Real-time position identification method and system for permanent-magnet semi-direct-drive wind generating set and medium

Abstract

The invention discloses a real-time position identification method and system for a permanent magnet semi-direct drive wind generating set and a medium. The method comprises the following steps: observing and obtaining a disturbance identification value caused by parameter mismatch and a periodic disturbance identification value caused by nonlinearity and space flux linkage harmonics of an inverter by adopting a harmonic filtering extended state observer based on an adaptive frequency algorithm; a back electromotive force expansion state observer is adopted to observe and obtain a # imgabs0 # shaft expansion back electromotive force identification value; obtaining a voltage identification value at the # imgabs1 # moment by the total disturbance based on a set control law; and then a phase-locked loop is used to obtain a rotor speed identification value # imgabs2 #, and the rotor speed identification value # imgabs3 # is subjected to integration to obtain a rotor position # imgabs4 #. The method aims at improving the current quality of the machine side of the permanent magnet direct-driven wind generating set, improving the identification precision of the rotating speed and the rotor position angle, and optimizing the performance of a position sensorless control system of the permanent magnet direct-driven wind generating set.

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 power generator set. Background Art

[0002] In recent years, people's increasing attention to energy production and environmental pollution has greatly promoted the use of renewable energy. As one of the most popular and cost-effective renewable energy sources in power production, wind energy has great potential. The variable speed constant frequency wind power generation method based on permanent magnet direct-drive wind turbines is highly efficient and has low maintenance costs. For permanent magnet direct-drive wind power generation systems, the key to achieving high-performance control is to obtain the rotor position information of the permanent magnet direct-drive wind turbine in real time, which is generally obtained by encoders. However, the installation of encoders will increase the size of permanent magnet direct-drive wind turbines, increase manufacturing costs, and reduce the reliability and stability of the control system. The real-time position identification method of permanent magnet semi-direct-drive wind turbines replaces the mechanical encoder to provide rotor position and speed information to the speed loop and coordinate transformation, and optimizes the control strategy of permanent magnet direct-drive wind turbines. It is divided into a model method suitable for medium and high speeds and a high-frequency injection method suitable for zero and low speeds. The model method includes sliding mode observers, Romberg observers, adaptive observers, and extended state observers, etc. In practical applications, due to the influence of inverter nonlinearity, spatial flux harmonics and parameter mismatch, the machine-side current of permanent magnet direct-drive wind turbines is distorted. The back-electromotive force signal directly observed by the model method has DC and high-order harmonics, which affects the identification accuracy of the rotor position. Some methods insert a filter after the observer to extract the fundamental frequency component in the back-electromotive force and output an ideal rotor position identification value, but ignore the improvement of current quality, and the control performance of permanent magnet direct-drive wind turbines still needs to be improved. Summary of the invention

[0003] Technical problem to be solved by the present invention: 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 a 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 a disturbance identification value caused by parameter mismatch and a periodic disturbance identification value caused by inverter nonlinearity and spatial flux harmonics:

[0006]

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

[0008]

[0009] in, and are the current identification values ​​of the harmonic filtering extended state observer at time k+1 and k, respectively, 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 inverter nonlinearity and spatial flux harmonics at time k+1, k and k-1 respectively, and are the γδ-axis extended back-EMF 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-EMF extended state observer at time k+1 and k respectively, h3 and h4 are the gains of the back-EMF 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 time k; the γδ axis extended back-electromotive force identification value at time k is Normalized to get the rotor position error identification value The rotor position error identification value The rotor speed identification value is obtained through the 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 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 function expression of the transfer function of the adaptive frequency algorithm is:

[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 get 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 The rotor speed identification value is obtained through the 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 comprises using a harmonic filtering extended state observer based on an adaptive frequency algorithm in an improved self-disturbance rejection controller to eliminate DC and harmonic components in the current; obtaining an extended back-electromotive force signal of the γδ axes through a back-electromotive force extended state observer in the improved self-disturbance rejection controller; generating a rotational speed and a rotor position angle through an orthogonal phase-locked loop from the extended back-electromotive force signal, which are used in a rotational speed loop and a coordinate transformation module, thereby significantly improving the current quality on the side of a permanent magnet direct-drive wind turbine generator set, improving the identification accuracy of the rotational 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 flow 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 4 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] Fig. 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] Fig.10 This is an experimental waveform diagram of the A-phase current without a harmonic filtering extended state observer in an embodiment of the present invention.

[0047] Fig.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] Fig.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 scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0050] like Figure 1 As shown, the real-time position identification method of the permanent magnet semi-direct drive wind turbine generator set in this embodiment includes the following steps: using the harmonic filtering extended state observer based on the adaptive frequency algorithm shown in the following formula to observe and obtain the disturbance identification value caused by parameter mismatch and the periodic disturbance identification value caused by the 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, respectively, 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 inverter nonlinearity and spatial flux harmonics at time k+1, k and k-1 respectively, and are the γδ-axis extended back-EMF 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-EMF extended state observer at time k+1 and k respectively, h3 and h4 are the gains of the back-EMF 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 time k; the γδ axis extended back-electromotive force identification value at time k is Normalized to get the rotor position error identification value The rotor position error identification value The rotor speed identification value is obtained through the PI controller The rotor speed identification value The rotor position is obtained by integration

[0055] like Figure 2 As shown, the control law, the harmonic filtering extended state observer based on the adaptive frequency algorithm and the back-electromotive force extended state observer in this embodiment together constitute the improved anti-disturbance controller in the method of this embodiment. Through the control law design, the total disturbance is used as the 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; the harmonic filtering extended state observer based on the adaptive frequency algorithm is used to identify the disturbances in the current caused by inverter nonlinearity, spatial flux harmonics and parameter mismatch, and obtain a γδ-axis current signal without distortion; the back-electromotive force extended state observer is used to output an 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 generator set through an orthogonal phase-locked loop for use in the speed loop and coordinate transformation module. As shown 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 is passed through the improved anti-disturbance controller in this embodiment to generate the d and q axis reference voltages

[0056] The voltage formula of permanent magnet direct-drive wind turbine in 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 amplitude of the back EMF, the 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 permanent magnet direct-drive wind turbine generator in the γδ coordinate system 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 is a spatial relationship diagram of the dq and estimated γδ synchronous rotating reference coordinate systems in this embodiment, where the dq synchronous rotating reference coordinate system is based on the rotation speed ω e Rotation, estimate γδ synchronous rotation reference coordinate system at speed Rotation, dq synchronous rotating reference frame lag γδ synchronous rotating reference frame

[0065] Figure 4 The block diagram of the improved self-disturbance rejection 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 disturbance identification value and the periodic disturbance identification value caused by the output parameter mismatch of the harmonic filtering state observer based on the adaptive frequency algorithm are used, and 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. The total disturbance is used as a feedforward compensation term to input into the designed control law, thereby reducing the influence 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 functional 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, respectively, 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 inverter nonlinearity and spatial flux harmonics at time k+1, k and k-1 respectively, and are the γδ-axis extended back-EMF 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-EMF extended state observer at time k+1 and k respectively, h3 and h4 are the gains of the back-EMF extended state observer, The current identification value of the back-EMF extended state observer.

[0070] As mentioned above, by designing the control law, the total disturbance is used as a feedforward compensation term to input the designed control law, thereby reducing the influence of inverter nonlinearity, spatial flux harmonics and parameter mismatch on system performance; in this embodiment, the function expression of 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 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 function expression of the transfer function of the adaptive frequency algorithm is:

[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 get the rotor position error identification value The rotor position error identification value The rotor speed identification value is obtained through the 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 get 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 The rotor speed identification value is obtained through the 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 FIG. 1 is an error frequency response diagram of the improved active disturbance rejection controller provided by this embodiment with or without an adaptive frequency algorithm; FIG. Figure 8 It can be seen that in the absence of an adaptive frequency algorithm, the improved active disturbance rejection controller has no ability to suppress periodic disturbances caused by inverter nonlinearity and spatial flux harmonics. When an adaptive frequency algorithm is used, the improved active disturbance rejection controller can significantly filter out the sixth harmonic. Fig. 9 and Fig.10 FIG. 1 is an experimental waveform diagram of the extended state observer without harmonic filtering provided by this embodiment; FIG. Fig. 9 and Fig.10 It can be seen that in the absence of 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 DC error and 5th and 7th harmonics in phase A current, which is the influence of inverter nonlinearity, spatial flux harmonics and parameter mismatch. Fig.11 and Fig.12 The experimental waveform diagram of the extended state observer with harmonic filtering provided by the embodiment of the present invention is shown in FIG. Fig.11 and Fig.12 It can be seen that in the case of 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 the periodic disturbances and non-periodic disturbances in the current. The disturbance identification value is input to the back-EMF extended state observer to output the ideal γδ-axis back-EMF identification value, which improves the identification accuracy of the rotor position and optimizes the control performance of the permanent magnet direct-drive wind turbine generator set.

[0094] In summary, the real-time position identification method of the permanent magnet semi-direct-drive wind turbine generator set of this embodiment includes using the harmonic filtering extended state observer based on the adaptive frequency algorithm in the improved self-disturbance rejection controller to eliminate the DC and harmonic components in the current; obtaining the extended back-electromotive force signal of the γδ axis through the back-electromotive force extended state observer in the improved self-disturbance rejection controller; and generating the speed and rotor position angle of the extended back-electromotive force signal through an orthogonal phase-locked loop for the speed loop and coordinate transformation module. The present invention significantly improves the current quality of the permanent magnet direct-drive wind turbine generator set, improves 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. The method of this embodiment effectively suppresses the DC and harmonic components in the side current of the permanent magnet direct-drive wind turbine generator set through a harmonic filtering extended state observer based on an adaptive frequency algorithm, and outputs an ideal extended back-EMF identification value through a back-EMF extended state observer, which significantly weakens the influence of inverter nonlinearity, spatial flux harmonics and parameter mismatch on the rotor speed and position identification accuracy, and optimizes the position-free control performance of the permanent magnet direct-drive wind turbine generator set. The method of this embodiment significantly improves the side current quality of the permanent magnet direct-drive wind turbine generator set, improves the identification accuracy of the speed and rotor position angle, and optimizes the performance of the position sensor-free 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 a microprocessor and a memory connected to each other, and 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 technical solutions provided by the embodiments of the present application may be in the form of methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt 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 codes. 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, can be implemented by computer program instructions. These computer program instructions can 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 processes 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 an instruction device, which implements the functions 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, so that the instructions executed on the computer or other programmable device provide for implementing the process in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0099] The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope 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, respectively, 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 inverter nonlinearity and spatial flux harmonics at time k+1, k and k-1 respectively, and are the γδ-axis extended back-EMF 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-EMF extended state observer at time k+1 and k respectively, h3 and h4 are the gains of the back-EMF 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 time k; the γδ axis extended back-electromotive force identification value at time k is Normalized to get the rotor position error identification value The rotor position error identification value The rotor speed identification value is obtained through the 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 is 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 EMF 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: Said The calculation function expression of the known model disturbance value at time is: ， In the above formula, is the standard value of stator resistance, and They are dq The standard value of shaft inductance is for The current value in the coordinate system, is the rotor speed identification value, is the matrix coefficient, 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 Moment Axis extended back EMF identification value Normalized to get the rotor position error identification value The function expression is: ， In the above formula, and They are Moment Axis extended back EMF 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 The rotor speed identification value is obtained through the 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 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 described 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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