Double-fed motor model predictive control method and device, electronic equipment and storage medium

Abstract

The application provides a doubly-fed motor model prediction control method and device, electronic equipment and storage medium, which are suitable for the technical field of motor control and pumped storage. The method comprises the following steps: obtaining two rotor-side voltages, two stator-side currents and two stator-side voltages; obtaining a corrected rotor resistance and a corrected stator-rotor mutual inductance value based on the two rotor-side voltages, the two stator-side currents and the two stator-side voltages; determining two expected currents based on the corrected stator-rotor mutual inductance value; determining two rotor-side output voltages based on the two expected currents, the corrected rotor resistance and the corrected stator-rotor mutual inductance value; and controlling the rotation of the doubly-fed motor based on the two rotor-side output voltages. The application embodiment can effectively solve the rotor current prediction deviation caused by the deviation of the core electrical parameters, provide accurate model parameter support for the model prediction current control, and meet the high real-time operation requirement of the model prediction current control.

Description

Technical Field

[0001] This application relates to the technical fields of motor control and pumped storage, and in particular to a doubly-fed motor model predictive control method, device, electronic equipment and storage medium. Background Technology

[0002] Doubly fed induction generators (DFIGs) are widely used in wind power generation and pumped storage due to their advantages such as small inverter capacity and independent decoupling of active and reactive power. In large-scale variable-speed pumped storage power stations, the AC excitation system built on the rotor-side bidirectional converter can not only widen the optimal operating head of the unit, but also realize flexible grid connection and bidirectional energy flow.

[0003] However, under complex actual operating conditions, internal temperature rise, main magnetic circuit saturation, and skin effect of the motor can cause significant time-varying drift in core electrical parameters such as stator-rotor mutual inductance and rotor resistance. Deviations in these core electrical parameters can lead to serious problems such as rotor current prediction errors, increased output power pulsation, and deterioration of power quality. Summary of the Invention

[0004] In view of this, embodiments of this application provide a doubly-fed motor model predictive control method, device, electronic device and storage medium to solve the serious problems of rotor current prediction deviation, increased output power pulsation and power quality degradation caused by the deviation of core electrical parameters in the prior art.

[0005] The first aspect of this application provides a doubly-fed motor model predictive control method, including: Obtain the two rotor-side voltages, two stator-side currents, and two stator-side voltages in a two-phase coordinate system; A variable step size parameter identification model is adopted. Based on two rotor-side voltages, two stator-side currents, and two stator-side voltages, the rotor resistance and stator-rotor mutual inductance values ​​are identified to obtain the corrected rotor resistance and stator-rotor mutual inductance values. Using a sliding mode controller, two desired currents in a two-phase coordinate system are determined based on the corrected stator-rotor mutual inductance values. A model predictive current control strategy is adopted to determine the output voltages on the two rotor sides in a two-phase coordinate system based on two expected currents, the corrected rotor resistance, and the corrected stator-rotor mutual inductance. The rotation of the doubly fed motor is controlled based on the output voltages of the two rotor sides.

[0006] In one possible implementation, a variable step-size parameter identification model is used. Based on two rotor-side voltages, two stator-side currents, and two stator-side voltages, parameter identification is performed on the rotor resistance and stator-rotor mutual inductance values ​​to obtain the corrected rotor resistance and corrected stator-rotor mutual inductance values, including: A variable step size parameter identification model is adopted to determine two theoretical current values ​​based on two rotor-side voltages, two stator-side voltages, and preset rotor resistance and preset rotor mutual inductance values. Based on two stator-side currents and two theoretical current values, two errors are determined accordingly. Based on two errors and variable step size parameters, the variable step size function of the identification model is determined accordingly, and the two step sizes are determined accordingly. Based on two errors and two step sizes, the preset rotor resistance and preset rotor mutual inductance value are corrected respectively until the variable step size function converges, and the corrected rotor resistance and corrected stator-rotor mutual inductance value are obtained.

[0007] In one possible implementation, the variable step size function is:

[0008] in, It is an adjustable variable. for Error at any given time.

[0009] In one possible implementation, a sliding mode controller is used to determine two desired currents in a two-phase coordinate system based on the corrected stator-rotor mutual inductance values, including: Obtain the actual active power, target active power, actual reactive power, and target reactive power; A sliding mode controller is used to determine two sliding surfaces based on the actual active power and target active power, as well as the actual reactive power and target reactive power. Using the output function of the sliding mode controller, based on the corrected stator-rotor mutual inductance values ​​and the two sliding surfaces, two desired currents in the two-phase coordinate system are determined respectively.

[0010] In one possible implementation, the output function of the sliding mode controller is used to determine two desired currents in the two-phase coordinate system based on the corrected stator-rotor mutual inductance values ​​and the two sliding surfaces, including: Obtain the stator inductance and rotor inductance; Based on the stator inductance, rotor inductance, and the corrected stator-rotor mutual inductance values, the variable parameters of the output function are determined; Based on two sliding surfaces and preset adjustable parameters, the values ​​of the two saturation functions of the output function are determined respectively; Based on the values ​​of the two saturation functions, the variable parameters, and the two sliding surfaces, the two desired currents in the two-phase coordinate system are determined respectively.

[0011] In one possible implementation, the output function is:

[0012] in, Let D be the desired current, and D be a variable parameter. It is the difference between the target active power and the actual active power, or the difference between the target reactive power and the actual reactive power. for The derivatives of , where a, ε, and k are all adjustable parameters with values ​​greater than 0. It is a sliding surface; The saturation function is:

[0013] in, These are preset adjustable parameters.

[0014] In one possible implementation, a model predictive current control strategy is employed to determine the two rotor-side output voltages in a two-phase coordinate system based on two desired currents, the corrected rotor resistance, and the corrected stator-rotor mutual inductance values. This includes: Based on two desired currents, the corrected rotor resistance, and the corrected stator-rotor mutual inductance, multiple voltage combinations for the inverter output at the next moment are determined. Based on each voltage combination, predict the two rotor-side currents in the two-phase coordinate system at the next moment for each voltage combination; Determine the error between the two rotor-side currents and the two desired currents corresponding to each voltage combination, and determine the two rotor-side currents with the smallest error to the two desired currents; Based on the voltage combination corresponding to the two rotor-side currents with the smallest determined error, the output voltages of the two rotor-side currents in the two-phase coordinate system are determined.

[0015] A second aspect of this application provides a doubly-fed motor model predictive control device, comprising: The acquisition module is used to acquire two rotor-side voltages, two stator-side currents, and two stator-side voltages in a two-phase coordinate system. The parameter identification module is used to identify the rotor resistance and stator-rotor mutual inductance values ​​based on two rotor-side voltages, two stator-side currents, and two stator-side voltages using a variable step size parameter identification model, and obtain the corrected rotor resistance and stator-rotor mutual inductance values. The sliding mode control module is used to determine two desired currents in a two-phase coordinate system based on the corrected stator-rotor mutual inductance values ​​using a sliding mode controller. The model predictive current control module is used to determine the output voltages of the two rotor sides in a two-phase coordinate system based on two expected currents, the corrected rotor resistance, and the corrected stator-rotor mutual inductance values, using a model predictive current control strategy. The voltage control module is used to control the rotation of the doubly fed motor based on the output voltages of the two rotor sides.

[0016] A third aspect of this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method of the first aspect.

[0017] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method of the first aspect.

[0018] Compared with the prior art, the embodiments of this application have at least the following technical effects: The doubly-fed induction generator (DFIG) model predictive control method of the first aspect of this application can obtain two rotor-side voltages, two stator-side currents, and two stator-side voltages in a two-phase coordinate system. Using a variable-step parameter identification model, based on the two rotor-side voltages, two stator-side currents, and two stator-side voltages, parameter identification is performed on the rotor resistance and stator-rotor mutual inductance values ​​to obtain corrected rotor resistance and corrected stator-rotor mutual inductance values. This achieves the identification of the two key electrical parameters, stator-rotor mutual inductance and rotor resistance. The identification results can directly reflect the true physical state of the current unit's magnetic circuit and thermodynamics, effectively reducing the deviation of core electrical parameters. Then, using a sliding mode controller, based on the corrected stator-rotor mutual inductance values, two desired currents are determined in the two-phase coordinate system. Using a model predictive current control strategy, based on the two desired currents, the corrected rotor resistance, and the corrected stator-rotor mutual inductance values, two rotor-side output voltages in the two-phase coordinate system are determined. Based on the two rotor-side output voltages, the rotation of the DFIG is controlled. Therefore, by identifying the two key electrical parameters, stator-rotor mutual inductance and rotor resistance, this application can effectively solve the serious problems caused by the deviation of core electrical parameters, such as rotor current prediction error, increased output power pulsation, and power quality degradation. Simultaneously, this application provides highly accurate model parameter support for rotor-side model predictive current control through the corrected rotor resistance and corrected stator-rotor mutual inductance values, meeting the high real-time computational requirements of model predictive current control.

[0019] It is understood that the beneficial effects of the second to fourth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart of a doubly-fed motor model predictive control method provided in an embodiment of this application; Figure 2 This is a schematic diagram of the framework of a doubly-fed motor model predictive control system provided in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of a doubly-fed motor model predictive control device provided in an embodiment of this application; Figure 4 This is a schematic diagram of the electronic device provided in the embodiments of this application. Detailed Implementation

[0022] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0023] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0024] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0025] In the description of this application, unless otherwise stated, the " / " used in this specification and appended claims indicates that the related objects are in an "or" relationship. For example, A / B can mean A or B. The "and / or" in this application merely describes the relationship between the related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. Furthermore, in the description of this application, unless otherwise stated, "multiple" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, a and b, a and c, b and c, or a, b, and c. Here, a, b, and c can be single or multiple.

[0026] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0027] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0028] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0029] Research has revealed that Model Predictive Current Control (MPCC), as an advanced strategy for AC excitation systems, is highly dependent on the accuracy of the discrete mathematical model. Parameter drift can easily lead to controller mismatch, causing serious problems such as rotor current prediction errors, increased output power pulsation, and deterioration of power quality.

[0030] Therefore, developing a rotor-side MPCC system with both high precision and fast convergence online parameter identification methods has become a key direction for solving the problem of AC excitation parameter mismatch in doubly-fed motors and improving grid-connected control efficiency.

[0031] Existing solutions, particularly the parameter identification control method based on the traditional recursive least squares (RLS) method, rely on a core module consisting of a least squares parameter identification module and rotor-side MPCC control equations. This method can identify motor parameters in real time without stopping unit operation and input the identification results into the MPCC control equations to achieve precise rotor current control. However, this traditional recursive least squares parameter identification control method suffers from significant convergence lag when facing dynamic unit operating conditions. The fixed forgetting factor mechanism cannot dynamically adjust the weights of historical sampled data based on the real-time operating status of the converter, leading to data saturation and significant identification delays in the parameter iteration process. This makes it unsuitable for the high real-time computational requirements of rotor-side model predictive current control.

[0032] The doubly fed motor model predictive control method, device, electronic equipment, and storage medium provided in this application are intended to solve the above-mentioned technical problems of the prior art.

[0033] The technical solution of this application and how it solves the above-mentioned technical problems are described in detail below with specific embodiments. It should be noted that the following embodiments can be referenced, borrowed, or combined with each other, and the same terms, similar features, and similar implementation steps in different embodiments will not be described again.

[0034] See Figure 1 As shown, this application provides a flowchart of a doubly-fed motor model predictive control method. Figure 1 As shown, the doubly fed motor model predictive control method of this application embodiment includes steps S101 to S105.

[0035] S101. Obtain the two rotor-side voltages, two stator-side currents, and two stator-side voltages in the two-phase coordinate system.

[0036] Optionally, the two-phase coordinate system can be a synchronous rotating coordinate system, the two rotor-side voltages are the q-axis rotor-side voltage and the d-axis rotor-side voltage, the two stator-side currents are the q-axis stator-side current and the d-axis stator-side current, and the two stator-side voltages are the q-axis stator-side voltage and the d-axis stator-side voltage.

[0037] S102. Using a variable step size parameter identification model, based on two rotor-side voltages, two stator-side currents, and two stator-side voltages, parameter identification is performed on the rotor resistance and stator-rotor mutual inductance values ​​to obtain the corrected rotor resistance and stator-rotor mutual inductance values.

[0038] Optionally, the variable step size parameter identification model can adopt a variable step size Adaline neural network parameter identification module, which is responsible for identifying key electrical parameters such as stator-rotor mutual inductance and rotor resistance of the doubly fed motor in real time, and can directly reflect the real physical state of the current unit's magnetic circuit and thermodynamics.

[0039] Furthermore, the variable step size parameter identification model can adopt a variable step size strategy, dynamically adjusting the network weights and updating the step size based on the transient prediction error, thereby significantly improving the convergence speed and steady-state accuracy of parameter identification, and providing extremely accurate model parameter support for rotor-side model predictive current control.

[0040] S103. Using a sliding mode controller, based on the corrected stator-rotor mutual inductance values, determine two desired currents in a two-phase coordinate system.

[0041] Optionally, the sliding mode controller can be improved by adjusting the saturation function to effectively suppress chattering and further improve the robustness of the system.

[0042] S104. Using a model predictive current control strategy, the output voltages on the two rotor sides in the two-phase coordinate system are determined based on two expected currents, the corrected rotor resistance, and the corrected stator-rotor mutual inductance.

[0043] Among them, the model predictive current control strategy is a high-performance control strategy in the field of power electronics and motor drive. It can predict the current generated by applying each switching state in the future by using the corrected rotor resistance and the corrected stator-rotor mutual inductance values, and then select the switching state that makes the current closest to the expected current to be applied directly.

[0044] S105. Based on the output voltages of the two rotor sides, control the rotation of the doubly fed motor.

[0045] The doubly-fed induction generator model predictive control method of this application can obtain two rotor-side voltages, two stator-side currents, and two stator-side voltages in a two-phase coordinate system. Using a variable step-size parameter identification model, based on the two rotor-side voltages, two stator-side currents, and two stator-side voltages, the rotor resistance and stator-rotor mutual inductance values ​​are identified to obtain corrected rotor resistance and corrected stator-rotor mutual inductance values. This achieves the identification of the two key electrical parameters, stator-rotor mutual inductance and rotor resistance. The identification results can directly reflect the true physical state of the current unit's magnetic circuit and thermodynamics, effectively reducing the deviation of core electrical parameters.

[0046] Then, in this embodiment, a sliding mode controller is used to determine two desired currents in a two-phase coordinate system based on the corrected stator-rotor mutual inductance values; a model predictive current control strategy is used to determine two rotor-side output voltages in a two-phase coordinate system based on the two desired currents, the corrected rotor resistance, and the corrected stator-rotor mutual inductance values; and the rotation of the doubly-fed motor is controlled based on the two rotor-side output voltages.

[0047] Therefore, by identifying the two key electrical parameters, stator-rotor mutual inductance and rotor resistance, this application can effectively solve the serious problems caused by the deviation of core electrical parameters, such as rotor current prediction error, increased output power pulsation, and power quality degradation. Simultaneously, this application provides highly accurate model parameter support for rotor-side model predictive current control through the corrected rotor resistance and corrected stator-rotor mutual inductance values, meeting the high real-time computational requirements of model predictive current control.

[0048] Furthermore, the embodiments of this application achieve adaptive optimization operation through the coordinated work of a variable step size parameter identification model, a sliding mode controller, and a model predictive current control strategy. By combining a variable step size Adaline neural network with improved sliding mode power control with parameter feedforward, control of each key converter can be realized. The rotor-side MPCC system, which has both high precision and fast convergence online parameter identification methods, has become a key direction for solving the problem of AC excitation parameter mismatch in doubly-fed motors and improving grid-connected control efficiency.

[0049] See Figure 2 As shown in the diagram, this application provides a schematic framework of a doubly-fed motor model predictive control system. Figure 2 As shown, U rd 、U rq 、I sd 、I sq 、U sd 、U sq These correspond to the d-axis rotor-side voltage, q-axis rotor-side voltage, d-axis stator-side current, q-axis stator-side current, q-axis stator-side voltage, and d-axis stator-side voltage, respectively. U rd 、U rq 、I sd 、I sq 、U sd 、U sqThe corrected rotor resistance Rs and the corrected stator-rotor mutual inductance values ​​are output through the variable step-size neural network parameter identification model. Lm.

[0050] Further research revealed that the traditional Adaline neural network algorithm uses only simple arithmetic operations in each iteration, without complex matrix transformations or calculations, and has very low requirements for initial values. However, the Fixed Step-Size Least Mean Square (LMS) algorithm it employs sets the step size factor for weight calculation to a fixed value, creating a contradiction between convergence speed, tracking capability of time-varying systems, and steady-state error. When the step size factor is set small, although it can suppress steady-state error, it will reduce convergence speed and tracking capability; when the step size factor is set large, it will improve convergence speed and tracking capability, but will lead to an increase in steady-state error.

[0051] The traditional LMS weight adjustment formula is as follows: (1) in: e(k) This represents the difference between the actual and expected output of the neural network. d(k) For the desired output, O(k) This is the actual output of the neural network. Wi(K) For neural network weights, X(k) This is the input to the neural network.

[0052] The weight adjustment algorithm should have good convergence, therefore the step size should be... μ Should meet: (2) Currently, traditional fixed-step LMS is generally improved by adding variable step size and momentum term. While adding momentum term can increase the adjustment of weights and shorten the time to converge to steady state, fixed step size will reduce the convergence speed and ultimately fail to achieve good convergence results. Variable step size method, on the other hand, designs a function between error and step size, so that the step size can track the change of error in real time. Currently, variable step size method mainly includes the following two categories: (1) Constructing a function relationship between error and step size. The variable step size function of this method generally adopts... sigmoid The function compensates more when the error is small. (2) The genetic variable step size method uses the autocorrelation estimate of the error between the current time and the previous time as the way to adjust the step size update. Although the convergence speed is fast, there is a problem that the convergence speed decreases when the signal-to-noise ratio is small.

[0053] In summary, both of the above methods have shortcomings. This application provides an improved variable step-size algorithm, which improves step-size identification. μA larger initial value will result in a faster convergence rate; in the later stages of the identification process, when the identification result approaches the convergence point, the step size should be increased to reduce the steady-state error. μ Take the smaller value.

[0054] In some embodiments, a variable step-size parameter identification model is used to identify the rotor resistance and stator-rotor mutual inductance values ​​based on two rotor-side voltages, two stator-side currents, and two stator-side voltages, obtaining the corrected rotor resistance and corrected stator-rotor mutual inductance values, including: A variable step size parameter identification model is adopted to determine two theoretical current values ​​based on two rotor-side voltages, two stator-side voltages, and preset rotor resistance and preset rotor mutual inductance values. Based on two stator-side currents and two theoretical current values, two errors are determined accordingly. Based on two errors and variable step size parameters, the variable step size function of the identification model is determined accordingly, and the two step sizes are determined accordingly. Based on two errors and two step sizes, the preset rotor resistance and preset rotor mutual inductance value are corrected respectively until the variable step size function converges, and the corrected rotor resistance and corrected stator-rotor mutual inductance value are obtained.

[0055] Optionally, embodiments of this application can correct a preset rotor resistance based on an error and a corresponding step size until the variable step size function converges to obtain the corrected rotor resistance. Similarly, a preset rotor mutual inductance value can be corrected based on another error and a corresponding step size until the variable step size function converges to obtain the corrected rotor mutual inductance value.

[0056] Optionally, the weight adjustment algorithm used in the variable step size parameter identification model of this application embodiment is: (3) in, It is a variable step size function; It is an adjustable variable; for example, it can be set to 2. for Error at any given time; d(k) For the desired output, W(K) For neural network weights, X(k) This is the input to the neural network.

[0057] The variable step-size parameter identification model in this application uses a neural network that operates with a "prediction-comparison-correction" cyclic mechanism. It first receives the aforementioned... U rd 、U rq 、I sd 、I sq 、Usd and U sq The actual measured value is used as the basis for calculating a theoretical current value that should be generated at this time, based on the currently stored preset rotor resistance and preset rotor mutual inductance value (i.e., neural network weights) and the input data. The theoretical current value calculated in the second step is then compared with the actual measured value read in the first step (i.e., the measured value). I sd 、I sq The two errors, e(k), are compared and subtracted to obtain two errors. The step size is adjusted according to the error; the step size is increased when the error is large. μ (Accelerate convergence), reduce step size when error is small μ (Stable fine-tuning). Then, based on the error and step size, correct the rotor resistance and stator-rotor mutual inductance values, output them, and enter the next cycle.

[0058] In some embodiments, a sliding mode controller is used to determine two desired currents in a two-phase coordinate system based on the corrected stator-rotor mutual inductance values, including: Obtain the actual active power, target active power, actual reactive power, and target reactive power; A sliding mode controller is used to determine two sliding surfaces based on the actual active power and target active power, as well as the actual reactive power and target reactive power. Using the output function of the sliding mode controller, based on the corrected stator-rotor mutual inductance values ​​and the two sliding surfaces, two desired currents in the two-phase coordinate system are determined respectively.

[0059] See Figure 2 As shown, P, P ref Q, Q ref These are the actual active power, the target active power, the actual reactive power, and the target reactive power, respectively.

[0060] Taking active power P-end control as an example, Q-end control is completely consistent. The sliding mode controller design takes the following state variables: (4) in, for The differential.

[0061] In some embodiments, the output function of the sliding mode controller is used to determine two desired currents in a two-phase coordinate system based on the corrected stator-rotor mutual inductance values ​​and two sliding surfaces, including: Obtain the stator inductance and rotor inductance; Based on the stator inductance, rotor inductance, and the corrected stator-rotor mutual inductance values, the variable parameters of the output function are determined; Based on two sliding surfaces and preset adjustable parameters, the values ​​of the two saturation functions of the output function are determined respectively; Based on the values ​​of the two saturation functions, the variable parameters, and the two sliding surfaces, the two desired currents in the two-phase coordinate system are determined respectively.

[0062] In some embodiments, the output function is: (5) in, Let D be the desired current, and D be a variable parameter. It is the difference between the target active power and the actual active power, or the difference between the target reactive power and the actual reactive power. for The derivatives of , where a, ε, and k are all adjustable parameters with values ​​greater than 0. It is a sliding surface.

[0063] Optionally, a, ε, and k can all be assigned values ​​based on the actual operating conditions of the motor.

[0064] Among them, the definition , , ; L s For stator inductance, L r This is the rotor inductance.

[0065] Furthermore, the saturation function is: (6) in, These are preset adjustable parameters that can be assigned values ​​based on the actual control effect of the motor.

[0066] The embodiments of this application use the saturation function sat(s,δ) instead of the general sgn(s), which can effectively suppress chattering and further improve the robustness of the system.

[0067] The sliding mode controller in this application uses parameter changes as feedforward compensation. When the load and motor parameters change, it can overcome the influence of load and parameter disturbances on the system, effectively suppress the chattering phenomenon of the system, and introduce a saturation function to further improve the robustness of the system.

[0068] See Figure 2 As shown, i qref and i dref These are two desired currents output after the sliding mode controller is improved. The two sliding surfaces are used to determine... i qref and idref Formula (6) is used, based on two sliding surfaces and preset adjustable parameters. The comparison results determine the piecewise expressions of the two saturation functions to be used, and then the values ​​of the two saturation functions are determined. Then, the results are compared with... i qref Substituting the corresponding values ​​of the saturation function, the sliding surface, and the variable parameters into formula (5), we can obtain... i qref Similarly, will be with i dref Substituting the corresponding values ​​of the saturation function, the sliding surface, and the variable parameters into formula (5), we can obtain... i dref .

[0069] Furthermore, the stability of sliding mode control is proven as follows: Setting up Lyapunov functions as follows: (7) According to Lyapunov's stability theorem, it is only necessary to prove... If the system is asymptotically stable, then we can obtain... (8) In the formula: ε>0, k>0, and sat(s)s>0; from this, we can obtain Therefore, the system error can converge to near 0 in a finite amount of time, making the system stable.

[0070] In some embodiments, a model predictive current control strategy is employed to determine the two rotor-side output voltages in a two-phase coordinate system based on two desired currents, a corrected rotor resistance, and a corrected stator-rotor mutual inductance value, including: Based on two desired currents, the corrected rotor resistance, and the corrected stator-rotor mutual inductance, multiple voltage combinations for the inverter output at the next moment are determined. Based on each voltage combination, predict the two rotor-side currents in the two-phase coordinate system at the next moment for each voltage combination; Determine the error between the two rotor-side currents and the two desired currents corresponding to each voltage combination, and determine the two rotor-side currents with the smallest error to the two desired currents; Based on the voltage combination corresponding to the two rotor-side currents with the smallest determined error, the output voltages of the two rotor-side currents in the two-phase coordinate system are determined.

[0071] See Figure 2 As shown, u d and u q These are the output voltages on the two rotor sides,I rd and I rq These are the two rotor-side currents, respectively.

[0072] The model predictive current control strategy in this application typically completes the following three steps within a very short control cycle: establishing a model and predicting the future, and utilizing the input real-time parameters. Rs, Lm and two rotor-side currents I rd and I rq A mathematical model of the motor is constructed. Then, the algorithm exhaustively enumerates all possible voltage combinations that the inverter can output at the next moment and calculates the predicted motor current for each voltage combination. For each of the two rotor-side currents calculated in the first step, the error between the predicted current and the two expected input currents is calculated; this error can be quantified as a cost function value. Based on the principle of minimizing error, the specific voltage combination that minimizes the error between the predicted current and the expected current is selected, thus obtaining... u d and u q The output is sent to the inverter for actual execution.

[0073] See Figure 2 As shown, the inverter output u a , u b , u c These represent the three-phase voltages on the rotor side, and 3s / 2r indicates the coordinate system transformation. I rabc This refers to the three-phase current on the rotor side. I sabc The coordinate transformation is used to obtain the three-phase current on the stator side. I sd and I sq , U sd , U sq , I sd and I sq The active power P can be obtained by power calculation.

[0074] In summary, this application presents a neural network-based online parameter identification method for doubly-fed generator (DFIG) model predictive control. The core of this method is the integration of a variable-step-size linear (Adaline) neural network to achieve step-by-step online identification of mutual inductance and rotor resistance. An improved sliding mode power control with parameter feedforward is employed to construct an integrated rotor-side MPCC control system. This application first identifies the mutual inductance parameters of the DFIG using a variable-step-size neural network, then uses the identification results to accurately identify the rotor resistance, updating the rotor-side control model parameters in real time. Simultaneously, a novel improved sliding mode power control with parameter feedforward is used in the outer loop, effectively suppressing power overshoot and fluctuations caused by large parameter identification errors during unit startup and grid connection or sudden changes in operating conditions. This significantly improves the stability and dynamic tracking performance of the active and reactive power decoupling control under parameter mismatch conditions.

[0075] See Figure 3 As shown in the diagram, this application provides a schematic diagram of the structure of a doubly-fed motor model predictive control device 30. Figure 3 As shown, the doubly fed motor model prediction control device 30 includes: an acquisition module 301, a parameter identification module 302, a sliding mode control module 303, a model prediction current control module 304, and a voltage control module 305.

[0076] The acquisition module 301 is used to acquire two rotor-side voltages, two stator-side currents, and two stator-side voltages in a two-phase coordinate system.

[0077] The parameter identification module 302 is used to identify the rotor resistance and stator-rotor mutual inductance values ​​based on two rotor-side voltages, two stator-side currents and two stator-side voltages using a variable step size parameter identification model, and obtain the corrected rotor resistance and stator-rotor mutual inductance values.

[0078] The sliding mode control module 303 is used to determine two desired currents in a two-phase coordinate system based on the corrected stator-rotor mutual inductance values ​​using a sliding mode controller.

[0079] The model predictive current control module 304 is used to determine the two rotor-side output voltages in a two-phase coordinate system based on two expected currents, the corrected rotor resistance, and the corrected stator-rotor mutual inductance values, using a model predictive current control strategy.

[0080] The voltage control module 305 is used to control the rotation of the doubly fed motor based on the output voltages of the two rotor sides.

[0081] Optionally, the parameter identification module 302 is used to employ a variable step size parameter identification model to determine two theoretical current values ​​based on two rotor-side voltages, two stator-side voltages, and preset rotor resistance and preset rotor mutual inductance values; to determine two errors based on the two stator-side currents and the two theoretical current values; to determine two step sizes based on the two errors and the variable step size function of the variable step size parameter identification model; and to correct the preset rotor resistance and preset rotor mutual inductance values ​​based on the two errors and the two step sizes, until the variable step size function converges, thereby obtaining the corrected rotor resistance and corrected stator-rotor mutual inductance values.

[0082] Optionally, the variable step size function is:

[0083] in, It is an adjustable variable. for Error at any given time.

[0084] Optionally, the sliding mode control module 303 is used to acquire the actual active power, target active power, actual reactive power, and target reactive power; using a sliding mode controller, based on the actual active power and target active power, and the actual reactive power and target reactive power, respectively, two sliding surfaces are determined; using the output function of the sliding mode controller, based on the corrected stator-rotor mutual inductance values ​​and the two sliding surfaces, two desired currents in the two-phase coordinate system are determined respectively.

[0085] Optionally, the sliding mode control module 303 is used to acquire the stator inductance and rotor inductance; determine the variable parameters of the output function based on the stator inductance, rotor inductance and the corrected stator-rotor mutual inductance values; determine the values ​​of the two saturation functions of the output function based on the two sliding surfaces and preset adjustable parameters; and determine the two desired currents in the two-phase coordinate system based on the values ​​of the two saturation functions, the variable parameters and the two sliding surfaces.

[0086] Optionally, the output function is:

[0087] in, Let D be the desired current, and D be a variable parameter. It is the difference between the target active power and the actual active power, or the difference between the target reactive power and the actual reactive power. for The derivatives of , where a, ε, and k are all adjustable parameters with values ​​greater than 0. It is a sliding surface; The saturation function is:

[0088] in, These are preset adjustable parameters.

[0089] Optionally, the model prediction current control module 304 is used to determine multiple voltage combinations output by the inverter at the next moment based on two expected currents, the corrected rotor resistance, and the corrected stator-rotor mutual inductance values; based on each voltage combination, predict the two rotor-side currents in the two-phase coordinate system corresponding to each voltage combination at the next moment; determine the error between the two rotor-side currents corresponding to each voltage combination and the two expected currents, and determine the two rotor-side currents with the smallest error with the two expected currents; based on the voltage combinations corresponding to the two rotor-side currents with the smallest determined error, determine the two rotor-side output voltages in the two-phase coordinate system.

[0090] In applications, the modules in the doubly fed motor model predictive control device 30 can be software program modules, or they can be implemented through different logic circuits integrated in the processor, or they can be implemented through multiple distributed processors.

[0091] The doubly fed motor model predictive control device 30 of this application embodiment can execute the method provided in this application embodiment. The implementation principle is similar. The actions performed by each module in the doubly fed motor model predictive control device 30 of each embodiment of this application correspond to the steps in the method of each embodiment of this application. For detailed functional descriptions of each module of the doubly fed motor model predictive control device 30, please refer to the descriptions in the corresponding methods shown above, which will not be repeated here.

[0092] See Figure 4 As shown, this application provides a schematic diagram of the structure of an electronic device 40. Figure 4 As shown, the electronic device 40 of this application embodiment includes: a memory 42, a processor 41, and a computer program 43 stored in the memory 42 and executable on the processor 41. When the processor 41 executes the computer program, it implements the steps of the methods of the various embodiments of this application.

[0093] Electronic device 40 may include, but is not limited to, processor 41 and memory 42. Those skilled in the art will understand that electronic device 40 may also include more or fewer components, or combinations of certain components, or different components, such as input / output devices, network access devices, etc.

[0094] The processor 41 can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0095] In some embodiments, memory 42 may be an internal storage unit, such as a hard disk or RAM. Memory 42 may be a removable / non-removable, volatile / non-volatile computer system storage medium; for example, memory 42 may be a non-volatile memory used for reading and writing non-volatile magnetic media. In other embodiments, memory 42 may be an external storage device, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc., provided on electronic device 40. Memory 42 is used to store operating systems, applications, bootloaders, data, and other programs, such as program code for computer programs. Memory 42 may also be used to temporarily store data that has been output or will be output.

[0096] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0097] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0098] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps in the above-described method embodiments.

[0099] If the integrated units described above are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a device / terminal equipment, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0100] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk drive (HDD), or solid-state drive (SSD), etc. The storage medium can also include combinations of the above types of memory.

[0101] This application provides a computer program product that, when run on a processor, enables the processor to execute the steps described in the various method embodiments above.

[0102] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0103] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0104] In the embodiments provided in this application, it should be understood that the disclosed apparatus / network devices and methods can be implemented in other ways. For example, the apparatus / network device embodiments described above are merely illustrative. For instance, the division of modules or units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0105] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0106] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A model predictive control method for a doubly-fed induction generator (DFIG), characterized in that, include: Obtain the two rotor-side voltages, two stator-side currents, and two stator-side voltages in a two-phase coordinate system; A variable step size parameter identification model is adopted to identify the rotor resistance and stator-rotor mutual inductance values ​​based on the two rotor-side voltages, the two stator-side currents, and the two stator-side voltages, so as to obtain the corrected rotor resistance and the corrected stator-rotor mutual inductance values. Using a sliding mode controller, two desired currents in the two-phase coordinate system are determined based on the corrected stator-rotor mutual inductance values; A model-predictive current control strategy is adopted to determine the two rotor-side output voltages in the two-phase coordinate system based on the two desired currents, the corrected rotor resistance, and the corrected stator-rotor mutual inductance values. The rotation of the doubly fed motor is controlled based on the two rotor-side output voltages.

2. The doubly-fed motor model predictive control method according to claim 1, characterized in that, The variable step-size parameter identification model, based on two rotor-side voltages, two stator-side currents, and two stator-side voltages, performs parameter identification on the rotor resistance and stator-rotor mutual inductance values ​​to obtain corrected rotor resistance and corrected stator-rotor mutual inductance values, including: A variable step size parameter identification model is adopted to determine two theoretical current values ​​based on the two rotor-side voltages, the two stator-side voltages, the preset rotor resistance, and the preset rotor mutual inductance value. Based on the two stator-side currents and the two theoretical current values, two errors are determined accordingly. Based on the two errors and the variable step size function of the variable step size parameter identification model, two step sizes are determined accordingly. Based on the two errors and the two step sizes, the preset rotor resistance and the preset rotor mutual inductance value are corrected respectively until the variable step size function converges, and the corrected rotor resistance and the corrected stator-rotor mutual inductance value are obtained.

3. The doubly-fed motor model predictive control method according to claim 2, characterized in that, The variable step size function is: in, It is an adjustable variable. for Error at any given time.

4. The doubly-fed motor model predictive control method according to claim 1, characterized in that, The method employs a sliding mode controller to determine two desired currents in the two-phase coordinate system based on the corrected stator-rotor mutual inductance values, including: Obtain the actual active power, target active power, actual reactive power, and target reactive power; Using a sliding mode controller, two sliding surfaces are determined based on the actual active power and the target active power, as well as the actual reactive power and the target reactive power. Using the output function of the sliding mode controller, based on the corrected stator-rotor mutual inductance values ​​and the two sliding surfaces, two desired currents in the two-phase coordinate system are determined respectively.

5. The doubly-fed motor model predictive control method according to claim 4, characterized in that, The step of using the output function of the sliding mode controller, based on the corrected stator-rotor mutual inductance values ​​and the two sliding surfaces, to determine two desired currents in the two-phase coordinate system includes: Obtain the stator inductance and rotor inductance; Based on the stator inductance, the rotor inductance, and the corrected stator-rotor mutual inductance values, the variable parameters of the output function are determined; Based on the two sliding surfaces and the preset adjustable parameters, the values ​​of the two saturation functions of the output function are determined respectively; Based on the values ​​of the two saturation functions, the variable parameters, and the two sliding surfaces, two desired currents in the two-phase coordinate system are determined respectively.

6. The doubly-fed motor model predictive control method according to claim 5, characterized in that, The output function is: in, Let D be the desired current, and D be the variable parameter. It is the difference between the target active power and the actual active power, or the difference between the target reactive power and the actual reactive power. for The derivatives of , where a, ε, and k are all adjustable parameters with values ​​greater than 0. It is a sliding surface; The saturation function is: in, The preset adjustable parameters are as described above.

7. The doubly-fed motor model predictive control method according to any one of claims 1-6, characterized in that, The model-predictive current control strategy, based on the two desired currents, the corrected rotor resistance, and the corrected stator-rotor mutual inductance, determines the two rotor-side output voltages in the two-phase coordinate system, including: Based on the two desired currents, the corrected rotor resistance, and the corrected stator-rotor mutual inductance, multiple voltage combinations output by the inverter at the next moment are determined. Based on each voltage combination, predict the two rotor-side currents in the two-phase coordinate system corresponding to each voltage combination at the next moment; Determine the error between the two rotor-side currents and the two desired currents corresponding to each voltage combination, and determine the two rotor-side currents with the smallest error to the two desired currents; Based on the voltage combination corresponding to the two rotor-side currents with the smallest determined error, the two rotor-side output voltages in the two-phase coordinate system are determined.

8. A doubly-fed motor model predictive control device, characterized in that, include: The acquisition module is used to acquire two rotor-side voltages, two stator-side currents, and two stator-side voltages in a two-phase coordinate system. The parameter identification module is used to identify the rotor resistance and stator-rotor mutual inductance values ​​based on the two rotor-side voltages, two stator-side currents and two stator-side voltages using a variable step size parameter identification model, so as to obtain the corrected rotor resistance and the corrected stator-rotor mutual inductance values. The sliding mode control module is used to determine two desired currents in the two-phase coordinate system based on the corrected stator-rotor mutual inductance values ​​using a sliding mode controller. The model predictive current control module is used to determine the two rotor-side output voltages in the two-phase coordinate system based on the two expected currents, the corrected rotor resistance, and the corrected stator-rotor mutual inductance values, using a model predictive current control strategy. A voltage control module is used to control the rotation of the doubly fed motor based on the two rotor-side output voltages.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 7.

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