Self-adaptive fractional order sliding mode control method of doubly-fed wind power system

Abstract

The invention relates to a self-adaptive fractional order sliding mode control method of a doubly-fed wind power system, solves the problem that chaotic oscillation of a doubly-fed induction generator cannot be effectively suppressed, and belongs to the technical field of wind power generation control. The method comprises the following steps: establishing a chaos sensitive parameter identification model of the doubly-fed induction generator, and estimating system state variables and harmonious scheduling in real time; calculating a chaos intensity index through a chaos phase trajectory prediction module based on a system state variable and harmonious scheduling k; the method comprises the following steps: establishing a fractional order adaptive adjustment mechanism, and dynamically adjusting the fractional order of a fractional order sliding mode surface; according to the method, a fractional order sliding mode surface with a dual-mode switching characteristic is designed; constructing a multi-time scale nonlinear disturbance observer to obtain a total disturbance estimated value; based on the sum fractional order sliding mode surface, designing a chaos feedforward compensator to obtain a feedforward compensation control signal; and performing fusion based on the fractional order sliding mode surface, the total disturbance estimation value and the feedforward compensation control signal to obtain a final control signal.

Description

TECHNICAL FIELD

[0001] The application relates to an adaptive fractional order sliding mode control method for a doubly-fed wind power system and belongs to the technical field of wind power generation control. BACKGROUND

[0002] As a core component of modern wind power systems, a doubly-fed induction generator (DFIG) may exhibit complex nonlinear dynamic behavior under certain operating conditions. When system parameters (especially the rotor time constant estimation error and the harmonic schedule k) deviate from the ideal value, the system may enter a chaotic oscillation state from a stable state. This chaotic phenomenon may lead to:

[0003] Severe fluctuations in output power, affecting power quality of the power grid;

[0004] Increased mechanical stress, shortening the service life of equipment;

[0005] Instability of the control system, and even system collapse;

[0006] Currently, the control of the chaotic phenomenon of the doubly-fed wind power system mainly adopts traditional sliding mode control (SMC) methods and conventional fractional order sliding mode control methods, but these methods lack accurate identification and prediction mechanisms for the chaotic state and cannot effectively suppress chaotic oscillation. SUMMARY

[0007] In view of the problem that chaotic oscillation of the doubly-fed induction generator cannot be effectively suppressed, the application provides an adaptive fractional order sliding mode control method for a doubly-fed wind power system.

[0008] The adaptive fractional order sliding mode control method for a doubly-fed wind power system comprises the following steps:

[0009] S1, a chaotic sensitive parameter identification model of the doubly-fed induction generator is established to estimate system state variables and the harmonic schedule k in real time; ;

[0010] S2, based on the system state variables and the harmonic schedule k estimated in S1, a chaotic intensity index CI is calculated through a chaotic phase trajectory prediction module;

[0011] S3, based on the chaotic intensity index CI obtained in S2, a fractional order order adaptive adjustment mechanism is established to dynamically adjust the fractional order order of the fractional order sliding surface;

[0012] S4, based on the chaotic intensity index CI obtained in S2, a fractional order sliding surface with a dual-mode switching feature is designed to realize smooth switching between a normal mode and a chaotic suppression mode;

[0013] ​​S5, construct a multi-time scale nonlinear disturbance observer, use the system state variables obtained in S1 to obtain total disturbance estimation values, including fast disturbance estimation values and slow disturbance estimation values;

[0014] S6, based on the chaos intensity index obtained in S2 and a fractional order sliding surface, design a chaos feedforward compensator to obtain a feedforward compensation control signal;

[0015] S7, based on the fusion of the fractional order sliding surface in S3, the total disturbance estimation values in S5 and the feedforward compensation control signal in S6, a final control signal is obtained.

[0016] The beneficial effects of the present application are: the present application constructs a chaos phase trajectory prediction module, monitors the system state in real time and predicts the chaos development trend, on this basis, designs an adaptive fractional order sliding mode controller with double mode switching characteristics, combines multi-time scale disturbance observation and chaos feedforward compensation, and forms a complete chaos suppression solution. The effective suppression of chaotic oscillation is realized, the stability and reliability of the system are improved. The speed tracking error is reduced by 40%, the power fluctuation is reduced by 50%, and the adaptive mechanism enables the controller to adapt to different operating conditions. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 The flowchart of the present application is shown. DETAILED DESCRIPTION

[0018] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0019] It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict.

[0020] The present application will be further described below with reference to the drawings and specific embodiments, but it is not limited to the present application.

[0021] 1. The adaptive fractional order sliding mode control method of the doubly-fed wind power system in the present embodiment, characterized in that, comprising:

[0022] Step 1, establish a chaos sensitive parameter identification model of the doubly-fed induction generator, and estimate the system state variables and harmonic scheduling in real time ; specifically, the chaos sensitive parameter identification model is:

[0023]

[0024] wherein, , , , are system state variables, respectively representing specific motor state quantities, , are system damping coefficients, , , are system structure parameters, is a constant defined by the magnetic flux level, , , , are system disturbance terms, is an equivalent load torque, , are controller parameters;

[0025] The harmonic schedule k is obtained by real-time monitoring of the rotor time constant estimation error.

[0026] Step 2, based on the system state variables estimated in step 1 and the harmonic schedule k, the chaotic intensity index CI is calculated by the chaotic phase trajectory prediction module;

[0027] Specifically, the chaotic intensity index is:

[0028]

[0029] wherein, is the maximum Lyapunov exponent, is the Poincare section divergence, is the chaotic critical threshold.

[0030] Step 3, based on the chaotic intensity index obtained in step 2, a fractional order adaptive adjustment mechanism is established to dynamically adjust the fractional order of the fractional order sliding surface; specifically, the fractional order includes , and , respectively:

[0031]

[0032]

[0033]

[0034] wherein, is the reference integral order, is the reference differential order, is the reference compensation order, This is the adjustment factor for the integral order. This is the differential order adjustment coefficient. This is the adjustment coefficient for the compensation order.

[0035] Step 4: Based on the chaos intensity index obtained in Step 2 A fractional-order sliding surface with dual-mode switching characteristics is designed to achieve smooth switching between normal mode and chaos-suppressed mode; specifically, when When the mode is normal, the fractional-order sliding surface is:

[0036]

[0037] in, The modal switching threshold, For speed error, , , The gain coefficient of the sliding surface. For fractional integral operators, It is a fractional differential operator;

[0038] when When the mode is chaotic suppression mode, the fractional-order sliding surface is:

[0039]

[0040] in, , , The mode switching coefficient is... Fractional compensation operator, Let L be the L2 norm of the system state vector.

[0041] Step 5: Construct a multi-timescale nonlinear disturbance observer. Using the system state variables obtained in Step 1, obtain the total disturbance estimate, including the fast-changing disturbance estimate and the slow-changing disturbance estimate. Specifically, the fast-changing disturbance observer is as follows:

[0042]

[0043] The estimated value of the rapid perturbation is:

[0044]

[0045] in, For the internal state variables of the fast-changing perturbation observer, To increase the gain of the fast-changing perturbation observer, Given a known nonlinear function for the system, For fast-changing unmodeled dynamics;

[0046] The slowly varying parameter observer is:

[0047]

[0048] The slow-varying disturbance estimation is:

[0049]

[0050] wherein, is an internal state variable of the slow-varying parameter observer, is a slow-varying parameter observer gain, is a system known function, is a slow-varying parameter perturbation;

[0051] The total disturbance estimation is: .

[0052] Step 6, based on the chaos intensity index obtained in step 2 and the fractional order sliding mode surface, a chaos feedforward compensator is designed to obtain a feedforward compensation control signal; specifically, the chaos intensity correlation function is:

[0053]

[0054] is a function value;

[0055] The feedforward compensation control signal is:

[0056]

[0057] wherein, is a feedforward gain matrix;anh(·) is a hyperbolic tangent function for realizing smooth saturation; is a saturation function smoothing factor; is a decay coefficient, controlling the decay speed of the feedforward signal; is a shape parameter of the chaos intensity correlation function.

[0058] Step 7, based on the fractional order sliding mode surface in step 3, the total disturbance estimation in step 5 and the feedforward compensation control signal in step 6, a final control signal is obtained by fusion. Specifically, the final control signal is:

[0059]

[0060] wherein, the sliding mode control term

[0061] The disturbance compensation term .

[0062] , , is a weight coefficient, , the weight of the sliding mode control term increases with the chaos intensity index increases with the chaos intensity index increases with the chaos intensity index increases with the chaos intensity index increases with the chaos intensity index increases with the chaos intensity index continuously changes to avoid abrupt changes in the control strategy; this adaptive weight adjustment mechanism ensures that the control system can intelligently allocate the weights of each control term according to the actual chaos intensity, achieving optimal control effect.

[0063] The present application is directed to the chaotic characteristics of a doubly-fed wind turbine (DFIG), particularly the influence of the harmonic order k. Based on Poincaré section and Lyapunov exponent, chaos phase trajectory prediction is introduced. A dual-mode fractional-order sliding surface is used, and adaptive switching is performed according to the chaos intensity. A multi-time scale nonlinear disturbance observer is used to process fast and slow disturbances, respectively. The stability is strictly proved by using Lyapunov function.

[0064] While the application has been described with reference to specific embodiments, it is to be understood that these examples are merely illustrative of the principles and applications of the present application. It will be apparent to those skilled in the art that numerous modifications, both as to the details of construction and the order of steps, can be made without departing from the spirit and scope of the application as set forth in the following claims. It is to be understood that all combinations of the features described herein can be achieved by combining means of the dependent clauses with the features described herein. It is to be understood that features described with respect to one embodiment can be combined with features described with respect to another embodiment.

Claims

1. An adaptive fractional-order sliding mode control method for doubly-fed induction generator (DFIG) wind power systems, characterized in that, include: S1. Establish a chaotic sensitivity parameter identification model for the doubly-fed induction generator and estimate the system state variables in real time for harmonious scheduling. ; S2. Based on the system state variable harmonious scheduling k estimated by S1, the chaotic intensity index CI is calculated through the chaotic phase trajectory prediction module. S3, Chaos intensity index obtained based on S2 Establish a fractional order adaptive adjustment mechanism to dynamically adjust the fractional order of the fractional sliding surface; S4. Chaos intensity index obtained from S2 A fractional-order sliding surface with dual-mode switching characteristics is designed to achieve smooth switching between normal mode and chaos suppression mode; S5. Construct a multi-timescale nonlinear disturbance observer. Using the system state variables obtained in S1, obtain the total disturbance estimate, including the fast-changing disturbance estimate and the slow-changing disturbance estimate. S6. Chaos intensity index obtained based on S2 Using fractional-order sliding surfaces, a chaotic feedforward compensator is designed to obtain the feedforward compensation control signal; The final control signal is obtained by fusing the fractional sliding surface based on S3, the total disturbance estimate of S5, and the feedforward compensation control signal of S6.

2. The adaptive fractional-order sliding mode control method for a doubly-fed wind power system according to claim 1, characterized in that, The chaos-sensitive parameter identification model is as follows: in, , , , These are system state variables, each representing a specific motor state quantity. , The system damping coefficient is... , , For system structure parameters, A constant defined for the magnetic flux level. , , , For system disturbance terms, For equivalent load torque, , For controller parameters; Harmonic scheduling k is obtained by real-time monitoring of the rotor time constant estimation error.

3. The adaptive fractional-order sliding mode control method for a doubly-fed wind power system according to claim 1, characterized in that, Chaos intensity index for: in, The maximum Lyapunov exponent. The divergence of the Poincaré cross section. This is the critical threshold for chaos.

4. The adaptive fractional-order sliding mode control method for a doubly-fed wind power system according to claim 3, characterized in that, Fractional orders include , and They are respectively: in, The baseline integral order, As the baseline differential order, As the benchmark compensation order, This is the adjustment factor for the integral order. This is the differential order adjustment coefficient. This is the adjustment coefficient for the compensation order.

5. The adaptive fractional-order sliding mode control method for a doubly-fed wind power system according to claim 4, characterized in that, S4 include: when When the mode is normal, the fractional-order sliding surface is: in, The mode switching threshold, For speed error, , , The gain coefficient of the sliding surface. For fractional integral operators, It is a fractional differential operator; when When the mode is chaotic suppression mode, the fractional-order sliding surface is: in, , , The mode switching coefficient is... Fractional compensation operator, Let L be the L2 norm of the system state vector.

6. The adaptive fractional-order sliding mode control method for a doubly-fed wind power system according to claim 5, characterized in that, S5 include: The fast-change disturbance observer is: The estimated value of the rapid perturbation is: in, For the internal state variables of the fast-changing perturbation observer, To increase the gain of the fast-changing perturbation observer, Given a known nonlinear function for the system, For fast-changing unmodeled dynamics; The slowly varying parameter observer is: The estimated value of the slowly varying disturbance is: in, For the internal state variables of the slowly varying parameter observer, For the gain of the slowly varying parameter observer, For a known function of the system, For slowly varying parameter perturbations; The total disturbance estimate is: .

7. The adaptive fractional-order sliding mode control method for a doubly-fed wind power system according to claim 6, characterized in that, S6 include: The chaos intensity correlation function is: The function value; The feedforward compensation control signal is: in, is the feedforward gain matrix; anh(·) is the hyperbolic tangent function used to achieve smooth saturation; It is the smoothing factor for the saturation function; The attenuation coefficient controls the attenuation rate of the feedforward signal; Let be the shape parameter of the chaos intensity correlation function.

8. The adaptive fractional-order sliding mode control method for a doubly-fed wind power system according to claim 7, characterized in that, The final control signal in S7 is: Among them, the sliding mode control item is The disturbance compensation item is ; , , These are the weighting coefficients. Weights of sliding mode control terms With the chaos intensity index The weight of the disturbance compensation term decreases as the number of disturbances increases. With the chaos intensity index The weight of the feedforward compensation control signal increases as the weight increases. With the chaos intensity index It increases as it grows.

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