CN113098057B - Multi-target high-dimensional multi-fractional order optimization method for parameters of double-fed fan - Google Patents
Multi-target high-dimensional multi-fractional order optimization method for parameters of double-fed fan Download PDFInfo
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- CN113098057B CN113098057B CN202110366160.8A CN202110366160A CN113098057B CN 113098057 B CN113098057 B CN 113098057B CN 202110366160 A CN202110366160 A CN 202110366160A CN 113098057 B CN113098057 B CN 113098057B
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/381—Dispersed generators
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/28—The renewable source being wind energy
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/70—Wind energy
- Y02E10/72—Wind turbines with rotation axis in wind direction
Abstract
The invention provides a multi-target high-dimensional multi-fractional order optimization method for parameters of a doubly-fed wind turbine, which can optimize parameters of a rotor-side converter controller in the doubly-fed wind turbine, find benefit balance points of reactive errors and rotor rotating speed deviation and coordinate the relation between target functions. The method introduces multidimensional information data of a high-dimensional multi-fractional order controller, and performs fractional order analysis and calculation on each dimension information data; the method provided by the invention applies the control theory to the optimization method, and accelerates the iteration speed; the method provided by the invention comprises two search parts of exploration and development; the method provided by the invention introduces order operators, automatically adjusts in the optimizing process, balances exploration and development of two search parts, and improves the global search capability; and the maximum power point tracking and low voltage ride through of the doubly-fed wind turbine are realized.
Description
Technical Field
The invention belongs to the field of control of wind power generation, and relates to an optimization method for parameter setting of a controller, which is suitable for maximum wind energy tracking in a double-fed wind power generation system.
Background
Fossil energy reserves are limited and cannot be regenerated, while the main energy structure of China is mainly coal, the use of the coal can generate atmospheric pollution and a large amount of discharged CO 2 Can cause global warming, and the adjustment of energy structure of China constitutes an urgent task. The wind energy storage capacity is large, the wind power generation technology is gradually improved, and the wind power generation becomes one of the most potential new energy power generation forms at present. The double-fed fan belongs to a variable-speed constant-frequency generator, has the advantages of high rotating speed, light weight, low cost, low rated capacity of a converter and high energy conversion efficiency, and becomes one of the mainstream of the current wind power technology.
The maximum wind energy capture is a main control target of the double-fed wind turbine, and the rotating speed of the generator is controlled to track the rotating speed of the wind turbine, so that the wind turbine generator can realize the maximization of power in a maximum wind energy capture area. The optimal tip speed method calculates the optimal tip speed ratio according to the aerodynamic characteristics of the blades of the wind turbine generator, but real-time data of the wind speed is difficult to obtain in real time; the torque feedback method and the power feedback method respectively use an optimal electromagnetic torque curve and an optimal power generation curve as references to realize optimal wind energy capture, but due to the turbulence of wind energy, the two methods cannot quickly track the running state of the wind turbine generator. Vector control is one of the mainstream control technologies of wind power generation at present, and by measuring and controlling the current vector of a fan stator, the exciting current and the torque current are controlled according to the magnetic field orientation principle, so that the aim of controlling the torque of the fan is fulfilled. The parameters of the controller in vector control have an important influence on the control of the fan.
The double-fed fan has two optimization targets, namely the minimum reactive error and the rotor rotation speed error, and belongs to the multi-target problem. The controller parameters can be set by a single-target intelligent optimization method, and each single target is weighted according to importance, but the single target is easily influenced by the subjectivity of a decision maker, so that the objectivity of a result is influenced. The invention provides a multi-target high-dimensional multi-fractional order optimization method, which applies the idea of a control theory to the optimization method to search a balance point with the minimum reactive error and the minimum rotating speed error, thereby achieving the maximization of benefits of the two, and improving the search speed and the global search capability.
Disclosure of Invention
The invention provides a multi-target high-dimensional multi-fractional order optimization method for parameters of a doubly-fed fan. The method applies the concept of a control theory to an intelligent optimization method to form a multi-target high-dimensional multi-fractional order optimization method, and is used for parameter setting of a high-dimensional multi-fractional order controller in a rotor-side converter in a double-fed fan to achieve maximum wind energy tracking.
The control strategy adopted by the double-fed fan is vector control, and modeling is carried out according to the structure of each part. The wind turbine is connected with the doubly-fed induction generator through a rotating shaft system, a stator of the doubly-fed fan is directly connected with a power grid, a rotor is connected with a back-to-back converter, and the rotor-side converter is used for adjusting the rotating speed and the reactive power of the rotor so as to achieve maximum wind energy capture.
The mathematical model of the wind turbine is as follows:
P m is the mechanical power output by the wind turbine; p v Is the input power of the wind turbine, i.e. the kinetic energy that the airflow has when passing through the sweep area; c P Is the wind energy utilization coefficient; λ is the tip speed ratio; β is the pitch angle; ρ is the air density at the hub; r is the radius of the wind wheel; lambda [ alpha ] i Is a function of the tip speed ratio λ and the pitch angle β; ω is the wind wheel rotational angular velocity; v. of wind Is the wind speed at the hub.
The mathematical model of the doubly-fed wind generator is as follows:
T e ＝(e' qs /w s )i qs +(e' ds /w s )i ds (9)
P e ＝e' qs i qs +e' ds i ds (10)
Q s ＝v qs i ds -v ds i qs ＝v qs i ds (11)
i ds and i qs D-axis stator current and q-axis stator current respectively; v. of ds And v qs D-axis stator external voltage and q-axis stator external voltage respectively; v. of dr And v qr Respectively representing d-axis rotor voltage and q-axis rotor voltage; e' ds And e' qs Internal voltages of d-axis and q-axis are represented, respectively; w is a b Represents a reference electrical angular velocity; w is a s Represents a synchronous angular velocity; w is a r Representing the rotor angular velocity; l is m Is mutual inductance; t is e Is the electromagnetic torque produced by the generator; p is e Is the active power of the generator; q s Is a reactive power; l' s Is the stator inductance. R l 、R 2 、T r 、L rr Is an intermediate amount, R 1 ＝R s +(L m +L rr ) 2 R r ，R 2 ＝(L m /L rr ) 2 R r ，T r ＝L rr /R r ，L rr ＝1.005×1.01L m ， L' s ＝1.01L m 。R s Representing a stator resistance; r is r Representing the rotor resistance. Setting the q axis to be the stator voltage direction and the d axis to be perpendicular to the q axis, then there is v ds And =0. And v is qs ＝v t ，v qs The value of (d) is the terminal voltage magnitude.
The mathematical model of the rotating shaft system in the doubly-fed wind turbine is as follows:
H m ＝H t +H g (12)
the rotating shaft system can be represented by a single concentrated inertial system. H m Represents a lumped inertia constant; h t Is the collective inertia constant of the wind turbine; h g Is the lumped inertia constant of the generator; the electromagnetic dynamic equation in the rotating shaft system is shown as the formula (13). w is a m Is the rotational speed of the concentrated inertial system, which is equal to the generator rotational speed w r . D represents the central inertial system damping, T m Represents a mechanical torque, and T m ＝P m /ω m 。
The operation control targets of the double-fed fan are two, namely two fitness degrees f 1 (x) And f 2 (x)，f 1 (x) The reactive power error is minimum in the running time; f. of 2 (x) The rotor speed error is minimized during operation time.
The multi-target high-dimensional multi-fractional order optimization method provided by the invention is used for carrying out parameter setting on a controller in a converter side converter. The double-fed wind power generation system is a high-order, multivariable, strong-coupling and nonlinear system, and the selection of the controller parameters plays an important role in the normal operation of the double-fed wind turbine. The multi-target high-dimensional multi-fractional order optimization method is divided into two parts, namely an exploration part and a development part, and is used for readjusting the positions of the controller individuals.
The survey controller part used for readjusting the position of the controller body is composed of the following parts:
sign(·)＝e -x (18)
k is the iteration number of the high-dimensional multi-fractional order optimization algorithm; sign () is a sign function used to perform a search for the range of solutions. e.g. of a cylinder -x Is a decreasing exponential function;
is a proportional parameter of the ith layer fractional order of the jth information of the exploration controller;
an order parameter of the ith layer fractional order of the jth information of the exploration controller; epsilon j,i And delta j,i Proportional gain and fractional order gain of the survey controller update strategy, respectively.
The development controller part for readjusting the position of the individual controller is composed of the following parts:
wherein
Is a proportional parameter of the ith layer fractional order of the jth information of the development controller;
is an order parameter of the ith layer fractional order of the jth information of the development controller; t is mil Is the value of millisecond, T, of the current time of the computer mil E.g., 000,999). Connect () is a numerical cascading function.
The value of the argument at the (k + 1) th iteration of the a-th controller
Comprises the following steps:
For the multi-objective optimization problem, the solution in which the objectives cannot be optimized continuously but other objective functions are not degraded is the Pareto optimal solution. For the minimum value of multi-objective optimization, if a solution vector x exists in a feasible domain 1 And x 2 When the following two conditions are satisfied, it is referred to as x 1 Dominating x 2 Or x 1 The dominant effect is.
In the formula, N obj In order to optimize the number of objective functions, it is 2 in the present invention. In the invention, a Pareto domination relation is adopted to determine the optimal value of the controller, namely, whether the controller generated in the current iteration dominates the optimal value of the controller generated in the last iteration is judged. Determining a global optimal solution using an external archive set, first saving the Pareto optimal solutions, calculating a crowding distance for each Pareto optimal solution of the external archive,the greater the crowding distance, the greater the probability of being selected as a global optimal solution.
Compared with the prior art, the invention has the following advantages and effects:
(1) The invention provides a high-dimensional multi-fractional order controller, which takes multi-dimensional feedback information as the input of the controller, analyzes and calculates the fractional order of the multi-dimensional information, and can output more accurate control instructions compared with the traditional proportional-integral-derivative controller.
(2) The invention provides a multi-target high-dimensional multi-fractional order optimization method under the inspiration of a high-dimensional multi-fractional order controller, and the idea of a control theory is used in the optimization method. The multi-target high-dimensional multi-fractional order optimization method comprises an exploration part and a development part, wherein the exploration part is used for dealing with the uncertainty of parameters and updating data in real time due to data disturbance and is used for exploring a global optimal solution; and the development part strengthens the randomness and is used for searching a local optimal solution. Compared with other single-target optimization methods, the multi-target high-dimensional multi-fractional order optimization method can reduce the subjectivity of a decision maker, and the set controller parameters are more objective and more in line with the actual situation.
Drawings
FIG. 1 is a graphical representation of wind turbine wind energy utilization coefficient versus tip speed ratio and pitch angle for the method of the present invention.
FIG. 2 is a control framework diagram of the doubly fed wind power generation system of the method of the present invention.
FIG. 3 is a graph of data trained by the method of the present invention.
FIG. 4 is a flow chart of a multi-objective high-dimensional multi-fractional order optimization method of the present invention.
Detailed Description
The invention provides a multi-target high-dimensional multi-fractional order optimization method for parameters of a double-fed fan, which is explained in detail by combining the accompanying drawings as follows:
FIG. 1 is a graphical representation of wind energy utilization coefficient versus tip speed ratio and pitch angle for a wind turbine using the method of the present invention. The change of the wind energy utilization coefficient is related to the blade tip speed ratio and the pitch angle. When wind flows through the wind turbine, the wind drives the fan blades to move, the fan blades are connected with the rotating shaft system to drive the rotor of the induction motor to rotate, and when the wind turbine is used, a prime mover in the double-fed wind power generation system can realize energy conversion. In the energy conversion process, the utilized energy is less than the energy blown across the blades of the wind turbine, and the wind energy utilization factor represents the utilization of wind energy. The magnitude of the wind energy utilization factor depends on the tip speed ratio and the pitch angle.
FIG. 2 is a control framework diagram of the doubly fed wind power generation system of the method of the present invention. The rotor-side converter adopts a controller loop formed by 4 high-dimensional multi-fractional order controllers to realize the maximum power point tracking of the doubly-fed wind power generation system. Wherein the external loop adjusts the generator rotor and the reactive power of the doubly-fed wind turbine to obtain the dq-axis rotor current reference values respectively
And
inner control loop control compensation term v qr2 And v dr2 Two currents related to produce the final output v of the controller qr And v dr 。
FIG. 3 is a graph of data trained by the method of the present invention. Wherein (a) the step wind speed; (b) ramping the wind speed; (c) a voltage sag. The step wind speed is used for simulating wind speed mutability, 4 continuous step changes are generated between 20s and 40s and are increased by 1 m/s; the gradual change wind speed is used for simulating gradual change of the wind speed, the wind speed change is generated between 20s and 50s, the characteristic of the gradual change wind speed is represented as sine change, the gradual change starts from 10m/s, the highest wind speed reaches 12m/s, the lowest wind speed reaches 8m/s, and finally the gradual change returns to 10m/s; the voltage drop is a voltage drop which has a rated value of 50% and lasts for 200 milliseconds and is applied to an alternating current power grid, and the voltage drop is used for testing the low-voltage ride through capability of the double-fed wind energy power generation system when the voltage drops.
FIG. 4 is a flow chart of a multi-objective high-dimensional multi-fractional order optimization method of the present invention. The calculation process of the multi-target high-dimensional multi-fractional order optimization method is as follows: randomly generating N controllers, and initializing parameters of the N controllers; (ii) putting the controller individuals into a model of the doubly-fed induction power generation system, calculating the fitness value of each controller individual, processing the non-dominated solution set by using a pareto principle, and storing the obtained data; (iii) determining a local optimum and a global optimum for each individual controller through the dominance relationship and the congestion distance; (iv) updating the controller individuals by adopting a high-dimensional multi-fractional order optimization method, and readjusting the positions of the controller individuals; (v) updating the stored data, storing the non-dominant solution, and deleting the dominant solution; (vi) updating the local optimal solution and the global optimal solution, judging whether the iteration requirement is met, if so, finishing the operation, and outputting control parameters of the doubly-fed fan controller; if not, returning to the step (ii) and continuing to carry out iterative computation.
Claims (1)
1. A multi-target high-dimensional multi-fractional order optimization method for parameters of a doubly-fed wind turbine is characterized by comprising the following steps in the using process:
step (i): randomly generating N controllers, and initializing parameters of the N controllers;
step (ii): putting the controller individuals into a model of a doubly-fed induction power generation system, calculating the fitness value of each controller individual, processing a non-dominated solution set by using a pareto principle, and storing obtained data;
the mathematical model of the wind turbine is:
λ i -1 ＝(λ+0.08β) -1 -0.035(β 3 +1) -1 (3)
in the formula, P m Is the mechanical power output by the wind turbine; p is v Is the input power of the wind turbine, i.e. the kinetic energy that the airflow has when passing through the sweep area; c P Is the wind energy utilization coefficient; λ is the tip speed ratio; β is the pitch angle; ρ is the air density at the hub; r is the radius of the wind wheel; lambda i Is a function of the tip speed ratio λ and the pitch angle β; ω is the wind wheel rotational angular velocity; v. of wind Is the wind speed at the hub;
the mathematical model of the doubly-fed wind generator is as follows:
T e ＝(e′ qs /w s )i qs +(e′ ds /w s )i ds (9)
P e ＝e′ qs i qs +e′ ds i ds (10)
Q s ＝v qs i ds -v ds i qs ＝v qs i ds (11)
in the formula i ds And j qs D-axis stator current and q-axis stator current respectively; v. of ds And v qs D-axis stator external voltage and q-axis stator external voltage respectively; v. of dr And v qr Respectively representing d-axis rotor voltage and q-axis rotor voltage; e' ds And e' qs Internal voltages of d-axis and q-axis are represented, respectively; w is a b Represents a reference electrical angular velocity; w is a s Representing a synchronous angular velocity; w is a r Representing the rotor angular velocity; l is a radical of an alcohol m Is mutual inductance; t is e Is the electromagnetic torque produced by the generator; p is e Is the active power of the generator; q s Is a reactive power; l' s Is the stator inductance; r is 1 、R 2 、T r 、L rr Is an intermediate amount, R 1 ＝R s +(L m +L rr ) 2 R r ，R 2 ＝(L m /L rr ) 2 R r ，T r ＝L rr /R r ，L rr ＝1.005×1.01L m ，L′ s ＝1.01L m ；R s Representing a stator resistance; r is r Representing the rotor resistance; setting the q axis to be the stator voltage direction and the d axis to be perpendicular to the q axis, then there is v ds =0; and v is qs ＝v t ，v qs The value of (d) is terminal voltage amplitude;
the mathematical model of the transfer shaft system in the double-fed fan is as follows:
H m ＝H t +H g (12)
in the formula, H m Represents a lumped inertia constant; h t Is the collective inertia constant of the wind turbine; h g Is the lumped inertia constant of the generator; an electromagnetic dynamic equation in the rotating shaft system is shown as a formula (13); w is a m Is the rotational speed of the concentrated inertial system, which is equal to the generator rotational speed w r (ii) a D represents the central inertial system damping, T m Represents a mechanical torque, and T m ＝P m /ω m ；
Two fitness degrees f 1 (x) And f 2 (x)，f 1 (x) The reactive power error is minimum in the running time; f. of 2 (x) To be at runtimeThe rotation speed error of the inner rotor is minimum;
step (iii): determining a local optimal value and a global optimal value of each controller individual through the dominance relation and the crowding distance;
step (iv): updating the controller individuals by adopting a high-dimensional multi-fractional order optimization method, and readjusting the positions of the controller individuals;
the survey controller part used for readjusting the position of the controller body is composed of the following parts:
sign(·)＝e -x (18)
in the formula, k is the iteration number of the high-dimensional multi-fractional order optimization algorithm; sign (-) is a sign function used to perform a search for the range of solutions; e.g. of the type -x Is a decreasing exponential function;
is a proportional parameter of the ith layer fractional order of the jth information of the exploration controller;
an order parameter of the ith layer fractional order of the jth information of the exploration controller; epsilon j，i And delta j，i Respectively, the survey controller updates the strategyA slight proportional gain and a fractional order gain;
the development controller section for readjusting the position of the individual controllers is composed of:
in the formula (I), the compound is shown in the specification,
is a proportional parameter of the ith layer fractional order of the jth information of the development controller;
is an order parameter of the ith layer fractional order of the jth information of the development controller; t is mil Is the value of millisecond, T, of the current time of the computer mil E.g. [000, 999); connect () is a numerical cascading function;
the value of the argument at the (k + 1) th iteration of the a-th controller
Comprises the following steps:
in the formula (I), the compound is shown in the specification,
is the fitness function value;
is a fractional order integral function;
is a fractional order differential function;
step (v): updating storage data, storing non-dominant solutions and deleting dominant solutions;
step (vi): updating the local optimal solution and the global optimal solution, judging whether the iteration requirement is met, if so, finishing the operation, and outputting the control parameters of the double-fed fan controller; if not, returning to the step (ii) and continuing the iterative computation.
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