Pelican optimization algorithm-based variable-pitch active-disturbance-rejection control method

Abstract

The invention discloses a variable-pitch active-disturbance-rejection control method based on a pelican optimization algorithm, a pelican optimization algorithm module is embedded in a variable-pitch active-disturbance-rejection controller, the variable-pitch active-disturbance-rejection controller is connected with a controlled fan, and the variable-pitch active-disturbance-rejection control method comprises the steps that S1, parameters in the variable-pitch active-disturbance-rejection controller are optimized through the pelican optimization algorithm module; and S2, total disturbance of the system is observed through a variable-pitch active disturbance rejection controller, and control over the size of the pitch angle when the wind speed of the unit is higher than the rated wind speed is achieved. According to the invention, the pitch angle can be quickly and accurately adjusted when the wind speed of the unit is higher than the rated wind speed, so that the output power can be smoothly and stably output.

Description

Technical Field

[0001] The invention relates to a variable pitch anti-disturbance control method based on a Pelican optimization algorithm. Background Art

[0002] Variable pitch control is the main method for wind turbines to smoothly output power and improve power quality in high wind speed environments. Due to the nonlinearity, time lag and sensitivity to wind speed changes of wind turbines, traditional PID control strategies cannot cope well with the influence of the system and the interference of external wind speed during the control process, making it difficult for the system to obtain satisfactory control effects.

[0003] Therefore, a variable pitch anti-disturbance control method based on the Pelican optimization algorithm is provided. Summary of the invention

[0004] The purpose of the present invention is to overcome the existing defects and provide a variable pitch anti-disturbance control method based on the Pelican optimization algorithm, which realizes the rapid and precise adjustment of the pitch angle of the unit when the wind speed is above the rated wind speed, so that the output power can be output smoothly and stably.

[0005] The technical solution to achieve the above purpose is:

[0006] The pitch control method based on the Pelican optimization algorithm is a variable pitch anti-disturbance control controller with a built-in Pelican optimization algorithm module and connected to the controlled wind turbine, including:

[0007] Step S1, optimizing the parameters in the pitch active disturbance rejection controller through the Pelican optimization algorithm module;

[0008] Step S2, observing the total disturbance of the system through the variable pitch anti-disturbance controller to control the pitch angle of the unit when the wind speed is above the rated wind speed.

[0009] Preferably, in step S1, the pelican optimization algorithm is divided into two stages, the first stage is an exploration stage, which simulates the behavior of the pelican approaching prey, and the second stage is a water flight stage, which simulates the behavior of the pelican searching for prey near the water surface;

[0010] At the beginning of the algorithm, the population is initialized first, and then the location of the prey is randomly selected. The population of pelicans is initialized as follows:

[0011] x ij = l j +rand·(u j -l j ),i=1,2,...,N,j=1,2,...,m;

[0012] In the formula, x ijis the j-th dimension position of the i-th pelican, N is the size of the pelican population, m is the dimension of the problem to be solved, l j 、u j They are the upper and lower bounds of the j-th dimension of the problem to be solved;

[0013] For the exploration phase, the main task of this phase is to determine the location of the prey and model the pelican's strategy of approaching the prey. The location of the prey is randomly generated in the search space and can be expressed as follows:

[0014]

[0015] In the formula, is the j-th dimensional position of the ith pelican after the exploration phase update, I is a random integer of 1 or 2, P j is the j-th dimension position of the prey, F p is the objective function value of the prey, F i for;

[0016] If the position where the objective function value is located is improved, the new position is accepted. If it is not improved, the position remains unchanged. This behavior is called effective update, which prevents it from moving to the non-optimal solution area. The modeling formula of this process is as follows:

[0017]

[0018] In the formula, is the new position of the i-th pelican, is the objective function value of the new position of the i-th pelican after the update in the first stage;

[0019] For the surface flight phase, the pelicans start searching in the nearby area after reaching the water surface, which allows them to catch more fish and find more solutions that meet the constraints, expressed as follows:

[0020]

[0021] In the formula, is the position of the i-th pelican in the j-th dimension after the update based on the second stage, R is a constant value, which is 0.2, t is the number of iterations, and T is the maximum number of iterations;

[0022] Valid updates are also used to accept or reject new pelican positions, and the modeling formula is as follows:

[0023]

[0024] In the formula, is the new position of the i-th pelican, is the objective function value of the new position of the i-th pelican after the second phase update.

[0025] Preferably, in step S1, optimizing the parameters in the pitch active disturbance rejection controller by using the Pelican optimization algorithm module includes:

[0026] Step S11, initialize the population, calculate the fitness of each pelican, and save the optimal solution of the current population;

[0027] Step S12, randomly selecting prey and saving the fitness of the prey;

[0028] Step S13, traverse the current population, calculate the new position of each pelican in the exploration phase, and update the position;

[0029] Step S14, traverse the current population, calculate the new position of each pelican in the water flight phase, and update the position;

[0030] Step S15, updating the optimal solution, judging whether the number of iterations has been reached, if not, proceeding to step S12, otherwise, ending and outputting the optimal solution and the optimal value;

[0031] Step S16, the Pelican optimization algorithm is used to calculate the parameters in the pitch active disturbance rejection controller to obtain the optimal parameter combination, and the optimal parameter combination is assigned to the pitch active disturbance rejection controller.

[0032] Preferably, in step S16, the parameters include: gain coefficients β1, β2, β3, and adjustable parameters k1 and k2.

[0033] Preferably, in step S2, the pitch control active disturbance rejection controller includes: a differential tracker, an extended state observer and a nonlinear feedback control law;

[0034] The tracking differentiator is a transition link for setting the jump system control target value v. The target value v plays a buffering role after the tracking differentiator, and then obtains its smooth tracking signal v1 and good differential signal v2. The second-order tracking differentiator is expressed as:

[0035] v1(t+1)=v1(t)+T·v2(t);

[0036] v2(t+1)=v2(t)+T·fhan(v1(t),v2(t),r,h);

[0037] Where T is the sampling period, t is, r is the speed factor, h is the filter factor, fhan is the nonlinear function, and the expression is as follows:

[0038]

[0039] d = rh;

[0040] d0 = hd;

[0041] y=v1+hv2;

[0042]

[0043] The extended state observer can simultaneously observe each state variable of the system and the total disturbance of the system. The extended state observer combines the external disturbance of the system and the uncertain internal state quantity to form the total disturbance of the system and observes it in real time. The extended state observer is expressed as:

[0044] e = z1-y;

[0045]

[0046] Where e is the tracking error, z1, z2 and z3 are all observed values ​​of the extended state observer, and It is the time derivative calculation of the change rate of z1, z2 and z3 based on the time domain, y is the actual output active power of the wind turbine, that is, the output active power P, b0 is the coefficient of the input quantity, u is the output control quantity of the active disturbance rejection control, α and δ are both nonlinear function parameters;

[0047] Among them, the expression of fal(e,α,δ) is as follows:

[0048]

[0049] The nonlinear feedback control law calculates the error signal of the output of the tracking differentiator and the output of the extended state observer, and then linearly combines the error signal after the nonlinear function configuration to obtain the uncompensated control variable u0. Finally, the extended state estimate of the extended state observer is used to compensate u0 to generate the actual control quantity u of the input controlled system. For a second-order nonlinear system, its nonlinear state error feedback control rate can be expressed as follows:

[0050] e1=v1-z1;

[0051] e2=v2-z2;

[0052] u0=k1fal(e1,α1,δ2)+k2fal(e2,α2,δ2);

[0053]

[0054] Where α1, α2 and δ2 are all nonlinear function parameters.

[0055] The beneficial effects of the present invention are as follows: the present invention optimizes and adjusts the parameters in the variable pitch anti-disturbance control controller through the Pelican optimization algorithm, completes the selection of the parameter group, and then realizes control by combining the tracking differentiator, the expanded state observer and the nonlinear feedback control law. The anti-disturbance control controller replaces the PID controller, thereby realizing fast and accurate adjustment of the pitch angle of the unit when the wind speed is above the rated wind speed, so that the output power can be output smoothly and stably. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 It is a flow chart of the pitch auto-disturbance rejection control method based on the Pelican optimization algorithm of the present invention;

[0057] Figure 2 It is a specific flow chart of optimizing the parameters in the variable pitch anti-disturbance control controller by using the Pelican optimization algorithm module in the present invention;

[0058] Figure 3 is a flow chart of the Pelican optimization algorithm in the present invention;

[0059] Figure 4 It is a structural block diagram of the active disturbance rejection controller in the present invention. DETAILED DESCRIPTION

[0060] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. In the description of the present invention, it should be noted that the terms "center", "up", "down", "left", "right", "vertical", "horizontal", "inside", "outside" and the like indicate directions or positional relationships based on the directions or positional relationships shown in the accompanying drawings, which are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as a limitation on the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance.

[0061] The present invention will be further described below in conjunction with the accompanying drawings.

[0062] like Figure 1 As shown in the figure, a pitch control method based on the Pelican optimization algorithm is provided. The pitch control controller is embedded with the Pelican optimization algorithm module and connected with the controlled wind turbine, including:

[0063] Step S1, optimizing the parameters in the variable pitch active disturbance rejection controller through the Pelican optimization algorithm module.

[0064] In the embodiment, the pelican optimization algorithm (POA) is divided into two stages, the first stage is the exploration stage, which simulates the behavior of the pelican approaching prey, and the second stage is the water flight stage, which simulates the behavior of the pelican searching for prey near the water surface;

[0065] At the beginning of the algorithm, the population is initialized first, and then the location of the prey is randomly selected. The population of pelicans is initialized as follows:

[0066] x ij = l j +rand·(u j -l j ),i=1,2,...,N,j=1,2,...,m;

[0067] In the formula, x ij is the j-th dimension position of the i-th pelican, N is the size of the pelican population, m is the dimension of the problem to be solved, l j 、u j They are the upper and lower bounds of the j-th dimension of the problem to be solved;

[0068] For the exploration phase, the main task of this phase is to determine the location of the prey and model the pelican's strategy of approaching the prey. The location of the prey is randomly generated in the search space and can be expressed as follows:

[0069]

[0070] In the formula, is the j-th dimensional position of the ith pelican after the exploration phase update, I is a random integer of 1 or 2, P j is the j-th dimension position of the prey, F p is the objective function value of the prey, F i for;

[0071] If the position where the objective function value is located is improved, the new position is accepted. If it is not improved, the position remains unchanged. This behavior is called effective update, which prevents it from moving to the non-optimal solution area. The modeling formula of this process is as follows:

[0072]

[0073] In the formula, is the new position of the i-th pelican, is the objective function value of the new position of the i-th pelican after the update in the first stage;

[0074] For the surface flight phase, the pelicans start searching in the nearby area after reaching the water surface, which allows them to catch more fish and find more solutions that meet the constraints, expressed as follows:

[0075]

[0076] In the formula, is the position of the i-th pelican in the j-th dimension after the update based on the second stage, R is a constant value, which is 0.2, t is the number of iterations, and T is the maximum number of iterations;

[0077] Valid updates are also used to accept or reject new pelican positions, and the modeling formula is as follows:

[0078]

[0079] In the formula, is the new position of the i-th pelican, is the objective function value of the new position of the i-th pelican after the second phase update.

[0080] like Figure 2 , 3 As shown in the figure, the parameters in the variable pitch anti-disturbance control controller are optimized through the Pelican optimization algorithm module, including:

[0081] Step S11, initialize the population, calculate the fitness of each pelican, and save the optimal solution of the current population;

[0082] Step S12, randomly selecting prey and saving the fitness of the prey;

[0083] Step S13, traverse the current population, calculate the new position of each pelican in the exploration phase, and update the position;

[0084] Step S14, traverse the current population, calculate the new position of each pelican in the water flight phase, and update the position;

[0085] Step S15, update the optimal solution, determine whether the number of iterations has been reached, if not, proceed to step S12, otherwise terminate and output the optimal solution and optimal value;

[0086] Step S16, the Pelican optimization algorithm is used to calculate the parameters in the pitch active disturbance rejection controller to obtain the optimal parameter combination, and the optimal parameter combination is assigned to the pitch active disturbance rejection controller.

[0087] In the embodiment, the parameters include: gain coefficients β1, β2, β3, and adjustable parameters k1 and k2.

[0088] In the embodiment, after the parameters to be optimized are clarified, an evaluation function needs to be designed. The function of the evaluation function is to evaluate the parameter optimization results of the algorithm. Selecting different evaluation functions will affect the optimization results and thus affect the performance of the controller. When selecting the evaluation function, the invention uses ITAE as the evaluation function, and the expression is as follows:

[0089]

[0090] Where e(t) is the error and t is the time.

[0091] Step S2, observing the total disturbance of the system through the variable pitch anti-disturbance controller to control the pitch angle of the unit when the wind speed is above the rated wind speed.

[0092] like Figure 4 As shown, the pitch control controller includes: a differential tracker, an extended state observer and a nonlinear feedback control law;

[0093] The tracking differentiator is a transition link for setting the jump system control target value v. The target value v plays a buffering role after the tracking differentiator, and then obtains its smooth tracking signal v1 and good differential signal v2. The second-order tracking differentiator is expressed as:

[0094] v1(t+1)=v1(t)+T·v2(t);

[0095] v2(t+1)=v2(t)+T·fhan(v1(t),v2(t),r,h);

[0096] Where T is the sampling period, t is, r is the speed factor, h is the filter factor, fhan is the nonlinear function, and the expression is as follows:

[0097]

[0098] d = rh;

[0099] d0 = hd;

[0100] y=v1+hv2;

[0101]

[0102] The extended state observer can simultaneously observe each state variable of the system and the total disturbance of the system. The extended state observer combines the external disturbance of the system and the uncertain internal state quantity to form the total disturbance of the system and observes it in real time. The extended state observer is expressed as:

[0103] e = z1-y;

[0104]

[0105] Where e is the tracking error, z1, z2 and z3 are all observed values ​​of the extended state observer, and It is the time derivative calculation of the change rate of z1, z2 and z3 based on the time domain, y is the actual output active power of the wind turbine, that is, the output active power P, b0 is the coefficient of the input quantity, u is the output control quantity of the active disturbance rejection control, α and δ are both nonlinear function parameters;

[0106] Among them, the expression of fal(e,α,δ) is as follows:

[0107]

[0108] The nonlinear feedback control law calculates the error signal of the output of the tracking differentiator and the output of the extended state observer, and then linearly combines the error signal after the nonlinear function configuration to obtain the uncompensated control variable u0. Finally, the extended state estimate of the extended state observer is used to compensate u0 to generate the actual control quantity u of the input controlled system. For a second-order nonlinear system, its nonlinear state error feedback control rate can be expressed as follows:

[0109] e1=v1-z1;

[0110] e2=v2-z2;

[0111] u0=k1fal(e1,α1,δ2)+k2fal(e2,α2,δ2);

[0112]

[0113] Where α1, α2 and δ2 are all nonlinear function parameters.

[0114] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments may still be modified, or some or all of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A variable pitch active disturbance rejection control method based on the Pelican optimization algorithm is characterized in that: The variable pitch anti-disturbance controller is embedded with the Pelican optimization algorithm module and connected to the controlled wind turbine, including: Step S1, optimizing the parameters in the pitch active disturbance rejection controller through the Pelican optimization algorithm module; Step S2, observing the total disturbance of the system through the variable pitch anti-disturbance controller to control the pitch angle of the unit when the wind speed is above the rated wind speed.

2. The pitch control method based on the Pelican optimization algorithm according to claim 1 is characterized in that: In step S1, the pelican optimization algorithm is divided into two stages, the first stage is the exploration stage, which simulates the behavior of the pelican approaching prey, and the second stage is the water flight stage, which simulates the behavior of the pelican searching for prey near the water surface; At the beginning of the algorithm, the population is initialized first, and then the location of the prey is randomly selected. The population of pelicans is initialized as follows: x ij =l j +rand·(u j -l j ),i=1,2,…,N,j=1,2,…,m; In the formula, x ij is the j-th dimension position of the i-th pelican, N is the size of the pelican population, m is the dimension of the problem to be solved, l i 、u i They are the upper and lower bounds of the j-th dimension of the problem to be solved; For the exploration phase, the main task of this phase is to determine the location of the prey and model the pelican's strategy of approaching the prey. The location of the prey is randomly generated in the search space and can be expressed as follows: In the formula, is the j-th dimensional position of the ith pelican after the exploration phase update, I is a random integer of 1 or 2, P j is the j-th dimension position of the prey, F p is the objective function value of the prey, F i for; If the position where the objective function value is located is improved, the new position is accepted. If it is not improved, the position remains unchanged. This behavior is called effective update, which prevents it from moving to the non-optimal solution area. The modeling formula of this process is as follows: In the formula, is the new position of the i-th pelican, is the objective function value of the new position of the i-th pelican after the update in the first stage; For the surface flight phase, the pelicans start searching in the nearby area after reaching the water surface, which allows them to catch more fish and find more solutions that meet the constraints, expressed as follows: In the formula, is the position of the i-th pelican in the j-th dimension after the update based on the second stage, R is a constant value, which is 0.2, t is the number of iterations, and T is the maximum number of iterations; Valid updates are also used to accept or reject new pelican positions, and the modeling formula is as follows: In the formula, is the new position of the i-th pelican, is the objective function value of the new position of the i-th pelican after the second phase update.

3. The pitch control method based on the Pelican optimization algorithm according to claim 2 is characterized in that: In step S1, the parameters in the pitch control active disturbance rejection controller are optimized by the Pelican optimization algorithm module, including: Step S11, initialize the population, calculate the fitness of each pelican, and save the optimal solution of the current population; Step S12, randomly selecting prey and saving the fitness of the prey; Step S13, traverse the current population, calculate the new position of each pelican in the exploration phase, and update the position; Step S14, traverse the current population, calculate the new position of each pelican in the water flight phase, and update the position; Step S15 updates the optimal solution and determines whether the number of iterations has been reached. If not, the process proceeds to step S12. Otherwise, the process ends and outputs the optimal solution and the optimal value. Step S16, the Pelican optimization algorithm is used to calculate the parameters in the pitch active disturbance rejection controller to obtain the optimal parameter combination, and the optimal parameter combination is assigned to the pitch active disturbance rejection controller.

4. The pitch control method based on the Pelican optimization algorithm according to claim 3 is characterized in that: In the step S16, the parameters include: gain coefficients β1, β2, β3, and adjustable parameters k1 and k2.

5. The pitch control method based on the Pelican optimization algorithm according to claim 1 is characterized in that: In step S2, the pitch control active disturbance rejection controller includes: a differential tracker, an extended state observer and a nonlinear feedback control law; The tracking differentiator is a transition link for setting the jump system control target value v. The target value v plays a buffering role after the tracking differentiator, and then obtains its smooth tracking signal v1 and good differential signal v2. The second-order tracking differentiator is expressed as: v1(t+1)=v1(t)+T·v2(t); V2(t+1)=V2(t)+T·fhan(v1(t), v2(t), r, h); Where T is the sampling period, t is, r is the speed factor, h is the filter factor, fhan is the nonlinear function, and the expression is as follows: d = rh; d0 = hd; y=V1+hv2; The extended state observer can simultaneously observe each state variable of the system and the total disturbance of the system. The extended state observer combines the external disturbance of the system and the uncertain internal state quantity to form the total disturbance of the system and observes it in real time. The extended state observer is expressed as: e = z1-y; Where e is the tracking error, z1, z2 and z3 are all observed values ​​of the extended state observer, and It is the time derivative calculation of the change rate of z1, z2 and z3 based on the time domain, y is the actual output active power of the wind turbine, that is, the output active power P, b0 is the coefficient of the input quantity, u is the output control quantity of the active disturbance rejection control, α and δ are both nonlinear function parameters; Among them, the expression of fal(e, α, δ) is as follows: The nonlinear feedback control law calculates the error signal of the output of the tracking differentiator and the output of the extended state observer, and then linearly combines the error signal after configuring the nonlinear function to obtain the uncompensated control variable u0. Finally, u0 is compensated using the extended state estimate of the extended state observer to generate the actual control variable u of the input controlled system. For a second-order nonlinear system, its nonlinear state error feedback control rate can be expressed as follows: e1=v1-z1; e2=v2-z2; u0=k1fal(e1, α1, δ2)+k2fal(e2, α2, δ2); Where α1, α2 and δ2 are all nonlinear function parameters.

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