Machine-side converter finite control set model prediction control method, device and equipment

Abstract

The invention relates to the technical field of converter control, and discloses a finite control set model prediction control method, device and equipment for a machine side converter, and the method comprises the steps: inputting the port voltage of the machine side converter at the current moment under a target coordinate and the stator current of a fan at the current moment under the target coordinate into a prediction model, the stator current of at least one future moment under the target coordinate is predicted through the prediction model; on the basis of the stator current of at least one future moment under the target coordinate and the machine side current reference value under the target coordinate, performing traversal calculation to obtain the optimal switching state of each bridge arm of the converter, which minimizes the value of the cost function; and controlling each bridge arm of the converter according to the optimal switching state. According to the method, the on-off state of each bridge arm of the machine side converter can be adaptively changed on line, tedious PI setting is reduced, decoupling is not needed, multiple times of conversion is not needed, and the operand is small.

Description

Technical Field

[0001] This invention relates to the field of converter control technology, specifically to a finite control set model predictive control method, device, and equipment for machine-side converters. Background Technology

[0002] To cope with increasing electricity demand and environmental pressures, power systems are trending towards a high proportion of renewable energy and power electronic equipment. Currently, most power electronic converters employ a control mode that decouples the prime mover input power from the grid-side electromagnetic power, generally lacking spinning reserve capacity and rotational inertia, and unable to provide an inertial response similar to traditional synchronous generators. Therefore, a high proportion of renewable energy integration will increase power system stability risks and may even lead to large-scale grid blackouts. Utilizing wind farms as black-start power sources can significantly accelerate grid recovery.

[0003] To support black start of wind turbines, grid-connected converters are recommended. By embedding the motion or electromagnetic equations of a synchronous generator into the grid-connected converter, under the assumption of unrestricted DC-side power, the converter can possess external characteristics similar to a synchronous generator. It can actively construct the voltage and frequency of the output port without relying on phase-locked loops and current information, thus enabling the grid-connected converter to autonomously synchronize with the grid and respond to grid frequency and voltage. Based on this principle, various methods for implementing grid-connected control have been proposed, mainly including droop control, power synchronization control, virtual synchronous machine control, DC capacitor inertial synchronization control, and virtual oscillator control.

[0004] Existing network-based control methods typically employ proportional-integral-differential (PID) controllers, which are simple in structure, robust, and easy to implement. However, in practical converter control, PID controllers require cumbersome PI tuning and multiple transformations, resulting in a large computational load. Summary of the Invention

[0005] In view of this, the present invention provides a model predictive control method, apparatus and equipment for a finite control set of generator-side converters to solve the technical problem of large computational load in existing grid-type control methods.

[0006] In a first aspect, the present invention provides a finite control set model predictive control method for a machine-side converter, comprising: acquiring the machine-side converter port voltage and the stator current of the wind turbine at the current moment in the target coordinates; acquiring a reference value of the machine-side current in the target coordinates; inputting the machine-side converter port voltage and the stator current of the wind turbine at the current moment in the target coordinates into a predictive model, and predicting the stator current at at least one future moment in the target coordinates through the predictive model; based on the stator current at at least one future moment in the target coordinates and the reference value of the machine-side current in the target coordinates, iteratively calculating the optimal switching state of each bridge arm of the converter that minimizes the cost function; and controlling each bridge arm of the converter according to the optimal switching state.

[0007] In some optional embodiments, obtaining the machine-side converter port voltage and the stator current of the wind turbine at the current time under the target coordinates includes: obtaining the machine-side converter port voltage and the stator current of the wind turbine at the current time under the initial coordinates; performing a preset coordinate transformation on the machine-side converter port voltage at the current time under the initial coordinates to obtain the machine-side converter port voltage at the current time under the target coordinates; and performing a preset coordinate transformation on the stator current of the wind turbine at the current time under the initial coordinates to obtain the stator current of the wind turbine at the current time under the target coordinates.

[0008] In some optional implementations, when the control mode of the generator-side converter is grid-following control, obtaining the generator-side current reference value under the target coordinates includes: obtaining the synchronous speed of the wind turbine motor; obtaining the maximum active and reactive power output reference value based on the synchronous speed of the motor using the maximum power tracking curve; and obtaining the generator-side current reference value under the target coordinates after proportional-integral adjustment and preset coordinate transformation of the maximum active and reactive power output reference value.

[0009] In some optional implementations, when the control mode of the generator-side converter is grid-type control, obtaining the generator-side current reference value under the target coordinates includes: obtaining the DC-side voltage and DC-side voltage reference value of the converter; comparing the DC-side voltage and DC-side voltage reference value of the converter to obtain a first difference; and obtaining the generator-side current reference value under the target coordinates after proportional-integral adjustment and coordinate transformation of the first difference.

[0010] In some alternative implementations, the initial coordinates are three-phase stationary coordinates, the target coordinates are two-phase rotating coordinates, and the prediction model is:

[0011]

[0012] In the formula, k represents the current time. and Let represent the d-axis and q-axis components of the stator current at time k+1 in the two-phase rotating coordinate system, respectively. and These represent the d-axis and q-axis components of the stator current at the current moment in the two-phase rotating coordinate system, respectively. and These represent the d-axis and q-axis components of the current generator-side converter port voltage in a two-phase rotating coordinate system, respectively. This represents the d-axis component of the stator voltage of the wind turbine at the current moment, where L and R represent the equivalent reactance and equivalent resistance of the wind turbine rotor, respectively, and T. s ω represents the sampling period. r Indicates the synchronous speed of the motor, ψ f This indicates the magnetic flux linkage of the rotor permanent magnet.

[0013] In some alternative implementations, the cost function is:

[0014]

[0015] In the formula, l g (k) represents the cost function, where N and m are both positive integers. and These represent the d-axis and q-axis components of the machine-side current reference value in two-phase rotating coordinate systems, respectively. Indicates the DC-side voltage reference value. and Let represent the d-axis and q-axis components of the machine-side current at the (k+m)th time step in a two-phase rotating coordinate system, respectively. λ1 represents the DC side voltage at the (k+m)th moment in a two-phase rotating coordinate system. When the control mode of the generator-side converter is grid-following control, λ1 = 0; when the control mode of the generator-side converter is grid-connected control, λ1 = 1.

[0016] In some alternative implementations, when traversing and calculating the optimal switching states of each bridge arm of the converter that minimize the cost function, an amplitude constraint condition needs to be satisfied. The amplitude constraint condition is as follows:

[0017]

[0018] Among them, i d,max and i q,max These represent the maximum values ​​of the d-axis and q-axis components of the stator current in the two-phase rotating coordinate system, respectively.

[0019] Secondly, the present invention provides a finite control set model predictive control device for a machine-side converter, comprising: a port voltage and current acquisition module for acquiring the machine-side converter port voltage and the stator current of the wind turbine at the current moment under the target coordinates; a current reference value acquisition module for acquiring the machine-side current reference value under the target coordinates; a current prediction module for inputting the machine-side converter port voltage and the stator current of the wind turbine at the current moment under the target coordinates into a prediction model, and predicting the stator current at at least one future moment under the target coordinates through the prediction model; a traversal optimization module for traversing and calculating the optimal switching state of each bridge arm of the converter that minimizes the cost function based on the stator current at at least one future moment under the target coordinates and the machine-side current reference value under the target coordinates; and a bridge arm control module for controlling each bridge arm of the converter according to the optimal switching state.

[0020] Thirdly, the present invention provides a computer device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the machine-side converter finite control set model predictive control method described in the first aspect or any corresponding embodiment thereof.

[0021] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the machine-side converter finite control set model predictive control method described in the first aspect or any corresponding embodiment thereof.

[0022] The present invention has the following beneficial effects:

[0023] This invention discloses a finite control set model predictive control method for a machine-side converter. It obtains the current machine-side converter port voltage and the current stator current of the wind turbine at the target coordinates, acquires a reference value for the machine-side current at the target coordinates, and inputs these values ​​into a predictive model. The predictive model then predicts the stator current at at least one future time in the target coordinates. Based on this stator current and the reference value, the optimal switching state of each bridge arm of the converter that minimizes the cost function is calculated. The method controls each bridge arm of the converter according to the optimal switching state, enabling online adaptive changes to the switching states of each bridge arm of the machine-side converter, maintaining stable system operation, reducing cumbersome PI tuning, eliminating the need for decoupling and multiple transformations, minimizing computational load, and avoiding complex controller parameter design. Attached Figure Description

[0024] 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.

[0025] Figure 1 This is a flowchart of the machine-side converter finite control set model predictive control method according to an embodiment of the present invention;

[0026] Figure 2 This is a topology diagram of the machine-side converter according to an embodiment of the present invention;

[0027] Figure 3 This is a block diagram illustrating the control structure principle of the machine-side converter according to an embodiment of the present invention;

[0028] Figure 4 This is a schematic diagram of the structure of the machine-side converter finite control set model predictive control device according to an embodiment of the present invention;

[0029] Figure 5 This is a schematic diagram of the structure of a computer device according to an embodiment of the present invention. Detailed Implementation

[0030] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0031] In practical converter control, PID controllers have limited bandwidth and cannot handle physical constraints such as voltage and current limits in real-time converter control, easily leading to overshoot, oscillation, and degraded power quality. This invention employs a Finite Control Horizon Model Predictive Control (FCS-MPC) algorithm, which eliminates the need for an external modulator and allows direct real-time control of transistor switching devices. This reduces computational complexity and avoids the need for complex controller parameter design.

[0032] This invention proposes a finite control set model predictive control method for machine-side converters, applicable to terminal devices such as computers and servers. Figure 1 As shown, the process includes the following steps:

[0033] Step S101: Obtain the current voltage of the generator-side converter at the target coordinates and the current stator current of the wind turbine at the target coordinates.

[0034] Specifically, the target coordinates are two-phase rotating coordinates, or the dq coordinate system. Two-phase rotating coordinates are a coordinate system used in power systems to describe vector analysis in three-phase AC systems. Two-phase rotating coordinates are obtained by transforming from three-phase stationary coordinates, converting the three-phase system into a two-dimensional coordinate system with DC or stator as the reference through rotation transformation.

[0035] A sampling period is set, and the grid current at the target coordinates at the current moment is sampled and calculated in each sampling period. It should be understood that each sampling period corresponds to a sampling moment, and the next moment in this application is the next sampling moment.

[0036] In some embodiments, step S101, obtaining the machine-side converter port voltage and the stator current of the wind turbine at the current time under the target coordinates, includes:

[0037] Step S1011: Obtain the current voltage of the machine-side converter port and the current stator current of the wind turbine at the current time under the initial coordinates.

[0038] Step S1012: Perform a preset coordinate transformation on the current machine-side converter port voltage at the initial coordinates to obtain the current machine-side converter port voltage at the target coordinates. Perform a preset coordinate transformation on the current stator current of the wind turbine at the initial coordinates to obtain the current stator current of the wind turbine at the target coordinates.

[0039] Specifically, the initial coordinates are three-phase stationary coordinates. The grid current i(k) at the current moment under the three-phase stationary coordinates is obtained through real-time measurement, where i(k) includes the grid currents ia, ib, and ic of phases a, b, and c. a (k), i b (k) and i c (k), the grid current i(k) at the current moment in the three-phase stationary coordinate system is transformed by Clarke transformation to obtain the grid current i at the current moment in the two-phase rotating coordinate system. dq (k).

[0040] Step S102: Obtain the reference value of the machine-side current at the target coordinates.

[0041] Specifically, the control modes of the generator-side converter include two types: grid-based control and grid-connected control.

[0042] When the control mode of the generator-side converter is grid-following control, step S102 involves obtaining the generator-side current reference value at the target coordinates, including:

[0043] Step S10211: Obtain the synchronous speed of the fan motor;

[0044] Step S10212: Based on the synchronous speed of the motor, obtain the reference value of the maximum active and reactive power output using the maximum power tracking curve;

[0045] Step S10213: After the maximum active and reactive power output reference value is adjusted by proportional-integral method and subjected to preset coordinate transformation, the machine-side current reference value under the target coordinate is obtained.

[0046] Specifically, the wind turbine can be a permanent magnet direct-drive wind turbine. The synchronous speed of the wind turbine motor is acquired in each sampling period. The turbine-side converter outputs the active and reactive power corresponding to this synchronous speed based on the Maximum Power Point Tracking (MPPT) curve, serving as the reference value for the maximum active and reactive power output. This reference value is input to a PI controller for proportional-integral regulation to obtain the turbine-side current reference value in three-phase stationary coordinates. This three-phase stationary coordinate reference value is then transformed using Clarke transformation to obtain the two-phase rotating coordinate reference value i. dq * .

[0047] When the control mode of the generator-side converter is grid-type control, step S102 involves obtaining the generator-side current reference value at the target coordinates, including:

[0048] Step S10221: Obtain the DC-side voltage and DC-side voltage reference value of the converter;

[0049] Step S10222: Compare the DC-side voltage of the converter with the DC-side voltage reference value to obtain the first difference;

[0050] Step S10223: After adjusting the first difference using proportional-integral ratio and performing coordinate transformation, the reference value of the machine-side current under the target coordinates is obtained.

[0051] Specifically, the DC-side voltage u of the converter is measured in each sampling period. dc and DC side voltage reference value Among them, DC side voltage u dc The DC-side voltage reference value is obtained through real-time sampling. This is obtained through command settings.

[0052] DC side voltage u dc and DC side voltage reference value The first difference is obtained by comparing and subtracting the values. This first difference is then input into a PI controller for proportional-integral adjustment to obtain the machine-side current reference value in three-phase stationary coordinates. The machine-side current reference value in three-phase stationary coordinates is then transformed by Clarke to obtain the machine-side current reference value i in two-phase rotating coordinates. dq * .

[0053] Machine-side current reference value i dq * After obtaining the corresponding d-axis components respectively and q-axis components

[0054] Step S103: Input the current generator port voltage and the current stator current of the wind turbine at the target coordinates to the prediction model, and predict the stator current at at least one future time at the target coordinates through the prediction model.

[0055] Specifically, based on the circuit topology, a predictive model is constructed using a finite control set model predictive control algorithm.

[0056] Finite control set model predictive control is a model-based predictive control algorithm that achieves control objectives by predicting the future behavior of the system and selecting the optimal control input.

[0057] The predictive model is built upon the dynamic characteristics of the converter and can predict future states based on the current state and control inputs. For example, the predictive model can predict the grid current at the next moment based on the current grid current in a two-phase rotating coordinate system, or predict the grid current at the next two moments, etc. The number of moments to be predicted can be set according to actual conditions.

[0058] Step S104: Based on the stator current at at least one future time under the target coordinates and the reference value of the machine-side current under the target coordinates, calculate the optimal switching state of each bridge arm of the converter that minimizes the value of the cost function.

[0059] The cost function is set according to control requirements and is used to measure the difference between the predicted state and the desired state.

[0060] To protect power devices, prevent excessive current from exceeding safety limits, and stabilize the DC bus voltage, when calculating the optimal switching states of each bridge arm of the converter that minimize the cost function, amplitude constraints must be satisfied. The amplitude constraints are as follows:

[0061]

[0062] Among them, i d,max and i q,maxThese represent the maximum values ​​of the d-axis and q-axis components of the stator current in the two-phase rotating coordinate system, respectively.

[0063] Step S105: Control each bridge arm of the converter according to the optimal switching state.

[0064] After obtaining the optimal switching state, it is input into the machine-side converter for control. The above control process is repeated in each sampling period to achieve online rolling optimization.

[0065] This invention discloses a finite control set model predictive control method for a generator-side converter. It acquires the current grid current and a reference value of the current grid current in a two-phase rotating coordinate system. The current grid current in the two-phase rotating coordinate system is input into a predictive model, which predicts the grid current at least one future time in the two-phase rotating coordinate system. Based on the grid current at at least one future time in the two-phase rotating coordinate system and the reference value of the grid current in the two-phase rotating coordinate system, the optimal switching state of each bridge arm of the converter that minimizes the cost function is calculated iteratively. The optimal switching state is then used to control each bridge arm of the converter. This method can adaptively change the switching state of the generator-side converter online according to the grid voltage or DC voltage, maintaining stable system operation, reducing cumbersome PI tuning, eliminating the need for decoupling, and avoiding multiple transformations.

[0066] The present invention discloses a model predictive control method for a machine-side converter with a finite control set, which does not require an external modulator and can directly control transistor switching devices in real time. It has low computational load and avoids complex controller parameter design.

[0067] The present invention provides a finite control set model predictive control method for machine-side converters, which can achieve a balance between control accuracy and computation time by selecting an appropriate number of iteration steps according to actual needs.

[0068] Furthermore, the generator-side converter topology is as follows: Figure 2 As shown, u a u b and u c These represent the three-phase voltages at the machine-side converter ports, i a i b and i c These represent the three-phase stator currents of the permanent magnet direct-drive fan, e a e b and e c These represent the three-phase stator voltages of the permanent magnet direct-drive fan, u dc Indicates the DC side voltage, i dc Representing the DC-side current, the process of constructing the prediction model includes:

[0069] 1. The three-phase voltage vector u, stator three-phase current vector i, and stator three-phase voltage vector e at the converter port on the machine side can be defined as follows:

[0070]

[0071] In the formula, A = e j2π / 3 u aN u bN and u cN These represent the three-phase output voltages of the converter to the neutral point, u and u respectively. aN u bN and u cN Represented as a switching function S x and DC side voltage u dc The product of, i.e.:

[0072] u xN =S x u dc (x = a, b, c)

[0073] Switching function S x The switching state of each phase arm of the generator-side converter is represented by:

[0074]

[0075] The above formula can be used to correlate the converter port voltage with the switching state of the transistors in each bridge arm.

[0076] As defined above, when the switching states of the transistors in each bridge arm are different, the resulting machine-side converter port three-phase voltage vector u, stator three-phase current vector i, and stator three-phase voltage vector e are all different. Consequently, the values ​​of these vectors after Clarke transformation to two-phase rotating coordinates are also different. Therefore, the stator current i in the two-phase rotating coordinates corresponding to each switching state combination can be obtained iteratively. dq and machine-side converter port voltage u dq The value of .

[0077] The voltage dynamic model of the permanent magnet direct-drive fan motor in two-phase rotating coordinates with respect to inductance and flux linkage is established as follows:

[0078]

[0079] In the formula, u d and u q Let i represent the d-axis and q-axis components of the converter port voltage on the machine side in a two-phase rotating coordinate system, respectively. d and i qLet L and R represent the d-axis and q-axis components of the stator current in a two-phase rotating coordinate system, respectively, and let L and R represent the equivalent reactance and equivalent resistance of the fan rotor, respectively. r Indicates the synchronous speed of the motor, ψ f This indicates the magnetic flux linkage of the rotor permanent magnet.

[0080] 2. Convert the voltage dynamic model into a discrete model, and let the sampling period be T. s When T s When << 1s, we have:

[0081]

[0082] In the formula, k represents the current time, and i dq (k) and i dq (k+1) represents the grid current at the current moment in the two-phase rotating coordinate system and the stator current at the (k+1)th sampling moment in the two-phase rotating coordinate system, respectively.

[0083] Discretizing the discrete model, the discrete model of the machine-side converter in two-phase rotating coordinates, i.e., the prediction model, is as follows:

[0084]

[0085] In the formula, and Let represent the d-axis and q-axis components of the stator current at time k+1 in the two-phase rotating coordinate system, respectively. and These represent the d-axis and q-axis components of the stator current at the current moment in the two-phase rotating coordinate system, respectively. and These represent the d-axis and q-axis components of the current generator-side converter port voltage in a two-phase rotating coordinate system, respectively. This represents the d-axis component of the stator voltage of the wind turbine at the current moment.

[0086] 3. Based on the above prediction model, the current value at time k+1 can be predicted by sampling the voltage and current values ​​of the machine-side converter at time k, thereby achieving accurate current tracking.

[0087] The generator-side converter has two control modes: grid-connected control and grid-connected control. In grid-connected control, the grid-connected converter maintains a stable DC voltage, while the generator-side converter outputs active and reactive power according to the MPPT curve. In grid-connected control, the grid-connected converter maintains a stable three-phase AC grid voltage, thus supporting the black start function of the wind turbine. The generator-side converter, in turn, needs to cooperate in maintaining the DC side voltage u. dcStable. In both modes, direct control of the converter bridge arm switching state is achieved by controlling the dq-axis current component. To achieve the above multi-objective control, a weighting factor is introduced, and the cost function of the FCS-MPC algorithm is defined as:

[0088]

[0089] Among them, the weighting coefficient λ1 is used to set the weight of the DC voltage tracking error in the cost function; and These represent the d-axis and q-axis components of the machine-side current reference value in two-phase rotating coordinate systems, respectively. Indicates the DC-side voltage reference value. and Let λ1 and q1 represent the d-axis and q-axis components of the machine-side current at the (k+1)th time step in a two-phase rotating coordinate system, respectively. When using grid-fed control, λ1 = 0. and The synchronous speed ω of the motor is obtained from the MPPT curve. r When using network control, set λ1 = 1. At each sampling time, iterate through and calculate all possible finite number of switching functions S. x Combining, choosing the cost function l g (k) Minimum switching function S x The combination represents the optimal switching state of each bridge arm of the converter at the current moment, and the corresponding dq-axis voltage vector at this time. and As the trigger pulse signal for the converter, it enables optimal control of the current vector.

[0090] To protect power devices, prevent excessive current from exceeding safety limits, and stabilize the DC bus voltage, when calculating the optimal switching states of each bridge arm of the converter that minimize the cost function, amplitude constraints must be satisfied. The amplitude constraints are as follows:

[0091]

[0092] Among them, i d,max and i q,max These represent the maximum values ​​of the d-axis and q-axis components of the stator current in the two-phase rotating coordinate system, respectively.

[0093] 4. Due to the time delay in controller calculation, the optimal switching state obtained will not be applied to the converter until the next time step, thus affecting control accuracy and response speed. Therefore, a multi-step prediction algorithm is adopted. Using the known state variables at the current time k, the prediction model iterates forward multiple steps to obtain the predicted value at the (k+m)th time step. The algorithm then iterates through the variables to obtain the switching state that minimizes the cost function. The optimal switching state obtained at the current time k is then applied to the actual converter, thus correcting the cost function of FCS-MPC to:

[0094]

[0095] In the formula, N and m are both positive integers. In practical engineering, considering the balance between online calculation time and control accuracy, the number of forward iteration steps can be taken as N = 2. Since the sampling period Ts << 1s, the DC side voltage u can be considered to be within N sampling periods. dc constant.

[0096] The control structure principle block diagram of the machine-side converter is as follows: Figure 3 As shown, when using the grid-following control mode, the DC side voltage u in the three-phase stationary coordinates is... dc DC voltage reference value u in three-phase stationary coordinates dc * The comparison yields the first difference, which is then input into a PI controller for proportional-integral adjustment to obtain the grid current reference value in three-phase stationary coordinates. This three-phase stationary coordinate reference value is then transformed using Clarke transformation to obtain the grid current reference value i in two-phase rotating coordinates. dq * When using a grid-based control mode, the grid voltage e(k) in the three-phase stationary coordinates and the grid voltage reference value e in the three-phase stationary coordinates are... * The comparison yields a second difference, which is then input into a PI controller for proportional-integral adjustment to obtain the grid current reference value in three-phase stationary coordinates. This three-phase stationary coordinate reference value is then transformed using Clarke transformation to obtain the grid current reference value i in two-phase rotating coordinates. dq * Among them, the DC voltage reference value u dc * and grid voltage reference value e * It is obtained through pre-setting control commands.

[0097] Collect the grid current i(k) in three-phase stationary coordinates and convert it to the grid current i in two-phase rotating coordinates. dq (k) is used to predict the grid current i at several future times through a predictive model. dq (k+m) represents the grid current i at several future times. dq (k+m) and grid current reference value idq * The optimal switching state is calculated by inputting the cost function and then input to the machine-side converter for control. This control process is repeated in each sampling period to achieve online rolling optimization.

[0098] This invention also provides a finite control set model predictive control device for a machine-side converter, such as... Figure 4 As shown, the device includes:

[0099] The port voltage and current acquisition module 401 is used to acquire the machine-side converter port voltage and the stator current of the wind turbine at the current time under the target coordinates.

[0100] The current reference value acquisition module 402 is used to acquire the machine-side current reference value under the target coordinates;

[0101] The current prediction module 403 is used to input the current converter port voltage at the target coordinate and the stator current of the wind turbine at the target coordinate to the prediction model, and predict the stator current at at least one future time at the target coordinate through the prediction model.

[0102] The traversal optimization module 404 is used to calculate the optimal switching state of each bridge arm of the converter that minimizes the cost function based on the stator current at at least one future time under the target coordinates and the reference value of the machine-side current under the target coordinates.

[0103] The bridge arm control module 405 is used to control each bridge arm of the converter according to the optimal switching state.

[0104] This invention discloses a finite control set model predictive control device for a machine-side converter. It acquires the current machine-side converter port voltage and the current stator current of the wind turbine at the target coordinates, obtains a reference value for the machine-side current at the target coordinates, and inputs these values ​​into a predictive model. The predictive model then predicts the stator current at at least one future time in the target coordinates. Based on this stator current and the reference value, it iterates through the calculations to obtain the optimal switching state of each bridge arm of the converter that minimizes the cost function. Controlling each bridge arm according to the optimal switching state allows for online adaptive changes to the switching states of each bridge arm of the machine-side converter, maintaining stable system operation, reducing cumbersome PI tuning, eliminating the need for decoupling and multiple transformations, minimizing computational load, and avoiding complex controller parameter design.

[0105] In some embodiments, the port voltage and current acquisition module 401 includes:

[0106] The initial voltage and current acquisition module is used to acquire the machine-side converter port voltage and the stator current of the wind turbine at the current moment under the initial coordinates.

[0107] The first coordinate transformation module is used to perform a preset coordinate transformation on the current machine-side converter port voltage under the initial coordinates to obtain the current machine-side converter port voltage under the target coordinates, and to perform a preset coordinate transformation on the current stator current of the wind turbine under the initial coordinates to obtain the current stator current of the wind turbine under the target coordinates.

[0108] In some embodiments, the current reference value acquisition module 402 includes:

[0109] The speed acquisition module is used to acquire the synchronous speed of the fan motor;

[0110] The power reference value acquisition module is used to obtain the maximum active and reactive power output reference value based on the synchronous speed of the motor and the maximum power tracking curve.

[0111] The second coordinate transformation module is used to obtain the machine-side current reference value under the target coordinates after the maximum active and reactive power output reference value is adjusted by proportional-integral method and subjected to preset coordinate transformation.

[0112] In some embodiments, the current reference value acquisition module 402 includes:

[0113] The DC voltage acquisition module is used to acquire the DC side voltage and DC side voltage reference value of the converter;

[0114] The voltage comparison module is used to compare the DC-side voltage of the converter with the DC-side voltage reference value to obtain the first difference;

[0115] The third coordinate transformation module is used to obtain the reference value of the machine-side current in the target coordinates after the first difference is adjusted by proportional-integral method and transformed by coordinate transformation.

[0116] In some embodiments, the initial coordinates are three-phase stationary coordinates, the target coordinates are two-phase rotating coordinates, and the prediction model is:

[0117]

[0118] In the formula, k represents the current time. and Let represent the d-axis and q-axis components of the stator current at time k+1 in the two-phase rotating coordinate system, respectively. and These represent the d-axis and q-axis components of the stator current at the current moment in the two-phase rotating coordinate system, respectively. and These represent the d-axis and q-axis components of the current generator-side converter port voltage in a two-phase rotating coordinate system, respectively. This represents the d-axis component of the stator voltage of the wind turbine at the current moment, where L and R represent the equivalent reactance and equivalent resistance of the wind turbine rotor, respectively, and T. s ω represents the sampling period. r Indicates the synchronous speed of the motor, ψ f This indicates the magnetic flux linkage of the rotor permanent magnet.

[0119] In some embodiments, the cost function is:

[0120]

[0121] In the formula, l g (k) represents the cost function, where N and m are both positive integers. and These represent the d-axis and q-axis components of the machine-side current reference value in two-phase rotating coordinate systems, respectively. Indicates the DC-side voltage reference value. and Let represent the d-axis and q-axis components of the machine-side current at the (k+m)th time step in a two-phase rotating coordinate system, respectively. λ1 represents the DC side voltage at the (k+m)th moment in a two-phase rotating coordinate system. When the control mode of the generator-side converter is grid-following control, λ1 = 0; when the control mode of the generator-side converter is grid-connected control, λ1 = 1.

[0122] In some embodiments, when traversing and calculating the optimal switching states of each bridge arm of the converter that minimize the cost function, an amplitude constraint condition needs to be satisfied. The amplitude constraint condition is as follows:

[0123]

[0124] Among them, i d,max and i q,max These represent the maximum values ​​of the d-axis and q-axis components of the stator current in the two-phase rotating coordinate system, respectively.

[0125] This invention also provides a schematic diagram of the structure of a computer device, such as... Figure 5As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 5 Take a processor 10 as an example.

[0126] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GPA), or any combination thereof.

[0127] The memory 20 stores instructions executable by at least one processor 10 to cause at least one processor 10 to perform the method shown in the above embodiments.

[0128] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0129] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0130] The computer device also includes an input device 30 and an output device 40. The processor 10, memory 20, input device 30, and output device 40 can be connected via a bus or other means. Figure 5 Taking the example of a connection between China and Israel via a bus.

[0131] Input device 30 can receive input numerical or character information, and generate key signal inputs related to user settings and function control of the computer device, such as a touchscreen, keypad, mouse, trackpad, touchpad, joystick, one or more mouse buttons, trackball, joystick, etc. Output device 40 may include display devices, auxiliary lighting devices (e.g., LEDs), and haptic feedback devices (e.g., vibration motors). The aforementioned display devices include, but are not limited to, liquid crystal displays, light-emitting diodes, displays, and plasma displays. In some alternative embodiments, the display device may be a touchscreen.

[0132] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0133] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and all such modifications and variations fall within the scope of protection.

Claims

1. A finite control set model predictive control method for a machine-side converter, characterized in that, include: Obtain the current voltage at the machine-side converter port and the current stator current of the wind turbine at the target coordinates at the current time. Obtain the reference value of the machine-side current at the target coordinates; The current generator port voltage and the current stator current of the wind turbine at the target coordinates are input into the prediction model, and the stator current at at least one future time at the target coordinates is predicted by the prediction model. Based on the stator current at at least one future moment under the target coordinates and the reference value of the machine-side current under the target coordinates, the optimal switching state of each bridge arm of the converter that minimizes the cost function is obtained through iterative calculation. The converter arms are controlled according to the optimal switching state.

2. The machine-side converter finite control set model predictive control method according to claim 1, characterized in that, Obtain the current machine-side converter port voltage and the current stator current of the wind turbine at the target coordinates at the current moment, including: Obtain the current voltage at the machine-side converter port and the current stator current of the wind turbine at the current time in the initial coordinate system. The turbine-side converter port voltage at the current moment under the initial coordinates is transformed by a preset coordinate transformation to obtain the turbine-side converter port voltage at the current moment under the target coordinates. The stator current of the wind turbine at the current moment under the initial coordinates is transformed by a preset coordinate transformation to obtain the stator current of the wind turbine at the current moment under the target coordinates.

3. The machine-side converter finite control set model predictive control method according to claim 1, characterized in that, When the control mode of the generator-side converter is grid-following control, the reference value of the generator-side current at the target coordinates is obtained, including: Obtain the synchronous speed of the fan motor; Based on the synchronous speed of the motor, the reference value of the maximum active and reactive power output is obtained using the maximum power tracking curve. After the maximum active and reactive power output reference value is adjusted by proportional-integral method and subjected to preset coordinate transformation, the machine-side current reference value under the target coordinate is obtained.

4. The machine-side converter finite control set model predictive control method according to claim 1, characterized in that, When the control mode of the generator-side converter is grid-type control, the reference value of the generator-side current under the target coordinates is obtained, including: Obtain the DC-side voltage and DC-side voltage reference value of the converter; The first difference is obtained by comparing the DC-side voltage of the converter with the DC-side voltage reference value. After adjusting the first difference using proportional-integral ratio and performing coordinate transformation, the reference value of the machine-side current under the target coordinates is obtained.

5. The finite control set model predictive control method for machine-side converters according to claim 1, characterized in that, The target coordinates are two-phase rotating coordinates, and the prediction model is: In the formula, k represents the current time. and Let represent the d-axis and q-axis components of the stator current at time k+1 in the two-phase rotating coordinate system, respectively. and These represent the d-axis and q-axis components of the stator current at the current moment in the two-phase rotating coordinate system, respectively. and These represent the d-axis and q-axis components of the current generator-side converter port voltage in a two-phase rotating coordinate system, respectively. This represents the d-axis component of the stator voltage of the wind turbine at the current moment, where L and R represent the equivalent reactance and equivalent resistance of the wind turbine rotor, respectively, and T. s ω represents the sampling period. r Indicates the synchronous speed of the motor, ψ f This indicates the magnetic flux linkage of the rotor permanent magnet.

6. The finite control set model predictive control method for machine-side converters according to claim 5, characterized in that, The cost function is: In the formula, l g (k) represents the cost function, where N and m are both positive integers. and These represent the d-axis and q-axis components of the machine-side current reference value in two-phase rotating coordinate systems, respectively. Indicates the DC-side voltage reference value. and Let represent the d-axis and q-axis components of the machine-side current at the (k+m)th time step in a two-phase rotating coordinate system, respectively. λ1 represents the DC side voltage at the (k+m)th moment in a two-phase rotating coordinate system. When the control mode of the generator-side converter is grid-following control, λ1 = 0; when the control mode of the generator-side converter is grid-connected control, λ1 = 1.

7. The finite control set model predictive control method for machine-side converters according to claim 6, characterized in that, When calculating the optimal switching states of each bridge arm of the converter that minimize the cost function, the amplitude constraint condition must be satisfied. The amplitude constraint condition is as follows: Among them, i d,max and i q,max These represent the maximum values ​​of the d-axis and q-axis components of the stator current in the two-phase rotating coordinate system, respectively.

8. A finite control set model predictive control device for a machine-side converter, characterized in that, include: The port voltage and current acquisition module is used to acquire the machine-side converter port voltage and the stator current of the wind turbine at the current time under the target coordinates. The current reference value acquisition module is used to acquire the machine-side current reference value under the target coordinates; The current prediction module is used to input the current voltage of the machine-side converter port and the current stator current of the wind turbine at the target coordinate to the prediction model, and predict the stator current at at least one future time at the target coordinate through the prediction model. The traversal optimization module is used to calculate the optimal switching state of each bridge arm of the converter that minimizes the cost function based on the stator current at at least one future time under the target coordinates and the reference value of the machine-side current under the target coordinates. The bridge arm control module is used to control each bridge arm of the converter according to the optimal switching state.

9. A computer device, characterized in that, include: The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to perform the machine-side converter finite control set model predictive control method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the machine-side converter finite control set model predictive control method as described in any one of claims 1 to 7.

Images