Fuzzy wind power system robust control method based on dynamic memory event triggering mechanism

Abstract

The invention discloses a fuzzy wind power system robust control method based on a dynamic memory event trigger mechanism, which belongs to the technical field of automatic control of a wind power generation system, and comprises the following steps: constructing a multi-region DFIG wind power system T-S fuzzy model considering a controller random fault and state time delay; designing a proportional-integral type controller structure; a memory type event triggering mechanism based on a dynamic threshold and a historical error variable is introduced; a Lyapunov-Krasovskii functional method and a linear matrix inequality method are utilized to construct a robustness criterion; according to the invention, unified guarantee of system control performance, robustness and stability is realized in a communication resource limited environment.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of automatic control of wind power generation systems, in particular to a fuzzy wind power system robust control method based on a dynamic memory event triggering mechanism. It is suitable for multi-area doubly-fed induction generator (DFIG) wind power systems. The method aims to improve the frequency stability of the wind power system and effectively improve the utilization efficiency of communication resources in the case of random controller failure and communication delay. BACKGROUND

[0002] With the wide access of wind power in modern power systems, the problem of system frequency stability is increasingly prominent. Due to the volatility of wind energy, the inertial response capability of the system is significantly reduced, especially in large-scale multi-area wind power systems, the frequency deviation phenomenon is more significant, which seriously threatens the safety and reliability of system operation. The traditional load frequency control (LFC) method usually relies on timed sampling and deterministic control strategy, although it can maintain system performance in the worst case, but it often causes a large amount of redundant data transmission, causing communication network congestion and waste of computing resources.

[0003] In order to alleviate the above problems, researchers have proposed an event-triggered control (ETC) method, which only updates data when the system state meets the triggering condition, thereby effectively reducing the communication burden. However, the static event triggering mechanism (METS) has poor adaptability when facing dramatic changes in system state, and it is difficult to cope with uncertain factors such as random controller failure or communication delay. Among them, random controller failure is an important non-deterministic factor that cannot be ignored in practical applications, common causes include component aging, communication channel failure, etc., which may cause abnormal actuator output.

[0004] Doubly-fed induction generator (DFIG) has become the mainstream model of modern wind power systems due to its good grid performance and energy capture capability. However, its corresponding mathematical model has high nonlinearity and coupling characteristics, such as valve restriction, load disturbance and frequency deviation, which bring many challenges to controller design.

[0005] Therefore, with the continuous development of smart grids, how to ensure control performance while improving system stability and control robustness, security, and saving communication resources has become an important direction of networked control system research. SUMMARY

[0006] The present application aims at solving the problems of insufficient frequency regulation ability, waste of communication resources and weak system robustness of the existing wind power system control method under the conditions of random failure of the controller, communication delay and nonlinear coupling, and provides a fuzzy wind power system robust control method based on a dynamic memory event triggering mechanism.

[0007] The present application is achieved by the following technical solutions: The present application provides a fuzzy wind power system robust control method based on a dynamic memory event triggering mechanism, which refers to Figure 1 and comprises the following steps: Step 1: establishing a T-S fuzzy model of a multi-region double-fed induction generator wind power system; Step 2: designing a PI controller with random fault modeling; Step 3: constructing a dynamic memory event triggering mechanism; Step 4: constructing a new Lyapunov-Krasovskii functional set and deriving a stability condition.

[0008] As a further improvement of the present application, the step 1 is specifically as follows: A T-S fuzzy model with the following fuzzy rules is established: Rule : If is ··· and is , then: wherein a=1, 2,..., l, , ..., ..., represent fuzzy sets, and l represents the number of fuzzy reasoning rules.

[0009] The fuzzy membership function is defined as follows: wherein q represents the number of fuzzy sets , i represents the number of fuzzy reasoning rules under the current rule, ) represents the change of steam flow entering the steam turbine. The premise value is bounded, i.e. and the subset function are selected as: and .

[0010] Using the central average fuzzifier, product reasoning, and monadic fuzzifier, the fuzzy model can be expressed as the following global model: in, , is the state variable of the system, is the derivative of the state variable of the system, represents the external disturbance, and the control input signal is , , , is the parameter matrix, Represents the system output variable, represents the output parameter matrix, Indicates the change in steam flow entering the turbine, represents a fuzzy membership function under a fuzzy inference rule, l represents the number of fuzzy inference rules, =1,2,.., l，i Indicates the number of the fuzzy inference rule under the current rule.

[0011] As a further improvement of the present invention, the step 2 is specifically as follows: To track the frequency variation of the multi-zone DFIG integrated WPS is achieved by using the following PI controller:

[0012] in, , represents the controller matrix, and Indicates the length of the integration interval.

[0013] for , The control input The control input of the actuator is redefined as coming from the zero-order hold, and the hold interval is , is satisfied Communication delay, is the sampling period, is the maximum allowed communication delay.

[0014] The holding interval of the zero-order holder can be divided into multiple sub-intervals, such as in, and Indicates that the current sampling instance Start to subsequent sampling instancesExamples of where and For , the time-varying delay in the sawtooth structure is modeled using The following are the constraints of Thus, the PI controller can be replaced by the following expression: .

[0015] In addition, the frequency response of the multi-area wind power system integrated with the doubly-fed converter is further monitored and analyzed, which more realistically reproduces the uncertainty failures that may occur in the power system. By introducing a dynamic adaptive mechanism, the robustness and fault tolerance of the system in the face of failures are effectively improved. At the same time, the PI controller with random faults is introduced as part of the control strategy, thereby enhancing the control performance of the system under non-ideal working conditions. On this basis, combined with the fuzzy center balancer, the following input controller expression is constructed: .

[0016] In order to enhance the robustness of the system, a random fault model is introduced into the PI controller. Through the simulation and modeling of random faults, the control system can more effectively adapt to various uncertain factors and failure situations that may occur in the actual operation process, thereby improving its stability and reliability in complex environments. The random fault model is as follows: where, is a random fault sample value, which is subject to a beta distribution defined in the interval (0,1). The beta distribution is selected to generate random faults, which not only ensures that the generated random variables are always within the effective range, but also enhances the flexibility and adaptability of the power system to uncertain factors. In the simulation environment where the controller components may fail, in order to ensure the stability and control performance of the system, the following fuzzy proportional integral (PI) control strategy is designed: Rule 2: If belongs to ··· and belongs to , then the fuzzy controller can be represented by the following expression:

[0017] where, is the integral control gain, is the proportional control gain, and .

[0018] In summary, the fuzzy proportional-integral control strategy can be processed into the following form by fuzzification:

[0019] wherein, represents a random fault sampling value, represents a time-varying delay, represents a fuzzy membership function under another fuzzy inference rule, and j represents the number of fuzzy inference rules under the current rule.

[0020] The above formula is brought into the global model to obtain: As a further improvement of the present application, the step 3 is specifically as follows: Static event triggering is generally used in power systems as a general event triggering as follows: wherein, represents a sequence of transmission instants. Meanwhile, define wherein, and are constants. In addition, represents a synchronous sampling interval of the system, represents a gain matrix.

[0021] If the system state satisfies the condition specified in the above formula, the following transmission event sequence will be used. Although the adoption of the standard event triggering strategy (ETS) reduces the usage rate of the required communication bandwidth, unnecessary data transmission may still occur due to the triggering condition set by the event triggering strategy even if the system has tended to be stable. Such redundant communication may still be triggered when the system state changes weakly or even is static, which reduces the utilization efficiency of the communication resources and is not conducive to the implementation of efficient networked control.

[0022] Therefore, the is redefined, thereby more effectively saving the communication resources, and a dynamic event triggering mechanism (DETM) containing a parameter function of an auxiliary dynamic variable is proposed. The method fully considers the problem of waste of communication resources possibly caused by controller failure. In order to achieve effective monitoring, the controller needs to access historical information to assist in judging the change of the system state. By grouping and accumulating the event triggering data at each time point and combining the auxiliary dynamic variable with the event state of the current time, the dynamic event triggering mechanism determines and confirms the sending time of the next sampling signal according to this. The mechanism not only improves the communication efficiency, but also The adaptability and robustness of the system in the face of uncertain factors are enhanced. The new trigger condition is as follows: wherein, denotes the sequence of transmission instants, denotes the sequence of next transmission instants, s is a constant, h denotes the synchronization sampling interval of the system, and min denotes the minimum value operation, denotes a diagonal matrix, denotes a threshold parameter, and both denote a matrix, and are both given constants, denotes the number of recently transmitted data packets, is a weighting function, and f denotes the data packet number of transmission, ) denotes a parameter function. , , , , , , .

[0023] In addition, in the formula: , Meanwhile wherein, is a matrix determined in subsequent analysis, and the threshold parameter .

[0024] Thus, for the threshold parameter , the expression is as follows: wherein and are located in the range of (0, 1) to represent the highest value and the lowest value of , and is a predetermined constant.

[0025] As a further improvement of the present application, the step 4 is specifically as follows: For a given scalar value , , , , the following matrix can be obtained: , , , , and an arbitrary matrix .

[0026] The above matrix and controller gain , These parameters can together satisfy the following conditions to ensure the asymptotic stability of the system. Some of the vectors are specified as follows: The matrix product and the multiple quadratic form in the above equation are as follows: , And these quadratic forms and matrices are dependent on the state difference, which also includes .

[0027] Therefore, in order to verify the asymptotic stability of the system, the Lyapunov-Krasovskii functional is constructed as follows: where represents the set of Lyapunov-Krasovskii functionals, and each sub-function corresponds to the current state term, the delayed state term, the historical derivative term, and the event-triggered disturbance term, represents the current state term, represents the delayed state term, represents the historical derivative term, represents the event-triggered disturbance term, and the overall derivative is represented as: Here is a linear combination of the state vector, is the constructed augmented matrix function.

[0028] After the above derivation, in order to verify and evaluate the stability of the system, under the premise of satisfying the condition , based on the reasoning of the V function, the weighted integral term of the system state derivative can be estimated as an upper bound of a quadratic function with respect to the state variable difference , , and the corresponding matrix of the function can satisfy the following LMI structure: where: , , .

[0029] In addition, when considering the nonlinear inequality constraints on the system under the event-triggered condition, the following relationship can be derived: where respectively contain the historical sampling state and the current sampling state.

[0030] In external interference Under the condition of , substituting the control law and event triggering strategy, the derivative of the system Lyapunov function can finally satisfy: .

[0031] As a further improvement of the present invention, it also includes: like It can be inferred that , thus ensuring the asymptotic stability of the entire wind power system.

[0032] Setting scalar parameters 、 and control parameters 、 , and introduce the positive definite matrix U, W, M, 、 On this basis, if there is a controller gain matrix in( ) and satisfy the following linear matrix inequality conditions: 1. All subitems Must meet ; 2. Block Matrix Form ; This indicates that the interference Under the existing conditions, the system can still meet the set Performance indicators , and maintains robust asymptotic stability under the action of the event triggering mechanism. Specifically: .

[0033] In order to facilitate the solution of the controller, we introduce the positive definite matrix , and reconstruct the controller gains: .

[0034] Based on the above verification process, if there are design variables With positive definite matrix etc., and can meet the following LMI conditions: 1. 2. If these series of conditions are met, the system The robust controller can be constructed by solving the LMI. The design can also be obtained by replacing the equivalent variables as follows: And based on the replacement, the expansion matrix can be introduced By multiplying a series of inequalities on the left and right, and applying the Schur complement formula for linearization, the original controller design problem can be transformed into a standard convex optimization problem, ensuring that the system can achieve robust stability and stability under the dynamic event triggering strategy. Performance goals.

[0035] It should be further explained that the technical features corresponding to the above options can be combined or replaced with each other to form a new technical solution if there is no conflict.

[0036] Compared with the prior art, the present invention has the following beneficial effects: This paper proposes a TS fuzzy control modeling method for multi-region doubly fed induction generator (DFIG) wind power systems, combining the random fault modeling mechanism of the PI controller with the dynamic memory event triggering mechanism (DMETS). This method not only ensures the robustness and fault tolerance of the system, but also significantly reduces the communication burden and improves bandwidth utilization. Based on the stability criterion constructed based on the new Lyapunov-Krasovskii functional family, the robust stability of the system under random fault and event triggering constraints is rigorously proved. Numerical simulation results further verify that the proposed control strategy is superior to traditional methods in terms of frequency regulation performance, communication efficiency, and fault adaptability, and has high engineering application value. Specific contributions include: (1) A TS fuzzy control modeling method suitable for multi-region DFIG wind power systems is proposed to effectively deal with the nonlinear characteristics of the system and improve the practicality and adaptability of the model.

[0037] (2) A PI controller that introduces random fault modeling is designed to realistically simulate the gain loss and signal interference that may occur in the actual operating environment of the controller, thereby enhancing the robustness and fault tolerance of the system.

[0038] (3) A dynamic memory event trigger mechanism is proposed, which comprehensively considers the current state of the system, historical errors and dynamically adjusted trigger thresholds, effectively reducing the communication frequency and improving bandwidth utilization efficiency.

[0039] (4) Construct a new Lyapunov-Krasovskii universal function set, combine dynamic time delay and nonlinear coupling processing mechanism, effectively reduce the conservatism of stability criterion, and strictly verify the control system Robust stability.

[0040] (5) Numerical simulations have shown that the proposed control strategy is superior to the traditional load frequency control (LFC) method in terms of frequency regulation effect, communication resource utilization, and fault adaptability, and has broad prospects for engineering promotion. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1Flow chart of the fuzzy robust control method of wind power system based on dynamic memory event triggering mechanism of the application; Figure 2 Isolated region dynamic memory event triggering graph in embodiment 1 of the application; Figure 3 Threshold parameter triggering behavior fluctuation graph in embodiment 2 of the application; Figure 4 Dynamic memory event triggering threshold dynamic characteristic schematic diagram in embodiment 2 of the application; Figure 5 Dynamic memory event triggering threshold dynamic characteristic schematic diagram in embodiment 2 of the application when disturbance is applied; Figure 6 In embodiment 2 of the application, when the threshold is 0.15, the change of the system triggering interval with time; Figure 7 In embodiment 2 of the application, when the threshold is 0.25, the change of the system triggering interval with time; Figure 8 In embodiment 2 of the application, when the threshold is 0.35, the change of the system triggering interval with time; Figure 9 In embodiment 2 of the application, when the threshold is 0.45, the change of the system triggering interval with time. DETAILED DESCRIPTION

[0042] The technical solutions of the application will be described clearly and completely below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the application, rather than all the embodiments. The components of the embodiments of the application described and shown in the accompanying drawings can be arranged and designed in various different configurations. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative work are within the protection scope of the application.

[0043] It should be noted that the defects in the above prior art solutions are the results obtained by the inventors after practice and careful study. Therefore, the discovery process of the above problems and the solutions proposed by the embodiments of the application to the above problems should be the contributions made by the inventors to the application during the process of invention and creation, and should not be understood as technical content known to those skilled in the art.

[0044] Embodiment 1: Single-region DFIG wind power system control based on event triggering mechanism This embodiment designs a robust control strategy with random fault tolerance ability for isolated single-region wind power system, mainly including: 1. System structure: First, we construct an independently operated wind power generation area. This area contains a doubly fed induction generator (DFIG) model and is not connected to other areas by tie lines, forming a local autonomous system structure. Define the following equation: , from which we can get the single-region wind power system under isolated conditions: On this basis, the system uses the TS fuzzy model to approximate nonlinear dynamics. By designing corresponding fuzzy rules, key state quantities such as regional frequency deviation, valve hysteresis, and power dynamics are linearized and expressed in the form of state space. Specifically, the TS fuzzy model with the following fuzzy rules is considered: rule :if yes ,So: Rule 2: If yes ,So: in, is the state variable of the system, represents external disturbance. The control input signal is A, B, F are parameter matrices.

[0045] Fuzzy membership function The following definitions are possible: ,in, 、 , from which the following formula can be derived: in, . From this we can derive the following formula: In summary, we get the following formula: 2. Design of proportional-integral controller with random faults: During the actual operation of the power system, the controller may encounter various random faults, such as sensor failure, actuator gain loss, signal interference, and hardware failure. These random problems can have a significant impact on the stability and performance of the system. This is especially important for critical infrastructure such as wind power systems, where ensuring safe and stable operation is particularly important. To this end, this paper designs a control model that considers random faults. The model introduces a random parameter that follows a Beta distribution. , to characterize the failure scenarios such as random drift of actuator gain, random switching or local interruption of communication channel, so as to enhance the robustness and fault tolerance of the controller in complex operating environment. Then the following controller input form is obtained: where, is a random fault sampling data, which is beta distributed between (0, 1), which can better grasp the random fault distribution of the controller.

[0046] 3. Function stability analysis: After constructing the system model and the controller model, the following Lyapunov-Krasovskii functional is constructed to analyze the stability of the system: , each sub-item considers the current state, delay state, derivative integral and event error, and uses integral inequality and lemma tools to derive the system derivative to satisfy: Then the stability analysis condition is converted to linear matrix inequality form, which is solved by MATLAB + YALMIP tool to obtain the controller gain and . Analyzing the stability of isolated area system, although the system tends to be stable, there is still a slight disturbance due to the fault of the controller.

[0047] 4. State response under the state of isolated area: In power systems, static event triggering is usually used as a general event trigger as follows: where, represents the sequence at the transmission moment. At the same time, is defined as and are constants. In addition, represents the synchronous sampling interval of the system, represents the gain matrix.

[0048] If the system state satisfies the conditions specified in the static event trigger, the following transmission event will be activated. The use of standard ETS reduces the usage rate of required communication bandwidth. However, even if the system is stable, unnecessary data transmission defined in the static event trigger may still occur through ETS.

[0049] Therefore, is redefined to more effectively save communication resources, and a parameter function containing auxiliary dynamic variables is proposed The dynamic event triggering mechanism (DETM) of the controller. This method takes into account the waste of communication resources that may be caused by controller failure. To achieve effective monitoring, the controller needs to access historical information to assist in determining changes in system state. By grouping and accumulating event-triggered data at each time point, and combining the auxiliary dynamic variables and the event state of the current time, the dynamic event triggering mechanism determines and determines the sending time of the next sampling signal accordingly. This mechanism not only improves communication efficiency, but also enhances the adaptability and robustness of the system in the face of uncertain factors. The new triggering condition is as follows: where, denotes the sequence at the transmission moment, denotes the sequence at the next transmission moment, s is a constant, h represents the synchronous sampling interval of the system, min represents the minimum value operation, denotes a diagonal matrix, denotes a threshold parameter, and both denote a matrix, and are given constants, denotes the number of recently transmitted data packets, is a weighted function, f denotes the data packet number, ) denotes a parameter function. , , , , , , .

[0050] In addition, in the formula: , At the same time where, is a matrix determined in subsequent analysis, and the threshold parameter .

[0051] Thus, the expression of the threshold parameter is as follows: where and are located in the range of (0, 1) to represent the highest and lowest values, and is a predetermined constant.

[0052] Finally, numerical simulation is performed and the results are as follows: Figure 2As shown, the state response has faster convergence speed and smaller steady-state error under the DMETS mechanism. Compared with the traditional ETS and DETS triggering mechanisms, DMETS greatly reduces the number of communications, verifying the superiority of the proposed method in isolated area control.

[0053] Embodiment 2: Based on the previous isolated area wind power system, this embodiment further optimizes and reforms the multi-area interconnected DFIG wind power system for collaborative control and anti-disturbance performance analysis. This embodiment expands to a wind power system including two areas, each area being configured with a DFIG power generation unit, and the areas transmitting power through a tie line, requiring frequency synchronization and power balance, which belongs to a typical distributed wide-area power system structure. On this basis, a unified fuzzy modeling method is used, and the state variables of each area include frequency deviation , power transmission error and internal control variables. In the modeling process, fuzzy rules are used to divide the working interval to obtain a T-S fuzzy system coupled with the areas.

[0054] Thereafter, the controller continues the PI structure in Embodiment 1, introduces the DMETS triggering mechanism, ensures that the controller is updated only when the system state changes significantly, avoids redundant signal transmission, and unifies the triggering condition as follows: , wherein the threshold function has a time-varying memory characteristic.

[0055] On the basis of the multi-area system, a Lyapunov-Krasovskii functional is further constructed, and the system stability conditions considering controller failure, communication time delay and external disturbance are derived. Subsequently, the obtained conditions are equivalent to the solvable linear matrix inequality (LMI) form, which is used to analyze whether the system satisfies the robust performance constraint as shown below: After completing the basic system establishment, the parameters in the following table are set for model simulation design.

[0056] Table 1 Simulation parameters of wind power system

[0057] After numerical simulation using the parameters listed in Table 1, the steady-state operation of the two-area interconnected system (WPS) can be obtained, and the system appears sustained low-amplitude oscillation phenomenon during dynamic operation. The main reason for this can be attributed to the small signal disturbance received by the terminal node. This type of oscillation has typical decay period characteristics, which are specifically manifested as follows: 1. The disturbance variable presents an alternating decay law, and its amplitude gradually converges as the system approaches the steady-state balance; 2. After entering steady state, the disturbance amplitude obeys uniform statistical distribution, which proves that the balanced energy exchange mechanism has been established between interconnected areas.

[0058] On this basis, the DMETS-based dynamic characteristics analysis is shown in Figure 4 . The research data show that the adjustment mechanism is significantly related to the system behavior shown in Figure 3 . Specifically, the parameter matrix of the nonlinear NCS that satisfies the two fuzzy rules can be expressed as follows: 1. When the system response shows significant fluctuations, it will approach the threshold value, at which time the frequency of triggering events increases significantly; 2. In the process of system stabilization and response error convergence to zero, it gradually approaches the reference value, and the triggering events decrease accordingly.

[0059] When the simulation of the system ends, this embodiment studies the difference between dynamic memory event triggering and traditional triggering in the multi-area wind power system under the T-S fuzzy environment, Figure 4 and shows the experimental results of the dynamic characteristics of the DMETS triggering threshold, where Figure 4 (a) is the change of triggering interval during operation, Figure 4 (b) is the evolution of the triggering threshold over time, which reflects the coordination and regulation ability of the mechanism in the dynamic response stage and its advantage in maintaining system consistency. Table 2 gives the comparison of different event triggering methods in terms of relative communication resource utilization.

[0060] Table 2 Comparison of different event triggering methods

[0061] As can be seen from Table 2, there are significant differences in relative communication resource utilization among ETS, METS, DETS, and DMETS technologies, among which various ETS technologies have obvious effect in reducing NCCs usage, especially DMETS and METS technologies, which perform particularly outstanding in improving communication efficiency.

[0062] To further verify the robustness of the controller under disturbance conditions, the operation process of the DMETS mechanism is tested under the condition of step disturbance applied to the system, and the results are shown in Figure 5 . Among them, Figure 5 (a) gives the change of triggering interval of DMETS under disturbance, Figure 5 (b) shows the corresponding triggering threshold The dynamic evolution over time reflects that the mechanism can quickly adjust the triggering frequency at the initial stage of disturbance and gradually extend the triggering interval at the system recovery stage, thereby maintaining system stability and consistency.

[0063] Figures 6-9 The triggering interval of the system over time under different threshold conditions is shown, corresponding to 0.15, 0.25, 0.35, and 0.45, respectively. Figure 6 The threshold is 0.15: the threshold is the lowest, the triggering condition is the strictest (more likely to trigger), the triggering interval is the shortest, the communication frequency is the highest, the initial triggering is dense, and there is almost no long interval section. Figure 7 The threshold is 0.25: the threshold is raised, the triggering interval is slightly extended, the communication frequency is relatively Figure 6 It has decreased, the initial triggering is still dense but sparse in the middle and late stages. Figure 8 The threshold is 0.35: the threshold is further raised, the triggering condition of the system is more relaxed, the triggering interval is significantly extended, and the communication frequency is further reduced. Figure 9 The threshold is 0.45: the threshold is the highest, the triggering condition is the most relaxed, the triggering interval is the longest, and the communication frequency is the lowest. The triggering is only concentrated in the stage of rapid change of system state. The DMETS mechanism does not affect the system response speed and output stability while the communication frequency is significantly reduced, and has stronger adaptability to disturbance changes, fully embodying the practicality and robustness of the proposed control method.

[0064] The above specific embodiments are detailed descriptions of the present application, which cannot be recognized as limited to these descriptions. For ordinary skilled persons in the technical field to which the present application belongs, without departing from the concept of the present application, a number of simple deductions and substitutions can be made, which should be regarded as belonging to the protection scope of the present application.

Claims

1. A fuzzy wind power system robust control method based on dynamic memory event triggering mechanism is characterized by: The following steps are involved: Step 1: Establish a TS fuzzy model of a multi-area doubly-fed induction generator wind power system; Step 2: Design a PI controller with random fault modeling; Step 3: Construct a dynamic memory event triggering mechanism; Step 4: Construct a new Lyapunov-Krasovskii functional set and derive Stability conditions.

2. The fuzzy wind power system robust control method based on dynamic memory event triggering mechanism according to claim 1 is characterized in that: The method of establishing a TS fuzzy model for a multi-region doubly-fed induction generator wind power system includes: Using the central average fuzzifier, product reasoning and monadic fuzzifier, the fuzzy model is expressed as follows Global Model: ,in, , is the state variable of the system, is the derivative of the state variable of the system, represents the external disturbance, and the control input signal is , , , is the parameter matrix, Represents the system output variable, represents the output parameter matrix, Indicates the change in steam flow entering the turbine, represents a fuzzy membership function under a fuzzy inference rule, l represents the number of fuzzy inference rules, ，i Indicates the number of the fuzzy inference rule under the current rule.

3. The fuzzy wind power system robust control method based on dynamic memory event triggering mechanism according to claim 2 is characterized in that: The PI controller is expressed as follows: ,in, represents the integral control gain, represents the proportional control gain, and , represents a random fault sampling value, represents the time-varying delay, represents the fuzzy membership function under another fuzzy inference rule, and j represents the number of the fuzzy inference rule under the current rule.

4. The fuzzy wind power system robust control method based on dynamic memory event triggering mechanism according to claim 1 is characterized in that: The construction of the dynamic memory event triggering mechanism includes: The trigger conditions are: ,in, represents the sequence of transmission moments, Indicates the sequence of the next transmission moment, s is a constant, h represents the synchronous sampling interval of the system, and min represents the minimum value operation. represents a diagonal matrix, represents the threshold parameter, and Both represent matrices, and are given constants, Indicates the number of packets transmitted recently, , is a weighted function, f represents the number of transmitted data packets, Represents a parametric function.

5. The fuzzy wind power system robust control method based on dynamic memory event triggering mechanism according to claim 1 is characterized in that: The Lyapunov-Krasovskii functional set is as follows: ,in, represents the Lyapunov-Krasovskii functional set, each subfunction They correspond to the current state item, delayed state item, historical derivative item and event-triggered disturbance item respectively. Indicates the current status item, Represents a delayed status item, represents the historical derivative term, Represents the event-triggered disturbance term.

6. The fuzzy wind power system robust control method based on dynamic memory event triggering mechanism according to claim 2 is characterized in that: The derivation Stability conditions, including: The stability condition is set as: ,in, is the attenuation performance index.

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