CN111911352B - Airflow generation wind power generation method - Google Patents

Airflow generation wind power generation method Download PDF

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CN111911352B
CN111911352B CN202010392442.0A CN202010392442A CN111911352B CN 111911352 B CN111911352 B CN 111911352B CN 202010392442 A CN202010392442 A CN 202010392442A CN 111911352 B CN111911352 B CN 111911352B
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CN111911352A (en
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郑紫微
李攀
季克宇
贺超宇
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Ningbo University
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    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03D—WIND MOTORS
    • F03D7/00—Controlling wind motors 
    • F03D7/02—Controlling wind motors  the wind motors having rotation axis substantially parallel to the air flow entering the rotor
    • F03D7/026—Controlling wind motors  the wind motors having rotation axis substantially parallel to the air flow entering the rotor for starting-up
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
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    • F03D17/00—Monitoring or testing of wind motors, e.g. diagnostics
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
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    • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
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Abstract

The invention relates to an airflow generation wind power generation method, which comprises the steps of obtaining distance weights of distances aiming at each wind turbine through calculation, obtaining a real-time wind speed numerical value fitting curve, a real-time air density value fitting curve and a real-time generator active power numerical value fitting curve of each wind turbine through fitting, and obtaining a self-adaptive power adjustment fluctuation value of the generator active power corresponding to each wind turbine through an ideal state generator active power mean value of each wind turbine; the target wind turbines for executing the wind power generation work are selected according to the fluctuation condition of the adaptive power adjustment fluctuation value, all inflection point time values of three fitting curves corresponding to each target wind turbine are calculated, the optimal working time period for executing the power generation work of each target wind turbine is selected according to the minimum inflection point time value and the maximum inflection point time value, each target wind turbine executes the wind power generation work in the optimal working time period, and the power generation efficiency of the whole wind turbine set is improved.

Description

Airflow generation wind power generation method
Technical Field
The invention relates to the field of wind power generation, in particular to a wind power generation method by airflow generation.
Background
With the increasing demand for energy from global economy, the existing non-renewable energy sources are far from meeting the long-term demand for energy. Renewable clean energy sources such as solar and wind energy are becoming new hopes for long-term economic development.
In the utilization of wind energy resources, the speed of airflow generated by the flow of air is mainly used to drive the blades (or called as vanes) of a wind turbine to rotate, and then a generator in the wind turbine is used to generate electricity. By locating wind turbines in areas where air flows relatively frequently, it is often easier to increase the efficiency of the wind turbine for generating electricity.
In practical situations, a plurality of wind turbines are generally arranged as a fan group in a certain area, such as an open area or a mountain. When the fan set works, the fan set does not depend on the work of one wind turbine but depends on the work of all the wind turbines, namely, as long as any one wind turbine in the fan set executes wind power generation work, the electric energy output outwards can be generated.
However, due to differences in the area position where the fan unit is disposed, the specific position where each wind turbine is located, the air density of the height where each wind turbine blade is located, and the air flow speed, the existing fan unit cannot select a proper time to perform wind power generation operation in time according to actual conditions, and finally the overall power generation efficiency of the fan unit is low.
Disclosure of Invention
The invention aims to solve the technical problem of providing an airflow generation wind power generation method aiming at the prior art.
The technical scheme adopted by the invention for solving the technical problems is as follows: an airflow generation wind power generation method is characterized by comprising the following steps:
step 1, acquiring spatial position coordinates of each wind turbine in a wind turbine set, forming a spatial position coordinate set for the wind turbine set, and calculating to obtain a set center coordinate of the wind turbine set; n wind turbines are arranged in the wind turbine set, and the nth wind turbine is marked as a Machine n Wind turbine Machine n Is recorded as a spatial position coordinate
Figure BDA0002486112330000011
N is more than or equal to 1 and less than or equal to N; the wind turbine has a cluster center marked O and a cluster center coordinate marked (x) o ,y o ,z o )：
Figure BDA0002486112330000021
Step 2, respectively calculating the distance between each wind turbine in the wind turbine set and the center of the wind turbine set to obtain a distance weight value respectively aiming at each wind turbine; wherein the wind turbine Machine n The distance from the center O of the unit is marked
Figure BDA0002486112330000022
For the wind turbine Machine n Is marked as a distance weight
Figure BDA0002486112330000023
Figure BDA0002486112330000024
Figure BDA0002486112330000025
Step 3, acquiring the wind speed numerical values of the height positions of the blades of each wind turbine in the wind turbine set respectively according to preset sampling moments in a preset time period, and respectively forming a wind speed numerical value sequence aiming at each wind turbine; wherein the preset time period is marked as T pre Wind turbine Machine n At a preset sampling time t i Is marked by the wind speed value of
Figure BDA0002486112330000026
I is more than or equal to 1 and less than or equal to I, wherein I represents a preset time period T pre Total number of preset sampling moments in the wind turbine n Is marked as a wind speed value sequence
Figure BDA0002486112330000027
Figure BDA0002486112330000028
Step 4, respectively collecting according to the preset collection in the preset time periodAcquiring the air density values of the height positions of the blades of each wind turbine in the wind turbine set at a sampling moment, and respectively forming air density value sequences aiming at each wind turbine; wherein the wind turbine Machine n At a preset sampling time t i Is marked by the air density value
Figure BDA0002486112330000029
Wind turbine Machine n Is marked as air density value series
Figure BDA00024861123300000210
Figure BDA00024861123300000211
Step 5, collecting the generator active power numerical values of each wind turbine in the wind turbine set respectively according to the preset sampling time within the preset time period, and respectively forming a generator active power numerical value sequence aiming at each wind turbine; wherein the wind turbine Machine n At a preset sampling time t i Is marked as the value of active power generated
Figure BDA00024861123300000212
Wind turbine Machine n Is marked as a series of values of the active power of the generator
Figure BDA00024861123300000213
Figure BDA00024861123300000214
Step 6, respectively fitting to obtain a real-time fitted curve of the wind speed numerical value, a real-time fitted curve of the air density value and a real-time fitted curve of the active power numerical value of the generator corresponding to each wind turbine in the preset time period according to the wind speed numerical value sequence, the air density value sequence and the active power numerical value sequence of the generator respectively formed by aiming at each wind turbine in the preset time period; wherein the wind turbine Machine n At a preset time period T pre Real-time simulation of corresponding wind speed numerical valueThe resultant curve, the real-time fitted curve of the air density value and the real-time fitted curve of the active power numerical value of the generator are respectively and correspondingly marked as
Figure BDA0002486112330000031
And
Figure BDA0002486112330000032
step 7, respectively calculating the wind speed mean value of the wind speed numerical sequence, the air density mean value of the air density value sequence and the generator active power mean value of the generator active power numerical sequence corresponding to each wind turbine in the wind turbine set; wherein the wind turbine Machine n Corresponding wind speed numerical value sequence
Figure BDA0002486112330000033
Is marked as the mean value of wind speed
Figure BDA0002486112330000034
Wind turbine Machine n Corresponding air density value sequence
Figure BDA0002486112330000035
Is marked as the mean value of air density
Figure BDA0002486112330000036
Wind turbine Machine n Corresponding generator active power numerical value sequence
Figure BDA0002486112330000037
Is marked as the mean value of the active power of the generator
Figure BDA0002486112330000038
Figure BDA0002486112330000039
Step 8, respectively obtaining the average value of the wind speed, the average value of the air density and the average value of the active power of the generator according to the obtained average value of the wind speed, the average value of the air density and the average value of the active power of each wind turbineThe mean value of the active power of the ideal state generator of each wind turbine; wherein the wind turbine Machine n Is marked as the mean value of active power of ideal state generator
Figure BDA00024861123300000310
Figure BDA00024861123300000311
Figure BDA00024861123300000312
Wherein, the first and the second end of the pipe are connected with each other,
Figure BDA00024861123300000313
representing wind turbine machines n The radius of the blades of (a) a,
Figure BDA00024861123300000314
indicating the wind turbine Machine n The maximum wind energy utilization coefficient of the wind turbine,
Figure BDA00024861123300000315
indicating the wind turbine Machine n In conjunction with the sum of the rotational inertia of the generator,
Figure BDA00024861123300000316
indicating the wind turbine Machine n The optimum torque coefficient in the optimum torque control,
Figure BDA00024861123300000317
indicating the wind turbine Machine n The optimum tip speed ratio;
step 9, obtaining a self-adaptive power adjustment fluctuation value aiming at the active power of the generator corresponding to each wind turbine in the wind turbine set according to the average value of the active power of the ideal-state generator corresponding to each wind turbine; wherein the wind turbine Machine n The adaptive power regulation fluctuation value mark of the corresponding active power of the generator
Figure BDA00024861123300000318
Figure BDA00024861123300000319
Step 10, screening out the wind turbines for executing wind power generation according to the adaptive power adjustment fluctuation values of the wind turbines:
when the adaptive power adjustment fluctuation value of any wind turbine is within the preset fluctuation value range, selecting the wind turbine as a target wind turbine for executing wind power generation work, and turning to step 11; otherwise, the any one wind turbine is not selected as a target wind turbine for executing the wind power generation work; the selected target wind turbine corresponds to an original number w in the wind turbine set, and the target wind turbine is marked as a Machine in the wind turbine set w ，1≤w≤N；
Step 11, calculating inflection point time of a real-time wind speed numerical value fitting curve, inflection point time of a real-time air density value fitting curve and inflection point time of a real-time generator active power numerical value fitting curve corresponding to each target wind turbine executing wind power generation work to obtain an inflection point time set corresponding to each target wind turbine; wherein:
target wind turbine Machine w The inflection points of the corresponding real-time fitting curve of the wind speed numerical value are U at the moment, and the mark of the U-th inflection point is
Figure BDA0002486112330000041
u∈[1,U](ii) a Target wind turbine Machine w E inflection points of the corresponding real-time fitting curve of the air density value are marked as
Figure BDA0002486112330000042
e∈[1,E](ii) a Target wind turbine Machine w G inflection points are arranged at the moment of the inflection point of the real-time fitted curve of the active power value of the corresponding generator, and the G-th inflection point moment is marked as
Figure BDA0002486112330000043
g∈[1,G](ii) a Target wind turbine Machine w The corresponding inflection point time set is marked as
Figure BDA0002486112330000044
Figure BDA0002486112330000045
Step 12, selecting an inflection point moment minimum value and an inflection point moment maximum value in each inflection point moment set, taking a time period defined by the inflection point moment minimum value and the inflection point moment maximum value as an optimal working time period for the corresponding target wind turbine to execute power generation work, and starting the target wind turbine to execute the power generation work at an initial time corresponding to the optimal working time period; and limiting the minimum value of the inflection point time of the optimal working time period as the starting time of the optimal working time period, and limiting the maximum value of the inflection point time of the optimal working time period as the ending time of the optimal working time period.
In the improved method for generating wind power by airflow generation, the preset time period is 365 days.
Preferably, in the method for generating wind power by airflow generation, each wind turbine in the wind turbine set has three blades.
Compared with the prior art, the invention has the advantages that: the method comprises the steps of obtaining a distance weight of a distance for each wind turbine by calculating the distance between each wind turbine in the wind turbine set and the center of the wind turbine set, fitting to obtain a real-time wind speed numerical value fitting curve, a real-time air density value fitting curve and a real-time generator active power numerical value fitting curve of each wind turbine according to a wind speed numerical value sequence, an air density value sequence and a generator active power numerical value of each wind turbine collected in a preset time period, and obtaining a self-adaptive power regulation fluctuation value of the generator active power corresponding to each wind turbine by calculating an ideal state generator active power mean value of each wind turbine; and selecting a target wind turbine for executing wind power generation according to the fluctuation condition of the adaptive power regulation fluctuation value, calculating all inflection point time values of three fitting curves corresponding to each target wind turbine, and finally selecting the optimal working time period for executing the power generation work of each target wind turbine according to the screening of the minimum inflection point time value and the maximum inflection point time value, so that each target wind turbine executes the wind power generation work in the optimal working time period, the optimal time selection starting work is achieved, and the power generation efficiency of the whole wind turbine set is improved.
Drawings
FIG. 1 is a schematic flow chart of a method for generating wind power by airflow in an embodiment of the invention.
Detailed Description
The invention is described in further detail below with reference to the accompanying examples.
Referring to fig. 1, the present embodiment provides an airflow generation wind power generation method, including the following steps:
step 1, acquiring spatial position coordinates of each wind turbine in a wind turbine set, forming a spatial position coordinate set for the wind turbine set, and calculating to obtain a set center coordinate of the wind turbine set; each wind turbine in the wind turbine set is provided with three blades, N wind turbines are arranged in the wind turbine set, and the nth wind turbine is marked as a Machine n Wind turbine Machine n Is recorded as a spatial position coordinate
Figure BDA0002486112330000051
N is more than or equal to 1 and less than or equal to N; the wind turbine has a cluster center marked O and a cluster center coordinate marked (x) o ,y o ,z o )：
Figure BDA0002486112330000052
Step 2, respectively calculating the distance between each wind turbine in the wind turbine set and the center of the wind turbine set to obtain a distance weight value respectively aiming at each wind turbine; wherein the wind turbine Machine n The distance from the center O of the unit is marked
Figure BDA0002486112330000053
For the wind turbine Machine n Is marked as a distance weight
Figure BDA0002486112330000054
Figure BDA0002486112330000055
Figure BDA0002486112330000056
It should be noted that after the unit center coordinates of the whole wind turbine unit are obtained in step 1, distance weights for the wind turbines are obtained through the distance weights, so that the influence of wind power generation working differences caused by different positions of the wind turbines in the same wind turbine unit is taken into consideration, and data support is provided for the subsequent calculation of active power of the corresponding generators in the wind turbine unit;
step 3, acquiring the wind speed numerical values of the height positions of the blades of each wind turbine in the wind turbine set respectively according to preset sampling moments in a preset time period, and respectively forming a wind speed numerical value sequence aiming at each wind turbine; wherein the preset time period is marked as T pre The preset time period T in this embodiment pre 365 days, wind Machine n At a preset sampling time t i Is marked as the wind speed value
Figure BDA0002486112330000061
I is more than or equal to 1 and less than or equal to I, wherein I represents the preset time period T pre Total number of preset sampling moments in the wind turbine n Is marked as a wind speed value sequence
Figure BDA0002486112330000062
Figure BDA0002486112330000063
Step 4, in a preset time period T pre Respectively acquiring the air density values of the height positions of the blades of each wind turbine in the wind turbine set according to the preset sampling time, and respectively forming an air density value sequence aiming at each wind turbine; wherein the wind turbine Machine n At a preset sampling time t i Is marked by the air density value
Figure BDA0002486112330000064
Wind turbine Machine n Is marked as air density value series
Figure BDA0002486112330000065
Figure BDA0002486112330000066
Step 5, in a preset time period T pre The method comprises the steps of collecting the active power numerical values of the generators of the wind turbines in the wind turbine set at preset sampling moments respectively to form generator active power numerical value sequences aiming at the generators of the wind turbines respectively; wherein the wind turbine Machine n At a preset sampling time t i Is marked as the value of active power generated
Figure BDA0002486112330000067
Wind turbine Machine n Is marked as a series of values of the active power of the generator
Figure BDA0002486112330000068
Figure BDA0002486112330000069
Step 6, setting a preset time period T for each wind turbine pre Respectively formed wind speed numerical value sequence
Figure BDA00024861123300000610
Air density value sequence
Figure BDA00024861123300000611
And generator active power numerical sequence
Figure BDA00024861123300000612
Respectively fitting to obtain the preset time period T of each wind turbine pre Real-time fitting curve of corresponding wind speed numerical value
Figure BDA00024861123300000613
Real-time fitting curve of air density value
Figure BDA00024861123300000614
And real-time fitted curve of active power value of generator
Figure BDA00024861123300000615
Step 7, respectively calculating the wind speed numerical value sequence corresponding to each wind turbine in the wind turbine set
Figure BDA00024861123300000616
Sequence of wind speed mean value and air density value
Figure BDA00024861123300000617
Air density mean value and generator active power numerical sequence
Figure BDA00024861123300000618
The average value of active power of the generator; wherein the wind turbine Machine n Corresponding wind speed numerical value sequence
Figure BDA00024861123300000619
Is marked as the mean value of wind speed
Figure BDA00024861123300000620
Wind turbine Machine n Corresponding air density value sequence
Figure BDA00024861123300000621
Is marked as the mean value of air density
Figure BDA00024861123300000622
Wind turbine Machine n Corresponding generator active power numerical value sequence
Figure BDA00024861123300000623
Is marked as the mean value of the active power of the generator
Figure BDA00024861123300000624
Figure BDA0002486112330000071
Step 8, according to the obtained wind speed mean value of each wind turbine
Figure BDA0002486112330000072
Mean value of air density
Figure BDA0002486112330000073
And the mean value of active power of generator
Figure BDA0002486112330000074
Respectively obtaining the mean value of the active power of the ideal state generator of each wind turbine; wherein the wind turbine Machine n Is marked as the mean value of active power of ideal state generator
Figure BDA0002486112330000075
Figure BDA0002486112330000076
Figure BDA0002486112330000077
Wherein the content of the first and second substances,
Figure BDA0002486112330000078
indicating the wind turbine Machine n The radius of the blades of (a) a,
Figure BDA0002486112330000079
indicating the wind turbine Machine n The maximum wind energy utilization coefficient of the wind turbine,
Figure BDA00024861123300000710
representing wind turbine machines n In conjunction with the sum of the rotational inertia of the generator,
Figure BDA00024861123300000711
indicating the wind turbine Machine n The optimum torque coefficient in the optimum torque control,
Figure BDA00024861123300000712
indicating the wind turbine Machine n The optimum tip speed ratio; parameters herein
Figure BDA00024861123300000713
And
Figure BDA00024861123300000714
each wind turbine is a fixed constant of each wind turbine;
9, according to the active power mean value of the ideal state generator corresponding to each wind turbine
Figure BDA00024861123300000715
Obtaining a self-adaptive power adjustment fluctuation value aiming at the active power of a generator corresponding to each wind turbine in the wind turbine set; wherein the wind turbine Machine n The adaptive power regulation fluctuation value mark of the corresponding active power of the generator
Figure BDA00024861123300000716
Figure BDA00024861123300000717
Step 10, screening out the wind turbines for executing wind power generation according to the adaptive power adjustment fluctuation values of the wind turbines:
when the adaptive power adjustment fluctuation value of any wind turbine is within the preset fluctuation value range, selecting the wind turbine as a target wind turbine for executing wind power generation operation, and turning to step 11; otherwise, the any one wind turbine is not selected as a target wind turbine for executing the wind power generation work; the selected target wind turbine corresponds to the original number w in the wind turbine set of the embodiment, and the target wind turbine is marked as a Machine in the wind turbine set w ，1≤w≤N；
Step 11, calculating inflection point time of a real-time wind speed numerical value fitting curve, inflection point time of a real-time air density value fitting curve and inflection point time of a real-time generator active power numerical value fitting curve corresponding to each target wind turbine executing wind power generation work to obtain an inflection point time set corresponding to each target wind turbine; wherein:
in this embodiment, it is assumed that the target wind turbine Machine w The inflection points of the corresponding real-time fitting curve of the wind speed numerical value are U at the moment, and the mark of the U-th inflection point is
Figure BDA0002486112330000081
u∈[1,U](ii) a Target wind turbine Machine w E inflection points of the corresponding real-time fitting curve of the air density value are marked as
Figure BDA0002486112330000082
e∈[1,E](ii) a Target wind turbine Machine w G inflection points are arranged at the moment of the inflection point of the real-time fitted curve of the active power value of the corresponding generator, and the G-th inflection point moment is marked as
Figure BDA0002486112330000083
g∈[1,G](ii) a Target wind turbine Machine w The corresponding inflection point time set is marked as
Figure BDA0002486112330000084
Figure BDA0002486112330000085
Step 12, selecting a set of inflection point moments
Figure BDA0002486112330000086
The time period defined by the minimum inflection point time value and the maximum inflection point time value is used as the optimal working time period for the corresponding target wind turbine to execute the power generation work, and the target wind turbine is started to execute the power generation work at the initial time corresponding to the optimal working time period; and limiting the minimum value of the inflection point time of the optimal working time period as the starting time of the optimal working time period, and limiting the maximum value of the inflection point time of the optimal working time period as the ending time of the optimal working time period.
For example, in step 12, the selected target wind turbine Machine is aimed at w Set of inflection points corresponding thereto
Figure BDA0002486112330000087
Minimum value of inflection point time of all the inflection point time is
Figure BDA0002486112330000088
Set of inflection points in time
Figure BDA0002486112330000089
The maximum value of the inflection point time of all the inflection point times is
Figure BDA00024861123300000810
That time, the time period is now divided
Figure BDA00024861123300000811
Target wind turbine Machine w An optimum operation period for performing the power generating operation. Wherein the inflection point time
Figure BDA00024861123300000812
Is the starting time of the optimal operating period,
Figure BDA00024861123300000813
is the end time of the optimal operating period.
Specifically, in the embodiment, by calculating all inflection point time values of three fitting curves corresponding to each target wind turbine, and finally screening according to the inflection point time minimum value and the inflection point time maximum value, the optimal working time period for each target wind turbine to execute power generation work is selected, so that each target wind turbine executes the wind power generation work in the optimal working time period, the optimal time selection starting work is achieved, and the power generation efficiency of the whole wind turbine set is improved.

Claims (3)

1. An airflow generation wind power generation method is characterized by comprising the following steps:
step 1, acquiring spatial position coordinates of each wind turbine in a wind turbine set, forming a spatial position coordinate set for the wind turbine set, and calculating to obtain a set center coordinate of the wind turbine set; n wind turbines are arranged in the wind turbine set, and the nth wind turbine is marked as a Machine n Wind turbine Machine n Is recorded as a spatial position coordinate
Figure FDA0003890251650000011
The wind turbine has a cluster center marked O and a cluster center coordinate marked (x) o ,y o ,z o )：
Figure FDA0003890251650000012
Step 2, respectively calculating the distance between each wind turbine in the wind turbine set and the center of the wind turbine set to obtain distance weights respectively aiming at each wind turbine; wherein the wind turbine Machine n Distance from the center O of the unit is marked
Figure FDA0003890251650000013
For the wind turbine Machine n Is marked as a distance weight
Figure FDA0003890251650000014
Figure FDA0003890251650000015
Figure FDA0003890251650000016
Step 3, acquiring the wind speed numerical values of the height positions of the blades of each wind turbine in the wind turbine set respectively according to preset sampling moments in a preset time period, and respectively forming a wind speed numerical value sequence aiming at each wind turbine; wherein the preset time period is marked as T pre Wind turbine Machine n At a preset sampling time t i Is marked as the wind speed value
Figure FDA0003890251650000017
I represents a preset time period T pre Total number of preset sampling moments in the wind turbine n Is marked as a wind speed value sequence
Figure FDA0003890251650000018
Step 4, acquiring the air density values of the height positions of the blades of the wind turbines in the wind turbine set respectively according to the preset sampling moments within the preset time period, and respectively forming air density value sequences aiming at the wind turbines; wherein the wind turbine Machine n At a preset sampling time t i Is marked by the air density value
Figure FDA0003890251650000019
Wind turbine Machine n Is marked as air density value series
Figure FDA00038902516500000110
Step 5, collecting the generator active power numerical values of each wind turbine in the wind turbine set respectively according to the preset sampling time within the preset time period, and respectively forming a generator active power numerical value sequence aiming at each wind turbine; wherein the wind turbine Machine n At a preset sampling time t i Is marked as the value of active power generated
Figure FDA0003890251650000021
Wind turbine Machine n Is marked by a sequence of values of the active power of the generator
Figure FDA0003890251650000022
Step 6, respectively fitting to obtain a real-time wind speed numerical value fitting curve, a real-time air density value fitting curve and a real-time generator active power numerical value fitting curve which correspond to each wind turbine in the preset time period according to the wind speed numerical value sequence, the air density value sequence and the generator active power numerical value sequence which are respectively formed in the preset time period for each wind turbine; wherein the wind turbine Machine n At a preset time period T pre The corresponding real-time fitted curve of the wind speed numerical value, the real-time fitted curve of the air density value and the real-time fitted curve of the active power numerical value of the generator are respectively and correspondingly marked as
Figure FDA0003890251650000023
And
Figure FDA0003890251650000024
step 7, respectively calculating the wind speed mean value of the wind speed numerical sequence, the air density mean value of the air density value sequence and the generator active power mean value of the generator active power numerical sequence corresponding to each wind turbine in the wind turbine set; wherein the wind turbine Machine n Corresponding wind speed numerical value sequence
Figure FDA0003890251650000025
Is marked as the mean value of wind speed
Figure FDA0003890251650000026
Wind turbine Machine n Corresponding air density value sequence
Figure FDA0003890251650000027
Is marked as the mean value of air density
Figure FDA0003890251650000028
Wind turbine Machine n Corresponding generator active power numerical value sequence
Figure FDA0003890251650000029
Is marked as the mean value of the active power of the generator
Figure FDA00038902516500000210
Figure FDA00038902516500000211
Step 8, respectively obtaining the ideal state generator active power mean value of each wind turbine according to the obtained wind speed mean value, the air density mean value and the generator active power mean value of each wind turbine; wherein the wind turbine Machine n Is marked as the mean value of active power of ideal state generator
Figure FDA00038902516500000212
Figure FDA00038902516500000213
Figure FDA00038902516500000214
Wherein the content of the first and second substances,
Figure FDA00038902516500000215
indicating the wind turbine Machine n The radius of the blade of (a) is,
Figure FDA00038902516500000216
indicating the wind turbine Machine n The maximum wind energy utilization coefficient of the wind turbine,
Figure FDA00038902516500000217
indicating the wind turbine Machine n In conjunction with the sum of the rotational inertia of the generator,
Figure FDA00038902516500000218
indicating the wind turbine Machine n The optimum torque coefficient in the optimum torque control,
Figure FDA0003890251650000031
indicating the wind turbine Machine n The optimum tip speed ratio;
step 9, obtaining a self-adaptive power adjustment fluctuation value aiming at the active power of the generator corresponding to each wind turbine in the wind turbine set according to the average value of the active power of the ideal-state generator corresponding to each wind turbine; wherein the wind turbine Machine n The adaptive power regulation fluctuation value mark of the corresponding active power of the generator
Figure FDA0003890251650000032
Figure FDA0003890251650000033
Step 10, screening out the wind turbines for executing wind power generation according to the adaptive power adjustment fluctuation values of the wind turbines:
adaptive power regulation for any wind turbineWhen the fluctuation value is within the preset fluctuation value range, selecting any one wind turbine as a target wind turbine for executing wind power generation operation, and turning to step 11; otherwise, the any one wind turbine is not selected as a target wind turbine for executing the wind power generation work; the selected target wind turbine corresponds to an original number w in the wind turbine set, and the target wind turbine is marked as a Machine in the wind turbine set w ，1≤w≤N；
Step 11, calculating inflection point time of a real-time wind speed numerical value fitting curve, inflection point time of a real-time air density value fitting curve and inflection point time of a real-time generator active power numerical value fitting curve corresponding to each target wind turbine executing wind power generation work to obtain an inflection point time set corresponding to each target wind turbine; wherein:
target wind turbine Machine w The inflection points of the corresponding real-time fitting curve of the wind speed numerical value are U at the moment, and the mark of the U-th inflection point is
Figure FDA0003890251650000034
Target wind turbine Machine w E inflection points of the corresponding real-time fitting curve of the air density value are marked as
Figure FDA0003890251650000035
Target wind turbine Machine w G inflection points are arranged at the moment of the inflection point of the real-time fitted curve of the active power value of the corresponding generator, and the G-th inflection point moment is marked as
Figure FDA0003890251650000036
Target wind turbine Machine w The corresponding inflection point time set is marked as
Figure FDA0003890251650000037
Step 12, selecting an inflection point moment minimum value and an inflection point moment maximum value in each inflection point moment set, taking a time period defined by the inflection point moment minimum value and the inflection point moment maximum value as an optimal working time period for the corresponding target wind turbine to execute power generation work, and starting the target wind turbine to execute the power generation work at an initial time corresponding to the optimal working time period; and limiting the minimum value of the inflection point time of the optimal working time period as the starting time of the optimal working time period, and limiting the maximum value of the inflection point time of the optimal working time period as the ending time of the optimal working time period.
2. The method of claim 1, wherein the predetermined period of time is 365 days.
3. The method of claim 1, wherein each wind turbine in the wind turbine group has three blades.
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CN104021424A (en) * 2013-02-28 2014-09-03 国际商业机器公司 Method and device used for predicting output power of blower in wind field
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