CN113098057A - Multi-target high-dimensional multi-fractional order optimization method for parameters of doubly-fed wind turbine - Google Patents

Multi-target high-dimensional multi-fractional order optimization method for parameters of doubly-fed wind turbine Download PDF

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CN113098057A
CN113098057A CN202110366160.8A CN202110366160A CN113098057A CN 113098057 A CN113098057 A CN 113098057A CN 202110366160 A CN202110366160 A CN 202110366160A CN 113098057 A CN113098057 A CN 113098057A
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controller
fractional order
wind turbine
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doubly
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CN113098057B (en
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殷林飞
陈立春
苏志鹏
高放
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Guangxi University
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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
    • 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]
    • 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
    • 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 carries out fractional order analysis and calculation on the multidimensional 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

Multi-target high-dimensional multi-fractional order optimization method for parameters of doubly-fed wind turbine
Technical Field
The invention belongs to the field of control of wind power generation, and relates to an optimization method for setting parameters of a controller, which is suitable for tracking the maximum wind energy 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 CO2Can 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 improved day by day, 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 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 are 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 acute 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 the aim of controlling the torque of a fan is achieved by measuring and controlling the current vector of a stator of the fan and controlling the exciting current and the torque current according to the magnetic field orientation principle. 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 parameter setting can be carried out by a single-target intelligent optimization method, and the weight is added to each single target according to the importance, but the influence of the subjectivity of a decision maker is easily caused, so that the objectivity of the 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 thought 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:
Figure BDA0003007569390000021
Figure BDA0003007569390000022
Figure BDA0003007569390000023
Figure BDA0003007569390000024
Pmis the mechanical power output by the wind turbine; pvIs the input power of the wind turbine, i.e. the kinetic energy that the airflow has when passing through the sweep area; cPIs 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 ]iIs the tip speed ratio λ and pitch angleA function of β; ω is the wind wheel rotational angular velocity; v. ofwindIs the wind speed at the hub.
The mathematical model of the doubly-fed wind generator is as follows:
Figure BDA0003007569390000025
Figure BDA0003007569390000026
Figure BDA0003007569390000027
Figure BDA0003007569390000028
Te＝(e'qs/ws)iqs+(e'ds/ws)ids (9)
Pe＝e'qsiqs+e'dsids (10)
Qs＝vqsids-vdsiqs＝vqsids (11)
idsand iqsD-axis stator current and q-axis stator current respectively; v. ofdsAnd vqsD-axis stator external voltage and q-axis stator external voltage respectively; v. ofdrAnd vqrRespectively representing d-axis rotor voltage and q-axis rotor voltage; e'dsAnd e'qsInternal voltages of d-axis and q-axis are represented, respectively; w is abRepresents a reference electrical angular velocity; w is asRepresenting a synchronous angular velocity; w is arRepresenting the rotor angular velocity; l ismIs mutual inductance; t iseIs the electromagnetic torque produced by the generator; peIs the active power of the generator; qsIs a reactive power; l is'sIs the stator inductance. Rl、R2、Tr、LrrIs an intermediate amount, R1＝Rs+(Lm+Lrr)2Rr，R2＝(Lm/Lrr)2Rr，Tr＝Lrr/Rr，Lrr＝1.005×1.01Lm，L's＝1.01Lm。RsRepresenting the stator resistance; rrRepresenting 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 ds0. And v isqs＝vt，vqsThe 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:
Hm＝Ht+Hg (12)
Figure BDA0003007569390000031
the rotating shaft system can be represented by a single concentrated inertial system. HmRepresents a lumped inertia constant; htIs the collective inertia constant of the wind turbine; hgIs the lumped inertia constant of the generator; the electromagnetic dynamic equation in the rotating shaft system is shown as the formula (13). w is amIs the rotational speed of the concentrated inertial system, which is equal to the generator rotational speed wr. D represents the central inertial system damping, TmRepresents a mechanical torque, and Tm＝Pm/ωm。
The operation control targets of the double-fed fan are two, namely two fitness degrees f1(x) And f2(x)，f1(x) The reactive power error is minimum in the running time; f. of2(x) The rotor speed error is minimized during operation time.
Figure BDA0003007569390000032
Figure BDA0003007569390000033
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:
Figure BDA0003007569390000034
Figure BDA0003007569390000035
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 the type-xIs a decreasing exponential function;
Figure BDA0003007569390000036
is a proportional parameter of the ith layer fractional order of the jth information of the exploration controller;
Figure BDA0003007569390000037
an order parameter of the ith layer fractional order of the jth information of the exploration controller; (ii) a Epsilonj,iAnd deltaj,iProportional gain and fractional order gain of the survey controller update strategy, respectively.
The development controller section for readjusting the position of the individual controllers is composed of:
Figure BDA0003007569390000041
Figure BDA0003007569390000042
wherein
Figure BDA0003007569390000043
Is a proportional parameter of the ith layer fractional order of the jth information of the development controller;
Figure BDA0003007569390000044
is an order parameter of the ith layer fractional order of the jth information of the development controller; t ismilIs the value of the current time in milliseconds, TmilE [000,999). Connect () is a numerical cascading function.
The value of the argument of the a-th controller at the (k +1) -th iteration
Figure BDA0003007569390000045
Comprises the following steps:
Figure BDA0003007569390000046
Figure BDA0003007569390000047
is a fitness function value, the physical meaning of which is equivalent to an error function value in the controller;
Figure BDA0003007569390000048
is a fractional order integral function;
Figure BDA0003007569390000049
is a fractional order differential function.
For the multi-objective optimization problem, the optimization cannot be continued for the target in the multi-objective optimization problem, but the other objective functions are not degradedIs the Pareto optimal solution. For the minimum value of multi-objective optimization, if a solution vector x exists in a feasible domain1And x2When the following two conditions are satisfied, it is referred to as x1Dominating x2Or x1The dominant effect is.
Figure BDA00030075693900000410
Figure BDA00030075693900000411
In the formula, NobjIn 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. The global optimal solution is determined by using an external archive set, the Pareto optimal solution is firstly stored, the crowding distance of each Pareto optimal solution of the external archive is calculated, and the greater the crowding distance is, the greater the probability of being selected as the global optimal solution is.
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 uncertainty of parameters and real-time updating of data through 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 doubly-fed wind turbine, which is explained in detail by combining the accompanying drawings as follows:
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. 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 the prime mover in the double-fed wind power generation system can realize energy conversion when the wind turbine is used. 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 value respectively
Figure BDA0003007569390000051
And
Figure BDA0003007569390000052
inner control loop control compensation term vqr2And vdr2Two currents related to produce the final output v of the controllerqrAnd vdr。
FIG. 3 is a graph of data trained by the method of the present invention. Wherein, (a) a step wind speed; (b) gradually changing the wind speed; (c) the voltage drops. The step wind speed is used for simulating wind speed mutation, 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 10 m/s; the voltage drop is a voltage drop which is applied to an alternating current power grid and has a rated value of 50% and a duration of 200 milliseconds, and the voltage drop is used for testing the low voltage ride through capability of the doubly-fed wind 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 by dominating relationships and congestion distances; (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:
Figure FDA0003007569380000011
Figure FDA0003007569380000012
λi -1＝(λ+0.08β)-1-0.035(β3+1)-1 (3)
Figure FDA0003007569380000013
in the formula, PmIs the mechanical power output by the wind turbine; pvIs the input power of the wind turbine, i.e. the kinetic energy that the airflow has when passing through the sweep area; cPIs 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 ]iIs a function of the tip speed ratio λ and the pitch angle β; ω is the wind wheel rotational angular velocity; v. ofwindIs the wind speed at the hub;
the mathematical model of the doubly-fed wind generator is as follows:
Figure FDA0003007569380000014
Figure FDA0003007569380000015
Figure FDA0003007569380000016
Figure FDA0003007569380000017
Te＝(e'qs/ws)iqs+(e'ds/ws)ids (9)
Pe＝e'qsiqs+e'dsids (10)
Qs＝vqsids-vdsiqs＝vqsids (11)
in the formula idsAnd iqsD-axis stator current and q-axis stator current respectively; v. ofdsAnd vqsD-axis stator external voltage and q-axis stator external voltage respectively; v. ofdrAnd vqrRespectively representing d-axis rotor voltage and q-axis rotor voltage; e'dsAnd e'qsInternal voltages of d-axis and q-axis are represented, respectively; w is abRepresents a reference electrical angular velocity; w is asRepresenting a synchronous angular velocity; w is arRepresenting the rotor angular velocity; l ismIs mutual inductance; t iseIs the electromagnetic torque produced by the generator; peIs the active power of the generator; qsIs a reactive power; l'sIs the stator inductance; rl、R2、Tr、LrrIs an intermediate amount, R1＝Rs+(Lm+Lrr)2Rr，R2＝(Lm/Lrr)2Rr，Tr＝Lrr/Rr，Lrr＝1.005×1.01Lm，L's＝1.01Lm；RsRepresenting the stator resistance; rrRepresenting 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 vds0; and v isqs＝vt，vqsIs a value ofA voltage amplitude;
the mathematical model of the rotating shaft system in the double-fed fan is as follows:
Hm＝Ht+Hg (12)
Figure FDA0003007569380000021
in the formula, HmRepresents a lumped inertia constant; htIs the collective inertia constant of the wind turbine; hgIs the lumped inertia constant of the generator; an electromagnetic dynamic equation in the rotating shaft system is shown as a formula (13); w is amIs the rotational speed of the concentrated inertial system, which is equal to the generator rotational speed wr(ii) a D represents the central inertial system damping, TmRepresents a mechanical torque, and Tm＝Pm/ωm；
Two fitness degrees f1(x) And f2(x)，f1(x) The reactive power error is minimum in the running time; f. of2(x) The rotor speed error is minimal during operation time;
Figure FDA0003007569380000022
Figure FDA0003007569380000023
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:
Figure FDA0003007569380000024
Figure FDA0003007569380000025
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-xIs a decreasing exponential function;
Figure FDA0003007569380000031
is a proportional parameter of the ith layer fractional order of the jth information of the exploration controller;
Figure FDA0003007569380000032
an order parameter of the ith layer fractional order of the jth information of the exploration controller; (ii) a Epsilonj,iAnd deltaj,iRespectively, the proportional gain and the fractional order gain of the updating strategy of the exploration controller;
the development controller section for readjusting the position of the individual controllers is composed of:
Figure FDA0003007569380000033
Figure FDA0003007569380000034
in the formula (I), the compound is shown in the specification,
Figure FDA0003007569380000035
is a proportional parameter of the ith layer fractional order of the jth information of the development controller;
Figure FDA0003007569380000036
is the jth message of the development controllerThe order parameter of the ith layer fractional order of information; t ismilIs the value of the current time in milliseconds, TmilE [000,999); connect () is a numerical cascading function;
the value of the argument of the a-th controller at the (k +1) -th iteration
Figure FDA0003007569380000037
Comprises the following steps:
Figure FDA0003007569380000038
in the formula (I), the compound is shown in the specification,
Figure FDA0003007569380000039
is the fitness function value;
Figure FDA00030075693800000310
is a fractional order integral function;
Figure FDA00030075693800000311
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 to carry out iterative computation.
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CN113872476B (en) * 2021-10-21 2024-02-09 国网江苏省电力有限公司电力科学研究院 PI parameter optimization method and device for doubly-fed induction motor

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