Anti-interference control method of permanent magnet direct drive wind turbine system based on fusion event triggering mechanism

Abstract

This invention discloses an anti-interference control method for a permanent magnet direct-drive wind turbine system based on a fusion event-triggered mechanism. The method first establishes a model of the permanent magnet direct-drive wind turbine system based on fuzzy control theory. Then, it designs an intermediate variable estimator to simultaneously estimate the system state and external disturbances. Next, it designs a fusion event-triggered mechanism based on ring events and a dynamic threshold update law composed of system input and error-dependent trigger parameters to balance trigger performance and system performance. Finally, it designs an event-triggered anti-interference controller to ensure stable system operation. When applied to a permanent magnet direct-drive wind turbine system, this method exhibits good anti-interference performance and significantly reduces communication frequency.

Description

Technical Field

[0001] This invention relates to an anti-interference control method, specifically an anti-interference control method for a permanent magnet direct-drive wind turbine system based on a fusion event triggering mechanism. Background Technology

[0002] Compared to traditional fossil fuels and nuclear energy, wind power is widely recognized as a clean and sustainable energy solution due to its higher cost-effectiveness. This advantage has sparked researchers' interest in the stability of wind turbine systems. The most commonly used generator in wind power generation is the permanent magnet synchronous generator (PMSG), which offers advantages such as direct drive capability, low speed, and low maintenance costs. It is well known that PMSG direct-drive wind turbine systems exhibit complex nonlinear dynamic characteristics, thus necessitating the development of robust control schemes to achieve the required performance and ensure system stability. To address nonlinear issues, the Takagi-Sugano (TS) fuzzy model method has been explored, demonstrating strong capabilities in describing nonlinear characteristics. By designing appropriate fuzzy sets and fuzzy membership functions, the nonlinear features can be approximated as a locally linear model. Therefore, it is necessary to apply TS fuzzy technology to develop robust control methods for PMSG direct-drive wind turbine systems.

[0003] Furthermore, with the widespread adoption of communication technologies and their integration with industrial systems, networked control has become a crucial pathway for the development of permanent magnet direct-drive wind turbine systems. In networked control frameworks, data exchange is conducted through network channels employing a time-triggered (periodic) mode. However, with the expansion of system scale and the indiscriminate release of redundant data packets, network congestion looms over the entire system. Therefore, there is an urgent need to adopt effective communication schemes in permanent magnet direct-drive wind turbine systems to reduce data transmission frequency and conserve limited network resources.

[0004] However, existing research mainly focuses on static event-triggered strategies, which offer limited reduction in network load. Furthermore, it does not consider the impact of outlier policy data on the event generator. Therefore, it is necessary to adopt a fusion event-triggered strategy to save bandwidth usage. Summary of the Invention

[0005] The purpose of this invention is to propose an anti-interference control method for a permanent magnet direct-drive wind turbine system based on a fusion event triggering mechanism, which can effectively improve the anti-interference performance of the permanent magnet direct-drive wind turbine system under conditions of limited bandwidth and the presence of outlier measurement data.

[0006] The specific technical solution of the present invention is as follows: an anti-interference control method for a permanent magnet direct-drive wind turbine system based on a fusion event triggering mechanism, comprising the following steps:

[0007] Based on fuzzy control theory, a model of a permanent magnet direct-drive wind turbine system is established:

[0008] According to the principles of aerodynamics, the power generated by wind energy is expressed as follows:

[0009]

[0010] Wherein, ρ(Kg / m 3 () represents air density, and π represents pi. Indicates the length of the turbine blades. Represents the power coefficient, ω w (m / s) represents wind speed. ω represents the tip speed ratio. t (rpm) represents the turbine speed, β (rad) represents the pitch angle. Additionally, δ i (t) is

[0011] To maximize the efficiency of wind turbines, consider The optimal value is δ = δ opt (t). The corresponding torque is calculated as follows:

[0012]

[0013] Next, the nonlinear model of the permanent magnet direct-drive wind turbine system is established as follows:

[0014]

[0015] In the formula, Indicates stator resistance. Represents the stator inductance on the d-axis. Represents the stator inductance on the q-axis. This represents the magnetic flux of a permanent magnet. Indicates generator torque. Represents the moment of inertia. Indicates the coefficient of friction. Indicates external interference. and Representing the q-axis and d-axis voltages respectively, ω r (t)(rpm) represents the rotor speed, i q (t) and i d (t) represents the q-axis and d-axis currents, respectively.

[0016] Then, let The following state-space model of the permanent magnet direct-drive wind turbine system can be obtained:

[0017]

[0018] In the formula, the system matrix is ​​as follows:

[0019]

[0020] For the nonlinear terms present in the system, fuzzy control theory is used for processing. Assume ω... r (t)∈[θ min θ max And select the fuzzy membership function as:

[0021]

[0022] Here, θ1(t) and θ2(t) are available premise variables.

[0023] Next, applying the fuzzy membership function to the state-space expression of the aforementioned permanent magnet direct-drive wind turbine system yields the following fuzzy model:

[0024] Fuzzy rule i: If ω r (t) is θ i (t), then

[0025]

[0026] in,

[0027]

[0028] At this point, the fuzzy model of the permanent magnet direct-drive wind turbine system has been successfully established.

[0029] Furthermore, an intermediate variable estimator is designed to simultaneously estimate the system state and external disturbances. The specific steps are as follows:

[0030] An intermediate estimator is introduced to simultaneously estimate the system state and external disturbances. First, an intermediate variable is designed as follows:

[0031]

[0032] In the formula, Let ζ(t) represent the estimator gain. The derivative of ζ(t) is as follows:

[0033]

[0034] To estimate x(t) and ζ(t), the intermediate estimator is constructed as follows:

[0035]

[0036] In the formula, and These are estimates of x(t), ζ(t), and d(t), respectively.

[0037] Furthermore, a fusion event triggering mechanism based on ring events and a dynamic threshold update law composed of system input and error-dependent triggering parameters are designed to balance triggering performance and system performance. The specific steps are as follows:

[0038] To reduce network transmission burden, the estimated signal is first verified using event criteria, and then transmitted to the controller via an ideal network. The fusion event triggering mechanism is constructed as follows:

[0039]

[0040] in, It is the triggering error of state estimation, e2(t)=θ(t) k )-θ(t) is the triggering error of the premise variable. Ω 1i and Ω 2i This is the weight matrix to be designed. t k Indicates the last time it was triggered, t k+1 Indicates the time of the next trigger. σ q (t)(q=1,2,3) is a dynamic threshold and is updated by the following formula:

[0041]

[0042] In the formula, μ q The weighting parameter γ represents the weights for the instantaneous and average components. aq and γ bq Let f represent the sensitivity parameter, and h represent the length of the circular queue. f2(t) is considered to reduce unnecessary transmissions caused by anomalous measurements (such as failures, network attacks, and erroneous measurements due to component aging). Furthermore, f3(t) is introduced to address the mismatch in preconditions between the system / estimator and the controller.

[0043] Furthermore, an event-triggered anti-interference controller is designed to ensure the stable operation of the system. The specific steps are as follows:

[0044] Establish the following event-triggered TS fuzzy controller:

[0045]

[0046] make The following closed-loop error system can be obtained:

[0047]

[0048] also, The above closed-loop error system can be simplified as follows:

[0049]

[0050] In the formula,

[0051] This anti-interference control scheme can guarantee the eventual uniform bounded stability of the closed-loop error system. The proof is as follows:

[0052] C001: Select the following form of composite energy function:

[0053]

[0054] C002: In the formula,

[0055] C003: Calculate the derivative of V(t), and we get:

[0056]

[0057] C004: According to axioms and assumptions We can obtain:

[0058]

[0059] C005: Next, select two positive scalars ε1 and ε2. Taking event triggering rules into account, we can obtain:

[0060]

[0061] C006: Substituting C005 into C004, we get:

[0062]

[0063] C007: To ensure that the closed-loop error system is eventually bounded, the following condition must be met:

[0064]

[0065] C008: In the formula,

[0066]

[0067] C009: Based on C007 and the closed-loop error system, we can obtain:

[0068]

[0069] C010:C009 is equivalent to:

[0070]

[0071] C011: For C009 to be effective, the following formula must be satisfied:

[0072]

[0073] C012: In the formula, Ψ = [Ψ ij ] 4×4 ≥0.

[0074] C013: Based on the event triggering mechanism, we can conclude that:

[0075]

[0076] C014:C013 can be guaranteed by the following set of inequalities:

[0077]

[0078] C015: Note Γ ij Nonlinear coupling exists within it. To overcome this challenge, a method is introduced... Combining this with Finsler's lemma, we can obtain:

[0079]

[0080] C016: Therefore, Γ ij ≤0 can be guaranteed by the following formula:

[0081]

[0082] C017: In the formula,

[0083]

[0084] C018: Next, according to Finsler's lemma, Γ ij ≤0 can be further guaranteed by the following formula:

[0085]

[0086] C019: Then, by applying Schuler's complement theorem to C018, we can obtain...

[0087]

[0088] C020: Following the steps in C007-C014, the sufficient condition for ensuring that the closed-loop error system is eventually bounded can be obtained as follows:

[0089]

[0090] C021: According to C020, the permanent magnet direct-drive wind turbine system is eventually uniformly bounded under the anti-interference controller based on the fusion event triggering mechanism designed in this invention. Attached Figure Description

[0091] Figure 1 This is a flowchart of a method according to an embodiment of the present invention;

[0092] Figure 2 This is a system state response diagram using the method proposed in this invention as an example.

[0093] Figure 3 For the example, the ω method proposed in this invention is used. r Plot of estimation error of (t);

[0094] Figure 4 The method proposed in this invention is used in the embodiment. q Plot of estimation error of (t);

[0095] Figure 5 The method proposed in this invention is used in the embodiment. d Plot of estimation error of (t);

[0096] Figure 6 This is an example of an interference estimation map using the method proposed in this invention;

[0097] Figure 7 This is a response diagram of the event triggering threshold under the method proposed in this invention, as shown in the embodiment.

[0098] Figure 8 This is a trigger diagram for event triggering using the method proposed in this invention, as shown in the embodiment. Detailed Implementation

[0099] The present invention will be further illustrated below with reference to specific embodiments. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. After reading the present invention, any modifications of the present invention in various equivalent forms by those skilled in the art will fall within the scope defined by the appended claims.

[0100] like Figure 1 As shown, an anti-interference control method for a permanent magnet direct-drive wind turbine system based on a fusion event triggering mechanism includes the following steps:

[0101] Step 1: Set initial values ​​for each parameter;

[0102] Step 2: Estimate the state and disturbances based on the sensor output y(t), and output... and

[0103] Step 2: Update the threshold parameter σq (t);

[0104] Step 3: Use the threshold parameter σ q (t) and state estimation Verify the event triggering conditions and update the trigger output.

[0105] Step 4: Use Update the controller and transmit the update to the actuator via the network;

[0106] Step 5: Repeat steps 2, 3, and 4 until the runtime ends.

[0107] An embodiment of the present invention is described below:

[0108] Consider the system parameters shown in the table below:

[0109]

[0110] Figure 1 The system structure diagram is shown in the embodiment of the present invention; the state response diagram of the permanent magnet direct-drive wind turbine system under the designed control method is shown in the figure below. Figure 2 As shown. The designed event triggering mechanism is compared with that in references [1] and [2], and the resulting state error response diagram is shown below. Figure 3-5 As shown, the interference estimation effect of the designed estimator is as follows: Figure 6 As shown, the designed dynamic threshold response diagram is as follows: Figure 7 As shown in the diagram, the trigger intervals for the three event triggering mechanisms are as follows: Figure 8 As shown. From Figure 2 As can be seen, the proposed anti-interference control method based on the fusion event triggering mechanism successfully ensures the solution to the problem of the permanent magnet direct-drive wind turbine system. From Figure 3-5 It can be seen that the proposed event triggering mechanism maintains almost the same performance as existing results. From Figure 7-8 It can be seen that the proposed event triggering mechanism, while ensuring the same system performance, further reduces the number of signal transmissions and lowers the network bandwidth usage.

[0111] References

[0112] [1] Yan, S., Gu, Z., Park, JH, & Xie, X. (2021). Adaptive memory-event-triggered static output control of TS fuzzy wind turbine systems. IEEE Transactions on Fuzzy Systems, 30(9), 3894-3904.

[0113] [2]Peng,C.,&Yang,T.C.(2013).Event-triggered communication and H∞control co-design for networked control systems.Automatica,49(5),1326-1332.

Claims

1. An anti-interference control method for a permanent magnet direct-drive wind turbine system based on a fusion event triggering mechanism, characterized in that, Includes the following steps: A model of a permanent magnet direct-drive wind turbine system is established based on fuzzy control theory. Design an intermediate variable estimator to simultaneously estimate the system state and external disturbances, as follows; First, design an intermediate variable. as follows: In the formula, Indicates a fuzzy rule index; Represents the fuzzy membership function. Indicates external interference. Indicates the estimator gain. Indicates the system status; The derivative is as follows: In the formula, It is a vector composed of voltages along the q-axis and d-axis; , , The system matrix is ​​known; To estimate the system state and intermediate variables The intermediate estimator is constructed as follows: In the formula, , and They are , and The estimate; A fusion event triggering mechanism based on ring events and a dynamic threshold update law composed of system input and error-dependent triggering parameters are designed to balance triggering performance and system performance, as follows; To reduce network transmission burden, the estimated signal is first verified using event criteria, and then transmitted to the controller via an ideal network; the fusion event triggering mechanism is constructed as follows: in, Indicates the last time it was triggered. Indicates the time of the next trigger; It is the triggering error of state estimation. This represents the state estimate at the time of the last triggering; ; It is the triggering error of the premise variable; The fuzzy membership function representing the last trigger moment; and This is the weight matrix to be designed; It is a dynamic threshold and is updated by the following formula: In the formula, express The upper realm, express The lower bound of them, their relationship satisfies ; The weighting parameters represent the instantaneous and average components; and Indicates the sensitivity parameter; Indicates the length of the circular queue; By utilizing the estimator output after triggering, an event-triggered anti-interference controller is designed to ensure the stable operation of the system, as follows: Establish the following event-triggered TS fuzzy anti-interference controller : in, Indicates a fuzzy rule index; make , The following closed-loop error system can be obtained: In addition, The above closed-loop error system can be simplified as follows: In the formula, .

2. The anti-interference control method for a permanent magnet direct-drive wind turbine system based on a fusion event triggering mechanism according to claim 1, characterized in that, Based on fuzzy control theory, a model of a permanent magnet direct-drive wind turbine system is established. The specific steps are as follows: According to the principles of aerodynamics, the power generated by wind energy is expressed as follows: in, Indicates air density, Represents pi (π). Indicates the length of the turbine blades. Indicates the power factor. Indicates wind speed. Indicates the tip speed ratio, Indicates turbine speed. Indicates the pitch angle; in addition, for To maximize the efficiency of wind turbines, consider The optimal value is The corresponding torque is calculated as follows: Next, the nonlinear model of the permanent magnet direct-drive wind turbine system is established as follows: In the formula, Indicates stator resistance. Represents the stator inductance on the d-axis. Represents the stator inductance on the q-axis. This represents the magnetic flux of a permanent magnet. Indicates generator torque. Represents the moment of inertia. Indicates the coefficient of friction. , Indicates external interference. and These represent the voltages along the q-axis and d-axis, respectively. Indicates the rotor speed. and These represent the q-axis and d-axis currents, respectively. Then, let , , The following state-space model of the permanent magnet direct-drive wind turbine system can be obtained: In the formula, the system matrix is ​​as follows: For the nonlinear terms present in the system, fuzzy control theory is used to handle them; assuming... And select the fuzzy membership function as: in, , These are available prerequisite variables; Next, applying the fuzzy membership function to the state-space expression of the aforementioned permanent magnet direct-drive wind turbine system yields the following fuzzy model: Fuzzy rules if yes ,So in, At this point, the fuzzy model of the permanent magnet direct-drive wind turbine system has been successfully established.

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