loop and generation of voltage commands, depend on this electrical coordinate system. Therefore, in the technical solution of this invention, through an accurate conversion process, the mechanical angle is converted into an electrical angle in real time to determine the instantaneous orientation of the dq coordinate system, thereby achieving accurate projection (Park transformation) of the stator current vector onto this rotating coordinate system. In addition, the calculation of electric angular velocity is equally important. It is not only a feedback signal for the speed control loop to achieve precise speed regulation, but also plays a role in the feedforward decoupling compensation of the current loop to improve the dynamic response performance of the system to load changes.

[0032] In this process, the mechanical rotor angle is first decoded and calibrated to obtain the calibrated mechanical angle. It should be understood that in practical applications, the raw signal output by the position sensor may not be a direct angle value. For example, an incremental encoder outputs a pulse signal, which needs to be converted into angle information through counting and decoding logic. Decoding refers to the controller parsing the raw data output by the paired position sensor (such as an absolute encoder, resolver, etc.) according to its communication protocol and signal format, restoring it to an uncorrected mechanical angle value. After decoding, due to unavoidable physical position deviations during sensor installation or zero-point drift of the sensor itself, the obtained initial angle value may have a fixed offset from the actual magnetic pole position of the motor. Therefore, calibration is necessary to compensate for this offset and correct the mechanical angle reading, thereby obtaining a calibrated mechanical angle that is strictly aligned with the electromagnetic characteristics of the motor, laying the foundation for the accuracy of subsequent calculations.

[0033] Next, the calibrated mechanical angle is subjected to mechanical angular velocity differential calculation and filtering to obtain the filtered mechanical angular velocity. As the derivative of the mechanical angle with respect to time, the mechanical angular velocity is typically approximated in discrete digital control systems using the backward difference method. Specifically, this involves subtracting the calibrated mechanical angle obtained in the current sampling period from the angle value of the previous sampling period, and then dividing by the sampling period duration of the control system.

[0034] However, since the angle signal itself may contain noise and the differential operation amplifies the noise, directly using the differential result will introduce large fluctuations, affecting the stability of the velocity loop. Therefore, in the technical solution of this invention, by introducing a low-pass filter, such as a first-order inertial filter or a moving average filter, the original velocity value calculated by the differential is processed, which can effectively filter out high-frequency noise and obtain a smooth filtered mechanical angular velocity that can accurately reflect the actual rotational speed change.

[0035] Subsequently, a linear transformation is performed on the calibrated mechanical angle and the filtered mechanical angular velocity to obtain the electrical angular velocity and the electrical angle. The core of this process is the utilization of a key intrinsic parameter of the motor—the number of pole pairs (p). Electrical Angle With calibrated mechanical angle The relationship between them, and electric angular velocity With filtered mechanical angular velocity The relationships between them all follow the following linear formula:

[0036]

[0037] By multiplying the calibrated mechanical angle and the filtered mechanical angular velocity by the inherent number of pole pairs of the motor, the electrical angle and electrical angular velocity directly used by the vector control algorithm can be accurately calculated.

[0038] Furthermore, a coordinate transformation is performed on the three-phase stator currents based on electrical angles to obtain the q-axis and d-axis currents. It should be understood that in the natural three-phase AC (A, B, C phase) coordinate system, the stator currents are three sinusoidal quantities that vary with time. Their relationship with the rotor flux linkage is complex and difficult to control directly to obtain the desired torque output. By transforming the coordinate system describing the stator currents from a stationary three-phase coordinate system (ABC coordinate system) to a two-phase orthogonal coordinate system (dq coordinate system) that rotates synchronously with the rotor flux linkage, the stator current vector can be decomposed into two DC components: one is the excitation component (d-axis current) aligned with the rotor flux linkage direction, and the other is the torque component (q-axis current) perpendicular to the rotor flux linkage direction. In this way, independent, precise, and linear control of the motor flux linkage and torque can be achieved by independently controlling the two DC current components on the d and q axes. This greatly simplifies the design of the control algorithm and improves the system's control performance and dynamic response speed.

[0039] In this process, firstly, the three-phase stator current is subjected to Clarke transformation to obtain the q-axis current and d-axis current in the two-phase stationary coordinate system; then, based on the electrical angle, the q-axis current and d-axis current in the two-phase stationary coordinate system are subjected to Park transformation to obtain the q-axis current and the d-axis current.

[0040] Specifically, in S3, online identification of motor parameters based on MRAS is performed on the q-axis and d-axis currents to obtain estimated stator resistance and estimated rotor flux linkage. It should be understood that the physical parameters of a permanent magnet synchronous motor are not constant. Specifically, the stator resistance increases significantly with increasing motor winding temperature, while the rotor flux linkage may change due to temperature variations or magnetic saturation. In traditional motor control schemes, controller parameters (such as the gain of a PI controller) are usually set based on motor parameters under specific conditions or offline measurements. Once the motor's operating temperature, load, and other conditions change during actual operation, these fixed parameters can no longer accurately describe the motor's actual physical characteristics. This parameter mismatch leads to a deterioration in the decoupling effect of field-oriented control (FOC), thereby reducing the motor's torque control accuracy, dynamic response speed, and overall operating efficiency. Therefore, to achieve truly high-performance and robust control, these changing parameters need to be identified online and in real-time, and accurate parameter values ​​need to be fed back to the control system, enabling the system to dynamically adjust itself to adapt to changing operating conditions. In the technical solution of this invention, online identification of motor parameters based on MRAS is performed on the q-axis current and d-axis current to obtain estimated stator resistance and estimated rotor flux linkage. Online motor parameter identification refers to the technique of estimating the internal physical parameters of the motor (such as resistance, inductance, flux linkage, etc.) in real time during normal motor operation using a specific algorithm. This differs from offline parameter measurement performed when the motor is stationary or under specific test conditions. It is worth noting that Model Reference Adaptive System (MRAS) is a control theory framework whose basic structure includes a reference model and an adjustable model. The reference model typically represents the ideal or actual behavior of the system, and its structure does not depend on the parameters to be identified; the adjustable model contains the parameters to be identified, and its output changes as these parameters are adjusted. By continuously adjusting the parameters in the adjustable model through an adaptive mechanism, the error between its output and the output of the reference model is minimized, thereby achieving accurate parameter estimation.

[0041] In practice, the first step is to calculate the estimated q-axis current for the current cycle based on the adjustable model. Here, the adjustable model is a software model based on the mathematical equations of the motor. It can calculate a theoretical q-axis current value based on known input quantities, such as dq-axis voltage commands, motor speed, and motor parameters including the parameters to be identified (i.e., the currently estimated stator resistance).

[0042] Next, the difference between the q-axis current and the estimated q-axis current for the current cycle is calculated as the resistance error signal. In this process, the q-axis current actually calculated in the previous stage (coordinate transformation module) is considered a reference value that truly reflects the current state of the motor. The system compares this actual q-axis current value with the estimated q-axis current value calculated by the adjustable model in the previous step. The deviation between these two values ​​is considered to be due to inaccurate stator resistance estimation used in the adjustable model.

[0043] Furthermore, based on the resistance error signal, the estimated resistance value is adjusted using a PI controller to obtain the estimated stator resistance. This PI controller, acting as an adaptive adjustment mechanism, continuously and dynamically adjusts the estimated stator resistance value to drive the resistance error signal towards zero. Specifically, the PI controller generates an adjustment amount based on the current error magnitude (proportional action) and the accumulation of error over time (integral action). This adjustment amount is used to update the estimated stator resistance value. This updated estimate is then used in the next calculation cycle for the adjustable model, forming a closed-loop correction process. Through this continuous iterative adjustment, the estimated stator resistance value will eventually converge and accurately track changes in its actual physical value.

[0044] Taking the scheme of this invention as an example, in the initial startup phase of the wind turbine's pitch drive, the estimated stator resistance value inside the system is set to an initial value based on factory data. As the motor starts running and gradually heats up, its actual stator resistance increases due to temperature effects. At this time, the actual q-axis current value calculated by the coordinate transformation module will begin to deviate from the theoretical q-axis current value calculated by the adjustable model based on the old, lower resistance estimate. For example, the q-axis current predicted by the adjustable model may be higher than the actual value. The system then calculates the difference between these two q-axis currents, forming a negative resistance error signal. This negative error signal is transmitted to the PI controller. Upon receiving this signal, the PI controller outputs a positive adjustment. This positive adjustment acts on the current estimated stator resistance value, causing it to be adjusted upwards, i.e., its value is increased. In the following control cycle, the updated, slightly increased stator resistance estimate is used for calculation by the new adjustable model. The new estimated q-axis current will be closer to the actual q-axis current, thereby reducing the absolute value of the error signal. This closed-loop identification and adjustment process continues at a very high frequency. As long as the motor temperature continues to change, the actual stator resistance will change, generating an error signal. The PI controller will then continuously fine-tune the estimated resistance value to ensure it closely tracks the actual changes in the physical entity. Ultimately, this precisely identified stator resistance value will be transmitted to the subsequent adaptive controller parameter tuning module to ensure the high-performance operation of the control system.

[0045] Specifically, in step S4, adaptive FOC controller parameter tuning is performed based on the estimated stator resistance and estimated rotor flux linkage to obtain new current loop gain and new speed loop gain. It should be understood that in field-oriented control strategies, proportional-integral (PI) controllers are typically used to construct the current loop and speed loop respectively. The performance of these PI controllers is directly determined by their proportional gain (Kp) and integral gain (Ki). In traditional designs, these gain values ​​are calculated or experimentally tuned based on fixed parameters of the motor under specific ideal conditions (such as factory-set stator resistance, rotor flux linkage, etc.). However, as revealed in the preceding steps, the stator resistance of the motor changes with temperature, and the rotor flux linkage may also change due to temperature or magnetic saturation effects. When these critical physical parameters change, if the controller gain remains constant, the original optimal control effect will be lost. For example, parameter mismatch may lead to overshoot, slow response, or even oscillation in current or speed control, severely affecting the pitch drive's ability to accurately control the blade angle. Therefore, in the technical solution of the present invention, by introducing an adaptive tuning stage, the parameters identified in real time are used to continuously and dynamically recalculate and adjust these controller gains to generate a new set of controller gains that are most suitable for the current operating conditions, thereby ensuring that the motor control system always operates in the best performance state.

[0046] Among them, adaptive FOC controller parameter tuning refers to automatically and continuously adjusting the gain of the PI controller in field-oriented control based on the real-time estimated motor parameters during motor operation. This is an advanced control strategy that adapts to changes in motor characteristics to maintain optimal control performance. Current loop gain refers to the proportional gain of the PI controller in the inner current control loop and the outer speed control loop of the FOC control system. ) and integral gain ( The current loop gain determines the tracking speed and accuracy of the dq axis current, while the speed loop gain determines the tracking performance of the motor speed.

[0047] In practice, the new current loop gain is obtained by self-tuning the PI gain of the current loop based on the estimated stator resistance. This step is responsible for adjusting the PI controller parameters of the inner loop (i.e., the current loop) that controls the dq-axis current of the motor. Specifically, the new gain is calculated using the estimated stator resistance value identified in real-time in the previous step, based on a clear mathematical formula. More specifically, the new proportional gain of the current loop is calculated by multiplying a pre-set design target, the desired current loop bandwidth, by the q-axis inductance value of the motor. This proportional gain primarily determines the response speed of the current loop. The new integral gain of the current loop is calculated by multiplying the same desired current loop bandwidth by the estimated stator resistance value, which is obtained from the online identification module and updated in real-time. Since the estimated stator resistance changes dynamically with motor temperature, the integral gain is also updated in real-time through this calculation. The main function of the integral gain is to eliminate steady-state errors. In this way, especially by adjusting the integral gain, which is strongly correlated with the resistance value, the control performance of the current loop remains stable and accurate when the motor temperature changes. Specifically, the process can be expressed by the following formula:

[0048]

[0049] in, For the desired current loop bandwidth, For q-axis inductance, This is the estimated stator resistance.

[0050] Similarly, the speed loop PI gain is self-tuned based on the estimated rotor flux linkage to obtain the new speed loop gain. This step is responsible for adjusting the PI controller parameters of the outer loop (i.e., the speed loop) that controls the motor speed. Its execution logic utilizes the estimated rotor flux linkage value identified in real-time in the previous step to adjust the speed loop performance. Specifically, in actual software engineering implementation, the desired speed loop natural frequency or damping ratio, which are key design indicators, are associated with the estimated rotor flux linkage value acquired in real-time. The strength of the rotor flux linkage directly affects the motor's torque output capability; therefore, using it as the basis for adjusting the dynamic response characteristics of the speed loop ensures that speed control can still achieve the desired dynamic performance and stability even when the flux linkage changes. Specifically, this process is expressed by the following formula:

[0051]

[0052] in, For the damping ratio, For the desired speed loop natural frequency, Let the system's rotational inertia be... For the estimated rotor flux, This represents the number of pole pairs of the motor.

[0053] Specifically, in step S5, based on the new current loop gain and the new speed loop gain, FOC torque and voltage commands are generated to obtain the dq-axis voltage command. Since motor parameters change significantly with temperature and operating conditions, traditional fixed-parameter field-oriented control (FOC) struggles to maintain optimal performance. However, through the preceding online parameter identification and adaptive tuning stages, the system has acquired the current loop and speed loop PI gains that reflect the motor's current true state. To overcome the performance degradation of permanent magnet synchronous motors caused by changes in stator resistance and rotor flux linkage during actual operation, and to ensure that the pitch drive always possesses high-precision torque control capabilities, excellent dynamic response characteristics, and reliable system stability, the technical solution of this invention generates FOC torque and voltage commands based on the new current loop gain and the new speed loop gain. This effectively suppresses response sluggishness, steady-state errors, or oscillation risks caused by parameter drift, ensuring that the pitch system can still achieve fast, stable, and accurate torque output when facing complex operating conditions and external disturbances, meeting the high reliability requirements of wind turbine drive systems.

[0054] In practice, the system first receives reference signals and actual feedback signals from the speed loop and current loop, and calculates the q-axis current reference value using the updated speed loop PI gain. Then, it adjusts the d-axis and q-axis current errors using the new current loop PI gain, generating d-axis and q-axis voltage commands respectively through the PI controller. Specifically, the q-axis voltage command is obtained by the torque control loop based on the speed error, output from the speed loop PI controller, and then adjusted by the current loop PI controller. The d-axis voltage command is directly generated by the excitation control loop based on the d-axis current error through the current loop PI controller. This process ensures that the system maintains excellent torque response and stability even under changes in motor parameters or external disturbances.

[0055] Taking the solution of this invention as an example, suppose the main controller of the wind turbine needs to quickly adjust the blades from their current position to a new windward angle, and therefore issues a target speed command to the pitch driver, requiring the motor to reach a speed of 50 revolutions per minute within a short period of time. At this moment, the command generation module starts working. First, the speed loop starts. It detects that the actual speed of the current motor is 0, while the target speed is 50 revolutions per minute, thus generating a large speed error. This error is sent to the speed PI controller. Since the system has just updated the new speed loop gain according to the latest parameters of the motor, the controller can very quickly and stably calculate a matching q-axis current command, for example, a command value of 30 amperes, to generate strong starting torque. At the same time, the d-axis current command remains at 0 amperes. Then, these two current commands ( The 30-amp command is passed to the current loop. The q-axis current loop compares the 30-amp command with the current actual q-axis current, which is 0, resulting in a large 30-amp error. The q-axis current PI controller, using its latest current loop gain, calculates a higher q-axis voltage command, such as 200 volts, to quickly build up the required current. Meanwhile, the d-axis current loop compares the 0-amp command with a small actual d-axis current measurement (e.g., a 0.2-amp disturbance), and its PI controller calculates a corresponding smaller d-axis voltage command, such as -5 volts, to suppress this unwanted d-axis current component. Finally, the output of this step at this point is a pair of specific voltage commands: , These commands will be sent to the subsequent hardware drive circuitry to ensure that the motor can accelerate precisely toward the target speed with optimal dynamic response.

[0056] Specifically, in step S6, the dq-axis voltage command undergoes inverse Park transformation and space vector pulse width modulation to obtain the gate drive signal. That is, the DC voltage control signal output from the Field Oriented Control (FOC) algorithm, represented in a synchronous rotating coordinate system, is converted into a PWM waveform capable of actually driving the power switching devices in the three-phase inverter bridge. This ultimately generates the required three-phase AC voltage with the specified amplitude, frequency, and phase on the stator windings of the permanent magnet synchronous motor, achieving high-precision torque and speed control.

[0057] In practice, the system first receives the d-axis voltage command generated by the preceding FOC control loop. and q-axis voltage command These two quantities are DC voltage signals that control the motor excitation and torque respectively in a synchronous rotating coordinate system. To drive a three-phase inverter, these two DC signals need to be converted back to AC voltage reference quantities in a two-phase stationary coordinate system (α-β coordinate system). This conversion is achieved through inverse Park transformation, which uses the current electrical angle information to project the voltage components in the rotating coordinate system to the stationary coordinate system. The resulting... and These represent the two orthogonal components of the desired stator voltage space vector in the stationary coordinate system. Subsequently, the space vector pulse width modulation (SVPWM) module... and As input, its core task is to synthesize a voltage vector that rotates in space, making its average value equal to that of the reference voltage vector. The implementation of SVPWM includes several sub-steps: First, determine the sector where the reference voltage vector is located and divide the voltage plane into 6 basic sectors; then, calculate the required action time of the two adjacent basic non-zero voltage vectors and the zero vector based on the sector; then, calculate the comparison value or duty cycle of each bridge arm of the three-phase inverter based on these time values; finally, generate six three-phase symmetrical PWM waveforms with specific duty cycles and complementary dead time through hardware timers, which are the final gate drive signals. These signals are isolated and amplified to directly control the turn-on and turn-off of the power devices in the three-phase full-bridge inverter.

[0058] It is worth mentioning that the inverse Park transformation is a mathematical inverse transformation that maps DC variables in a synchronous rotating coordinate system (dq axis) back to a two-phase stationary coordinate system (α-β axis), and its transformation relationship is highly dependent on real-time electrical angle information. Space vector pulse width modulation is a modulation strategy that approximates an ideal circular rotating magnetic field by combining the basic switching states of the inverter.

[0059] In summary, the method for a permanent magnet pitch drive for a wind turbine according to embodiments of the present invention is explained. It utilizes a Model Reference Adaptive System (MRAS) to identify key parameters of the pitch drive motor, such as stator resistance and rotor flux linkage, online to obtain accurate estimates in real time. The identified real-time parameters are seamlessly fed back to the FOC controller for dynamic adjustment of the core control gains of the current and speed loops, forming a closed-loop intelligent adjustment mechanism of "identification-tuning." This ensures that the control model always accurately matches the actual physical state of the motor. In this way, the pitch system can maintain high-precision, high-dynamic-response control performance under complex and changing operating conditions, thereby improving the operational stability and power generation efficiency of the wind turbine.

[0060] Furthermore, a permanent magnet pitch driver for a wind turbine is also provided.

[0061] Figure 3This is a block diagram of a permanent magnet pitch driver for a wind turbine according to an embodiment of the present invention. Figure 3 As shown, the permanent magnet pitch driver 300 for a wind turbine according to an embodiment of the present invention includes: a data acquisition module 310 for acquiring three-phase stator current, DC bus voltage, and mechanical rotor angle; a coordinate transformation module 320 for calculating electrical angular velocity and electrical angle based on the mechanical rotor angle, and performing coordinate transformation on the three-phase stator current based on the electrical angle to obtain q-axis current and d-axis current; an online motor parameter identification module 330 for performing MRAS-based online motor parameter identification on the q-axis current and d-axis current to obtain estimated stator resistance and estimated rotor flux; an adaptive FOC controller parameter tuning module 340 for performing adaptive FOC controller parameter tuning based on the estimated stator resistance and estimated rotor flux to obtain new current loop gain and new speed loop gain; a command generation module 350 for generating FOC torque and voltage commands based on the new current loop gain and new speed loop gain to obtain dq-axis voltage commands; and a gate drive signal generation module 360 ​​for performing inverse Park transformation and space vector pulse width modulation on the dq-axis voltage commands to obtain gate drive signals.

[0062] As described above, the permanent magnet pitch driver 300 for wind turbines according to embodiments of the present invention can be implemented in various wireless terminals, such as servers with permanent magnet pitch drive algorithms for wind turbines. In one possible implementation, the permanent magnet pitch driver 300 for wind turbines according to embodiments of the present invention can be integrated into a wireless terminal as a software module and / or a hardware module. For example, the permanent magnet pitch driver 300 for wind turbines can be a software module in the operating system of the wireless terminal, or it can be an application developed for the wireless terminal; of course, the permanent magnet pitch driver 300 for wind turbines can also be one of many hardware modules of the wireless terminal.

[0063] Alternatively, in another example, the permanent magnet pitch driver 300 of the wind turbine and the wireless terminal can also be separate devices, and the permanent magnet pitch driver 300 of the wind turbine can be connected to the wireless terminal via wired and / or wireless networks, and transmit interactive information in accordance with an agreed data format.

[0064] It is understood that the above embodiments are merely exemplary implementations used to illustrate the principles of the present invention, and the present invention is not limited thereto. For those skilled in the art, various modifications and improvements can be made without departing from the spirit and essence of the present invention, and these modifications and improvements are also considered to be within the scope of protection of the present invention.

Claims

1. A method for a permanent magnet pitch drive for a wind turbine, characterized in that, include: Obtain the three-phase stator current, DC bus voltage, and mechanical rotor angle; Based on the mechanical rotor angle, the electrical angular velocity and electrical angle are calculated, and the coordinate transformation of the three-phase stator current is performed based on the electrical angle to obtain the q-axis current and d-axis current. For the q-axis current and d-axis current, perform online identification of motor parameters based on MRAS to obtain the estimated stator resistance and the estimated rotor flux. Adaptive FOC controller parameter tuning is performed based on the estimated stator resistance and estimated rotor flux to obtain new current loop gain and new speed loop gain. Based on the new current loop gain and the new speed loop gain, FOC torque and voltage commands are generated to obtain dq axis voltage commands. The gate drive signal is obtained by performing inverse Park transformation and space vector pulse width modulation on the dq axis voltage command.

2. The method for a permanent magnet pitch driver for a wind turbine according to claim 1, characterized in that, Based on the mechanical rotor angle, calculate the electrical angular velocity and electrical angle, including: Decode and calibrate the mechanical rotor angle to obtain the calibrated mechanical angle; The mechanical angular velocity difference is calculated and filtered after calibration to obtain the filtered mechanical angular velocity. The calibrated mechanical angle and the filtered mechanical angular velocity are linearly transformed to obtain the electrical angular velocity and the electrical angle.

3. The method for a permanent magnet pitch driver for a wind turbine according to claim 1, characterized in that, Based on electrical angles, coordinate transformation of the three-phase stator currents is performed to obtain the q-axis current and d-axis current, including: The Clarke transformation is performed on the three-phase stator currents to obtain the q-axis and d-axis currents in a two-phase stationary coordinate system. Based on the electrical angle, the Park transformation is applied to the q-axis current and the d-axis current in the two-phase stationary coordinate system to obtain the q-axis current and the d-axis current.

4. The method for a permanent magnet pitch driver for a wind turbine according to claim 1, characterized in that, For the q-axis and d-axis currents, online motor parameter identification based on MRAS is performed to obtain estimated stator resistance and estimated rotor flux linkage, including: The q-axis current is estimated for the current cycle based on an adjustable model. The difference between the q-axis current and the estimated q-axis current for the current cycle is calculated as the resistance error signal; The estimated stator resistance is obtained by adjusting the estimated resistance value based on the resistance error signal and using a PI controller.

5. The method for a permanent magnet pitch driver for a wind turbine according to claim 1, characterized in that, Adaptive FOC controller parameter tuning is performed based on estimated stator resistance and estimated rotor flux linkage to obtain new current loop gain and new speed loop gain, including: The new current loop gain is obtained by self-tuning the current loop PI gain based on the estimated stator resistance. The new speed loop gain is obtained by self-tuning the speed loop PI gain based on the estimated rotor flux linkage.

6. The method for a permanent magnet pitch driver for a wind turbine according to claim 5, characterized in that, The new current loop gain is obtained by self-tuning the current loop PI gain based on the estimated stator resistance, including: The new current loop gain is obtained by self-tuning the PI gain of the current loop based on the estimated stator resistance using the following formula: in, For the desired current loop bandwidth, For q-axis inductance, This is the estimated stator resistance.

7. The method for a permanent magnet pitch driver for a wind turbine according to claim 5, characterized in that, The new speed loop gain is obtained by self-tuning the speed loop PI gain based on the estimated rotor flux linkage, including: The new speed loop gain is obtained by self-tuning the speed loop PI gain based on the estimated rotor flux linkage using the following formula: in, For the damping ratio, For the desired speed loop natural frequency, Let the system's rotational inertia be... For the estimated rotor flux, This represents the number of pole pairs of the motor.

8. A permanent magnet pitch drive for a wind turbine, characterized in that, include: The data acquisition module is used to acquire three-phase stator current, DC bus voltage, and mechanical rotor angle. The coordinate transformation module is used to calculate the electrical angular velocity and electrical angle based on the mechanical rotor angle, and to perform coordinate transformation on the three-phase stator current based on the electrical angle to obtain the q-axis current and d-axis current. The online motor parameter identification module is used to perform MRAS-based online identification of q-axis current and d-axis current to obtain estimated stator resistance and estimated rotor flux. An adaptive FOC controller parameter tuning module is used to adaptively tune the FOC controller parameters based on the estimated stator resistance and the estimated rotor flux linkage to obtain new current loop gain and new speed loop gain. The instruction generation module is used to generate FOC torque and voltage instructions based on the new current loop gain and the new speed loop gain to obtain the dq axis voltage instruction. The gate drive signal generation module is used to perform inverse Park transformation and space vector pulse width modulation on the dq axis voltage command to obtain the gate drive signal.

9. The permanent magnet pitch driver for a wind turbine according to claim 8, characterized in that, The coordinate transformation module is further used for: Decode and calibrate the mechanical rotor angle to obtain the calibrated mechanical angle; The mechanical angular velocity difference is calculated and filtered after calibration to obtain the filtered mechanical angular velocity. The calibrated mechanical angle and the filtered mechanical angular velocity are linearly transformed to obtain the electrical angular velocity and the electrical angle.

10. The permanent magnet pitch driver for a wind turbine according to claim 8, characterized in that, The coordinate transformation module is further used for: The Clarke transformation is performed on the three-phase stator currents to obtain the q-axis and d-axis currents in a two-phase stationary coordinate system. Based on the electrical angle, the Park transformation is applied to the q-axis current and the d-axis current in the two-phase stationary coordinate system to obtain the q-axis current and the d-axis current.

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