Self-adaptive fractional order permanent magnet synchronous wind driven generator fuzzy control method and system

Abstract

The invention relates to the technical field of wind power generation control, and discloses a self-adaptive fractional order permanent magnet synchronous wind driven generator fuzzy control method and system. Aiming at the nonlinear characteristic of a permanent magnet synchronous generator and the problem that the performance of a traditional control method is limited under a dynamic working condition, a Lyapunov function is constructed by utilizing a fractional order Lyapunov stability theory, and a T-S fuzzy controller is designed to be applied to a fractional order PMSG wind power generation system in combination with an adaptive quantization mechanism. By establishing a fuzzy logic control method based on an observer, the actual effect of an adaptive quantization mechanism on the asymptotic stability of the fractional order PMSG system is verified; constructing a function by means of a fractional order Lyapunov stability theory to cope with random dynamic behaviors of the model; and analyzing the interaction between the fractional order and the controller, and determining the optimal order and the influence of the order on the service life of the controller. According to the method, the stability and robustness of the system under uncertain conditions are improved, and compared with a traditional control strategy, the method shows better performance in fractional order PMSG wind power generation system control.

Description

Technical Field

[0001] This invention belongs to the field of wind power generation control technology, and relates to an adaptive fractional-order permanent magnet synchronous wind turbine fuzzy control method and system, which is suitable for improving the stability and robustness of wind power generation systems under complex operating conditions. Background Technology

[0002] With the surge in global demand for clean energy, wind power, as a core component of renewable energy, has seen its installed capacity continuously expand. Permanent magnet synchronous generators (PMSGs) have become the mainstream equipment for wind power generation due to their advantages such as high efficiency, low energy consumption, and strong disturbance resistance. However, PMSGs have strong nonlinear and multivariate coupling characteristics. Their mathematical models involve multiple coupled variables such as current, voltage, and speed. Under the influence of disturbances such as wind speed fluctuations and load changes, they are prone to chaotic phenomena and dynamic instability, which may lead to fluctuations in output power, accelerated wear of the unit, and even threaten the safe operation of the power grid.

[0003] Currently, research on PMSG control mainly focuses on the combination of traditional and intelligent control. Nonlinear bifurcation analysis can be used to study the dynamic characteristics of simple systems, but in actual PMSG systems, the control links of power electronic devices, external environmental disturbances (such as wind speed and humidity), and internal parameter coupling (such as resistance and inductance fluctuations) interact with each other. A single bifurcation analysis method is difficult to capture the dynamic behavior under complex operating conditions and cannot provide effective guidance for control strategy design.

[0004] Current PMSG control methods have significant limitations: PI control relies on linearized models and has poor robustness under nonlinear conditions; sliding mode control has strong disturbance rejection capabilities, but chattering can exacerbate device losses; fuzzy control is suitable for nonlinear systems, but its performance degrades when the rule base is complex and state changes are large; fractional-order control can accurately describe dynamic characteristics, but its design is complex and computationally intensive; quantization control can reduce communication bandwidth, but its fixed quantization strategy leads to error accumulation, affecting system stability. All these methods struggle to balance control accuracy, disturbance rejection, and real-time performance under complex conditions.

[0005] Therefore, how to integrate fractional-order theory, TS fuzzy control and adaptive quantization mechanism to overcome the shortcomings of traditional control methods in nonlinear processing, quantization error suppression and stability assurance, so as to improve the stability and robustness of fractional-order PMSG wind power generation system under parameter fluctuations and external disturbances, is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0006] In view of this, the present invention provides an adaptive fractional-order permanent magnet synchronous wind turbine fuzzy control method and system to solve some of the technical problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention adopts the following technical solution:

[0008] An adaptive fractional-order permanent magnet synchronous wind turbine fuzzy control method includes the following steps:

[0009] S1. Construct a fractional permanent magnet synchronous wind turbine model based on fractional calculus theory;

[0010] S2. Establish a TS fuzzy fractional-order permanent magnet synchronous wind turbine chaotic model using membership functions;

[0011] S3. Design the corresponding TS fuzzy control model by combining adaptive quantization mechanism and observer method;

[0012] S4. Verify the stability of the designed control system based on fractional Lyapunov stability theory;

[0013] S5. The simulation experiment verifies the suppression effect of the designed control system on the chaotic oscillation behavior of the permanent magnet synchronous wind turbine; the control system is the TS fuzzy control model of the fractional permanent magnet synchronous wind turbine based on the adaptive quantization mechanism constructed according to steps S1-S3.

[0014] Preferably, the fractional-order permanent magnet synchronous wind turbine generator model construction process in step S1 includes:

[0015] S11. Using the fundamental lemmas and definitions in fractional calculus theory, construct a mathematical model of the fractional PMSG system and obtain the state equation of the fractional PMSG in the dq synchronous rotating coordinate system.

[0016] S12. Using the stator voltage vector orientation control method, the q-axis of the dq synchronous rotating coordinate system is oriented on the stator voltage vector, and the rotor motion equation of the fractional-order PMSG is obtained;

[0017] Preferably, the fractional permanent magnet synchronous wind turbine model constructed based on fractional calculus theory is as follows:

[0018]

[0019] in, Describes a fractional differential operator. Indicates stator resistance. Represents electric angular velocity, stator voltage is determined by and It can be expressed that the state currents in the dq coordinate system are respectively , The stator reactance in the dq coordinate system is expressed as: Magnetic flux is The generator speed is determined by The electrical rotational speed is expressed as... ,in The extreme logarithm is given; furthermore, the mechanical characteristics of the PMSG-based wind power generation system are as follows:

[0020]

[0021] Where J is the moment of inertia. Indicates electromagnetic torque. Indicates load torque. This represents the damping coefficient.

[0022] Preferably, the fractional-order permanent magnet synchronous wind turbine TS chaotic model mentioned in step S2 is as follows:

[0023]

[0024] in, This represents the fractional derivative under the definition of caputo. The membership degree in is ;make For all Satisfaction: , ,and here .

[0025] Preferably, the TS fuzzy control model in step S3 specifically includes:

[0026] S31. Construct a TS fuzzy model of the observer based on TS fuzzy theory;

[0027] S32. Based on the adaptive quantization mechanism, a local gain matrix is ​​introduced by rules to explain the operation of the quantizer. By considering various cases and rigorously defining it, the quantizer's responsibility to map intervals to specific quantization levels and the resulting division of the mapped intervals are explained, along with its performance under special conditions. From this, the definition of the quantizer and the conditions it satisfies are derived, ultimately leading to the overall control model including control gain and membership functions. S32. Numerical simulations are performed to obtain the time-series characteristics of each state variable under normal operation and clustered oscillation states.

[0028] Preferably, the specific content of the stability verification of the control system in step S4 includes:

[0029] S41. Based on the fractional Lyapunov stability theory and the adaptive law conditions, sufficient conditions for guaranteeing the asymptotic stability of the augmented closed-loop fractional system are derived. By constructing the Lyapunov function and applying relevant lemmas, it is proved that the designed control law enables the observer's TS fuzzy system to achieve global ML stability.

[0030] S42. An adaptive quantization controller scheme is proposed. The quantizer is determined based on Filippov theory to obtain the error system. To address the shortcomings of theoretical analysis on the stability of the adaptive quantization controller, a pairwise theorem is given. By assuming a specific form of Lyapunov function and deriving and proving it, it is concluded that the adaptive closed-loop system can achieve global asymptotic stability through the adaptive quantization controller.

[0031] S43. In the process of jointly designing the gain of the required observer and controller, the Schur supplementary lemma and other derivations were used to determine that when there are positive definite matrices satisfying inequalities, the adaptive closed-loop system is asymptotically stable, thus establishing the design basis for the gain matrices of the controller and observer.

[0032] Preferably, the TS fuzzy control model of the fractional-order permanent magnet synchronous wind turbine based on the adaptive quantization mechanism mentioned in step S5 is specifically as follows:

[0033]

[0034] in express Fractional derivative under Caputo's definition; Represents the state variables of the system; The disturbances experienced by the system; For system output; control input ; , , The real matrix in the formula is as follows:

[0035] , ,

[0036] .

[0037] This invention also provides an adaptive fractional-order permanent magnet synchronous wind turbine fuzzy control system. The system consists of a state acquisition module that collects data such as shaft current, stator voltage, and rotational speed through sensors. The state reconstruction and quantization module outputs quantized state quantities through a fractional-order TS fuzzy observer and an adaptive quantizer. The controller module then generates control signals through a fuzzy rule base, an LMI solving unit, and a control law generation unit. The execution module converts these signals into drive signals through an inverter and a power converter. Finally, the feedback module monitors the system output and calculates the control error, forming a closed-loop control.

[0038] The hardware implementation of the fractional-order TS fuzzy observer includes: an embedded processor that runs the observer algorithm, a storage unit that stores the membership function parameters and the observer gain matrix, and a communication interface that supports the CAN bus protocol and is used to receive raw sensor data and output reconstructed state variables.

[0039] As can be seen from the above technical solutions, compared with the prior art, this invention, based on fractional calculus theory and adaptive quantization mechanism, discloses an adaptive fractional permanent magnet synchronous wind turbine fuzzy control method. This method is used to improve the stability of wind power systems adapted to fractional permanent magnet synchronous generators (PMSGs) through adaptive quantization. The observer-based adaptive fuzzy control exhibits excellent control effect and stability when dealing with internal parameter fluctuations and external disturbances, showing significant advantages compared to sliding mode control, PID control, and other methods. Research on the controller performance response under different fractional orders shows that the control system achieves the best control effect when the fractional order is 0.985. Furthermore, when the fractional order is smaller, the controller input frequency decreases, operating losses decrease, and the service life is extended. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, 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 embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0041] Figure 1 A schematic diagram of the adaptive fractional-order permanent magnet synchronous wind turbine fuzzy control method provided by the present invention;

[0042] Figure 2 This is a schematic diagram of the overall system control provided by the present invention;

[0043] Figure 3 This is a schematic diagram of the overall system model provided by the present invention;

[0044] Figure 4A schematic diagram of the chaotic model of the fractional-order PMSG model provided by the present invention;

[0045] Figure 5 The fractional-order PMSG state provided by the present invention after adding a controller Time-domain response diagram;

[0046] Figure 6 The fractional-order PMSG state provided by the present invention after adding a controller Time-domain response diagram;

[0047] Figure 7 The fractional-order PMSG state provided by the present invention after adding a controller Time-domain response diagram;

[0048] Figure 8 The fractional-order PMSG provided by this invention has different system parameters. Time-domain response diagram;

[0049] Figure 9 The fractional-order PMSG provided by this invention has different system parameters. Time-domain response diagram;

[0050] Figure 10 The fractional-order PMSG provided by this invention has different system parameters. Time-domain response diagram;

[0051] Figure 11 The fractional-order PMSG provided by this invention is Time-domain response diagram of the time error system;

[0052] Figure 12 The fractional-order PMSG system provided by this invention operates at different fractional orders. Time-domain response diagram;

[0053] Figure 13 The fractional-order PMSG system provided by this invention operates at different fractional orders. Time-domain response diagram;

[0054] Figure 14 The fractional-order PMSG system provided by this invention operates at different fractional orders. Time-domain response diagram;

[0055] Figure 15 The graph showing the performance variation of the controller in a fractional-order PMSG at different fractional orders is provided by the present invention.

[0056] Figure 16 The time-domain response diagram of the controller input for the fractional-order PMSG provided by this invention when the fractional order is 1.02;

[0057] Figure 17 The present invention provides a time-domain response diagram of the controller input for a fractional-order PMSG with a fractional order of 0.98.

[0058] Figure 18 A comparison chart of the control effects of the fractional-order PMSG provided by this invention at a fractional order of 0.98.

[0059] Figure 19 This is a schematic diagram of system parameter settings provided by the present invention.

[0060] Figure 20 This is a schematic diagram illustrating the performance changes of the controller provided by the present invention. Detailed Implementation

[0061] 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, and 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.

[0062] This invention discloses an adaptive fractional-order permanent magnet synchronous wind turbine fuzzy control method, such as... Figure 1 As shown, it includes the following steps:

[0063] S1. Construct a fractional permanent magnet synchronous wind turbine model based on fractional calculus theory;

[0064] S2. Establish a TS fuzzy fractional-order permanent magnet synchronous wind turbine chaotic model using membership functions;

[0065] S3. Design the corresponding TS fuzzy control model by combining adaptive quantization mechanism and observer method;

[0066] S4. Verify the stability of the designed control system based on fractional Lyapunov stability theory;

[0067] S5. The simulation experiment verifies the suppression effect of the designed control system on the chaotic oscillation behavior of the permanent magnet synchronous wind turbine; the control system is the TS fuzzy control model of the fractional permanent magnet synchronous wind turbine based on the adaptive quantization mechanism constructed according to steps S1-S3.

[0068] To further implement the above technical solution, the specific content of step S1 includes:

[0069] S11. Using the fundamental lemmas and definitions in fractional calculus theory, a mathematical model of the fractional PMSG system is constructed, and the state equations of the fractional PMSG in the dq synchronous rotating coordinate system are obtained to describe the electromagnetic and mechanical dynamics inside the generator:

[0070]

[0071] in, Describes a fractional differential operator. Indicates stator resistance. Represents electric angular velocity, stator voltage is determined by and It can be expressed that the state currents in the dq coordinate system are respectively , The stator reactance in the dq coordinate system is expressed as: Magnetic flux is The generator speed is determined by... The electrical rotational speed is expressed as... ,in It is an extreme logarithm.

[0072] S12. Using stator voltage vector orientation control, the q-axis of the dq synchronous rotating coordinate system is oriented onto the stator voltage vector, and the rotor motion equations of the fractional-order PMSG are obtained:

[0073]

[0074] Where J is the moment of inertia. Indicates electromagnetic torque. Indicates load torque. This represents the damping coefficient.

[0075] To further implement the above technical solution, step S2 includes the following:

[0076] A TS fuzzy fractional PMSG chaotic model was established using the membership function.

[0077] Rule 1: If correspond Until correspond Then we have:

[0078]

[0079] in, This represents the fractional derivative under the definition of caputo, where the system's state variables and control variables are respectively represented by... and It means that, among them , . For bounded external disturbance variables and The output is The superscripts n, m, and q indicate the dimension of the vector. In addition, and These are real matrices with a certain dimension. In summary, the fuzzy model can be represented as:

[0080]

[0081] in, The membership degree in is .make For all Satisfaction: , ,and here ; It is used to measure the first Fuzzy rules and current input The intensity of the rule incentive for matching degree is obtained by multiplying the membership functions of the input variables. p is the number of input variables.

[0082] Therefore, we can derive the following TS fuzzy model:

[0083]

[0084] By using fuzzy fusion, a simple linear model is used to approximate the global nonlinear behavior.

[0085] To further implement the above technical solution, step S3 includes the following:

[0086] S31. Construct a TS fuzzy model of the observer based on TS fuzzy theory;

[0087] The observer rule is similar to rule 1, and will not be repeated here. The following formula is the TS fuzzy model of the observer:

[0088]

[0089] Let f(x) and f(x) represent the state matrix corresponding to the i-th fuzzy rule, respectively. Control input matrix, Output feedback gain matrix.

[0090] S32. Based on the adaptive quantization mechanism, the local gain matrix is ​​introduced by rules to explain the working mode of the quantizer. By considering various cases and strictly defining it, it is explained that the quantizer is responsible for mapping the interval to a specific quantization level and the mapping interval forms a division, as well as its performance in special cases. Thus, the definition of the quantizer and the conditions it satisfies are derived, and finally the overall control model containing control gain and membership function is derived.

[0091] Next, the state variables will be quantized, and their corresponding fuzzy control methods will be designed:

[0092] Rule 2: If correspond Until correspond Then we have: .

[0093] The local gain matrix with appropriate dimensions is: According to its description, the quantizer works as follows:

[0094]

[0095] in, Let be the k-th symmetric subquantizer, and it has the following symmetry property:

[0096]

[0097] Represents the Kth state variable;

[0098] Based on its quantization level, it can be expressed as:

[0099]

[0100] in, This indicates the density of the quantizer. Indicates the initial quantization level. For To give a strict definition, the following situations must be considered:

[0101]

[0102] in, These are the quantizer parameters. By definition, the quantizer parameters are determined by its definition, which states that the quantizer is responsible for mapping intervals to specific quantization levels. Furthermore, the intervals mapped by the quantizer form a... Partitioning of the (real number field). It is important to note that when... Sometimes:

[0103]

[0104] Therefore, the quantizer is defined as:

[0105]

[0106] And it also satisfies: Based on this, we can derive the overall control model:

[0107]

[0108] Wherein, the control gain is The membership function is .

[0109] To provide a clear and complete description of the system model framework, this example is drawn as follows: Figure 2 The system control block diagram shown is as follows:

[0110] During the control response process, it is necessary to track the error of the system state variables in real time. The estimated error is expressed as: (where 'e' represents the error, the first value is the actual value, and the second value is the target value), the error system can be expressed in the following form:

[0111]

[0112] in, .make We can obtain the following augmented closed-loop fractional model:

[0113]

[0114] in

[0115] .

[0116] To further implement the above technical solution, step S4 includes the following:

[0117] S41. Based on the fractional Lyapunov stability theory and the adaptive law conditions, sufficient conditions for guaranteeing the asymptotic stability of the augmented closed-loop fractional system are derived. By constructing the Lyapunov function and applying relevant lemmas, it is proved that the designed control law enables the observer's TS fuzzy system to achieve global ML stability.

[0118] This section presents the derivation of the asymptotic stability and stabilization criteria for a state-quantized TS fuzzy fractional PMSG model expressed by the linear matrix inequality (LMI).

[0119] First, by utilizing fractional Lyapunov stability theory and adaptive law conditions, we derive sufficient conditions to guarantee the asymptotic stability of the extended system.

[0120] Theorem 1: For a given scalar and gain , If there exists a matrix P > 0 satisfying the following inequality, then the closed-loop system is considered globally asymptotically stable in the ML sense:

[0121]

[0122] in

[0123] .

[0124] Proof: As shown below, construct a Lyapunov function:

[0125]

[0126] in Let represent a positive definite matrix.

[0127] The fractional derivative can be determined as:

[0128]

[0129] have:

[0130]

[0131] We can obtain:

[0132]

[0133] Using Schur complement, we can deduce that:

[0134]

[0135] in .

[0136] have:

[0137]

[0138] Thus obtain therefore

[0139]

[0140] This demonstrates that the control law of the design enables the system to achieve global ML stability.

[0141] Adaptive quantization controller The proposed solution is as follows:

[0142]

[0143] in, Represents a positive constant. This represents the control gain. We can use a quantization controller to obtain the error system. , , .

[0144] Quantizer It is determined based on the Filippov theory. , , .

[0145] Furthermore, the following equation describes the error system:

[0146]

[0147] in , .

[0148] S42. An adaptive quantization controller scheme is proposed. The quantizer is determined based on the Filippov theory to obtain the error system. To address the shortcomings of theoretical analysis of the stability of the adaptive quantization controller, a pairwise theorem is given. By assuming a specific form of Lyapunov function and deriving and proving it, it is concluded that the adaptive closed-loop system can achieve global asymptotic stability through the adaptive quantization controller.

[0149] To verify that the quantization control scheme guarantees the stability of the error system, this example provides relevant theorems and proofs.

[0150] Theorem 2: For a given scalar and gain , An adaptive closed-loop system can achieve global asymptotic stability through an adaptive quantization controller, provided that there exists a positive definite matrix P that satisfies the following inequality:

[0151]

[0152] Among them, matrix elements The parts are as follows:

[0153]

[0154] , ,

[0155]

[0156] Proof: Assume the Lyapunov function has the following form:

[0157]

[0158] in, Let represent an adaptive constant, the value of which will be determined later. According to Lemma 2 in Section 2, the fractional derivative is determined as follows:

[0159]

[0160] By using Lemma 3, we can obtain:

[0161]

[0162] and

[0163]

[0164] Appropriate selection To make the above inequality hold, and combining this with the above formula, we can obtain:

[0165]

[0166] Using Schur's lemma, the following results are obtained:

[0167]

[0168] Here According to Theorem 1, we obtain:

[0169]

[0170] therefore, ,when hour, It is bounded. Therefore, when At that time, there exists M > 0 satisfying Therefore, It can be done through Conclusion This means that the system is globally asymptotically stable.

[0171] S43. In the process of jointly designing the gain of the required observer and controller, the Schur supplementary lemma and other derivations were used to determine that when there are positive definite matrices that satisfy the inequalities, the adaptive closed-loop system is asymptotically stable, thus establishing the design basis for the gain matrices of the controller and observer.

[0172] The process of jointly designing the required observer and controller gain involves using matrix SVD to obtain the solution of LMI. The following are the required theorems and proofs.

[0173] Theorem 3: For a given scalar An adaptive closed-loop system is asymptotically stable if there exists a positive definite matrix P that satisfies the following inequality:

[0174]

[0175] The matrix is ​​defined as follows:

[0176]

[0177] Furthermore, the gain matrices of the controller and the observer are: and .

[0178] Proof: By using Schur's supplementary lemma, we obtain the following inequality:

[0179]

[0180] definition ,in , By multiplying both sides of the inequality by P, we get:

[0181]

[0182] for

[0183]

[0184] Determine the existence of a matrix make This is established. It is confirmed by using Schur's supplementary lemma. The inequality holds only if the LMI is satisfied. Therefore, the LMI is a sufficient condition for the inequality, and thus the controller can ensure the stability of the system. This also establishes the completeness of the proof.

[0185] To further implement the above technical solution, step S5 includes the following specific contents:

[0186] Figure 3 The actual implementation procedure of the proposed control system in the wind turbine generator system is clearly explained. To verify the feasibility of the above system, this section will use MATLAB for simulation and provide a detailed description of the process of converting the theoretical derivation and verification results into an actual PMSG simulation system.

[0187] PMSG is considered an ideal generator for large wind farms due to its large air gap, low density, high torque handling capacity, reliable operation characteristics, and low cost.

[0188] The fractional-order TS fuzzy model of PMSG can be obtained from the formula above:

[0189]

[0190] in express Fractional derivative under Caputo's definition; Represents the state variables of the system; The disturbances experienced by the system; For system output; control input ; , , The real matrix in the formula is as follows:

[0191] , ,

[0192] , , , .

[0193] Parameter settings in the formula, , Load torque disturbance Other parameters were selected as , , , For other relevant parameters of PMSG, please refer to [link / reference]. Figure 19 The details will not be elaborated here. A feasible solution is obtained by solving the formula using MATLAB. Its control gain matrix is ​​as follows:

[0194]

[0195] The proposed controller and observer gain matrices ensure the closed-loop system

[0196] Figure 4 This is a chaotic model of a fractional-order PMSG model, with time on the x-axis and state variables on the y-axis. It also illustrates the chaotic characteristics of the system without control input at different fractional orders, with the left and right sides representing the fractional orders. and Chaotic phase diagram and time sequence diagram.

[0197] Fractional order of fractional model They have specific advantages because they have more freedom and memory properties.

[0198] The simulation results show that... The choice of has a profound impact on the system's frequency response. When a smaller value is chosen... When the frequency change is small, the fractional-order memory effect is small, and the system can automatically recover to steady state after a certain period of time without additional control. However, when a larger value is selected... When the system frequency changes significantly, the fractional-order memory effect also increases, and without additional control, it cannot transition to a steady state on its own, thus continuing to maintain a state of chaotic motion.

[0199] To verify the control effect of the proposed control method on the system's operating characteristics, we conducted simulation verification and obtained the following results: Figures 5-7 The response characteristic curve shown is as follows:

[0200] In actual PMSG operation, we need to consider the impact of losses or disturbances on system parameters. In order to explore the impact of fractional PMSG parameter uncertainty on system operating characteristics, we selected different parameter values ​​of resistance, inductance and moment of inertia, taking 30% and 60% of the parameters respectively, and observed the system's response to the three state variables after control was applied.

[0201] pass Figure 8-10 We can see that the proposed control method effectively stabilizes the operating state of the fractional PMSG under parameter changes, but we can also see that as the percentage of system parameter values ​​gradually decreases, it will have a negative impact on the control effect of the controller.

[0202] According to the above text Figure 4 Based on the observed results, when adding a controller to a fractional-order PMSG system for its control, we also need to consider the impact of changes in the fractional-order order on the control performance of the control system, such as... Figure 11 As shown, this is the time-domain response of the error system when the fractional order is 0.98.

[0203] Depend on Figure 11 It can be seen that the controller affects the fractional-order PMSG. The system still exhibits excellent control performance under different fractional orders. Therefore, we need to conduct a detailed study on the control performance at different fractional orders.

[0204] Figure 12-14 The time-domain response curves of the error system for a fractional-order PMSG with fractional orders of 1.02, 1.00, and 0.96 are given. , , , , , Let be the target value of the controller. It can be seen that all three state variables of the system are well controlled. To determine at what fractional order the system's control performance is maximized, we studied the controller performance within the order range of 0.96 to 1.02 and obtained the following results: Figure 15 The diagram shows the controller performance characteristics.

[0205] pass Figure 15 We can clearly see that blue represents rise time, red represents overshoot, and pink represents response time. As the fractional order gradually increases from 0.96 to 1.02, the rise time of the control system first decreases and then increases, while the overshoot and response time show a steady upward trend. Figure 20 Considering the specific time variations and various aspects of economy and control performance, this example concludes that the controller achieves optimal control of the fractional-order PMSG when the fractional-order order is 0.985.

[0206] also, Figure 16-17 Explained the control trajectory and The time-domain response.

[0207] We compared the time-domain response of the controller input when the system order is 1.02 and 0.98, respectively. We found that when the system order is smaller, the controller's action amplitude and frequency will decrease under the same control effect. This indicates that the controller's losses will also decrease under a smaller system order, and the controller's service life will also be improved.

[0208] Finally, considering the stochastic effects of external disturbances on the system, we verified the superior control performance of the proposed adaptive quantization fuzzy control by comparing it with sliding mode control and PID control. The control performance is comparable to, for example, that of, sliding mode control and PID control. Figure 18 As shown.

[0209] Specifically, this example proposes an observer-based fuzzy control method applicable to wind turbine systems equipped with fractional-order PMSGs, employing an adaptive quantization mechanism. The implementation steps are as follows:

[0210] First, the necessity of employing an observer-based fuzzy control method lies in addressing the nonlinearity and uncertainty of fractional-order PMSG. In this process, the introduction of quantization techniques can effectively reduce disturbances in the control system. Next, in the practical analysis, the control gain matrix is ​​determined by solving linear matrix inequalities using MATLAB. Specifically, this involves the gain matrices of the controller and the observer. and These gain matrices are calculated using linear matrix inequalities. Once determined, they are used to construct the control law to ensure the asymptotic stability of the closed-loop system and satisfy the relevant inequality requirements specified in the linear matrix inequalities.

[0211] Furthermore, adaptive parameters were introduced into the controller to improve the stability and robustness of the PMSG. After obtaining the complete model of the controller system, not only was the control performance of the controller studied, but the influence of system random parameters, fractional order, and external disturbances on the dynamic response of the controller was also investigated. Finally, by comparing different control schemes, the intended stability performance of this scheme in the face of various complex operating conditions was verified.

[0212] Based on the same inventive concept, this invention also provides an adaptive fractional-order permanent magnet synchronous wind turbine fuzzy control system. The system consists of a state acquisition module that collects data such as shaft current, stator voltage, and rotational speed through sensors, a state reconstruction and quantization module that outputs quantized state quantities through a fractional-order TS fuzzy observer and an adaptive quantizer, a controller module that generates control signals through a fuzzy rule base, an LMI solving unit, and a control law generation unit, an execution module that converts these signals into drive signals through an inverter and a power converter, and finally a feedback module that monitors the system output and calculates the control error to form a closed-loop control.

[0213] The hardware implementation of its fractional-order TS fuzzy observer includes: an embedded processor that runs the observer algorithm, a storage unit that stores the membership function parameters and the observer gain matrix, and a communication interface that supports the CAN bus protocol and is used to receive raw sensor data and output reconstructed state variables.

[0214] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0215] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An adaptive fractional-order permanent magnet synchronous wind turbine fuzzy control method, characterized in that, Includes the following steps: S1. Construct a fractional permanent magnet synchronous wind turbine model based on fractional calculus theory; S2. Establish a TS fuzzy fractional-order permanent magnet synchronous wind turbine chaotic model using membership functions; S3. Design the corresponding TS fuzzy control model by combining adaptive quantization mechanism and observer method; S4. Verify the stability of the designed control system based on fractional Lyapunov stability theory; S5. The simulation experiment verifies the suppression effect of the designed control system on the chaotic oscillation behavior of the permanent magnet synchronous wind turbine; the control system is the TS fuzzy control model of the fractional permanent magnet synchronous wind turbine based on the adaptive quantization mechanism constructed according to steps S1-S3.

2. The adaptive fractional-order permanent magnet synchronous wind turbine fuzzy control method according to claim 1, characterized in that, The fractional-order permanent magnet synchronous wind turbine generator model construction process in step S1 includes: S11. Using the fundamental lemmas and definitions in fractional calculus theory, construct a mathematical model of the fractional PMSG system and obtain the state equation of the fractional PMSG in the dq synchronous rotating coordinate system. S12. Using the stator voltage vector orientation control method, the q-axis of the dq synchronous rotating coordinate system is oriented on the stator voltage vector, and the rotor motion equation of the fractional-order PMSG is obtained.

3. The adaptive fractional-order permanent magnet synchronous wind turbine fuzzy control method according to claim 1, characterized in that, The fractional permanent magnet synchronous wind turbine model constructed based on fractional calculus theory is as follows: ； in, Describes a fractional differential operator. Indicates stator resistance. Represents electric angular velocity, stator voltage is determined by and It can be expressed that the state currents in the dq coordinate system are respectively , The stator reactance in the dq coordinate system is expressed as: Magnetic flux is The generator speed is determined by The electrical rotational speed is expressed as... ,in The extreme logarithm is given; furthermore, the mechanical characteristics of the PMSG-based wind power generation system are as follows: ； Where J is the moment of inertia. Indicates electromagnetic torque. Indicates load torque. This represents the damping coefficient.

4. The adaptive fractional-order permanent magnet synchronous wind turbine fuzzy control method according to claim 1, characterized in that, The fractional-order permanent magnet synchronous wind turbine TS chaotic model mentioned in step S2 is as follows: ； in, This represents the fractional derivative under the definition of caputo. The membership degree in is ;make For all Satisfaction: , ,and here .

5. The adaptive fractional-order permanent magnet synchronous wind turbine fuzzy control method according to claim 1, characterized in that, The specific content of the TS fuzzy control model in step S3 includes: S31. Construct a TS fuzzy model of the observer based on TS fuzzy theory; S32. Based on the adaptive quantization mechanism, a local gain matrix is ​​introduced by rules to explain how the quantizer works. By considering various cases and strictly defining it, it is explained that the quantizer is responsible for mapping intervals to specific quantization levels and forming divisions of the mapping intervals, as well as its performance in special cases. Thus, the definition of the quantizer and the conditions it satisfies are derived, and finally, the overall control model containing control gain and membership function is derived.

6. The adaptive fractional-order permanent magnet synchronous wind turbine fuzzy control method according to claim 1, characterized in that, The specific content of the stability verification of the control system in step S4 includes: S41. Based on the fractional Lyapunov stability theory and the adaptive law conditions, sufficient conditions for guaranteeing the asymptotic stability of the augmented closed-loop fractional system are derived. By constructing the Lyapunov function and applying relevant lemmas, it is proved that the designed control law enables the observer's TS fuzzy system to achieve global ML stability. S42. An adaptive quantization controller scheme is proposed. The quantizer is determined based on the Filippov theory to obtain the error system. To address the shortcomings of theoretical analysis of the stability of the adaptive quantization controller, a pairwise theorem is given. By assuming a specific form of Lyapunov function and deriving and proving it, it is concluded that the adaptive closed-loop system can achieve global asymptotic stability through the adaptive quantization controller. S43. In the process of jointly designing the gain of the required observer and controller, the Schur supplementary lemma and other derivations were used to determine that when there are positive definite matrices satisfying inequalities, the adaptive closed-loop system is asymptotically stable, thus establishing the design basis for the gain matrices of the controller and observer.

7. The adaptive fractional-order permanent magnet synchronous wind turbine fuzzy control method according to claim 1, characterized in that, The TS fuzzy control model for the fractional permanent magnet synchronous wind turbine based on the adaptive quantization mechanism mentioned in step S5 is specifically as follows: ； in express Fractional derivative under Caputo's definition; Represents the state variables of the system; The disturbances experienced by the system; For system output; control input ; , , The real matrix in the formula is as follows: ， ， 。 8. An adaptive fractional-order permanent magnet synchronous wind turbine fuzzy control system, characterized in that, include: The status acquisition module, including a current sensor, a voltage sensor, and a speed sensor, is used to acquire the dq-axis current. , Stator voltage , and speed ; The state reconstruction and quantization module is connected to the state acquisition module. It has a built-in fractional-order TS fuzzy observer and adaptive quantizer, and outputs the quantized state quantity q(x). The controller module connects to the state reconstruction and quantization module, and includes a fuzzy rule base, an LMI solving unit, and a control law generation unit, outputting a control signal u; The execution module, connected to the controller module, includes an inverter and a power converter, which converts the control signals into drive signals for the PMSG. The feedback module connects the execution module and the state reconstruction and quantization module, and monitors the system output in real time and calculates the control error.

9. The system according to claim 8, characterized in that, The hardware of the fractional-order TS fuzzy observer includes: Embedded processor, running observer algorithm; Storage unit, storing membership function parameters and observer gain matrix. ; The communication interface receives raw sensor data and outputs reconstructed state values, supporting the CAN bus protocol.

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