CN108460228B - Wind power plant equivalence method based on multi-objective optimization algorithm - Google Patents

Wind power plant equivalence method based on multi-objective optimization algorithm Download PDF

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CN108460228B
CN108460228B CN201810233629.9A CN201810233629A CN108460228B CN 108460228 B CN108460228 B CN 108460228B CN 201810233629 A CN201810233629 A CN 201810233629A CN 108460228 B CN108460228 B CN 108460228B
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fan
equivalent
power plant
parameter
wind
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CN108460228A (en
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李坚
黄琦
王妮
胡维昊
王鹏
张真源
易建波
井实
蔡东升
桂勋
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University of Electronic Science and Technology of China
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/30—Circuit design
    • G06F30/36—Circuit design at the analogue level
    • G06F30/367—Design verification, e.g. using simulation, simulation program with integrated circuit emphasis [SPICE], direct methods or relaxation methods
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    • G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
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Abstract

The invention discloses a wind power plant equivalence method based on a multi-objective optimization algorithm, which divides fans into K machine groups through a K-means algorithm, and performs current collection system equivalence and fan equivalence on the interior of each machine group, wherein the fan equivalence method is mainly improved: firstly, selecting parameters to be identified by adopting a track sensitivity method based on wind speed fluctuation and fault conditions, then selecting an equivalent fan with the largest capacity as the fan to be identified, and finally identifying the parameters of the equivalent fan by adopting an NSGA-II multi-objective optimization algorithm, thereby obtaining a wind power plant equivalent model simultaneously suitable for wind speed fluctuation disturbance and fault disturbance.

Description

Wind power plant equivalence method based on multi-objective optimization algorithm
Technical Field
The invention belongs to the field of power systems, and particularly relates to a method for equivalence of a wind power plant based on a multi-objective optimization algorithm.
Background
With the advent of the new century, social economy has rapidly developed, the demand for energy has increased dramatically, and the problem of energy shortage has become more and more prominent. Data of the Chinese renewable energy society show that the installed capacity of wind power in China is gradually increased year by year from 2006 to 2016, and the accumulated installed capacity reaches 1.69 hundred million kilowatts in 2016. The world weather organization (WMO) estimates that the amount of wind energy available on earth is about 200 hundred million kilowatts, which is 10 times greater than the total amount of electricity that can be harnessed on earth. In the future social development, wind power generation plays a very important role, and wind power generation becomes an indispensable part of a power system in many countries.
With the continuous increase of the scale of the wind power plant, the randomness, the volatility and the uncertainty of wind resources and wind power bring challenges to the safety and the stability of a power system. The large wind power plant has numerous units and complex structure, and the problems of high-order model, complex calculation and the like can be caused by one-to-one depiction of each fan during detailed modeling. In order to analyze the safety and stability of a power grid containing a large-scale wind power plant, accurate and effective equivalent modeling on the wind power plant becomes an urgent problem to be solved.
The main disturbance of the wind power plant is divided into a wind speed fluctuation condition and a fault condition, and when a power system comprising a large wind power plant is subjected to simulation analysis, equivalent wind electric fields are required to have good equivalent effects under the two conditions. However, due to the fact that the time scales of two types of disturbance are different, the time scale of wind speed fluctuation is in the second level, and the time scale of a fault is in the millisecond level, the single target parameter identification result based on a certain disturbance condition cannot be necessarily suitable for another disturbance condition, and equivalence accuracy and effectiveness of an equivalent wind power plant when multiple disturbances occur simultaneously cannot be guaranteed.
For example, the document "Y.Wang, C.Lu, L.Zhu, et al," Comprehensive Modeling and Parameter Identification of Wind farm Based on Wide-Area Measurement Systems, "J.Mod.Power Syst. clean Energy (2016)4: 383" proposes a method for Modeling and Parameter Identification of Wind Farms Based on measurements of synchronous vector units. Firstly, selecting main parameters by adopting the track sensitivity to obtain an equivalent model, then identifying the parameters by adopting an improved genetic algorithm, and finally verifying the equivalent accuracy of the method under the conditions of fault and wind speed fluctuation. However, wind speed fluctuation and fault disturbance often coexist in an actual wind power plant, and an equivalent model obtained by parameter identification based on a certain disturbance in the article may generate an error under the condition of another disturbance, so that the equivalent accuracy and the effectiveness of the equivalent model in power system analysis are difficult to guarantee by a single-target optimization parameter identification method.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a method for wind power plant equivalence based on a multi-objective optimization algorithm, wherein equivalent fan parameters are identified through the multi-objective optimization algorithm, so that a wind power plant equivalence model suitable for two conditions of wind speed fluctuation disturbance and fault disturbance is obtained.
In order to achieve the aim, the invention provides a method for equivalence of a wind power plant based on a multi-objective optimization algorithm, which is characterized by comprising the following steps of:
(1) clustering wind turbine generator sets
Dividing the wind turbine sets with similar operation modes into the same cluster by adopting a K-means clustering algorithm and taking the initial wind speed of each fan as a clustering index to obtain K fan sets;
(2) current collecting system and the like
Establishing a line and transformer model by utilizing PSASP software, and equating parameters of the line and the transformer in the same cluster by adopting a capacity weighting method;
(3) fan equivalent
(3.1) establishing a fan equivalent model by using PSASP software;
(3.2) selecting parameters to be distinguished;
(3.2.1) setting wind speed fluctuation disturbance, taking an output curve at the outlet of the fan as an observation object, calculating the relative track sensitivity of each parameter in the equivalent model of the fan, and selecting the parameter with higher relative track sensitivity as a key parameter under the wind speed fluctuation disturbance;
(3.2.2) setting fault disturbance, taking an output curve at the outlet of the fan as an observation object, calculating the relative track sensitivity of each parameter in the equivalent model of the fan, and selecting a parameter with higher relative track sensitivity as a key parameter of the fault disturbance;
and (3.2.3) solving and merging the key parameters obtained in the steps (3.2.1) and (3.2.2) to obtain the final parameter to be identified.
(3.3) determining the equivalent fan to be identified
Representing the clustered K fan groups by K equivalent fans, selecting the equivalent fan with the largest capacity as the equivalent fan to be identified, and equating the remaining K-1 equivalent fans by adopting a capacity weighting method;
(3.4) performing parameter identification by using NSGA-II multi-objective optimization algorithm
(3.4.1) in the equivalent fan to be identified, taking the final parameter to be identified obtained in the step (3.2.3) as an optimization object, and establishing an objective function;
Figure BDA0001603313630000031
wherein N is1The total number of sampling points of the wind power plant output curve under the condition of wind speed fluctuation, N2The total number of sampling points of an output curve of a wind power plant under a fault condition is shown, P is active power output by the wind power plant, Q is reactive power output by the wind power plant, subscript E represents a wind power plant equivalent model, subscript D represents a wind power plant detailed model, subscript wind represents a wind speed fluctuation condition, subscript fault represents a fault condition, and P isE_windRepresenting the output active power of the wind power plant equivalent model under the condition of wind speed fluctuation;
(3.4.2) solving the established objective function through an NSGA-II multi-objective optimization algorithm to obtain an optimal leading edge solution set, and selecting a solution which enables the fan equivalent model to have the smallest error under the conditions of wind speed fluctuation disturbance and fault disturbance in the optimal leading edge solution set to serve as an optimal solution;
(3.5) updating the equivalent model of the fan
And (4) bringing the parameters to be identified corresponding to the optimal solution into a fan equivalent model to obtain a wind power plant equivalent model simultaneously suitable for wind speed fluctuation disturbance and fault disturbance.
The invention aims to realize the following steps:
the invention relates to a wind power plant equivalence method based on a multi-objective optimization algorithm, which divides fans into K machine groups through a K-means algorithm, and performs current collection system equivalence and fan equivalence on the interior of each machine group, wherein the fan equivalence method is mainly improved as follows: firstly, selecting parameters to be identified by adopting a track sensitivity method based on wind speed fluctuation and fault conditions, then selecting an equivalent fan with the largest capacity as the fan to be identified, and finally identifying the parameters of the equivalent fan by adopting an NSGA-II multi-objective optimization algorithm, thereby obtaining a wind power plant equivalent model simultaneously suitable for wind speed fluctuation disturbance and fault disturbance.
Meanwhile, the method for equating the wind power plant based on the multi-objective optimization algorithm further has the following beneficial effects:
(1) the key parameters selected under the two conditions of wind speed fluctuation and fault are combined to serve as the final parameters to be identified, so that the parameters to be identified have high sensitivity under two disturbances;
(2) only the equivalent fan with the largest capacity is selected for parameter identification, so that the problem of excessive identification parameters can be avoided, and the calculated amount and the running time of an algorithm program are effectively reduced;
(3) and the equivalent error under the conditions of wind speed fluctuation and fault is taken as two objective functions, and the NSGA-II multi-objective optimization algorithm is adopted to carry out parameter identification, so that the accuracy of the parameter identification result in operation under two disturbance conditions is ensured.
Drawings
FIG. 1 is a schematic structural diagram of a detailed model of an actual wind farm in Ningxia;
FIG. 2 is a schematic diagram of a portion of an external system architecture;
FIG. 3 is a flow chart of a method for wind power plant equivalence based on a multi-objective optimization algorithm;
FIG. 4 is a structural schematic diagram of an equivalent model of a wind power plant;
FIG. 5 is a schematic structural diagram of a permanent magnet direct-drive wind driven generator;
FIG. 6 is a Pareto optimal leading edge solution;
FIG. 7 is an equivalent effect diagram of the multi-objective optimization parameter identification method;
FIG. 8 is an equivalent effect plot of a single target (wind speed fluctuation) identification method;
fig. 9 is an equivalent effect diagram of a single target (fault) identification method.
Detailed Description
The following description of the embodiments of the present invention is provided in order to better understand the present invention for those skilled in the art with reference to the accompanying drawings. It is to be expressly noted that in the following description, a detailed description of known functions and designs will be omitted when it may obscure the subject matter of the present invention.
Examples
In this embodiment, a PSASP software is used to build an actual wind farm in Ningxia, and a schematic structural diagram of the actual wind farm is shown in fig. 1. The wind power plant comprises 66 1.5MW permanent magnet direct-drive wind driven generators, wherein the fans are boosted and converged on a PCC bus through a 0.62/35kV generator-end transformer, and then the structural schematic diagram of the external system connected with the external system through an 35/110kV wind power plant outlet transformer is shown in FIG. 2.
The wind speed fluctuation conditions are set as follows: the initial wind speed is 11m/s, and gusts occur from the 2 nd to the 6 th, with a maximum of 3 m/s. Considering wake effect and time lag influence, the input wind speed of each fan is obtained through calculation of a wake model. The simulation time duration is set to 20 s.
The fault conditions are set as: two-phase grounding short-circuit fault occurs at 50% of the line between the external system bus 9 and the bus 7, the fault time is set to be 2.0s, and 2.2s is cut off. The simulation duration is set to 5 s. In the following, we will describe in detail a method for wind farm equivalence based on a multi-objective optimization algorithm according to a certain practical wind farm in Ningxia, as shown in FIG. 3, including the following steps:
s1, clustering wind turbines
Considering that the wake effect in a large wind power plant has a large influence on the running state of a wind turbine generator, a K-means clustering algorithm is adopted, the initial wind speed of each fan is taken as a clustering index, 66 fans are divided into 3 classes, and clustering results are shown in table 1;
table 1 is a wind turbine cluster result table.
Clustering results Blower serial number
Class
1 1～3,6～11,13,19,23,25,28,30～34,45,51～53,57,61,64,66
Class 2 4,12,15,18,21,24,27,37～39,41,42,44,47,48,55,56,59,60,62,63,65
Class 3 5,14,16,17,20,22,26,29,35,36,40,43,46,49,50,54,58
TABLE 1
Therefore, the wind power plant can be represented by three equivalent wind turbines, the equivalent model structure of which is shown in fig. 4, and then the current collection system parameters and the wind turbine parameters in the model are mainly equivalent, which is explained in detail below.
S2, current collecting system, etc
The equivalent of the power collection system is mainly to perform equivalent on the line and the transformer in the system, and in the embodiment, the line and the transformer in the power collection system are equivalent by using a capacity weighting method;
s2.1, line equivalence
Equating the impedance and admittance of the line according to the principle that the voltage drop is not changed before and after equating, wherein the line capacitance can be ignored according to the following formula;
Figure BDA0001603313630000061
wherein m is the number of fans in the group, n is the number of fans in trunk-line branches in the group, and P isi、PjIs the active output of the ith and jth fans, ZkIs the impedance of the k-th line in the trunk branch, YiIs the admittance of the ith segment of the line.
S2.2, transformer equivalent
The capacity and the impedance of the transformer are equalized, and the formula is as follows:
Figure BDA0001603313630000062
wherein S isTCapacity of a single transformer, ZTIs a single transformer impedance.
S3, equivalent of fan
S3.1, establishing a fan equivalent model by utilizing PSASP software;
although the double-fed fan is the most widely applied machine type at present, with the increase of the single machine capacity of the wind generating set, the problem of the fault of the high-speed transmission part of the gear box in the double-fed fan is increasingly prominent, and the advantage of the permanent magnet direct-driven fan without a transmission mechanism is gradually highlighted. Under the background that a high-power current transformation technology and a high-performance permanent magnet material are developed and improved day by day, the situation that a permanent magnet direct-drive fan is adopted in a large range to build a field is the mainstream trend in the future.
Therefore, in this embodiment, an 11-type permanent magnet direct-drive wind turbine model in the PSASP software is used as an established equivalent model of the wind turbine, and the structure of the model is as shown in fig. 5, and the model mainly includes a wind turbine and a control module thereof, a converter and a control module thereof, and a generator module.
S3.2, selecting parameters to be identified;
in the identification process, too many parameters easily cause the problems of complex calculation, low efficiency, multiple solutions and the like, the influence capacity of different parameters on the dynamic characteristics of the system is inconsistent, and the parameters with weak sensitivity cannot cause larger influence on the dynamic characteristics of the system even if the parameters change larger values, so that the resource waste is caused in the calculation process. Therefore, it is necessary to select key parameters having strong influence on the dynamic characteristics of the system as required and perform key identification on the key parameters. In the embodiment, the parameter to be identified is selected from 69 parameters of the model 11 type permanent magnet direct-drive wind turbine.
S3.2.1, setting wind speed fluctuation disturbance, taking an output curve at the outlet of the fan as an observation object, calculating the relative track sensitivity of each parameter, and selecting the front N with larger relative track sensitivity1Taking 10 parameters as key parameters under the wind speed fluctuation disturbance;
s3.2.2, setting fault disturbance, taking the output curve at the outlet of the fan as an observation object, calculating the relative track sensitivity of each parameter, and selecting the front N with larger relative track sensitivity27 parameters are used as key parameters of fault disturbance;
in the above steps, the method for calculating the relative trajectory sensitivity of a certain parameter comprises:
Figure BDA0001603313630000071
Figure BDA0001603313630000072
wherein, TSRjIs the jth parameter thetajThe relative track sensitivity of the wind speed sensor is obtained, N is the total sampling point number of an output curve at the outlet of the fan, and N is taken as the sampling point number N under the condition of wind speed fluctuation1And taking N as the sampling point number N under the fault condition2，ΔθjIs a parameter thetajOf the change value of thetaj0Is a parameter thetajGiven value of (a), yi+Is a parameter thetajIncrease of Delta thetajValue of ith sampling point, y of output curve at outlet of rear fani-Is a parameter thetajDecrease delta thetajAnd the value of the ith sampling point of the output curve at the outlet of the rear fan, wherein K is 69 which is the total number of the parameters.
S3.2.3, obtaining the final parameter to be identified by merging the key parameters obtained in steps S3.2.1 and S3.2.2.
In the embodiment, active and reactive output curves at the outlet of a single fan are taken as observation targets, the parameter change rate is set to be 10%, parameter sensitivity analysis is respectively carried out under the condition of wind speed fluctuation and the condition of fault, and sampling points N are taken1＝2000，N2If the relative trajectory sensitivity of a certain parameter is too small to be nearly 0 in table 2, which indicates that the influence of the parameter on the dynamic response of the fan under the disturbance is negligible, the TSR value of the parameter is directly represented by 0.
Table 2 is a table of the parameter selection results to be identified;
Figure BDA0001603313630000073
Figure BDA0001603313630000081
TABLE 2
S3.3, determining equivalent fan to be identified
In step S1, the wind farm is divided into 3 groups, each group is represented by one equivalent fan, 16 parameters to be identified are selected for each equivalent fan in step S3.2, if each fan is to be identified, there are 48 parameters to be identified, too many parameters will cause the optimization algorithm to easily fall into a local optimal solution, and considering that the most capacity equivalent fan among three equivalent fans has the largest dynamic response to the equivalent wind farm, only the most capacity equivalent fan is subjected to parameter identification, and the remaining two equivalent fans adopt capacity weighting method equivalence, and the formula is as follows:
Figure BDA0001603313630000082
wherein S isi，Pi，QiRespectively, the capacity, active power and reactive power, theta, of the fans i in the groupi,jRepresenting the jth parameter in fan i.
S3.4, performing parameter identification by using NSGA-II multi-objective optimization algorithm
The NSGA-II multi-objective optimization algorithm aims to solve the problem that multiple objectives coexist and even conflict with each other. Usually, it is impossible to make multiple sub-targets simultaneously reach the best, and only the coordination and compromise processing can be performed on each sub-target to obtain a solution set, called Pareto solution set, which makes each sub-target reach the best as possible. In the conventional wind power plant equivalence method for single-target identification, the obtained equivalence model has poor effect when being applied to other disturbances based on a certain disturbance condition; and the key parameters obtained under the condition of wind speed fluctuation and the condition of failure cannot be completely decoupled, and the parameter set of the equivalent fan can be identified only under two disturbance conditions.
S3.4.1, in the equivalent fan to be identified, establishing a target function by taking the final parameter to be identified obtained in the step S3.2.3 as an optimization object;
Figure BDA0001603313630000091
s3.4.2, solving the established objective function through an NSGA-II multi-objective optimization algorithm to obtain an optimal leading edge solution set, and selecting a solution which enables the fan equivalent model to have the smallest error under the conditions of wind speed fluctuation disturbance and fault disturbance in the optimal leading edge solution set to serve as an optimal solution;
in this embodiment, 16 parameters in table 1 are identified, the obtained optimal leading edge solution set is shown in fig. 6, and then the solution with the minimum error under the wind speed fluctuation condition and the minimum error under the fault condition is selected as the optimal solution, that is, the final solution identified in the diagram.
S3.5, updating the equivalent model of the fan
And (4) bringing the parameters to be identified corresponding to the optimal solution into a fan equivalent model to obtain a wind power plant equivalent model simultaneously suitable for wind speed fluctuation disturbance and fault disturbance.
In this embodiment, the identification results of the 16 parameters corresponding to the final solution are introduced into the equivalent model of the wind farm, simulation is performed under the conditions of wind speed fluctuation and fault respectively, and comparison is performed with the detailed model of the wind farm, the active and reactive output curves under the two disturbance conditions are shown in fig. 7, and the output curves of the equivalent model obtained by the multi-objective optimization parameter identification method in fig. 7 under the two disturbance conditions can be better overlapped with the detailed model.
In order to embody the advantages of the method, the wind power plant equivalence is carried out by adopting a single-target optimization parameter identification method, namely, the single-target identification is carried out by adopting a particle swarm algorithm by respectively taking the wind power plant output curves of wind speed fluctuation and fault conditions as targets. FIG. 8 presents output curves of the equivalent model and the detailed model obtained by single-target optimization parameter identification under the condition of wind speed fluctuation, and FIG. 9 presents output curves of the equivalent model and the detailed model obtained by single-target optimization parameter identification under the condition of fault. As can be seen from the figure, the error of the single target (wind speed fluctuation) identification method is smaller than that of the multi-target identification method under the condition of wind speed fluctuation, but the single target (wind speed fluctuation) identification method is directly switched off under the condition of fault, so that the active and reactive outputs of the wind power plant are all reduced to 0, and the misoperation cannot occur during the analysis of the power system, which causes great error and is difficult to restore the dynamic characteristics of the wind power plant; also the single target (fault) identification method can only perform well under fault conditions and is too low accurate under wind speed fluctuation conditions. Therefore, the multi-objective optimization parameter identification method provided by the invention makes up the defects of a single-objective optimization parameter identification method, can ensure that the equivalent model can keep higher consistency with the response of the original wind power plant under two disturbance conditions, and embodies the equivalent accuracy and effectiveness of the multi-objective optimization parameter identification method.
Although illustrative embodiments of the present invention have been described above to facilitate the understanding of the present invention by those skilled in the art, it should be understood that the present invention is not limited to the scope of the embodiments, and various changes may be made apparent to those skilled in the art as long as they are within the spirit and scope of the present invention as defined and defined by the appended claims, and all matters of the invention which utilize the inventive concepts are protected.

Claims (3)

1. A wind power plant equivalence method based on a multi-objective optimization algorithm is characterized by comprising the following steps:
(1) clustering wind turbine generator sets
Dividing the wind turbine sets with similar operation modes into the same cluster by adopting a K-means clustering algorithm and taking the initial wind speed of each fan as a clustering index to obtain K fan sets;
(2) current collecting system and the like
Establishing a line and transformer model by utilizing PSASP software, and equating parameters of the line and the transformer in the same cluster by adopting a capacity weighting method;
(3) fan equivalent
(3.1) establishing a fan equivalent model by using PSASP software;
(3.2) selecting parameters to be distinguished;
(3.2.1) setting wind speed fluctuation disturbance, taking an output curve at the outlet of the fan as an observation object, calculating the relative track sensitivity of each parameter in the equivalent model of the fan, and selecting the parameter with higher relative track sensitivity as a key parameter under the wind speed fluctuation disturbance;
(3.2.2) setting fault disturbance, taking an output curve at the outlet of the fan as an observation object, calculating the relative track sensitivity of each parameter in the equivalent model of the fan, and selecting a parameter with higher relative track sensitivity as a key parameter of the fault disturbance;
(3.2.3) solving and combining the key parameters obtained in the step (3.2.1) and the step (3.2.2) to obtain the final parameter to be identified;
(3.3) determining the equivalent fan to be identified
Representing the clustered K fan groups by K equivalent fans, selecting the equivalent fan with the largest capacity as the equivalent fan to be identified, and equating the remaining K-1 equivalent fans by adopting a capacity weighting method;
(3.4) performing parameter identification by using NSGA-II multi-objective optimization algorithm
(3.4.1) in the equivalent fan to be identified, taking the final parameter to be identified obtained in the step (3.2.3) as an optimization object, and establishing an objective function;
Figure FDA0002982180270000021
wherein N is1The total number of sampling points of the wind power plant output curve under the condition of wind speed fluctuation, N2The total number of sampling points of an output curve of a wind power plant under a fault condition is shown, P is active power output by the wind power plant, Q is reactive power output by the wind power plant, subscript E represents a wind power plant equivalent model, subscript D represents a wind power plant detailed model, subscript wind represents a wind speed fluctuation condition, subscript fault represents a fault condition, and P isE_windRepresenting the output active power of the wind power plant equivalent model under the condition of wind speed fluctuation;
(3.4.2) solving the established objective function through an NSGA-II multi-objective optimization algorithm to obtain an optimal leading edge solution set, and selecting a solution which enables the fan equivalent model to have the smallest error under the conditions of wind speed fluctuation disturbance and fault disturbance in the optimal leading edge solution set to serve as an optimal solution;
(3.5) updating the equivalent model of the fan
Bringing the parameters to be identified corresponding to the optimal solution into a fan equivalent model to obtain a wind power plant equivalent model simultaneously suitable for wind speed fluctuation disturbance and fault disturbance;
the method for calculating the relative track sensitivity comprises the following steps:
Figure FDA0002982180270000022
Figure FDA0002982180270000023
wherein, TSRjIs the jth parameter thetajThe relative track sensitivity of the wind speed sensor is obtained, N is the total sampling point number of an output curve at the outlet of the fan, and N is taken as the sampling point number N under the condition of wind speed fluctuation1And taking N as the sampling point number N under the fault condition2，ΔθjIs a parameter thetajOf the change value of thetaj0Is a parameter thetajGiven value of (a), yi+Is a parameter thetajIncrease of Delta thetajValue of ith sampling point, y of output curve at outlet of rear fani-Is a parameter thetajDecrease delta thetajAnd the value of the ith sampling point of the output curve at the outlet of the rear fan, wherein K is the total number of the parameters.
2. The method for equating the wind power plant based on the multi-objective optimization algorithm as claimed in claim 1, wherein the capacity weighting method for equating the parameters of the lines and the transformers in the same cluster comprises the following steps:
2.1) line equivalence
Equating the impedance and admittance of the line according to the principle that the voltage drop is unchanged before and after equating, wherein the formula is as follows:
Figure FDA0002982180270000031
wherein m is the number of fans in the group, n is the number of fans in trunk-line branches in the group, and P isi、PjIs the active output of the ith and jth fans, ZkIs the impedance of the k-th line in the trunk branch, YiAdmittance of the ith section of line;
2.2) transformer equivalent
The capacity and the impedance of the transformer are equalized, and the formula is as follows:
Figure FDA0002982180270000032
wherein S isTCapacity of a single transformer, ZTIs a single transformer impedance.
3. The wind power plant equivalence method based on the multi-objective optimization algorithm according to claim 1, wherein the capacity weighting method for equating the remaining K-1 equivalent fans comprises the following steps:
Figure FDA0002982180270000041
wherein S isi，Pi，QiRespectively, the capacity, active power and reactive power, theta, of the fans i in the groupi,jRepresenting the jth parameter in fan i.
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Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103094920A (en) * 2013-01-06 2013-05-08 宁夏电力公司电力科学研究院 Equivalence method of direct-drive-type wind turbine generator wind power plant
CN103887815A (en) * 2014-02-21 2014-06-25 华南理工大学 Wind power plant parameter identification and dynamic equivalence method based on operation data
CN103942736A (en) * 2014-05-08 2014-07-23 华北电力大学 Wind power station multi-machine equivalent modeling method
US9683552B2 (en) * 2014-03-06 2017-06-20 General Electric Company System and method for robust wind turbine operation
CN106873359A (en) * 2015-12-11 2017-06-20 中国电力科学研究院 A kind of wind-powered electricity generation noise rating method based on cluster analysis and neutral net
CN107104462A (en) * 2017-05-18 2017-08-29 电子科技大学 A kind of method dispatched for wind power plant energy storage
CN107330546A (en) * 2017-06-14 2017-11-07 武汉大学 One kind considers wind power output and the probabilistic Optimization Scheduling of demand response
CN107341593A (en) * 2017-06-19 2017-11-10 东北电力大学 A kind of electric heating integrated system based on scene partitioning abandons wind consumption coordinative dispatching model
CN107482690A (en) * 2017-09-26 2017-12-15 广东工业大学 The electric power system dispatching optimization method and system of wind-powered electricity generation and electric automobile cooperative scheduling
CN107579546A (en) * 2016-11-10 2018-01-12 中国能源建设集团广东省电力设计研究院有限公司 Marine wind electric field optimal reactive power allocation method based on double-fed fan motor unit blower fan topological structure
CN107679679A (en) * 2017-11-20 2018-02-09 国网辽宁省电力有限公司电力科学研究院 Cogeneration machine unit scheduling operation method

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106058937B (en) * 2016-07-29 2018-05-08 河海大学 A kind of power distribution network broad sense load modeling method of the wind power plant containing direct-drive permanent-magnetism
CN107689638B (en) * 2017-09-28 2020-11-06 中国电力科学研究院 Transient coordination control method for wind power-containing power system based on phase trajectory analysis

Patent Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103094920A (en) * 2013-01-06 2013-05-08 宁夏电力公司电力科学研究院 Equivalence method of direct-drive-type wind turbine generator wind power plant
CN103887815A (en) * 2014-02-21 2014-06-25 华南理工大学 Wind power plant parameter identification and dynamic equivalence method based on operation data
US9683552B2 (en) * 2014-03-06 2017-06-20 General Electric Company System and method for robust wind turbine operation
CN103942736A (en) * 2014-05-08 2014-07-23 华北电力大学 Wind power station multi-machine equivalent modeling method
CN106873359A (en) * 2015-12-11 2017-06-20 中国电力科学研究院 A kind of wind-powered electricity generation noise rating method based on cluster analysis and neutral net
CN107579546A (en) * 2016-11-10 2018-01-12 中国能源建设集团广东省电力设计研究院有限公司 Marine wind electric field optimal reactive power allocation method based on double-fed fan motor unit blower fan topological structure
CN107104462A (en) * 2017-05-18 2017-08-29 电子科技大学 A kind of method dispatched for wind power plant energy storage
CN107330546A (en) * 2017-06-14 2017-11-07 武汉大学 One kind considers wind power output and the probabilistic Optimization Scheduling of demand response
CN107341593A (en) * 2017-06-19 2017-11-10 东北电力大学 A kind of electric heating integrated system based on scene partitioning abandons wind consumption coordinative dispatching model
CN107482690A (en) * 2017-09-26 2017-12-15 广东工业大学 The electric power system dispatching optimization method and system of wind-powered electricity generation and electric automobile cooperative scheduling
CN107679679A (en) * 2017-11-20 2018-02-09 国网辽宁省电力有限公司电力科学研究院 Cogeneration machine unit scheduling operation method

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
Comprehensive modeling and parameter identification of wind farms based on wide-area measurement systems;Yinfeng WANG 等;《J. Mod. Power Syst. Clean Energy》;20161231;第383–393页 *
基于改进诱导有序加权算子的风电功率预测;何亚 等;《可再生能源》;20170131;第35卷(第1期);第86-91页 *

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