CN114069711A - Virtual inertia control system for offshore wind power - Google Patents

Virtual inertia control system for offshore wind power Download PDF

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CN114069711A
CN114069711A CN202111413530.5A CN202111413530A CN114069711A CN 114069711 A CN114069711 A CN 114069711A CN 202111413530 A CN202111413530 A CN 202111413530A CN 114069711 A CN114069711 A CN 114069711A
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virtual inertia
wind power
offshore wind
inertia control
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李泰�
孙苏南
朱志宇
赵黎
曾庆军
钱慧敏
王乐秋
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Jiangsu University of Science and Technology
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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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/004—Artificial life, i.e. computing arrangements simulating life
    • G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
    • 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
    • H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
    • H02J3/241—The oscillation concerning frequency
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P9/00—Arrangements for controlling electric generators for the purpose of obtaining a desired output
    • H02P9/007—Control circuits for doubly fed generators
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P9/00—Arrangements for controlling electric generators for the purpose of obtaining a desired output
    • H02P9/10—Control effected upon generator excitation circuit to reduce harmful effects of overloads or transients, e.g. sudden application of load, sudden removal of load, sudden change of load
    • H02P9/105—Control effected upon generator excitation circuit to reduce harmful effects of overloads or transients, e.g. sudden application of load, sudden removal of load, sudden change of load for increasing the stability
    • 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
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P2101/00—Special adaptation of control arrangements for generators
    • H02P2101/15—Special adaptation of control arrangements for generators for wind-driven turbines
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    • 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
    • Y02E10/76—Power conversion electric or electronic aspects

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Abstract

The invention discloses an offshore wind power virtual inertia control system. Belonging to the technical field of wind power system control; the system comprises a double-fed wind motor power device, an alternating current bus, a transformer, a target power grid, VSC-HVDC, a swarm active disturbance rejection control module, a wind motor rotor side converter, a wind motor grid side converter and other equipment which are connected with each other; according to the method, the error between the frequency of the power system and the reference frequency is calculated by an expansion state observation module in the active disturbance rejection to obtain the observed quantity of the frequency; eliminating errors of the difference value of the frequency observed quantity and the reference frequency deviation through a nonlinear feedback control law to obtain a power value of the offshore wind turbine needed to be compensated; optimizing corresponding parameters of the extended state observer through a swarm optimization algorithm, and adjusting the compensation power output by the offshore wind turbine; the invention can realize the stabilization of the frequency of the offshore wind turbine and the power grid, and ensure the safety of the power system.

Description

Virtual inertia control system for offshore wind power
Technical Field
The invention belongs to the technical field of wind power system control, relates to an offshore wind power virtual inertia control system, and is suitable for a control method of double-fed wind power unit virtual inertia control.
Background
With the continuous improvement of the proportion of installed wind power capacity in a power grid, constant-frequency double-Fed Induction Generators (DFIGs) become mainstream models in the wind power market, but because the DFIG is decoupled from the electromagnetic power of a system, the rotating speed is decoupled from the power grid frequency, and the inertia of a wind turbine is decoupled from the system, the fast response to the system frequency is lost. In addition, as the permeability of the wind power plant increases, the overall inertia of the system decreases, and when large disturbances occur to the power grid, the frequency stability of the system will be affected. The virtual inertia control is a concept used for solving the problem of inertia loss in a power grid caused by large-scale new energy grid connection, adopts power electronic components or other system elements, is used for simulating the actual inertia existing in a physical system under the assistance of a corresponding control algorithm, and has wide application in a direct-current micro-grid and a power grid containing large-scale new energy.
In recent years, the active disturbance rejection controller has the advantage of disturbance rejection capability, and the technology is applied in the field of industrial control to reach maturity. The active disturbance rejection controller utilizes the reasonable 'transition process', 'tracking differentiator', 'nonlinear combination' and 'extended state observer', not only retains the advantages of the PID, but also overcomes the defect of poor anti-interference capability of the PID, and achieves better control requirements.
Disclosure of Invention
The purpose of the invention is as follows: in order to overcome the defects in the prior art, the invention aims to solve the problems of high lowest value of transient frequency, long time for the frequency to reach the lowest value, poor anti-interference capability and the like when the double-fed wind turbine generator is controlled by virtual inertia by adopting the active disturbance rejection controller with the swarm algorithm; therefore, the offshore wind power virtual inertia control system is provided.
The technical scheme is as follows: the invention relates to an offshore wind power virtual inertia control system,
the offshore wind power virtual inertia control system comprises a double-fed wind motor power device (1), an alternating current bus (2), a transformer (3), a target power grid (4), a VSC-HVDC (5), a swarm active disturbance rejection control module (6), a wind motor rotor side converter (9) and a wind motor grid side converter (10) which are connected with one another;
the VSC-HVDC (5) comprises a rectifier (7) and an inverter (8) connected by power electronics;
the double-fed wind power generation device is characterized in that the double-fed wind power generation device (1) boosts current through a transformer (3), then the current is connected to an alternating current bus (2) together, then the current is transmitted to a rectifier (7) in a VSC-HVDC (5) through a direct current cable, and the boosted current is finally input into a target power grid (4) through the transformer (3) and the alternating current bus (2) through a converter (8) connected with the rectifier (7).
Further, the double-fed wind motor power device (1) comprises at least three groups of same wind power generators;
the wind driven generator comprises a wind motor rotor side converter (9) and a wind motor grid side converter (10), wherein the wind motor rotor side converter (9) and the wind motor grid side converter (10) adopt voltage prototype back-to-back PWM converters and are connected through a direct current circuit;
and the signal input ends of the three groups of wind motor rotor side converters (9) are connected to the bee colony active disturbance rejection control module (6) together.
Further, in the offshore wind power virtual inertia control system,
the control method of the swarm active disturbance rejection control module (6) specifically comprises the following steps:
s1, firstly, establishing a power system frequency response equation after wind power additional virtual inertia control;
s2, acquiring a standard form of the extended state observer according to the frequency response equation of the system power system;
s3, acquiring a nonlinear feedback law standard form;
and S4, carrying out real-time optimization on the parameters of the extended state observer in S2 by using an artificial bee colony algorithm.
Further, in step (1), the power system frequency response equation is specifically as follows:
Figure BDA0003374414020000021
where Δ f is the system frequency offset, H is the system inertial time constant, PVICActive reference increment, P, generated for virtual inertia control of a wind turbineMPPTAnd D is the load damping coefficient of the system, and t is a time variable.
Further, in step (2), the standard form of the extended state observer is specifically as follows:
Figure BDA0003374414020000022
wherein z is1＝Δf，z2＝(PVIC+PMPPT-DΔf)/2H,
Figure BDA0003374414020000023
And
Figure BDA0003374414020000024
are each z1And z2E is the actual value z1And the estimated value
Figure BDA0003374414020000025
U is the input of the state observer, u is PVIC,β01And beta02Are parameters of the state observer.
Further, in step (3), the standard form of the nonlinear feedback control law is specifically as follows:
u0(t)＝β03(Δfref-z1)
wherein, Δ frefIs a reference frequency deviation, beta03Are control parameters.
Further, in the step (4), a specific optimization manner of the artificial bee colony algorithm is as follows:
the parameters of the extended state observer in the S2 are optimized in real time through an artificial bee colony algorithm, and the bee colony algorithm is utilized to carry out beta01And beta02Optimizing and setting two parameters;
in the bee colony algorithm, each honey source represents a solution of an optimization problem, and the quality of the honey source, namely the fitness function, represents the quality of the solution; randomly generating an initial population containing N solutions by using a bee colony algorithm; each solution xiIs a multidimensional vector, where i ∈ {1, 2.., N }; it is demonstrated that ADRC requires tuning of the parameter [ beta ]01,β02And then, the bees circulate all the honey sources for the number of times P.
Further, the fitness function is specifically shown as the following formula:
Figure BDA0003374414020000031
wherein e (t) is the instantaneous error of the system, and J is the dynamic performance index of the system.
Further, the dynamic performance index J of the system may overshoot during the calculation process, and therefore, the specific formula of the dynamic performance index J is shown as follows:
Figure BDA0003374414020000032
in the formula, w2＞＞w1，w1Is the overshoot; wherein when the overshoot is not present, then w1And (4) obtaining the optimal controller parameter when the fitness function F is maximum.
Has the advantages that: compared with the prior art, the invention has the characteristics that the defects in the prior art are overcome, and the problems of poor anti-interference capability of the traditional virtual inertia, higher lowest value of transient frequency, longer time for the frequency to reach the lowest value and the like are effectively solved by adopting the swarm-optimized active disturbance rejection controller; the DSP is adopted to realize the function of the mode switching system controller, so that the hardware development cost is effectively reduced.
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FIG. 1 is a schematic view of the overall structure of the present invention;
FIG. 2 is a schematic diagram of a control model according to the present invention;
FIG. 3 is a flow chart of a swarm optimization algorithm in the present invention;
FIG. 4 is a block diagram of a control structure processor implementation of the present invention;
FIG. 5 is a comparison graph of grid frequency under different control methods in an embodiment of the present invention;
in the figure, 1 is a doubly-fed wind generator power device, 2 is an alternating current bus, 3 is a transformer, 4 is a power grid, 5 is VSC-HVDC, 6 is a swarm active disturbance rejection control module, 7 is a rectifier, 8 is an inverter, 9 is a wind generator rotor side converter, and 10 is a wind generator grid side converter.
Detailed Description
The invention is further described below with reference to the following figures and specific examples.
As shown in the figure, the invention relates to an offshore wind power virtual inertia control system,
the offshore wind power virtual inertia control system comprises a double-fed wind motor power system 1, an alternating current bus 2, a transformer 3, a target power grid 4, a VSC-HVDC5, a swarm active disturbance rejection control module 6, a wind motor rotor side converter 9 and a wind motor grid side converter 10 which are connected with one another;
the VSC-HVDC5 comprises a rectifier 7 and an inverter 8 connected by power electronics;
the doubly-fed wind motor power device 1 boosts current through a transformer 3, then connects the boosted current to an alternating current bus 2 together, then transmits the boosted current to a rectifier 7 in VSC-HVDC5 through a direct current cable, and finally inputs the boosted current into a target power grid 4 through the transformer 3 and the alternating current bus 2 through a converter 8 connected with the rectifier 7.
Further, the doubly-fed wind turbine generator unit 1 comprises at least three groups of identical wind generators; the wind driven generator comprises a wind motor rotor side converter 9 and a wind motor grid side converter 10, wherein the wind motor rotor side converter 9 and the wind motor grid side converter 10 adopt voltage prototype back-to-back PWM converters and are connected through a direct current circuit; the signal input ends of the three groups of wind motor rotor side converters 9 are connected to the swarm active disturbance rejection control module 6 together.
In order to achieve the above purpose, the control method of the swarm active disturbance rejection control module 6 of the invention comprises the following steps:
s1, firstly, establishing a power system frequency response equation after wind power additional virtual inertia control;
s2, acquiring a standard form of the extended state observer according to the system frequency response equation;
s3, acquiring a nonlinear feedback control law standard form;
and S4, carrying out real-time optimization on the parameters of the extended state observer in S2 by using an artificial bee colony algorithm.
Further, the frequency response equation of the power system is as follows:
Figure BDA0003374414020000041
where Δ f is the system frequency offset, H is the system inertial time constant, PVICActive reference increment, P, generated for virtual inertia control of a wind turbineMPPTThe maximum wind energy tracking power of the fan is D, the load damping coefficient of the system is D, and t is a time variable;
if x is Δ f, the system frequency response state equation is obtained as:
Figure BDA0003374414020000042
wherein g (x, w (t)) (P)MPPT-DΔf)/2H,u(t)＝PVIC，b＝1/2H。
In order to realize the disturbance estimation of the system, the state variable expansion is needed, and the total disturbance term g (x, w (t)) of the system shown in the formula (2) is used for expanding the state variable x2Instead, one can obtain:
Figure BDA0003374414020000043
in the formula, x1＝Δf，x2G (x, w (t)) is an expansion variable, g (t) represents an unknown function;
implementing a state variable x by extending a state observer1And x2The real-time observation of the land is realized by the following specific algorithm:
Figure BDA0003374414020000051
in the formula, z1、z2For system to state variable x1And x2The observed quantity of (1); beta is a01And beta02Parameters of a state observer; fal (e, a)2δ) is a non-linear function;
Figure BDA0003374414020000052
in the formula, delta and alpha are constants; sign (e) is a symbol constant;
the nonlinear feedback module in the active disturbance rejection controller is as follows:
u0(t)＝β03(Δfref-z1) (6)
in the formula,. DELTA.frefIs a reference frequency deviation, beta03Is a control parameter;
measure z of the disturbance2If u (t) is introduced into the control system as a control compensation amount, u (t) is0(t)-z2/b；
Extended state observer counterpartState variable x1Is observed quantity z1Deviation from reference frequency Δ frefIs used as the input of the swarm controller, and beta in the output formula (4) is output through the swarm control module01And beta02The parameter (c) of (c).
In the embodiment of the invention, the artificial bee colony algorithm is used for controlling the control parameter beta of the extended state observer in the active disturbance rejection controller01And beta02The adjustment is carried out, the instantaneous error e in fig. 2 is selected, and the time integral of the instantaneous error e is used as an objective function to evaluate the dynamic performance of the system, wherein the function is as follows:
Figure BDA0003374414020000053
the fitness function of the x system obtained from equation 7 is as follows:
Figure BDA0003374414020000054
overshoot occurs in the calculation process, and the overshoot amount is also one of the optimal solutions, so that:
Figure BDA0003374414020000055
w2＞＞w1，w1is the overshoot; when overshoot is not present, w1We get the optimal controller parameter when the fitness function F is maximum, 0.
The artificial bee colony algorithm sets the algorithm flow of parameter optimization of the active disturbance rejection controller as follows:
(7.1) initializing, and generating an initial solution set x in a set rangeijEach xijRepresents a set of ADRC controllers { beta }01，β02Parameters, and then calculating an adaptive value of each solution;
(7.2) searching bees nearby according to the formula:
Figure BDA0003374414020000061
to generate new solutions, calculating an adaptation value for each solution;
in the formula, k ∈ [1,2],j∈,1,2,…,N]K and j are randomly selected, k satisfies k ≠ i,
Figure BDA0003374414020000062
is [ -1, 1 [ ]]Random number in, this parameter controlling xijGeneration of a new solution nearby; substituting the control parameters expressed by the solution set into ADRC for simulation, and calculating a fitness value;
(7.3) greedy selection is carried out on the new solution and the original solution by the employing bee, and the optimal solution is reserved, namely parameters enabling ADRC control performance to be better are reserved;
(7.4) calculating a probability value using the following formula:
Figure BDA0003374414020000063
wherein f isiRepresents the applicability of solution I;
(7.5) following bee according to probability PiSelecting a solution and repeating the step (7.2);
(7.6) selecting a greedy new solution and an original solution (the solution of the selected employing bee) by the exploring bee, and keeping the optimal solution;
(7.7) after the limit cycle is finished, if the fitness of a solution is not improved, abandoning the solution, changing the employed bee into a scout bee to continue searching the honey source, and updating the position as follows:
xij＝xminj+rand(0,1)(xmaxj-xminj)
(7.8) recording the optimal solution, and increasing the cycle number by 1; determining whether a stopping condition is met (the maximum number of loops can be reached generally), if so, ending the loops, otherwise, skipping to the step (2);
the fitness function corresponding to the adaptive value is as follows:
Figure BDA0003374414020000064
wherein e (t) is the instantaneous error of the system, and J is the dynamic performance index of the system.
The dynamic performance index J of the system may overshoot during the calculation process, so that:
Figure BDA0003374414020000065
wherein, w2＞＞w1，w1Is the overshoot;
when overshoot is not present, w1We get the optimal controller parameter when the fitness function F is maximum, 0.
The invention further improves that the control operation is mainly as follows: the actual operation frequency f of the system is used as the input of the digital signal processor, and the compensation power P to be provided is calculated through the calculation of the digital processorVIC。
The system controller is realized by adopting a Digital Signal Processor (DSP) with the model number of TMS320F2812, and a control system realization diagram of the invention is shown in FIG. 4; the actual running frequency f of the fan system is used as input and connected with the DI port, and the actually required compensation power P is calculated through ADRC and a bee colony optimization calculation formulaVIC。
The present invention is described in detail below with reference to examples:
taking a doubly-fed wind generator with rated power of 1.5MW as an example, the main parameters are as follows: air density ρ 1.22kg/m3(ii) a Rated voltage 575V; rated power is 1.5 MW; wind turbine inertia constant HWT4.32 s; rated wind speed vr11 m/s; in order to verify the feasibility and the superiority of the method, the frequency regulation performance of the following three control methods under the same condition is compared and analyzed:
no inertia control: the DFIG has no frequency regulation function;
PD inertia control: the DFIG utilizes PD inertia control to realize transient frequency support;
and (3) carrying out active disturbance rejection inertial control on the swarm: the DFIG uses the controller presented herein to achieve transient frequency support.
And when the frequency deviation of the system is higher than 0.03Hz, activating an inertia control method, inputting the acquired frequency into respective controllers, transmitting the calculated power compensation into a doubly-fed wind power generator power compensation system, then monitoring the frequency deviation, and circulating in sequence.
Fig. 5 is a comparison of the grid frequency under different control methods, and it can be clearly seen that the load with the grid frequency of 60s suddenly drops when the load is suddenly switched on; the bee colony active disturbance rejection control (ABC-ADRC) can rapidly and obviously inhibit the frequency and enables the frequency to tend to be stable.
The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above-mentioned embodiments, and all technical solutions belonging to the idea of the present invention belong to the protection scope of the present invention. It should be noted that modifications and embellishments within the scope of the invention may be made by those skilled in the art without departing from the principle of the invention.

Claims (9)

1. An offshore wind power virtual inertia control system is characterized in that,
the offshore wind power virtual inertia control system comprises a double-fed wind motor power device (1), an alternating current bus (2), a transformer (3), a target power grid (4), a VSC-HVDC (5), a swarm active disturbance rejection control module (6), a wind motor rotor side converter (9) and a wind motor grid side converter (10) which are connected with one another;
the VSC-HVDC (5) comprises a rectifier (7) and an inverter (8) connected by power electronics;
the double-fed wind power generation device is characterized in that the double-fed wind power generation device (1) boosts current through a transformer (3), then the current is connected to an alternating current bus (2) together, then the current is transmitted to a rectifier (7) in a VSC-HVDC (5) through a direct current cable, and the boosted current is finally input into a target power grid (4) through the transformer (3) and the alternating current bus (2) through a converter (8) connected with the rectifier (7).
2. The offshore wind power virtual inertia control system of claim 1, wherein;
the double-fed wind motor power device (1) comprises at least three groups of same wind power generators;
the wind driven generator comprises a wind motor rotor side converter (9) and a wind motor grid side converter (10), wherein the wind motor rotor side converter (9) and the wind motor grid side converter (10) adopt voltage prototype back-to-back PWM converters and are connected through a direct current circuit;
and the signal input ends of the three groups of wind motor rotor side converters (9) are connected to the bee colony active disturbance rejection control module (6) together.
3. An offshore wind power virtual inertia control system according to claims 1 and 2,
the control method of the swarm active disturbance rejection control module (6) specifically comprises the following steps:
s1, firstly, establishing a power system frequency response equation after wind power additional virtual inertia control;
s2, acquiring a standard form of the extended state observer according to the frequency response equation of the system power system;
s3, acquiring a nonlinear feedback law standard form;
and S4, carrying out real-time optimization on the parameters of the extended state observer in S2 by using an artificial bee colony algorithm.
4. The offshore wind power virtual inertia control system of claim 3,
in step (1), the frequency response equation of the power system is specifically shown as follows:
Figure FDA0003374414010000011
where Δ f is the system frequency offset, H is the system inertial time constant, PVICActive reference increment, P, generated for virtual inertia control of a wind turbineMPPTAnd D is the load damping coefficient of the system, and t is a time variable.
5. The offshore wind power virtual inertia control system of claim 3,
in step (2), the standard form of the extended state observer is specifically as follows:
Figure FDA0003374414010000021
wherein z is1＝Δf，z2＝(PVIC+PMPPT-DΔf)/2H,
Figure FDA0003374414010000022
And
Figure FDA0003374414010000023
are each z1And z2E is the actual value z1And the estimated value
Figure FDA0003374414010000024
U is the input of the state observer, u is PVIC,β01And beta02Are parameters of the state observer.
6. The offshore wind power virtual inertia control system of claim 3,
in step (3), the standard form of the nonlinear feedback control law is specifically as follows:
u0(t)＝β03(Δfref-z1)
wherein, Δ frefIs a reference frequency deviation, beta03Are control parameters.
7. The offshore wind power virtual inertia control system of claim 3,
in the step (4), the specific optimization mode of the artificial bee colony algorithm is as follows:
the parameters of the extended state observer in the S2 are optimized in real time through an artificial bee colony algorithm, and the bee colony algorithm is utilized to carry out beta01And beta02Optimizing and setting two parameters;
in the bee colony algorithm, each honey source represents a solution of an optimization problem, and the quality of the honey source, namely the fitness function, represents the quality of the solution; randomly generating an initial population containing N solutions by using a bee colony algorithm; each solution xiIs a multidimensional vector, where i ∈ {1, 2.., N }; it is demonstrated that ADRC requires tuning of the parameter [ beta ]01,β02And then, the bees circulate all the honey sources for the number of times P.
8. The offshore wind power virtual inertia control system of claim 7,
the fitness function is specifically shown as the following formula:
Figure FDA0003374414010000025
wherein e (t) is the instantaneous error of the system, and J is the dynamic performance index of the system.
9. The offshore wind power virtual inertia control system of claim 8,
the dynamic performance index J of the system is overshot in the calculation process, so that the dynamic performance index J exists, and the specific formula is shown as the following formula:
Figure FDA0003374414010000026
in the formula, w2＞＞w1，w1Is the overshoot; wherein when the overshoot is not present, then w1And (4) obtaining the optimal controller parameter when the fitness function F is maximum.
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