CN110826927B - Wind power plant available inertia evaluation method - Google Patents
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Abstract
The invention discloses a method for evaluating available inertia of a wind power plant, which comprises the following steps: based on the space-time distribution characteristic of the wind field average wind speed under the influence of physical factors, and by utilizing a mixed Copula function, constructing an instantaneous wind speed conditional probability distribution model under the influence of atmospheric turbulence; an estimation model of available inertia kinetic energy and inertia power increment of the wind turbine is established in an operation mode under the virtual inertia control of the double-fed wind turbine generator, and the operation working conditions of all wind turbines of the wind field in actual operation are additionally considered, so that an interval evaluation curve of the available inertia of the full wind field under a certain confidence coefficient is obtained. The confidence interval evaluation result provided by the application has smaller error and higher reliability.
Description
Technical Field
The invention belongs to the technical field of wind power evaluation, and particularly relates to a method for evaluating available inertia of a wind power plant.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
Wind power generation is one of the most developed new energy power generation forms at present due to the characteristics of cleanness, high efficiency, reproducibility and the like, but as the permeability of wind power in a power grid is continuously improved, a large number of thermal power generating units are replaced by the wind power, the available rotational inertia of the whole power system is continuously reduced, and the frequency response capability is reduced. Under the condition that the ever-decreasing thermal power generating units are gradually difficult to independently bear the ever-increasing frequency modulation pressure, the power grid requires the wind power generating units to have the capacity of participating in system frequency adjustment to a certain extent. The capability comprises frequency response modes of inertia response, primary frequency modulation, secondary frequency modulation and the like in various time scales.
The invention finds that researches on wind turbine generator participating in inertial response by scholars at home and abroad are mostly focused on optimizing a fan frequency modulation control strategy, and the literatures' Morren J, Haan S W H D, Kling W L, et al.
Aiming at the phenomenon, a novel wind power plant virtual inertia cooperative control strategy [ J ] is adopted in the literatures of Chengyou, Wanggang, waiqiao, Paishingjun, Jiangtao and Huanghe, the power system automation 2015, 39(05):27-33 ] introduces the concept of a fan rotor kinetic energy evaluation factor, and coordinates the mode of each fan participating in frequency response according to the parameter, so that the aim of inhibiting the frequency secondary falling is fulfilled.
The document 'Diileo, Yi Shang Yao, Wang Tong Xiao, Jiang Ji Ping, Cheng Famin, Si Jun Cheng' combines the frequency control strategy [ J ] of a double-fed fan of overspeed standby and simulated inertia power grid technology, 2015, 39(09): 2385-.
The documents "Wu L, Infield D.Power system frequency management tables [ C ]// reusable Power Generation reference. IET, 2013" and "Wu L, Infield D.Toware an assessment of a Power system frequency response-modeling aggregate response [ J ]. IEEE Transactions on Power Systems,2013, 28(3): 2283. 2291, a method for estimating the wind farm's capacity by a virtual inertia-based approach based on the principle of virtual inertia distribution under the virtual inertia-based process model is proposed.
On the basis of the research, "Wu L, Infield D.A robust approach to accessing combined drop and inertia response from wind and plant [ C ]// Renewable Power Generation conference. IET, 2014" comprehensively evaluates the joint frequency modulation effect of virtual inertia control and droop control in the inertia response process, and the result shows that the coordinated operation of the two control modes can improve the output capacity of the active power of the wind power plant and improve the frequency modulation performance of the wind power plant.
Furthermore, the strategy in the document "L ee J, Muljadi E, Sorensen P, et al, reusable kinetic-based inertial control of a DFIG wind power plant [ J ]. IEEE Transactionson Sustainable Energy,2015: 1-10" is to adjust the gains of the virtual inertia control loop and the droop control loop in the wind turbine control system by evaluating the available inertial Kinetic Energy (KE) of the wind turbine.
Therefore, the above researches all propose methods for determining the available inertia of the wind field under the inertia control strategy. However, the proposed method neglects the influence of environmental factors such as wake effect and wind shear effect, and does not consider the spatial and temporal distribution characteristics of the wind speed of each fan in the wind field and the wind speed fluctuation characteristics under the action of atmospheric turbulence in the inertial response process. Meanwhile, the influence of the fault shutdown state of the fan on the available inertia of the fan during real-time operation is not taken into account in each algorithm, and the difference value between the obtained evaluation result and the actual available inertia value is large. Therefore, it is necessary to construct an evaluation method for inertia available in consideration of wind speed conditions and operating conditions of each fan in a wind farm.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides the wind power plant available inertia evaluation method, which is a probabilistic evaluation algorithm considering the wind speed distribution of the fan and the available inertia of the unit operation condition, can reduce the error between an algorithm evaluation result and an actual value, and provides a high-reliability wind power plant available inertia evaluation result for the operation scheduling of the power system.
In order to achieve the above object, one or more embodiments of the present invention provide the following technical solutions:
a wind farm available inertia assessment method comprises the following steps:
based on the space-time distribution characteristic of the wind field average wind speed under the influence of physical factors, and by utilizing a mixed Copula function, constructing an instantaneous wind speed conditional probability distribution model under the influence of atmospheric turbulence;
an estimation model of available inertia kinetic energy and inertia power increment of the wind turbine is established in an operation mode under the virtual inertia control of the double-fed wind turbine generator, and the operation working conditions of all wind turbines of the wind field in actual operation are additionally considered, so that an interval evaluation curve of the available inertia of the full wind field under a certain confidence coefficient is obtained.
According to the further technical scheme, the position distribution and time change characteristics of wind energy in a wind field are obtained by analyzing three physical influence factors including a wake effect, a wind shear effect and a time delay effect, and a short-time wind speed condition probability distribution model under the influence of atmospheric turbulence is constructed by utilizing the advantages of a Copula function in correlation analysis.
According to the further technical scheme, data samples of wind field second-level wind speed, turbulence intensity and average wind speed are obtained, an edge distribution function of each variable is established by utilizing a kernel density estimation method, a proper Copula function is selected, relevant parameters are estimated through a maximum likelihood estimation method, and a ternary combined distribution function is obtained.
According to the further technical scheme, the wind turbine generator system can be divided into a starting area, a maximum power tracking area, a constant rotating speed area, a constant power area and a cut-out wind speed area according to the conditions of capturing active power and rotor rotating speed of the wind turbine generator system under the full wind condition.
According to the further technical scheme, the available inertia of the wind turbine is divided into available inertia kinetic energy stored on the wind turbine and available inertia power increment provided by the inertia kinetic energy in inertia response from the aspects of energy and power.
According to the further technical scheme, the available inertia evaluation algorithm can divide the available inertia of a single fan in the wind field into three states according to the fan fault condition and the wind speed partition condition, wherein the three states are respectively non-available inertia, constant available inertia and variable available inertia;
the states of unavailable inertia and constant available inertia are represented by a single probability value, and the variable available inertia states represent the distribution condition of the available variables of the wind turbine through continuous probability density functions.
In the further technical scheme, in the evaluation method, the wind speed relation only exists among the fans in the wind field under the influence of wake effect, and the available inertia of each fan is processed according to independent random variables when the interval evaluation of the algorithm is carried out under the condition that the wind speed distribution is known.
According to a further technical scheme, the steps of acquiring the available inertia confidence interval of the full wind field are as follows:
step1, respectively obtaining probability density functions f (E) and f (P) of the available inertia of each wind turbine generator set when the wind farm runs in real time according to the available inertia evaluation method of the wind turbines;
step2, integrating f (E) and f (P) to obtain probability distribution functions phi (E) and phi (P) of the available inertia;
step3, calculating the available inertia distribution function by using a binary search-numerical integration method
And
inertia values corresponding to the fans j at the probability values, namely confidence intervals of the available inertia of the fans j under the confidence α are respectively obtained;
and Step4, superposing the available inertia of each fan of the wind field to obtain a confidence interval of the available inertia of the full wind field.
The invention discloses an evaluation system for available inertia of a wind power plant, which comprises the following components:
the distribution model establishing module is used for establishing an instantaneous wind speed conditional probability distribution model under the influence of atmospheric turbulence by utilizing a mixed Copula function based on the space-time distribution characteristic of the wind field average wind speed under the influence of physical factors;
and the solving module is used for establishing an estimation model of available inertia kinetic energy and inertia power increment of the fan in an operation mode under the virtual inertia control of the double-fed wind turbine generator, additionally considering the operation working conditions of all fans of the wind field in actual operation, and obtaining an interval evaluation curve of the available inertia of the full wind field based on a certain confidence coefficient.
The above one or more technical solutions have the following beneficial effects:
aiming at the problem that the inertia response capability is lack of accurate estimation when a wind power plant participates in frequency modulation service, a wind field available inertia probabilistic evaluation method considering wind speed distribution of a wind field and operation conditions of a fan is provided. From both the energy and power aspects, an evaluation curve based on the wind field available inertial kinetic energy and available inertial power increments at a certain confidence level is obtained.
The confidence interval evaluation result provided by the application has smaller error and higher reliability.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
FIG. 1 is a schematic diagram of two-machine wake linear expansion according to an embodiment of the present invention;
FIG. 2 is a flow chart of a wind field and wind speed joint distribution function construction according to an embodiment of the present invention;
FIG. 3 is a schematic view illustrating the operation status of a DFIG unit under full wind conditions according to an embodiment of the present invention;
FIG. 4 is a schematic layout diagram of wind power plant fans according to an embodiment of the present invention;
FIG. 5 is a graph of wind velocity versus inertia available to the wind farm at 90% confidence according to an embodiment of the present invention;
FIG. 6 is a confidence interval diagram of available inertial kinetic energy of a wind farm when the confidence of the embodiment of the invention is 90%;
FIG. 7 is a graph of confidence intervals of increment of available inertial power of a wind farm when the confidence of the embodiment of the invention is 90%;
FIG. 8 is a graph illustrating wind farm available inertia estimation at different confidence levels according to an embodiment of the present invention;
FIG. 9 is a statistical chart of simulation results according to an embodiment of the present invention.
Detailed Description
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The embodiments and features of the embodiments of the present invention may be combined with each other without conflict.
The general idea provided by the invention is as follows:
the wind turbines in the wind power plant generally adopt a determined inertial response strategy, but due to the influence of complex environmental factors such as wake flow, turbulence and the like, the wind speed space-time distribution and real-time operation conditions of all wind turbines in the plant have single-machine difference, and a large error may exist between the available inertial value and the nominal value of the wind power plant. Therefore, a probabilistic evaluation algorithm considering the wind speed distribution of the fan and the available inertia of the unit operation condition is provided. The method comprises the steps of establishing an instantaneous wind speed conditional probability distribution model under the influence of atmospheric turbulence by researching the space-time distribution characteristic of the wind field average wind speed under the influence of physical factors and utilizing a mixed Copula function. An estimation model of available inertia kinetic energy and inertia power increment of a fan is established in an operation mode under virtual inertia control of a doubly-fed wind generator set (DFIG), and operation conditions of all fans of a wind field in actual operation are additionally considered, so that an interval evaluation curve of the available inertia of the full wind field under certain confidence coefficient is obtained. And taking the structure and the operation data of the actual wind power plant as examples to evaluate the available inertia of a single wind power plant, building a wind power plant model through a Simulink simulation platform, and verifying the validity of the confidence interval evaluation result by using statistical data.
Example one
The embodiment discloses an available inertia evaluation method for a wind power plant. On the basis, the inertia release condition of virtual inertia control in the frequency response of the power grid is analyzed, and the probabilistic evaluation method of the real-time available inertia of the wind turbine generator is further provided from the two aspects of energy and power.
Firstly, wind field wind speed modeling based on a mixed Copula function is introduced, and wind field wind speed distribution is an important factor influencing the running condition of a fan. Therefore, the position distribution and the time variation characteristic of the wind energy in the wind field can be obtained by analyzing three physical influence factors of the wake effect, the wind shear effect and the time delay effect. And further, a short-time wind speed conditional probability distribution model under the influence of atmospheric turbulence is constructed by utilizing the advantages of the Copula function in correlation analysis.
Regarding the Jensen wake model, the wake effect describes the phenomenon that the wind speed decreases when natural wind blows from an upstream fan to a downstream fan. At present, a plurality of models are provided for wake effect simulation, the most widely used model is a Jensen wake model, and the calculation basis is a fan wake expansion theory, namely under the condition of not considering turbulence influence, the influence of wake is approximately considered to be in a linear expansion trend along with distance, and natural wind is diffused in a conical shape after passing through a fan wind wheel and is uniformly distributed along the section. A schematic diagram of a two-machine linear dilation is shown in fig. 1.
Comprehensively considering the wake effect of each fan in the wind field on the fan j, the method comprises
In the formula: v. of0Representing the natural wind speed of an upstream unit; b (j, k) represents the wake influence coefficient of the fan k on the fan j, and the shielding area A of the unit k on the unit jjkSwept area A of the sum unit jjDetermining the ratio of (A) to (B); v. ofwkThe wind speed after k wake flows of the unit.
Regarding the wind shear effect, the wind shear effect refers to the phenomenon that the wind speed increases along with the increase of the vertical height, and for wind turbines in complex terrains such as mountain wind farms, the wind shear effect has a significant influence on the wind speed at the height of the hub. Wind speed calculations taking into account the wind shear effect typically use an exponential model:
in the formula: v (z) is the wind speed at z-height; v. ofhRepresenting the wind speed at the hub height, α is the wind shear coefficient.
Regarding the time delay effect, the real-time wind condition of the wind power plant is transmitted from the upwind unit to the downwind unit with obvious time delay, and under the condition of considering the wake effect, the time delay phenomenon that the wind speed is influenced by the wake is more obvious. When the delay time of the wind speed from the wind power plant end to the fan j is tau, the wind speed at the fan j under the time delay effect is tau in the combination formula (1)
The Copula function is a connection function describing multivariate dependence relationship, is mainly used for probability measurement space theory at first and then is used for determining nonparametric estimation among random variables after improvement, and can further accurately describe the probability distribution condition of the instantaneous wind speed under the influence of atmospheric turbulence by utilizing the characteristics of the Copula connection functionnComprises the following steps:
F(x1,x2,...,xn)＝C[F1(x1),F(x2),...,F(xn)](5)
in the formula: f. ofi(xi) Is a random variable xiAn edge density function of; c is Copula probability density function, let ui＝Fi(xi) Then c can be represented as
The correlation structure of the mixed Copula function is more flexible than that of a single Copula function, and can reflect correlation modes among different random variables. The prior art document proposes the concept of a two-way hybrid Copula function, which can be similarly obtained as
In the formula: omega1，ω2，ω3For the weight coefficient of each Copula function, satisfy omega1+ω2+ω3α, β and gamma are the correlation coefficients of the respective Copula functions.
Regarding the wind speed probability distribution model, the literature has been accurate depiction of the second-level instantaneous wind speed in the related research of wind field wind speed modeling. In the prior art, instantaneous wind speed waveforms considering various influence factors such as turbulence, white noise and the like are simulated through simulation. On the basis, the wind speed of the wind field has larger fluctuation under the action of atmospheric turbulence in real-time operation, namely the wind speed at the hub height of the wind turbine is considered to be changed in inertial response. Therefore, the application proposes to establish a ternary joint distribution function comprehensively considering the influence of the instantaneous wind speed, the atmospheric turbulence intensity and the average wind speed by using a hybrid Copula function, and the construction flow is shown in fig. 2.
The Kernel Density Estimation (KDE) is one of the widely applied nonparametric estimation methods at present, and in step2, the influence of different kernel functions in the KDE on the nonparametric estimation result is small, and the probability density function which can be constructed through the KDE
In the formula: n is the number of samples; h represents a window width; k (-) represents a kernel function; x is the number ofiIs the data sample point i. While the gaussian kernel function K () is used herein.
The instantaneous wind speed v and the average wind speed can be obtained by carrying out KDE processing on the wind speed and the meteorological data of the wind power plant
And the edge density functions f (v), f (i) and of the turbulence intensity i
Obtaining the required edge distribution functions F (v), F (i) and F (i) by integrating the density function
Clayton-Copula, Gumbel-Copula and Frank-Copula are suitable for describing the dependence of the lower tail, the upper tail and the symmetric data, respectively, in the family of Archimedes functions. Thus, steps 3 and 4 combine the advantages of the three Copula functions to establish a ternary hybrid Copula function of wind field wind speed as
In the formula: theta1，θ2，θ3Three classes of Copula functions relating to V, I and
correlation coefficients of three random variables.
Finally, step 5 is that a ternary combined density function describing the instantaneous wind condition of the wind farm can be obtained through the formulas (6), (7) and (11)
The direct purpose of constructing the ternary distribution function of the instantaneous wind speed, the turbulence intensity and the average flying speed of the wind power plant is to base the establishment of the instantaneous wind speed conditional probability distribution model of the formula (13) so as to establish a model suitable for describing the second-level fine wind speed condition in the inertial response process, thereby more accurately estimating the inertia condition of the wind power plant.
The subsequent multivariate distribution model is mainly substituted in formula (13)
And obtained by the same method
In (1).
During inertial response, for a wind turbine generator which runs in real time, the condition probability distribution condition of the instantaneous wind speed is determined under the condition of known average wind speed and turbulence intensity. From this, a probability density function based on the instantaneous wind speed at a specific average wind speed and turbulence intensity can be given
In the formula:
the conditional distribution function is expressed, and the derivation process is shown as equation (14). Wherein the content of the first and second substances,
the combined density function is obtained through the combined density function construction process.
Based on the probability density function of the instantaneous wind speed under the specific average wind speed and the turbulence intensity, the probability distribution condition of the instantaneous wind speed in the wind field in real-time operation can be more accurately described, so that a probability distribution model of the available inertia of a single machine and a full wind field is established according to the distribution of the wind speed. That is, of formulae (26) and (27)
And
two formulas are shown.
Available inertia assessment methods: virtual inertia control: with the increasing of the weight of the wind wheel of the double-fed wind turbine generator, the total inertial kinetic energy stored in the structures such as the wind wheel, the rotor and the like becomes huge in normal operation. The rotational inertia of the fan is fully excavated, so that the lowest point (highest point) of frequency fluctuation during power grid frequency accidents can be greatly reduced, and the frequency recovery speed is increased. At present, an inertia control link is applied to a wind turbine generator through power electronic equipment, so that the wind turbine generator can release or store the rotational kinetic energy of a rotating part of a fan like a synchronous machine when the wind turbine generator faces a frequency accident, and the control mode is called as virtual inertia control. Under the condition of not considering the active load shedding operation of the fan, when the DFIG adopts virtual inertia control, the inertia kinetic energy stored by the fan is
In the formula: omega is the rotor speed of the fan in normal operation; j is the total moment of inertia of various rotating structures including a generator rotor, a wind wheel and the like.
From the power perspective, the inertial power support which can be provided by the wind turbine in real time can be obtained by differentiating the inertial kinetic energy stored in the rotating rotor of the doubly-fed wind turbine and the related coupling rotating component
In the formula: omeganBeing fansRated rotating speed; f is the grid frequency.
The rotating speed of the fan rotor under the full wind condition is as follows: as shown in a schematic diagram 3 of an operating condition of the doubly-fed generator set, the doubly-fed generator set can be divided into a starting region, a Maximum Power Point Tracking (MPPT) region, a constant rotating speed region, a constant power region and a cut-out wind speed region according to the conditions of capturing active power and rotor rotating speed of the wind turbine set under the full wind condition.
The corresponding probability values are calculated for the different partitions according to the wind speed segments, see equation (30).
The obtained probability values of the wind speed regions are mainly used in equations (28), (29) and (31), and the available inertia of the wind field is classified through the wind speed regions, so that the available inertia probability distribution condition of the whole wind field is described.
I & V starting area and cut-out wind speed are above: at the moment, the fan cannot normally run in a grid-connected mode due to the limitation of the wind speed condition. Therefore, in the wind speed area, the wind turbine generator does not participate in the frequency response of the power system, and no available inertia exists.
II, maximum power tracking area: as shown in fig. 3, at a medium and low wind speed, the output power is at the highest point of the output curve when the fan normally operates through the MPPT control of the fan. Mechanical power P captured by a fanmAs shown in the following formula
In the formula: ρ is the air density; cpThe wind energy conversion efficiency is obtained; λ is tip speed ratio, defined as λ ═ ωtR/v, wherein: omegatThe rotating angular speed of the impeller of the fan, β the pitch angle and A the swept area of the fan.
When the fan works in the area II, the wind energy conversion efficiency CpAt maximum, at this point, pitch angle β is 0, and fan operation satisfies equation (18)
λ＝λopt(19)
Obtaining the optimum tip speed ratio lambda by the above formulaoptSpeed ratio of blade tipThe linear relation between the rotating speed and the wind speed of the fan can be obtained by defining
In the formula: omega is the rotating speed of the rotor of the wind driven generator; and G is the transmission coefficient of the gearbox of the fan.
And III, a constant rotating speed area, wherein the rotating speed of the fan in the constant rotating speed area is slightly changed along with the increase of the wind speed, but the capture power of the fan still shows an ascending trend at the moment. An equation of the approximately linearized rotor rotational speed can be derived from the fan power curve relationship proposed in the prior art, wherein the variables correspond to fig. 3.
And IV, in the constant power area, when the wind speed at the hub of the fan exceeds the rated wind speed, the output power of the fan cannot be increased continuously, the rotating speed and the output power of the rotor of the wind driven generator are constant, and the rotating speed of the rotor is at the highest rotating speed.
ω＝ωmax(22)
According to the available inertia evaluation method, when the fans in the wind power plant are in real-time grid-connected operation, factors influencing the available inertia of the fans in the wind power plant are mainly classified into two aspects of wind conditions of the wind power plant and operation conditions of the fans. The propagation of wind in the station is analyzed through a physical model, and the instantaneous fluctuation characteristics of the wind speed of each fan in the inertial response process are described in a probability distribution mode. In this section, the operating condition of the fan is introduced into the available inertia evaluation algorithm of a single fan, and the fault condition of a wind field unit and the condition of an inertia control strategy of the fan in the operating process are mainly considered. Meanwhile, the available inertia of the wind turbine is divided into the available inertia kinetic energy stored on the wind turbine and the available inertia power increment provided by the inertia kinetic energy in the inertia response from two aspects of energy and power in the analysis of the application.
Under the normal operation condition (no load shedding control), the rotating speed of the fan is in different areas along with the change of the wind speed condition, and under the consideration of the wake flow influence, different rotating speed areas in a wind fieldThe available inertia of the fan has great difference due to different wind speed conditions. Therefore, starting from the available inertia of a single fan, the estimation accuracy can be remarkably improved by a mode of estimating the available inertia of the full wind field. For a single doubly-fed wind turbine generator, the rotational kinetic energy stored in the rotating mechanism of the single doubly-fed wind turbine generator cannot be completely released in the inertial response process, because when the rotating speed of the rotor of the wind turbine generator is reduced to the lowest allowable rotating speed omegamin(typically 0.7 p.u.) the blower will be taken out of service. Thus, the available rotor inertia kinetic energy of a certain fan j in the wind farm is
As can be seen from FIG. 3, the operating conditions of the fans have large differences at full wind speed. After the fan is in grid-connected operation, the double-fed fan in a medium-low wind speed state can keep the MPPT state (region II) operation, but the E state needs to be ensuredj>0, actual wind speed at the hub of the fan
When (region II)2) The fan will begin to have inertial response capability. And when the rated wind speed is reached, the rotating speed of the rotor of the fan j is kept constant, and the available inertia is a determined value. Finally, the fan in the cut-out wind speed area cannot normally operate, and the fan loses the inertia response capability, namely, no available inertia exists. In summary, the available inertial kinetic energy of the fan j under the full wind condition can be divided into four stages as shown in the following formula according to the wind speed condition.
Similarly, in combination with equations (16) and (20-22), from a power perspective, the available inertial power increment for a wind turbine j under virtual inertial control at full wind speed conditions can be expressed as
Here is obtainedThe available kinetic energy and power support distribution models g (v) and h (v) of the single fan j are used in equations (28) and (31), i.e. the inverse function g-1(Ej) And h-1(Pj)。
Two relations g (v) and h (v) of the available inertia of the fan j to the wind speed are given in the analysis, and it can be seen that the available inertia of a single fan can be determined by the wind speed regardless of the operation state of the unit. Meanwhile, the wind speed of the fan j in the inertial response can be controlled by
And describing the distribution of the functions. Therefore, the probability density function model of the available inertia kinetic energy and the available inertia power increment of the fan j under the specific average wind speed and the specific turbulence intensity in real-time operation can be obtained by combining the random variable inverse function probability density theory
In addition, under the condition that the installed capacity of a single fan is continuously increased, the running state of the fan cannot be ignored in the real-time evaluation process, and the inertia output capacity of the whole wind field is directly reduced due to the fault of the single high-capacity wind turbine. Suppose that the failure rate of fan j is p obtained from fan statistical datajThe probability density function of the available inertia of the single machine j is
pξ＝pI+pII1+pIV+pV(29)
In the formula: p is a radical ofI,pII1,pIV,pVIndicating that the wind turbine operates in the corresponding wind speed regions I, II shown in FIG. 31Probability values for IV and V. For any wind speed region x, pxIs expressed as
It can be seen that the available inertia evaluation algorithm can divide the available inertia of a single fan in a wind field into three states, namely, no available inertia, constant available inertia and variable available inertia according to the fan fault condition and the wind speed partition condition. The states of unavailable inertia and constant available inertia are represented by a single probability value, and the variable available inertia states represent the distribution condition of the available variables of the wind turbine through continuous probability density functions. Accordingly, from the power release point of view, the distribution of available inertia is
The evaluation method mainly processes the wind speed model by two points: 1) the probability values corresponding to the wind speed partitions are obtained by applying equation (30). 2) Applied to formulae (28) and (31)
And
to describe EjAnd PjThe probability density value of (c).
In the whole evaluation model processing, firstly, the available inertia of a single fan under each partition condition is obtained by analyzing the condition of the virtual inertia control strategy of the fan, namely, the equations (24) and (25). And establishing the distribution of the available inertia of the single fan under the condition of considering the fault probability of the fan and the wind speed probability distribution (28) and (31) on the basis of the fault probability of the single fan. And finally, obtaining confidence interval results of the available inertia of the full wind field by using a superposition and summation mode, namely equations (32) - (35).
Available inertia confidence interval: compared with the deterministic evaluation result, the wind farm available inertia evaluation result given in the form of a confidence interval under a certain confidence degree is more referential and instructive to system scheduling. In the evaluation algorithm, the wind speed relation only exists among all the fans in the wind field under the influence of wake effect, and under the condition that the wind speed distribution is known, the available inertia of all the fans is processed according to independent random variables during the interval estimation of the algorithm. Thus, the steps of obtaining the available inertia confidence interval for the full wind farm are as follows:
and Step1, respectively obtaining probability density functions f (E) and f (P) of the available inertia of each wind turbine generator when the wind power plant runs in real time according to the available inertia evaluation method of the wind turbine j.
Step2 obtaining probability distribution functions phi (E) and phi (P) of available inertia by integrating f (E) and f (P)
Step3, calculating the available inertia distribution function by using a binary search-numerical integration method
And
inertia values corresponding to the fans j at the probability values, namely confidence intervals of available inertia of the fans j under the confidence α are respectively obtained
ΩEj＝[Ejd,Eju](32)
ΩPj＝[Pjd,Pju](33)
Step4, superposing the available inertia of each fan in the wind field to obtain the confidence interval of the available inertia of the full wind field
Calculation and simulation: taking a certain practical wind power plant in northwest (wind field A for short) as an example, the wind speed data actually measured by the wind field is adopted to evaluate the available inertia of the single wind field. The wind power plant A comprises 108 double-fed asynchronous wind power generation sets in total in 6 rows, and the schematic layout of the fans in the wind power plant is shown in FIG. 4.
Evaluation algorithm comparative analysis:
the method comprises the steps of carrying out assessment on available inertia of a full-wind-speed wind field by using a structural layout A of a wind power plant, setting the whole assessment example to take the situation that the frequency of a power system falls under the condition of sudden load increase as a scene, and analyzing the condition of real-time releasable inertia kinetic energy of the wind field.
The wind field a is dominated by west wind directions, i.e., θ is 90 °. The confidence level of available inertia evaluation is taken to be 0.9, when the wind farm receives a grid frequency drop signal, the control system controls the fan to participate in inertia response, and the evaluation result of the available inertia interval of the full wind farm under different wind farm end wind speeds is obtained through an evaluation algorithm and is shown in fig. 5. In the calculation example, the nominal value curve is an evaluation result of the available inertia of the wind field obtained by neglecting the difference of each unit in the wind field and adopting single-machine equalization processing.
As can be seen from fig. 5, when the wind speed is less than the cut-out wind speed, the rotational kinetic energy stored in the wind farm generally increases with the wind speed at the wind farm end. In a small part of wind speed area after the wind speed is started, the wind field still has no inertial response capability, which is mainly because the rotating speed of each fan rotor in the wind field A in the wind speed area is lower than the lowest rotating speed omegaminThe rotational speed has no down regulation margin, so the wind field still cannot provide inertia response support for the power system.
When the wind field is in a medium and high wind speed area, the wind field starts to have the capability of participating in inertial response, the available inertia of the wind field is rapidly increased along with the rise of the wind speed, the corresponding confidence interval is continuously expanded, and the nominal algorithm result is always higher than the evaluation result of the algorithm because the running difference of each fan is ignored. For a unit which runs after part of the unit reaches rated wind speed, the rotating speed of the generator is not increased, inertia kinetic energy is kept constant, but the total available inertia of the full wind field still presents an ascending trend due to the existence of wake flow. At this time, as can be seen from the curve of fig. 5, all the units in the nominal algorithm are regarded as a constant-speed operation state, the available inertia of the wind field is in a constant state, and the obtained evaluation result is higher.
When the wind speed at the wind field end exceeds the cut-out wind speed (25m/s), part of the front exhaust wind turbine units quit operation, the phenomenon of sudden drop of the total available inertia of the wind field occurs, all the turbine units in the nominal algorithm are disconnected with the power grid, and the evaluation curve is suddenly dropped to a value of 0. Compared with the two algorithms, the method can effectively reduce the error between the estimation result and the actual value, and meanwhile, the evaluation curve in the form of the confidence interval can provide a more intuitive and credible reference result for the power grid operation scheduling.
And (4) running in real time, and carrying out available inertia evaluation by using the wind speed data measured from 6 am to 12 am on a certain summer day of the wind power plant A. The evaluation algorithm comprehensively considers various influence factors such as wake effect, time delay effect, wind shear effect, atmospheric turbulence and the like. From an energy analysis of the inertial response, the available inertia variation of the whole wind farm is shown in a graph 6.
The wind speed at the wind field end was kept varying between 9.5-14m/s, taking the time window of 6 to 12 am, with most of the fans operating in region ii (mppt) and region III (constant rpm region) shown in fig. 3. As can be known by combining the evaluation curve of FIG. 6, the whole available inertia evaluation curve basically shows the phenomenon of synchronous change along with the real-time wind speed change trend, and can provide an intuitive time-varying condition of the available inertia of the wind field for the power system scheduling. For any time, the wind farm available inertia truth value at 0.9 confidence may be considered to be between the lower confidence bound and the upper confidence bound.
On the other hand, from the perspective of active power release in the inertial response process of the doubly-fed wind turbine in the wind farm, a real-time wind farm available inertia evaluation curve is shown in fig. 7.
As can be seen from fig. 6 and 7, the rotational kinetic energy stored in the wind field fan is substantially consistent with the real-time evaluation curve of the active power which can be increased in a short time, and both the rotational kinetic energy and the real-time evaluation curve can display the variation trend of the available inertia of the wind field in real time. The phenomenon that the available inertia value rises steeply or falls steeply appears in both the two types of stepped evaluation curves is mainly caused by the fact that the time interval exists in the calculation of the provided real-time evaluation algorithm, and the larger the change of the wind speed in the adjacent time interval is, the more violent the change of the available inertia result of the wind field is.
The evaluation results at various confidence levels can highlight the margin size of the available inertia of the wind farm. The available inertia curve of the wind farm can be obtained as shown in fig. 8 by selecting different confidence levels.
As shown in fig. 8, the evaluation curve shown by the black line regardless of the turbulent condition. It can be seen that the evaluation of the available inertial kinetic energy at each confidence level under consideration of atmospheric turbulence remains in synchronism with the trend of the curve without turbulence. With the increase of the confidence degree, the confidence interval of the available inertia is increased continuously, namely the risk of interval evaluation is reduced, but the fluctuation range of the output evaluation result of the corresponding algorithm is enlarged. Therefore, when a system decision maker makes a scheduling plan, an optimal frequency modulation scheme based on specific risk can be made according to the running condition of a real-time power grid and available inertia evaluation curves of each wind power plant under different confidence degrees, and meanwhile, the real-time inertia response frequency modulation strategy of the wind power plant can be adjusted in a targeted mode according to the change condition of the available inertia evaluation curves.
Verifying the validity of a confidence interval evaluation mode: the above example results prove that the provided algorithm can effectively reduce the error between the available inertia evaluation result and the actual value, but the validity of the obtained confidence interval evaluation result is not verified. At this stage, the inertial response capability of a wind turbine can be regarded as a capability of "hiding" the wind turbine, and is usually revealed only when a grid frequency accident signal is received. The current research is difficult to directly acquire data information of inertial response conditions of a large number of wind fields in real-time operation. Therefore, an equivalent wind field model of 6 rows of double-fed asynchronous wind generation sets is built on a Simulink platform, and multiple groups of wind power sets are applied to each wind generation set
The distributed wind speed simulates the real-time wind speed condition of an actual wind field, a virtual inertia control link is applied to each fan, frequency accidents of the system in specific time are set, power output curves under various wind conditions are recorded, the rotating kinetic energy release condition of the fans is obtained, and finally the validity of the interval evaluation result is verified by counting a large number of simulation results.
By counting 320 sets of simulation results, the statistical results are shown in fig. 9. When turbulence influence is not considered, the wind turbine joint rotational inertia J calculation is estimated through different systems, so that the result obtained by the algorithm is compared with the statistical result, the available inertial kinetic energy of the wind turbine is obtained, and the error level is kept low, and the rotational inertia J of the wind turbine of the simulation system is estimated. When the turbulence influence is considered, the estimation is carried out by the proposed interval estimation method, and the error ratio of the algorithm result exceeding the confidence interval is compared with the actual statistical data, for example, as shown in table 1. The method has the advantages that the error is small when the confidence coefficient is high, and the error exceeds the upper risk limit under each confidence coefficient, so that the inertial response state and the inertial response capacity of the wind farm in real-time operation can be well evaluated by the algorithm.
TABLE 1 different confidence levels to assess the error of the results
The method for estimating the available inertia probability of the wind field by considering the wind speed distribution of the wind field and the operation condition of the fan aims at solving the problem that the inertia response capability is lack of accurate estimation when the wind field participates in frequency modulation service. From both the energy and power aspects, an evaluation curve based on the wind field available inertial kinetic energy and available inertial power increments at a certain confidence level is obtained. The evaluation algorithm proposed herein is arithmetically analyzed with the structure and wind speed data of a certain actual wind field in northwest. The results show that the method provided by the invention has strong reference and guidance. Meanwhile, an equivalent wind field model is built by using Simulink for simulation verification, and a large number of statistical results show that the confidence interval evaluation result provided by the method has small error and high reliability. In addition, with the increase of the number of the wind fields subsequently participating in frequency modulation services such as inertial response and the like, the effectiveness of the method can be further verified through actual wind field inertial response data.
Example two
The invention discloses an evaluation system for available inertia of a wind power plant, which comprises the following components:
the distribution model establishing module is used for establishing an instantaneous wind speed conditional probability distribution model under the influence of atmospheric turbulence by utilizing a mixed Copula function based on the space-time distribution characteristic of the wind field average wind speed under the influence of physical factors;
and the solving module is used for establishing an estimation model of available inertia kinetic energy and inertia power increment of the fan in an operation mode under the virtual inertia control of the double-fed wind turbine generator, additionally considering the operation working conditions of all fans of the wind field in actual operation, and obtaining an interval evaluation curve of the available inertia of the full wind field based on a certain confidence coefficient.
EXAMPLE III
The present embodiment aims to provide a computing device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to implement the following steps, including:
based on the space-time distribution characteristic of the wind field average wind speed under the influence of physical factors, and by utilizing a mixed Copula function, constructing an instantaneous wind speed conditional probability distribution model under the influence of atmospheric turbulence;
an estimation model of available inertia kinetic energy and inertia power increment of the wind turbine is established in an operation mode under the virtual inertia control of the double-fed wind turbine generator, and the operation working conditions of all wind turbines of the wind field in actual operation are additionally considered, so that an interval evaluation curve of the available inertia of the full wind field under a certain confidence coefficient is obtained.
Example four
An object of the present embodiment is to provide a computer-readable storage medium.
A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, performs the steps of:
based on the space-time distribution characteristic of the wind field average wind speed under the influence of physical factors, and by utilizing a mixed Copula function, constructing an instantaneous wind speed conditional probability distribution model under the influence of atmospheric turbulence;
an estimation model of available inertia kinetic energy and inertia power increment of the wind turbine is established in an operation mode under the virtual inertia control of the double-fed wind turbine generator, and the operation working conditions of all wind turbines of the wind field in actual operation are additionally considered, so that an interval evaluation curve of the available inertia of the full wind field under a certain confidence coefficient is obtained.
The steps involved in the apparatuses of the above second, third and fourth embodiments correspond to the first embodiment of the method, and the detailed description thereof can be found in the relevant description of the first embodiment. The term "computer-readable storage medium" should be taken to include a single medium or multiple media containing one or more sets of instructions; it should also be understood to include any medium that is capable of storing, encoding or carrying a set of instructions for execution by a processor and that cause the processor to perform any of the methods of the present invention.
Those skilled in the art will appreciate that the modules or steps of the present invention described above can be implemented using general purpose computer means, or alternatively, they can be implemented using program code that is executable by computing means, such that they are stored in memory means for execution by the computing means, or they are separately fabricated into individual integrated circuit modules, or multiple modules or steps of them are fabricated into a single integrated circuit module. The present invention is not limited to any specific combination of hardware and software.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.
Claims (9)
1. A method for evaluating available inertia of a wind power plant is characterized by comprising the following steps:
based on the space-time distribution characteristic of the wind field average wind speed under the influence of physical factors, and by utilizing a mixed Copula function, constructing an instantaneous wind speed conditional probability distribution model under the influence of atmospheric turbulence;
analyzing three physical influence factors of wake effect, wind shear effect and time delay effect to obtain the position distribution and time change characteristics of wind energy in a wind field, and further constructing a short-time wind speed conditional probability distribution model under the influence of atmospheric turbulence by using the advantages of a Copula function in correlation analysis;
an estimation model of available inertia kinetic energy and inertia power increment of the wind turbine is established in an operation mode under the virtual inertia control of the double-fed wind turbine generator, and the operation working conditions of all wind turbines of the wind field in actual operation are additionally considered, so that an interval evaluation curve of the available inertia of the full wind field under a certain confidence coefficient is obtained.
2. The method for evaluating the available inertia of the wind power plant according to claim 1, wherein data samples of second-level wind speed, turbulence intensity and average wind speed of the wind power plant are obtained, an edge distribution function of each variable is established by using a kernel density estimation method, a proper Copula function is selected, and related parameters are estimated by using a maximum likelihood estimation method to obtain a ternary combined distribution function.
3. The method for evaluating the available inertia of the wind power plant according to claim 1, wherein the wind power plant under the full wind condition can be divided into a starting region, a maximum power tracking region, a constant rotating speed region, a constant power region and a cut-out wind speed region according to the conditions of capturing active power by the wind turbine generator and the rotating speed of the rotor.
4. A wind farm available inertia estimation method according to claim 1, characterised in that the available inertia of the wind turbine is divided, both in terms of energy and power, into available inertial kinetic energy stored on the wind turbine and available inertial power increments provided by the inertial kinetic energy in the inertial response.
5. The method for evaluating the available inertia of the wind power plant according to claim 4, wherein the available inertia evaluation algorithm divides the available inertia of a single fan in the wind power plant into three states according to the fan fault condition and the wind speed partition condition, wherein the three states are respectively non-available inertia, constant available inertia and variable available inertia;
the states of unavailable inertia and constant available inertia are represented by a single probability value, and the variable available inertia states represent the distribution condition of the available variables of the wind turbine through continuous probability density functions.
6. The method for evaluating the available inertia of the wind power plant according to claim 1, wherein in the evaluation method, the wind speed relation only exists among all wind turbines in the wind power plant under the influence of wake effect, and under the condition that the wind speed distribution is known, the available inertia of all the wind turbines is processed according to independent random variables during interval evaluation of the algorithm;
the method comprises the following steps of:
step1, respectively obtaining a probability density function f (E) of inertia kinetic energy of an available rotor of each wind turbine generator set and a probability density function f (P) of an available inertia power increment when the wind power plant runs in real time according to an available inertia evaluation method of the wind turbines;
step2, integrating f (E) and f (P) to obtain a probability distribution function phi (E) of available inertial kinetic energy of the rotor and a probability distribution function phi (P) of available inertial power increment;
step3, calculating the available inertia distribution function by using a binary search-numerical integration method
And
inertia values corresponding to the fans j at the probability values, namely confidence intervals of the available inertia of the fans j under the confidence α are respectively obtained;
and Step4, superposing the available inertia of each fan of the wind field to obtain a confidence interval of the available inertia of the full wind field.
7. An available inertia evaluation system for a wind power plant is characterized by comprising:
the distribution model establishing module is used for establishing an instantaneous wind speed conditional probability distribution model under the influence of atmospheric turbulence by utilizing a mixed Copula function based on the space-time distribution characteristic of the wind field average wind speed under the influence of physical factors;
the distribution model establishing module analyzes three physical influence factors of wake effect, wind shear effect and time delay effect to obtain the position distribution and time change characteristics of wind energy in a wind field, and further establishes a short-time wind speed conditional probability distribution model under the influence of atmospheric turbulence by utilizing the advantages of Copula functions in correlation analysis;
and the solving module is used for establishing an estimation model of available inertia kinetic energy and inertia power increment of the fan in an operation mode under the virtual inertia control of the double-fed wind turbine generator, additionally considering the operation working conditions of all fans of the wind field in actual operation, and obtaining an interval evaluation curve of the available inertia of the full wind field based on a certain confidence coefficient.
8. A computing device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to perform steps comprising:
based on the space-time distribution characteristic of the wind field average wind speed under the influence of physical factors, and by utilizing a mixed Copula function, constructing an instantaneous wind speed conditional probability distribution model under the influence of atmospheric turbulence;
analyzing three physical influence factors of wake effect, wind shear effect and time delay effect to obtain the position distribution and time change characteristics of wind energy in a wind field, and further constructing a short-time wind speed conditional probability distribution model under the influence of atmospheric turbulence by using the advantages of a Copula function in correlation analysis;
an estimation model of available inertia kinetic energy and inertia power increment of the wind turbine is established in an operation mode under the virtual inertia control of the double-fed wind turbine generator, and the operation working conditions of all wind turbines of the wind field in actual operation are additionally considered, so that an interval evaluation curve of the available inertia of the full wind field under a certain confidence coefficient is obtained.
9. A computer-readable storage medium, having a computer program stored thereon, the program, when executed by a processor, performing the steps of:
based on the space-time distribution characteristic of the wind field average wind speed under the influence of physical factors, and by utilizing a mixed Copula function, constructing an instantaneous wind speed conditional probability distribution model under the influence of atmospheric turbulence;
analyzing three physical influence factors of wake effect, wind shear effect and time delay effect to obtain the position distribution and time change characteristics of wind energy in a wind field, and further constructing a short-time wind speed conditional probability distribution model under the influence of atmospheric turbulence by using the advantages of a Copula function in correlation analysis;
an estimation model of available inertia kinetic energy and inertia power increment of the wind turbine is established in an operation mode under the virtual inertia control of the double-fed wind turbine generator, and the operation working conditions of all wind turbines of the wind field in actual operation are additionally considered, so that an interval evaluation curve of the available inertia of the full wind field under a certain confidence coefficient is obtained.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106230024A (en) * | 2016-08-22 | 2016-12-14 | 张雪敏 | Power system power failure Risk Calculation method containing double-fed fan motor field |
| CN106528912A (en) * | 2016-09-19 | 2017-03-22 | 国网浙江省电力公司经济技术研究院 | Method for estimating frequency regulation capacity of wind power plant |
| CN108334672A (en) * | 2018-01-14 | 2018-07-27 | 浙江大学 | Variable Speed Wind Power Generator maximal wind-energy capture method based on effective wind speed estimation |
| CN110425092A (en) * | 2019-07-30 | 2019-11-08 | 华北电力大学 | A kind of wind-electricity integration system oscillation source positioning system and method based on energy frequency spectrum |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104699936B (en) * | 2014-08-18 | 2018-05-04 | 沈阳工业大学 | Wind power plant sector management method based on Fluid Mechanics Computation short-term wind speed forecasting |
| US20170298904A1 (en) * | 2016-04-18 | 2017-10-19 | Siemens Aktiengesellschaft | Method for responding to a grid event |
| US20170346375A1 (en) * | 2016-05-26 | 2017-11-30 | Sidney Shlomo BEN-DAN | Gravity Induced, Bucky-Ball Based, Power Plant - "Shlomo Pillar" |
-
2019
- 2019-11-11 CN CN201911094061.8A patent/CN110826927B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106230024A (en) * | 2016-08-22 | 2016-12-14 | 张雪敏 | Power system power failure Risk Calculation method containing double-fed fan motor field |
| CN106528912A (en) * | 2016-09-19 | 2017-03-22 | 国网浙江省电力公司经济技术研究院 | Method for estimating frequency regulation capacity of wind power plant |
| CN108334672A (en) * | 2018-01-14 | 2018-07-27 | 浙江大学 | Variable Speed Wind Power Generator maximal wind-energy capture method based on effective wind speed estimation |
| CN110425092A (en) * | 2019-07-30 | 2019-11-08 | 华北电力大学 | A kind of wind-electricity integration system oscillation source positioning system and method based on energy frequency spectrum |
Non-Patent Citations (1)
| Title |
|---|
| Releasable kinetic energy-based inertial control of a DFIG wind power plant;Jinsik Lee 等;《IEEE transactions on sustainable energy》;20151203;第7卷(第1期);第279-288页 * |
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