Permanent magnet half-direct drive wind turbine generator position real-time identification method, system and medium

Abstract

The application discloses a permanent magnet half-direct-drive wind turbine generator position real-time identification method and system and a medium, and the method comprises the following steps: obtaining disturbance identification values caused by parameter mismatch and periodic disturbance identification values caused by inverter nonlinearity and space flux linkage harmonics by using a harmonic filtering extended state observer based on an adaptive frequency algorithm; obtaining shaft extended back electromotive force identification values by using a back electromotive force extended state observer; obtaining voltage identification values at a time point of total disturbance based on a set control law; and then obtaining rotor speed identification values by using a phase-locked loop, and obtaining rotor position by integrating the rotor speed identification values. The application aims to improve the machine side current quality of the permanent magnet direct-drive wind turbine, improve the identification accuracy of the speed and the rotor position angle, and optimize the performance of the position sensorless control system of the permanent magnet direct-drive wind turbine.

Description

Technical Field

[0001] The present invention relates to the field of wind power generation, and in particular to a method, system and medium for real-time position identification of a permanent magnet semi-direct drive wind turbine generator set. Background Art

[0002] In recent years, increasing attention to energy production and environmental pollution has greatly promoted the use of renewable energy. Wind energy, as one of the most popular and cost-effective renewable energy sources for electricity production, holds enormous potential. Variable-speed constant-frequency wind power generation based on permanent magnet direct-drive wind turbines offers high efficiency and low maintenance costs. For permanent magnet direct-drive wind turbine systems, the key to achieving high-performance control lies in real-time acquisition of rotor position information on the turbine side, typically obtained using an encoder. However, the installation of an encoder increases the size and manufacturing cost of the permanent magnet direct-drive wind turbine, while also reducing the reliability and stability of the control system. A real-time position identification method for permanent magnet semi-direct-drive wind turbines replaces mechanical encoders to provide rotor position and speed information to the speed loop and coordinate transformation, optimizing the control strategy for permanent magnet direct-drive wind turbines. The method includes a model-based approach suitable for medium and high speeds and a high-frequency injection approach suitable for low and zero speeds. Model-based approaches include sliding mode observers, Romberg observers, adaptive observers, and extended state observers. In practical applications, the machine-side current of permanent magnet direct-drive wind turbines is distorted due to inverter nonlinearity, spatial flux harmonics, and parameter mismatch. The back-EMF signal directly observed using modeling methods contains DC and higher-order harmonics, which affect the accuracy of rotor position identification. Some methods insert a filter after the observer to extract the fundamental frequency component of the back-EMF and output an ideal rotor position identification value. However, these methods neglect the improvement of current quality, leaving the control performance of permanent magnet direct-drive wind turbines to be improved. Summary of the Invention

[0003] The technical problem to be solved by the present invention is as follows: In view of the above-mentioned problems in the prior art, a method, system and medium for real-time position identification of a permanent magnet semi-direct-drive wind turbine generator set are provided. The present invention aims to improve the quality of machine-side current, improve the identification accuracy of rotational speed and rotor position angle, and optimize the performance of the position sensorless control system of a permanent magnet direct-drive wind turbine generator set.

[0004] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0005] A method for real-time position identification of a permanent magnet semi-direct drive wind turbine generator set comprises the following steps: using a harmonic filtering extended state observer based on an adaptive frequency algorithm as shown in the following formula to observe and obtain disturbance identification values ​​caused by parameter mismatch and periodic disturbance identification values ​​caused by inverter nonlinearity and spatial flux harmonics:

[0006]

[0007] The back electromotive force extended state observer shown in the following formula is used to observe and obtain the γδ axis extended back electromotive force identification value

[0008]

[0009] in, and are the current identification values ​​of the harmonic filtering extended state observer at time k+1 and k, T s is the sampling period, B0 is the standard value of the coefficient matrix, is the known model disturbance value at time k, as well as are the disturbance identification values ​​caused by parameter mismatch at time k+1, k and k-1 respectively, as well as are the periodic disturbance identification values ​​caused by the inverter nonlinearity and spatial flux harmonics at time k+1, k, and k-1 respectively. and are the γδ-axis extended back-electromotive force identification values ​​at time k+1 and k, respectively; h1 and h2 are the gains of the harmonic filtering extended state observer; i(k) is the γδ-axis current at time k; and G0(z) is the transfer function of the adaptive frequency algorithm; and are the current identification values ​​of the back electromotive force extended state observer at time k+1 and k respectively, h3 and h4 are the gains of the back electromotive force extended state observer, is the current identification value of the back electromotive force extended state observer; the total disturbance composed of the periodic disturbance identification value, the disturbance identification value caused by parameter mismatch, the γδ axis extended back electromotive force identification value and the known model disturbance value is obtained based on the set control law to obtain the voltage identification value u(k) at the time k; the γδ axis extended back electromotive force identification value at the time k is Normalized to obtain the rotor position error identification value The rotor position error identification value Obtain rotor speed identification value through PI controller The rotor speed identification value The rotor position is obtained by integration

[0010] Optionally, the function expression for obtaining the voltage identification value u(k) at time k based on the set control law is:

[0011]

[0012] In the above formula, k p is the proportional gain, i *(k) is the reference value of the γδ axis current, i(k) is the γδ axis current at time k, B0 is the standard value of the coefficient matrix, is the total disturbance, and:

[0013]

[0014] in, is the periodic disturbance identification value caused by the inverter nonlinearity and spatial flux harmonics at time k, is the known model disturbance value at time k, is the γδ-axis extended back electromotive force identification value at time k, is the disturbance identification value caused by parameter mismatch at time k;

[0015]

[0016] Among them, L d0 is the standard value of stator d-axis inductance.

[0017] Optionally, the calculation function expression of the known model disturbance value at the k moment is:

[0018]

[0019] In the above formula, R s0 is the standard value of stator resistance, L d0 and L q0 are the standard values ​​of dq axis inductance, i γδ is the current value in the γδ coordinate system, is the rotor speed identification value, J is the matrix coefficient, and:

[0020]

[0021] Optionally, the transfer function of the adaptive frequency algorithm is expressed as follows:

[0022]

[0023] In the above formula, G o (z) is the transfer function of the adaptive frequency algorithm, μ is the gain of the adaptive frequency algorithm, ω h is the resonant angular frequency, T s is the sampling period; z is a variable in the complex domain, which is used to represent the frequency response of the discrete-time system.

[0024] Optionally, the γδ axis extended back electromotive force identification value at time k is Normalized to obtain the rotor position error identification value The function expression is:

[0025]

[0026] In the above formula, and They are the γδ-axis extended back electromotive force identification values ​​at time k The γ and δ axis extended back electromotive force identification values.

[0027] Optionally, the rotor position error identification value Obtain rotor speed identification value through PI controller The function expression is:

[0028]

[0029] In the above formula, T s is the sampling period, z is a variable in the complex domain, k i is the proportionality coefficient, k p is the integration coefficient.

[0030] Optionally, the rotor speed identification value The rotor position is obtained by integration The function expression is:

[0031]

[0032] In the above formula, T s is the sampling period, and z is a variable in the complex domain.

[0033] In addition, the present invention also provides a permanent magnet semi-direct drive wind turbine generator set position real-time identification system, comprising a microprocessor and a memory connected to each other, wherein the microprocessor is programmed or configured to execute the permanent magnet semi-direct drive wind turbine generator set position real-time identification method.

[0034] In addition, the present invention also provides a computer-readable storage medium, which stores a computer program or instruction, and the computer program or instruction is programmed or configured to execute the real-time position identification method of the permanent magnet semi-direct drive wind turbine generator set through a processor.

[0035] In addition, the present invention also provides a computer program product, including a computer program or instructions, which are programmed or configured to execute the method for real-time position identification of a permanent magnet semi-direct drive wind turbine generator set through a processor.

[0036] Compared with the prior art, the present invention mainly has the following advantages: the present invention includes using a harmonic filtering extended state observer based on an adaptive frequency algorithm in an improved active disturbance rejection controller to eliminate DC and harmonic components in the current; obtaining an extended back electromotive force signal of the γδ axis through a back electromotive force extended state observer in the improved active disturbance rejection controller; generating a speed and a rotor position angle from the extended back electromotive force signal through an orthogonal phase-locked loop, which are used in a speed loop and a coordinate transformation module, significantly improving the current quality on the side of a permanent magnet direct-drive wind turbine generator set, improving the identification accuracy of the speed and the rotor position angle, and optimizing the performance of a position sensorless control system of a permanent magnet direct-drive wind turbine generator set. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 Schematic diagram of the basic process of the method of the embodiment of the present invention.

[0038] Figure 2 Schematic diagram of the control principle of the method according to an embodiment of the present invention.

[0039] Figure 3 1 is a spatial relationship diagram of the dq and estimated γδ synchronously rotating reference coordinate systems in an embodiment of the present invention.

[0040] Figure 4 Schematic diagram of the control principle of the improved active disturbance rejection controller in an embodiment of the present invention.

[0041] Figure 5 Schematic diagram of the control principle of the harmonic filtering extended state observer in an embodiment of the present invention.

[0042] Figure 6 Schematic diagram of the control principle of the back electromotive force extended state observer in an embodiment of the present invention.

[0043] Figure 7 Schematic diagram of the control principle of an orthogonal phase-locked loop in an embodiment of the present invention.

[0044] Figure 8 1 is an error frequency response diagram of the improved active disturbance rejection controller with and without an adaptive frequency algorithm in an embodiment of the present invention.

[0045] Figure 9 This is a waveform diagram of a position experiment without a harmonic filtering extended state observer in an embodiment of the present invention.

[0046] Figure 10 This is an experimental waveform diagram of the A-phase current without the harmonic filtering extended state observer in an embodiment of the present invention.

[0047] Figure 11 This is a waveform diagram of a position experiment with a harmonic filtering extended state observer in an embodiment of the present invention.

[0048] Figure 12 This is an experimental waveform diagram of the A-phase current in the case of a harmonic filtering extended state observer in an embodiment of the present invention. DETAILED DESCRIPTION

[0049] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0050] like Figure 1 As shown, the method for real-time position identification of a permanent magnet semi-direct drive wind turbine generator set in this embodiment includes the following steps: using a harmonic filtering extended state observer based on an adaptive frequency algorithm as shown in the following formula to observe and obtain a disturbance identification value caused by parameter mismatch and a periodic disturbance identification value caused by inverter nonlinearity and spatial flux harmonics:

[0051]

[0052] The back electromotive force extended state observer shown in the following formula is used to observe and obtain the γδ axis extended back electromotive force identification value:

[0053]

[0054] in, and are the current identification values ​​of the harmonic filtering extended state observer at time k+1 and k, T s is the sampling period, B0 is the standard value of the coefficient matrix, is the known model disturbance value at time k, as well as are the disturbance identification values ​​caused by parameter mismatch at time k+1, k and k-1 respectively, as well as are the periodic disturbance identification values ​​caused by the inverter nonlinearity and spatial flux harmonics at time k+1, k, and k-1 respectively. and are the γδ-axis extended back-electromotive force identification values ​​at time k+1 and k, respectively; h1 and h2 are the gains of the harmonic filtering extended state observer; i(k) is the γδ-axis current at time k; and G0(z) is the transfer function of the adaptive frequency algorithm; and are the current identification values ​​of the back electromotive force extended state observer at time k+1 and k respectively, h3 and h4 are the gains of the back electromotive force extended state observer, is the current identification value of the back electromotive force extended state observer; the total disturbance composed of the periodic disturbance identification value, the disturbance identification value caused by parameter mismatch, the γδ axis extended back electromotive force identification value and the known model disturbance value is obtained based on the set control law to obtain the voltage identification value u(k) at the time k; the γδ axis extended back electromotive force identification value at the time k is Normalized to obtain the rotor position error identification value The rotor position error identification value Obtain rotor speed identification value through PI controller The rotor speed identification value The rotor position is obtained by integration

[0055] like Figure 2 As shown, in this embodiment, the control law, the harmonic filtering extended state observer based on the adaptive frequency algorithm, and the back electromotive force extended state observer together constitute the improved self-disturbance rejection controller in the method of this embodiment. Through the control law design, the total disturbance is used as the feedforward compensation item to input the designed control law, thereby reducing the impact of the inverter nonlinearity, spatial flux harmonics and parameter mismatch on the system performance; the harmonic filtering extended state observer based on the adaptive frequency algorithm is used to identify the disturbances in the current caused by the inverter nonlinearity, spatial flux harmonics and parameter mismatch, and obtain the γδ axis current signal without distortion; the back electromotive force extended state observer is used to output the ideal γδ axis extended back electromotive force. The extended back electromotive force signal is used to generate the speed and rotor position angle of the permanent magnet direct drive wind turbine through an orthogonal phase-locked loop for use in the speed loop and coordinate transformation module. As shown in FIG. Figure 2 As shown, the control method of the permanent magnet direct drive wind turbine generator set in this embodiment includes: sampling the three-phase current i A ,i B ,i C , after Clark transformation, the α, β axis stator current i is obtained α ,i β Then, the d and q axis currents i are obtained by Park coordinate transformation. d ,i q ; S2, reference speed ω ref and speed identification value The difference is passed through the PI regulator of the speed loop to generate the q-axis reference current d-axis reference current S3, d,q axis reference current and d,q axis current i d ,i q The difference between the two is passed through the improved active disturbance rejection controller in this embodiment to generate the d and q axis reference voltages

[0056] The voltage formula of the permanent magnet direct-drive wind turbine in the dq coordinate system is:

[0057]

[0058] Among them, i dq is the current value in the dq coordinate system, u dq is the voltage value in the dq coordinate system, L d is the d-axis inductance, L q is the q-axis inductance, R s is the stator resistance, R ex To expand the magnitude of the back electromotive force, its expression is:

[0059] R ex =(L d -L q )(ω e i d -pi q )+ω e ψ f ,

[0060] Among them, p = d / dt is the differential operator, ω e is the actual value of the rotor speed, ψ f is the motor rotor flux, i d and i q is the current value i in the dq coordinate system dq The d,q axis components of the permanent magnet direct-drive wind turbine generator model based on the dq coordinate system requires accurate motor rotor position information, which is unknown in the position sensorless control system. Therefore, the estimated γδ coordinate system is used to replace the dq coordinate system. The voltage formula in the γδ coordinate system of the permanent magnet direct-drive wind turbine generator is:

[0061]

[0062] Among them, i γδ is the current value in the γδ coordinate system, u γδ is the voltage value in the γδ coordinate system, L d is the d-axis inductance, L q is the q-axis inductance, R s is the stator resistance, e γδ is the extended back electromotive force in the γδ coordinate system, and its expression is:

[0063]

[0064] Among them, E ex To expand the amplitude of the back EMF, is the rotor position error identification value, is the rotor speed identification value, ω e is the actual value of the rotor speed, L q is the q-axis inductance. Figure 3 The spatial relationship diagram of the dq and estimated γδ synchronous rotating reference coordinate systems in this embodiment is shown in FIG. The dq synchronous rotating reference coordinate system is rotated at a speed of ω e Rotation, estimate γδ synchronous rotation reference coordinate system at speed Rotation, dq synchronous rotating reference frame hysteresis γδ synchronous rotating reference frame

[0065] Figure 4 The block diagram of the improved ADRC controller provided by the embodiment of the present invention includes three parts: a harmonic filtering extended state observer based on an adaptive frequency algorithm, a back-electromotive force extended state observer, and a control law design. The harmonic filtering state observer based on the adaptive frequency algorithm outputs disturbance identification values ​​and periodic disturbance identification values ​​caused by parameter mismatch, and the disturbance identification values ​​are input into the back-electromotive force extended state observer to output the ideal γδ-axis back-electromotive force identification values. The total disturbance is used as a feedforward compensation term to input the designed control law, thereby reducing the impact of inverter nonlinearity, spatial flux harmonics, and parameter mismatch on system performance. Figure 4 and Figure 5 As shown, the function expression of the harmonic filtering extended state observer based on the adaptive frequency algorithm in this embodiment is:

[0066]

[0067] like Figure 4 and Figure 6 As shown, the function expression of the back electromotive force extended state observer in this embodiment is:

[0068]

[0069] in, and are the current identification values ​​of the harmonic filtering extended state observer at time k+1 and k, T s is the sampling period, B0 is the standard value of the coefficient matrix, is the known model disturbance value at time k, as well as are the disturbance identification values ​​caused by parameter mismatch at time k+1, k and k-1 respectively, as well as are the periodic disturbance identification values ​​caused by the inverter nonlinearity and spatial flux harmonics at time k+1, k, and k-1 respectively. and are the γδ-axis extended back-electromotive force identification values ​​at time k+1 and k, respectively; h1 and h2 are the gains of the harmonic filtering extended state observer; i(k) is the γδ-axis current at time k; and G0(z) is the transfer function of the adaptive frequency algorithm; and are the current identification values ​​of the back electromotive force extended state observer at time k+1 and k respectively, h3 and h4 are the gains of the back electromotive force extended state observer, The current identification value of the back electromotive force extended state observer.

[0070] As mentioned above, through control law design, the total disturbance is used as a feedforward compensation term to input the designed control law, thereby reducing the impact of inverter nonlinearity, spatial flux harmonics, and parameter mismatch on system performance. In this embodiment, the function expression for the voltage identification value u(k) at time k obtained based on the set control law is:

[0071]

[0072] In the above formula, k p is the proportional gain, i * (k) is the reference value of the γδ axis current, i(k) is the γδ axis current at time k, B0 is the standard value of the coefficient matrix, is the total disturbance, and:

[0073]

[0074] in, is the periodic disturbance identification value caused by the inverter nonlinearity and spatial flux harmonics at time k, is the known model disturbance value at time k, is the γδ-axis extended back electromotive force identification value at time k, is the disturbance identification value caused by parameter mismatch at time k;

[0075]

[0076] Among them, L d0 is the standard value of stator d-axis inductance.

[0077] In this embodiment, the calculation function expression of the known model disturbance value at time k is:

[0078]

[0079] In the above formula, R s0 is the standard value of stator resistance, L d0 and L q0 are the standard values ​​of dq axis inductance, i γδ is the current value in the γδ coordinate system, is the rotor speed identification value, J is the matrix coefficient, and:

[0080]

[0081] In this embodiment, the transfer function of the adaptive frequency algorithm is expressed as follows:

[0082]

[0083] In the above formula, G o (z) is the transfer function of the adaptive frequency algorithm, μ is the gain of the adaptive frequency algorithm, ω h is the resonant angular frequency, T s is the sampling period; z is a variable in the complex domain, which is used to represent the frequency response of the discrete-time system.

[0084] Figure 7 In order to adopt the orthogonal phase-locked loop block diagram provided by the embodiment of the present invention, the γδ axis at the time k is expanded to identify the back electromotive force Normalized to obtain the rotor position error identification value The rotor position error identification value Obtain rotor speed identification value through PI controller The rotor speed identification value The rotor position is obtained by integration In this embodiment, the γδ axis extended back electromotive force identification value at time k is Normalized to obtain the rotor position error identification value The function expression is:

[0085]

[0086] In the above formula, and They are the γδ-axis extended back electromotive force identification values ​​at time k The γ and δ axis extended back electromotive force identification values.

[0087] In this embodiment, the rotor position error identification value Obtain rotor speed identification value through PI controller The function expression is:

[0088]

[0089] In the above formula, T s is the sampling period, z is a variable in the complex domain, k i is the proportionality coefficient, k p is the integration coefficient.

[0090] In this embodiment, the rotor speed identification value The rotor position is obtained by integration The function expression is:

[0091]

[0092] In the above formula, T s is the sampling period, and z is a variable in the complex domain.

[0093] Figure 8 The error frequency response diagram of the improved active disturbance rejection controller provided by this embodiment with or without the adaptive frequency algorithm; Figure 8 It can be seen that without the adaptive frequency algorithm, the improved active disturbance rejection controller has no ability to suppress the periodic disturbances caused by the inverter nonlinearity and spatial flux harmonics. When the adaptive frequency algorithm is used, the improved active disturbance rejection controller can significantly filter out the sixth harmonic. Figure 9 and Figure 10 This is an experimental waveform diagram of the extended state observer without harmonic filtering provided by this embodiment; Figure 9 and Figure 10 It can be seen that in the absence of a harmonic filtering extended state observer, the motor rotor position identification value and position error waveform show obvious 6th harmonic pulsation. The FFT analysis of phase A current shows that there is a DC error and 5th and 7th harmonics in phase A current, which is affected by the inverter nonlinearity, spatial flux harmonics and parameter mismatch. Figure 11 and Figure 12 This is an experimental waveform diagram of the case where the harmonic filtering extended state observer provided by the embodiment of the present invention is used; Figure 11 and Figure 12 It can be seen that with the harmonic filtering extended state observer, the harmonic pulsation of the motor rotor position identification waveform is effectively suppressed, and the position error is controlled within 3.2°. The FFT analysis of the A-phase current shows that the proportion of DC and 5th and 7th harmonic components in the A-phase current is significantly reduced. This is because the harmonic filtering state observer based on the adaptive frequency algorithm can identify periodic disturbances and non-periodic disturbances in the current. The disturbance identification value is input into the back-electromotive force extended state observer to output the ideal γδ-axis back-electromotive force identification value, thereby improving the identification accuracy of the rotor position and optimizing the control performance of the permanent magnet direct-drive wind turbine generator set.

[0094] In summary, the real-time position identification method for a permanent magnet semi-direct-drive wind turbine generator set in this embodiment includes eliminating DC and harmonic components in the current using a harmonic filtering extended state observer based on an adaptive frequency algorithm in an improved active disturbance rejection controller; obtaining an extended back-EMF signal for the γδ axes using a back-EMF extended state observer in the improved active disturbance rejection controller; and generating a rotational speed and rotor position angle from the extended back-EMF signal through an orthogonal phase-locked loop for use in the rotational speed loop and coordinate transformation module. This invention significantly improves the machine-side current quality of a permanent magnet direct-drive wind turbine generator set, enhances the identification accuracy of the rotational speed and rotor position angle, and optimizes the performance of the position sensorless control system for a permanent magnet direct-drive wind turbine generator set. This embodiment method effectively suppresses the DC and harmonic components in the machine-side current of a permanent magnet direct-drive wind turbine generator set through a harmonic filtering extended state observer based on an adaptive frequency algorithm. The back-EMF extended state observer outputs an ideal extended back-EMF identification value, significantly reducing the impact of inverter nonlinearity, spatial flux harmonics, and parameter mismatch on rotor speed and position identification accuracy, thereby optimizing the positionless control performance of the permanent magnet direct-drive wind turbine generator set. This embodiment method significantly improves the machine-side current quality of the permanent magnet direct-drive wind turbine generator set, increases the identification accuracy of the speed and rotor position angle, and optimizes the performance of the position sensorless control system of the permanent magnet direct-drive wind turbine generator set.

[0095] In addition, this embodiment also provides a permanent magnet semi-direct drive wind turbine generator set position real-time identification system, including an interconnected microprocessor and a memory, wherein the microprocessor is programmed or configured to execute the permanent magnet semi-direct drive wind turbine generator set position real-time identification method.

[0096] In addition, this embodiment also provides a computer-readable storage medium, which stores a computer program or instruction, and the computer program or instruction is programmed or configured to execute the real-time position identification method of the permanent magnet semi-direct drive wind turbine generator set through a processor.

[0097] In addition, this embodiment also provides a computer program product, including a computer program or instructions, which are programmed or configured to execute the method for real-time position identification of a permanent magnet semi-direct drive wind turbine generator set through a processor.

[0098] Those skilled in the art will appreciate that the embodiments of the present application may provide a technical solution in the form of a method, system, or computer program product. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-readable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, may be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 These computer program instructions can also be stored in a computer-readable memory that can guide a computer or other programmable data processing device to work in a specific way, so that the instructions stored in the computer-readable memory produce a product including the instruction device, which implements the function specified in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0099] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiment. All technical solutions based on the concept of the present invention are within the scope of protection of the present invention. It should be noted that for those skilled in the art, various improvements and modifications that do not depart from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A method for real-time position identification of a permanent magnet semi-direct drive wind turbine generator set, characterized in that: The method comprises the following steps: using a harmonic filtering extended state observer based on an adaptive frequency algorithm as shown in the following formula to observe and obtain a disturbance identification value caused by parameter mismatch and a periodic disturbance identification value caused by inverter nonlinearity and spatial flux harmonics: The back electromotive force extended state observer shown in the following formula is used to observe and obtain the γδ axis extended back electromotive force identification value: in, and are the current identification values ​​of the harmonic filtering extended state observer at time k+1 and k, T s is the sampling period, B0 is the standard value of the coefficient matrix, is the known model disturbance value at time k, as well as are the disturbance identification values ​​caused by parameter mismatch at time k+1, k and k-1 respectively, as well as are the periodic disturbance identification values ​​caused by the inverter nonlinearity and spatial flux harmonics at time k+1, k, and k-1 respectively. and are the γδ-axis extended back electromotive force identification values ​​at time k+1 and k, respectively; h1 and h2 are the gains of the harmonic filtering extended state observer; i(k) is the γδ-axis current at time k; and G0() is the transfer function of the adaptive frequency algorithm; and are the current identification values ​​of the back electromotive force extended state observer at time k+1 and k respectively, h3 and h4 are the gains of the back electromotive force extended state observer, is the current identification value of the back electromotive force extended state observer; the total disturbance composed of the periodic disturbance identification value, the disturbance identification value caused by parameter mismatch, the γδ axis extended back electromotive force identification value and the known model disturbance value is obtained based on the set control law to obtain the voltage identification value u(k) at the time k; the γδ axis extended back electromotive force identification value at the time k is Normalized to obtain the rotor position error identification value The rotor position error identification value Obtain rotor speed identification value through PI controller The rotor speed identification value The rotor position is obtained by integration 2. The method for real-time position identification of a permanent magnet semi-direct drive wind turbine generator set according to claim 1, characterized in that: The control law based on the setting is obtained Voltage identification value at the moment The function expression is: ， In the above formula, is the proportional gain, for Shaft current reference value, for Moment Shaft current, is the standard value of the coefficient matrix, is the total disturbance, and: ， in, for The periodic disturbance identification value caused by the inverter nonlinearity and spatial flux harmonics at the moment, for The known model perturbation value at time , for Moment Axis extended back electromotive force identification value, for Disturbance identification value caused by parameter mismatch at the moment; ， in, is the standard value of stator d-axis inductance.

3. The method for real-time position identification of a permanent magnet semi-direct drive wind turbine generator set according to claim 1, characterized in that: described The calculation function expression of the known model disturbance value at the moment is: ， In the above formula, is the standard value of stator resistance, and They are dq The standard value of shaft inductance, for The current value in the coordinate system, is the rotor speed identification value, are matrix coefficients, and we have: 。 4. The method for real-time position identification of a permanent magnet semi-direct drive wind turbine generator set according to claim 1, characterized in that: The function expression of the transfer function of the adaptive frequency algorithm is: ， In the above formula, is the transfer function of the adaptive frequency algorithm, is the gain of the adaptive frequency algorithm, is the resonant angular frequency, is the sampling period; is a variable in the complex domain that represents the frequency response of a discrete-time system.

5. The method for real-time position identification of a permanent magnet semi-direct drive wind turbine generator set according to claim 1, characterized in that: The said Moment Axis extended back electromotive force identification value Normalized to obtain the rotor position error identification value The function expression is: ， In the above formula, and They are Moment Axis extended back electromotive force identification value in Axis extended back EMF identification value.

6. The method for real-time position identification of a permanent magnet semi-direct drive wind turbine generator set according to claim 1, characterized in that: The rotor position error identification value Obtain rotor speed identification value through PI controller The function expression is: ， In the above formula, is the sampling period, is a variable in the complex domain, is the proportionality coefficient, is the integration coefficient.

7. The method for real-time position identification of a permanent magnet semi-direct drive wind turbine generator set according to claim 1, characterized in that: The rotor speed identification value The rotor position is obtained by integration The function expression is: ， In the above formula, is the sampling period, is a variable in the complex domain.

8. A permanent magnet semi-direct drive wind turbine generator set position real-time identification system, comprising a microprocessor and a memory connected to each other, characterized in that: The microprocessor is programmed or configured to execute the method for real-time position identification of a permanent magnet semi-direct drive wind turbine generator set as claimed in any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program or instruction stored therein, characterized in that: The computer program or instruction is programmed or configured to execute the method for real-time position identification of a permanent magnet semi-direct drive wind turbine generator set as claimed in any one of claims 1 to 7 through a processor.

10. A computer program product comprising a computer program or instructions, characterized in that The computer program or instruction is programmed or configured to execute the method for real-time position identification of a permanent magnet semi-direct drive wind turbine generator set as claimed in any one of claims 1 to 7 through a processor.

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