CN106992545B - Electromechanical transient model of weakly-consistent wind speed distribution mountain wind power plant and modeling method - Google Patents
Electromechanical transient model of weakly-consistent wind speed distribution mountain wind power plant and modeling method Download PDFInfo
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Abstract
The invention discloses an electromechanical system of a mountain wind power plant with weakly consistent wind speed distributionA transient model and modeling method, comprising: step 1, grouping fans in a specific wind field according to wind speed gradient and obtaining an aerodynamic model PmechConstructing; step 2, constructing a DFIG model in the n groups of wind speed areas; step 3, constructing a box type step-up transformer model; step 4, current collection circuit construction; step 5, constructing a main booster transformer model; step 6, building a reactive power compensation device model; 7, constructing a line model of the wind field access power system; the method solves the technical problems that the traditional wind power plant modeling method cannot reflect the characteristic of weak consistency of plateau mountain wind power spatial-temporal distribution, and is difficult to adapt to dynamic characteristic analysis of a wind power plant access system in the southwest mountain area and the like.
Description
Technical Field
The invention belongs to the technical field of wind power generation grid connection, and particularly relates to an electromechanical transient model and a modeling method of a weak-consistency wind speed distribution mountain wind power plant.
Background
By 2015, 92981 wind turbines are installed in China in an accumulated mode, the accumulated installed capacity is 145362MW, and the installed quantity of wind power is innovative and increased by 26.8% on a same scale. Wind power generation has become the main form of new energy power generation in China, so that the wind energy proportion is gradually increased. However, wind power output has randomness, intermittence and anti-peak-shaving characteristics, a series of new problems are provided for safe and stable operation of a power system, and extensive attention and deep research of experts in the industry and scholars in the fields of power and energy at home and abroad are aroused.
Recently, the key development areas of inland wind power in China gradually turn to regions in south China, the south China and the east China from 'three north regions' with serious electricity limitation, the regions are high in altitude, complex in terrain and meteorological conditions, and the running characteristics of wind power plants in mountainous regions have obvious difference characteristics. The special plateau mountain area meteorological performance of Guizhou determines that the environment of wind energy has the characteristics of high altitude, high humidity and the like, and the mountain area wind power is obviously different from plain and offshore wind power in other areas of China. With the gradual increase of the installed proportion of wind power, a new challenge is brought to the safe and stable operation of an alternating current-direct current transmission end power system represented by a Guizhou power grid. Therefore, research on the plateau mountain wind power plant electromechanical transient model and the modeling method thereof aiming at the cloud plateau geography and climate environment characteristics is urgently needed, and the dynamic characteristic of high-proportion wind power accessed to the power system is better mastered, so that a foundation is laid for scientific promotion of power grid planning construction and operation scheduling in southwest and similar environment areas.
Traditionally, wind farm modeling mainly considers the intrinsic relation of a Doubly Fed Induction Generator (DFIG) based wind turbine electromechanical transient model, and some early documents do not consider flexible alternating current transmission devices such as Static Var Compensator (SVC), svg (Static Var generator) and the like commonly used in a current wind farm booster station. However, when the wind power plant is connected to the grid, the reactive power sending or absorbing capacity is limited, and the voltage adjustment of the power grid is difficult to undertake, and particularly, the fluctuation of the voltage and the frequency of a Point of Common Coupling (PCC) is easily caused by the wind power characteristics of the wind power plant in the plateau mountain region. Therefore, in the electromechanical transient analysis of the grid in the wind power plant access region, the access of models such as SVC and SVG should be considered. In addition, more importantly, most documents often adopt average wind speed in wind power plant prime motor model side wind speed simulation, and are influenced by landscape, acceleration effect, delay effect and the like in mountainous areas, and the wind speed variation of each fan in the highland mountain wind power plant is large. Wind speed increases with elevation in mountains. Wind speed at mountaintop, mountaintop and gorge wind gap is high, and wind speed at basin, valley bottom and leeward is low. The wind speed on the alpine is generally high at night, small in the daytime and smallest in the afternoon, and the foot and valley are opposite. Local circulating currents can also be generated in mountainous regions, such as valley wind (wind blowing from valley to valley of valley due to thermal differences between air near the top and valley of valley) and wind blowing from peak to valley of valley at night (mountain wind), mountain wind and valley wind are collectively called valley wind, and wind burning (Foehn) is a local form of air movement caused by mountainous regions — a local form of air movement caused by mountainous regions-an endemic wind in which a cross-over airflow sinks on a leeward slope and becomes dry-hot), slope wind (an endemic wind formed due to diurnal thermal differences between a slope and air near the slope). Therefore, the traditional wind power plant modeling method cannot reflect the characteristic of weak consistency of plateau mountain wind power spatial-temporal distribution, and is difficult to adapt to the technical problems of dynamic characteristic analysis and the like of the wind power plant access system in the southwest mountain area.
Disclosure of Invention
The technical problems to be solved by the invention are as follows: the method aims to solve the technical problems that the traditional wind power plant modeling method in the prior art cannot reflect the characteristic of weak consistency of plateau mountain wind power spatial-temporal distribution, is difficult to adapt to dynamic characteristic analysis of a wind power plant access system in the southwest mountainous area, and the like.
The technical scheme of the invention is as follows:
an electromechanical transient model and a modeling method of a weakly consistent wind speed distribution mountain wind power plant comprise the following steps:
and 7, constructing a line model of the wind field access power system.
step 1.1, collecting wind field wind data of a specific area and constructing a wind database; evaluating data according to wind resources of wind power plant and wind power plant wind
The method comprises the steps of evaluating force distribution data, carrying out data cleaning, and constructing a wind database of the mountain wind power plant in a microclimate environment;
step 1.2, clustering the spatial wind speed distribution of the wind power plant at different time intervals;
according to the designed wind power or estimated wind power database of the mountain wind power plant, aiming at different seasons, different typical working days and different working periods, according to VstepWind spreadingElectric field space wind speed distribution clustering, and grouping wind turbine units through wind speed area division; for mountain wind farms, VstepCalculated according to the formulas (1) and (2);
n＝round(Nsum/Ngroup) (1)
wherein N is the number of unit groups, NsumAnd NgroupThe round () represents an integer function for the total number of the wind power plant units and the average number of the wind speed groups; vmaxAnd VminEstimated values or actual values of the maximum wind speed and the minimum wind speed of the wind power plant in a time period to be analyzed;
step 1.3, according to the clustering parameter VstepForm by VstepA wind field wind speed zone of gradient;
step 1.4, establishing equivalent wind turbine generators in n groups of wind speed areas according to the grouping number n of the clustering parameter generators;
step 1.5, combining the step 1.1 to the step 1.4 to obtain a spatial distribution model of the clustering wind speed in the expected event scene;
and step 1.6, selecting a wind speed time distribution model which comprises a slope wind model, a gust model, a Mexico straw hat wind model and a 'custom piecewise linear function wind speed' model.
Step 1.7, solving a wind energy utilization coefficient Cp;
Cp＝0.5(r-0.022β2-5.6)e-0.17r(3)
wherein β is the pitch angle, r satisfies the formula r 2.237 Vw/omega, VwIs the wind speed, m/s. Omega is the angular velocity of the fan rotor, rad/s.
Step 1.8, calculating an air density reduction factor a in the wind power plant in the mountainous areaTM-H
In the formula: in the formula: rhoHIs an altitude ofAir density at H, g/m3；ρ0The density of air is 1.225g/m at sea level and 15 deg.C under standard conditions of normal temperature and standard atmospheric pressure3(ii) a H is the altitude, unit m; t is0Taking 273 ℃ as absolute temperature; a is the air temperature gradient, and the temperature is taken to be 0.0065 ℃/m; a isHIs an altitude depreciation factor;
the relationship between density and temperature, relative humidity and atmospheric pressure is
Wherein t is air temperature, DEG C; p is atmospheric pressure, hPa；
Relative humidity,%; a isTMIs the air density reduction factor under temperature and relative humidity;
finally, the relationship between the air density and the altitude, the temperature, the relative humidity and the atmospheric pressure is obtained as follows:
ρ＝αTMαHρ0＝αTM-Hρ0(6)
step 1.9, wind power plant fan mechanical power PmechObtaining
Calculating the mechanical power of the n groups of fan units according to a standard aerodynamic model, wherein the formula is shown in (3-5)
Wherein S is the swept area of the wind wheel (S ═ Pi R)2＝3770m2) R is the radius of the wind wheel blade m; rho is air density, g/m3；VwIs the wind speed, m/s.
The invention has the beneficial effects that:
according to the method, aiming at the characteristic that the wind speed consistency of the mountain wind power plant is weak, an equivalent wind speed area is obtained through wind speed clustering of all fans, then a wind speed value reflecting the weak consistency distribution of the mountain wind power plant is introduced by means of a wind speed and aerodynamic model self-defining method, and compared with the prior art, the time domain dynamic characteristic of the mountain wind power plant is simulated more accurately;
in general, compared with the prior art, the invention has the following obvious beneficial effects:
1) the model introduces a wind speed value reflecting weak consistency distribution by means of a wind speed and aerodynamic self-defining method, and accurately simulates the wind speed conditions of different machine sites in the wind power plant; the problem that analysis errors of the wind power plants in the mountainous area are large due to the fact that only the average wind speed of the wind power plants in the mountainous area is considered is solved.
2) The model comprises a complete wind power plant model, and V is established by combining historical wind measurement data of the wind power plantstepThe wind speed area of the wind speed gradient is constructed based on a wind speed model of spatial distribution clustering, fans in each wind speed area are clustered into a group, then a wind speed space-time distribution model under a specific scene is set according to wind energy distribution evaluation data of a wind power plant, an equivalent wind speed area is established, and a classification cluster is formed. The wind turbines of the wind power plants in the mountainous area can be clustered into 3-5 groups generally. The problem of the loss of the plateau mountain wind power plant model is effectively solved.
The method solves the technical problems that the traditional wind power plant modeling method cannot reflect the characteristic of weak consistency of plateau mountain wind power spatial-temporal distribution, and is difficult to adapt to the dynamic characteristic analysis of the wind power plant access system in the southwest mountain area of China and the like.
Description of the drawings:
FIG. 1 is a general computational flow diagram to which the present invention relates;
FIG. 2 is a flow chart of the aerodynamic module calculation involved in the present invention;
FIG. 3 is a schematic diagram of geographical distribution of a certain mountain wind farm in the present invention;
FIG. 4 is a schematic diagram of a DFIG model in the PSS/E software;
FIG. 5 is a schematic diagram of an SVC model in the PSS/E software;
FIG. 6 is a detailed connection diagram of wind turbines of a certain mountain wind farm in the invention;
FIG. 7 is a block diagram of a conventional simplified model in the present invention;
FIG. 8 is a schematic diagram comparing the simulation results of active power and reactive power of the detailed model of the present invention and the simplified model of the conventional system under the condition of cut-in wind speed;
FIG. 9 is a schematic diagram showing the comparison between the simulation results of active power of the group 6 cluster in the detailed model and the conventional simplified model under the condition of low wind speed;
FIG. 10 is a schematic view of the Mexico "grass hat wind" waveform;
FIG. 11 is an active power comparison in the Mexico "grass hat wind" rated wind speed scenario;
FIG. 12 is a comparison of reactive power in a Mexico "grass hat wind" rated wind speed scenario;
FIG. 13 is a graph of wind speed values of each fan in the present invention;
FIG. 14 is a graph of wind speed values for each set of wind turbines in the simplified model of the present invention;
FIG. 15 is a schematic diagram showing the comparison of the simulation results of active power and reactive power between the detailed model of the present invention and the conventional simplified model under the condition of weakly-consistent distributed wind speed.
Detailed Description
An electromechanical transient model and a modeling method of a weakly consistent wind speed distribution mountain wind power plant comprise (as shown in figure 1):
step 1.1, collecting wind field wind data of a specific area and constructing a wind database; according to wind power plant wind resource evaluation data and wind power distribution evaluation data of a wind power plant, carrying out data cleaning, and constructing a wind power database of the mountain wind power plant in a microclimate environment;
step 1.2, clustering the spatial wind speed distribution of the wind power plant at different time intervals;
according to the designed wind power or estimated wind power database of the mountain wind power plant, aiming at different seasons and different dictionariesType working day, different working time period, according to VstepCarrying out spatial wind speed distribution clustering of a wind power plant, and realizing grouping of wind turbine units through wind speed area division; for mountain wind farms, VstepCalculated according to the formulas (1) and (2);
n＝round(Nsum/Ngroup) (1)
wherein N is the number of unit groups, NsumAnd NgroupThe round () represents an integer function for the total number of the wind power plant units and the average number of the wind speed groups; vmaxAnd VminEstimated values or actual values of the maximum wind speed and the minimum wind speed of the wind power plant in a time period to be analyzed;
step 1.3, according to the clustering parameter VstepForm by VstepA wind field wind speed zone of gradient;
step 1.4, establishing equivalent wind turbine generators in n groups of wind speed areas according to the grouping number n of the clustering parameter generators;
step 1.5, obtaining a spatial distribution model of the clustered wind speed in an expected event scene according to the steps 1.1 to 1.4;
and step 1.6, selecting a wind speed time distribution model, wherein the wind speed time distribution model comprises a slope wind model, a gust wind model, a Mexico straw hat wind model and a 'custom piecewise linear function wind speed' model.
Step 1.7, wind energy utilization coefficient CpObtaining;
Cp＝0.5(r-0.022β2-5.6)e-0.17r(3)
wherein β is the pitch angle, and r satisfies the formula r 2.237Vw/ω；VwIs wind speed, m/s; omega is angular speed of the fan rotor, rad/s; for example, 9.6m/s, r is 14.362; thus Cp＝0.381。
Step 1.8, calculating an air density reduction factor a in the wind power plant in the mountainous areaTM-H
In the formula: in the formula: rhoHIs the air density at altitude H, g/m3；ρ0The density of air is 1.225g/m at sea level and 15 deg.C under standard conditions of normal temperature and standard atmospheric pressure3(ii) a H is the altitude, unit m; t is0Taking 273 ℃ as absolute temperature; a is the air temperature gradient, and the temperature is taken to be 0.0065 ℃/m; a isHIs an altitude depreciation factor;
the relationship between the air density and the temperature, relative humidity and atmospheric pressure is
Wherein t is air temperature, DEG C; p is atmospheric pressure, hPa；
Relative humidity,%; a isTMIs the air density reduction factor under temperature and relative humidity;
finally, the relationship between the air density and the altitude, the temperature, the relative humidity and the atmospheric pressure is obtained as follows:
ρ＝αTMαHρ0＝αTM-Hρ0(6)
step 1.9, wind power plant fan mechanical power PmechObtaining
Calculating the mechanical power of the n groups of fan units according to a standard aerodynamic model, wherein the formula is shown in (3-5)
Wherein S is the swept area of the wind wheel (S pi R2 pi 3770 m)2) R is the radius of the wind wheel blade m; ρ is the air density; vwIs the wind speed.
The system comprises a power generation/current converter model, an electrical control model, a shafting model and a pitch angle control model. Generally, the terminal voltage of the DFIG wind driven generator of the mountain wind farm is 0.69 kV.
The invention adopts an oil-immersed box type transformer model with the model number of S11-2200/35. Generally, the wind driven generator of the mountain wind farm adopts a 0.69/38.5kV box type step-up transformer to boost up to 38.5kV.
Generally, in a mountain wind farm, 6-12 wind power generators are connected by a current collecting line and then converged into a 35kV bus of the wind farm according to installed capacity and environmental conditions, the current collecting line generally comprises two forms of a power cable and an overhead line, and a current collecting line model is constructed by adopting a cable with the model number of YJLY23-3 × 240.
A three-phase, double-winding and self-cooling oil-immersed low-loss on-load voltage-regulating power transformer with the model number of SZ11-100000/110 is adopted. Generally, mountain wind farms use 35/121kV (or 35/230kV) to boost the voltage to 121kV (or 230kV) and are grid-connected nearby via access system lines.
Generally, a voltage boosting transformer substation of a mountain wind farm adopts an SVC (scalable video coding) model, an SVG (scalable vector graphics) model or a FC (fiber channel) and SVG (scalable vector graphics) combined compensation model as a reactive compensation device of the wind farm, and the capacity of the voltage boosting transformer substation is generally about 20% of the total capacity of the wind farm.
And 7, constructing a line model of the wind power plant access electric power system, wherein the line model of the wind power plant access electric power system is constructed by adopting a power cable with the model number of YJY23-3 × 240.
And finally forming an electromechanical transient model of the weak-consistency wind speed distribution mountain wind power plant according to the results of the 7 steps.
The technical scheme of the invention is further explained by taking a wind power plant in a certain mountain area in Guizhou as an example. The mountain land belongs to the low wind speed category, the wind speed is easily influenced by the terrain, and the difference of the wind speeds at different positions is large. The wind speed is mainly distributed in a wind speed section of 2-10 m/s. The cut-in wind speed of the wind power station cluster is 3m/s, the average wind speed is 6.5m/s, the rated wind speed is 9.5m/s, and the cut-out wind speed is 20 m/s. The wind farm fan distribution is shown in fig. 3.
1. Firstly, an aerodynamic model P is constructedmech. The method comprises a mountainous wind power plant wind speed space-time distribution model and a mountainous wind power plant aerodynamic model. The value of n is 6, V calculated according to the formulas (1) and (2)stepThe value was 1.1 m/s.
n＝round(Nsum/Ngroup) (1)
Wherein N issumA value of 50, NgroupSet to 9. In the formula (2), VmaxA value of 9.5m/s, VminThe value was 3 m/s.
And secondly, forming a wind field wind speed area with the wind speed gradient of 1.1m/s and establishing 6 groups of equivalent wind turbine generators in the wind speed area. Wind farm clustering according to wind speed gradient clustering is shown in table 1.
TABLE 1 wind farm grouping according to wind speed distribution
Thirdly, a spatial distribution model of the clustered wind speeds in the expected event scene is established according to the step 1.5. Combining the time dimension evolution characteristics of wind speed, adding time limit conditions into four classical wind speed models (respectively basic wind, slope wind, gust and random wind) to obtain Mexican straw hat wind and self-defined piecewise linear function wind speed, and selecting a wind speed time distribution model from the slope wind model, the gust model, the Mexican straw hat wind model and the self-defined piecewise linear function wind speed model according to the step 1.6. And (3) carrying out time dimension matching of different seasons, different typical working days and different working periods by combining a specific wind power plant operation scene, and constructing a space-time distribution model of the clustering wind speed in the required operation scene according to the step 1.5 and the step 1.6.
The model of the slope wind, the model of the gust wind, the model of the Mexico straw hat wind and the model of the custom piecewise linear function wind speed are shown as follows.
Step 1.6, selecting a wind speed time distribution model which comprises a slope wind model, a gust model, a Mexico straw hat wind model and a 'custom piecewise linear function wind speed' model;
the slope wind model is mainly described by the following 3 parameters: starting time tsrEnd time terAmplitude of increase in wind speed Ar. The mathematical expression of the slope wind is:
in the formula, Dr＝ter–tsr。
The gust model is mainly described by the following 3 parameters: starting time tsgEnd time tegMaximum wind speed Vmax. The mathematical expression for a gust is:
in the formula, Ag＝(Vmax–V0)/2，Dg＝teg–tsg。
The "mexican grass hat wind" model is described mainly by the following parameters: v0Is the initial wind speed, VmaxIs the maximum wind speed, VminIs the minimum wind speed.
The "custom piecewise linear function wind speed" model is described by the following parameters: v0Is the initial wind speed, V1、V2、V3、V4、V5The wind speed at each time point.
Finally, the mechanical power P of the wind power plant fanmechAnd (6) obtaining. Including the coefficient of wind energy utilization CpAir density reduction factor aTM-H。
2. And constructing a DFIG model in the n groups of wind speed areas. The system comprises a power generation/current converter model, an electrical control model, a shafting model and a pitch angle control model. Generally, the terminal voltage of the DFIG wind driven generator of the mountain wind farm is 0.69 kV. The DFIG model in the PSS/E software is shown in fig. 4, and the DFIG model consists of a generator/frequency converter model, an electrical control model, a shafting model, a pitch angle control model, a wind turbine aerodynamic model and a wind speed model. The wind turbine shafting model and the doubly-fed asynchronous motor model can be respectively expressed by the following formulas (7) and (8):
in the formula, TM、TGInertia time constants of the wind turbine and the generator are respectively; kSIs the stiffness coefficient of the shaft; dM、DGDamping coefficients of a wind turbine rotor and a generator rotor are respectively set; thetaSIs the relative angular displacement between the two masses; mM、MERespectively are the mechanical torque of a wind turbine and the electromagnetic torque of a generator; omegaM、ωGThe rotational speeds, omega, of the wind turbine and generator rotors, respectively0For its synchronous speed; xrIs rotor leakage reactance; xmIs an excitation reactance; p is a differential operator; s is slip; xsIs a synchronous reactance; x'sIs a transient reactance; t'0Is the rotor winding transient open-circuit time constant; u. ofds、 uqsD and q axis components of the stator voltage; u. ofdr、uqrD and q axis components of the rotor voltage; u'dr＝udrXm/(Xr+Xm)、u'qr＝uqrXm/(Xr+Xm) Is an intermediate variable; i.e. ids、iqsD and q axis components of the stator current; e'd、E'qD, q-axis components of the transient potential; omegasIs the angular velocity of rotation of the coordinate system, and ωs＝ω0
3. And constructing a box type step-up transformer model. An oil-immersed box type transformer model with the model number of S11-2200/35 is adopted. Generally, the wind driven generator of the mountain wind farm adopts a 0.69/38.5kV box type step-up transformer to boost up to 38.5kV.
4. The method comprises the steps of building a collecting line, connecting 6-12 wind power generators by the collecting line according to installed capacity and environmental conditions of a mountain wind farm, and then collecting the wind power generators into a 35kV bus of the wind farm, wherein the collecting line generally comprises two forms of a power cable and an overhead line, and a collecting line model is built by using a cable with the model number of YJLY23-3 × 240.
5. And constructing a main step-up transformer model. Constructing a main booster transformer model; a three-phase, double-winding and self-cooling oil-immersed low-loss on-load voltage-regulating power transformer with the model number of SZ11-100000/110 is adopted. Generally, mountain wind farms use 35/121kV (or 35/230kV) to boost the voltage to 121kV (or 230kV) and are grid-connected nearby via access system lines.
6. And constructing a reactive compensation device model. Generally, a SVC model, an SVG model or a combined compensation model of FC and SVG is adopted by a mountain wind farm as a wind farm reactive power compensation device, and the capacity is about 20% of the total capacity of the wind farm. An SVG model in the PSS/E software is shown in FIG. 5, and the model corresponding to the SVG model is a CSVGN5 model. In the SVG model, bus voltage is collected, filtered and subtracted from a voltage set value to generate a voltage error value, the voltage error value is subtracted from an auxiliary signal to generate a susceptance error value through a regulator, and finally the voltage error value and the susceptance error value are converted through fast judgment logic and susceptance-reactance to generate compensation reactance.
7. The method comprises the steps of constructing a wind farm access electric power system line model by adopting an electric power cable with the model number of YJY23-3 × 240
The wind farm grouping obtained from steps 1 to 7 and the simplified system are shown in fig. 6 and 7.
Scene-low wind speed fan cut-in
In the step 1, the wind speed of the self-defined piecewise linear function is selected, and the wind speed is gradually increased to 7m/s from 0.5 m/s. And when the wind speed is increased to the cut-in wind speed, the fan is put into operation. The wind farm PCC bus power output is shown in FIG. 8.
As can be seen from fig. 8, the maximum deviation of the instantaneous active output of the simplified model is 17.5MW, the maximum deviation of the instantaneous reactive output is 3.75Mvar, and the injected active fluctuation at the PCC bus is large in the simplified model. The scene shows that in the process of gradually increasing the wind speed from 0.5m/s, the dynamic simulation of the wind power system is carried out by using the simplified model, and larger deviation can be generated.
Scene two low wind speed fan outage
The position of the sixth group of the wind power plant is relatively dispersed. Therefore, the 'custom piecewise linear function wind speed' is selected in the step 1, and in the sixth group of the cluster, the wind speeds of the fans # 1 to #7 are gradually reduced to be near 3m/s from the rated wind speed, and the wind speeds of the fans # 12 and #13 are near the rated wind speed. In this case, the sixth group output power is as shown in fig. 9.
As can be seen from FIG. 9, when the wind speed of the wind farm is gradually reduced, the instantaneous active output error of the simplified model is larger. The scene shows that in the process of gradually reducing the wind speed, the simplified model is used for developing the dynamic simulation of the wind power system, and larger deviation can be generated.
Rated wind speed of three Mexico 'grass hat wind' scene
In step 1, Mexico ' grass hat wind ' is selected, the initial wind speed value is selected to be the rated wind speed (9.5m/s), and a schematic diagram of the ' Mexico ' grass hat wind ' is shown in figure 10. In the simplified model and the detailed model, the pair of the power output of the wind power plant is shown in fig. 11 and 12. As can be seen from fig. 11, the deviation of the active power in the simplified model from the detailed model is about 2.5MW, and the relative deviation is about 3.45%. As can be seen from fig. 12, the deviation of the reactive power in the simplified model from the detailed model is about 1MW, and the relative deviation is about 7.55%. Therefore, in the Mexico 'grass hat wind' scene, the relative deviation of the active power and the reactive power of the simplified model is large.
Scene four self-defined piecewise linear function wind speed "
In order to highlight the wind speed changes of the fans at different positions, the 'self-defined piecewise linear function wind speed' is selected in the step 1, the rated wind speed (9.5m/s) is selected for the wind speed value, the PSS/E software self-defined function is utilized, and the wind speed is defined according to the formula (2), so that the average values of different wind speeds at the positions of 50 fans are established, as shown in fig. 13. According to the formula (1), the wind speed of the equivalent unit in the simplified model is obtained by fitting the wind speed of each group of clusters in the detailed model, as shown in fig. 14. The detailed model and simplified model dependent variable pairs are shown in fig. 15. Therefore, the wind speed of the self-defined piecewise linear function has smaller relative deviation compared with the scene of 'grass hat wind' in Mexico. But the relative deviation of the simplified models of the two is within a reasonable range. As can be seen from FIG. 15, the instantaneous active maximum deviation of the simplified model is 2.1MW, and the instantaneous reactive maximum deviation is 1.9 Mvar.
To illustrate the rationality of the simplified model, the active power mean deviation E is definedPAnd mean deviation of reactive power EQAs evaluation indexes, the calculation formula is as follows:
in the formula, PinAnd QinRespectively representing active power and reactive power at PCC of the simplified wind power plant; pnAnd QnRespectively representing active power and reactive power at PCC of the detailed wind power plant; n represents the sequence number of the integration calculation for each simulation step.
The relative deviations of the simplified models under different scenarios are shown in table 2. The fault disturbance is a three-phase short circuit of a connecting circuit between the PCC bus and a power grid side bus, and the disturbance time is set to be 0.25 s.
TABLE 2 deviation between simplified and detailed models
As can be seen from Table 2, when the wind speeds are the same, the wind farm output active power deviation is smaller. When the wind speed distribution changes, the output active power deviation is increased to 1.32%. When the system fails, the reactive deviation changes greatly, and is increased to 5.77% for the same wind speed and 5.42% for different wind speeds. The power deviation is within a reasonable range. Therefore, the simplified model can accurately reflect the power output characteristics of the wind power plant under the condition that the wind speed varies around the rated value.
According to the analysis of the space-time distribution scene of the wind speeds of different wind power plants, when the wind speeds change near the rated value, the relative deviation of the simplified model is in a reasonable range, and the method can be used for analyzing the dynamic characteristics of a regional system accessed by the wind power. When the wind speed in the wind power plant is small or part of the units are in a low wind speed operation state, the simplified model is used for analyzing the dynamic response of the wind power plant access system, and large deviation can be generated. In addition, the simplified model cannot reflect the condition of the outage of a fan in the wind power plant, and at the moment, a detailed model is adopted to analyze the dynamic response of the wind power accessed to the local power grid.
In summary, the invention provides an electromechanical transient model and a modeling method of a weakly consistent wind speed distribution mountain wind power plant, and the calculation results of the implementation cases show that:
1) the electromechanical transient model of the weak-consistency wind speed distribution mountain wind power plant and the modeling method have effectiveness and practicability;
2) when partial units are cut in or shut down due to wind speed changes in the mountain wind power plant, the relative deviation of the simplified die is large. At the moment, the simplified model is not suitable for electromechanical transient analysis of the wind power access local system.
3) The detailed model can reflect weak consistency wind speed distribution characteristics of the mountain wind power plant and is suitable for analysis of low wind speed change scenes, internal fault disturbance analysis and dynamic characteristic analysis of a high-density wind power access system in the mountain wind power plant.
The above description is only an example of the present invention, and is not intended to limit the present invention in any way, and all the embodiments of the present invention are within the scope of the present invention.
Claims (1)
1. A modeling method of an electromechanical transient model of a weakly consistent wind speed distribution mountain wind power plant comprises the following steps:
step 1, grouping fans in a wind field of a specific area according to wind speed gradient and carrying out aerodynamic model PmechConstructing; the method comprises the steps of constructing a wind speed space-time distribution model of the mountain wind power plant and an aerodynamic model of the mountain wind power plant, and specifically comprises the following steps:
step 1.1, collecting wind field wind data of a specific area and constructing a wind database; according to wind power plant wind resource evaluation data and wind power distribution evaluation data of a wind power plant, carrying out data cleaning, and constructing a wind power database of the mountain wind power plant in a microclimate environment;
step 1.2, clustering the spatial wind speed distribution of the wind power plant at different time intervals;
according to the designed wind power or estimated wind power database of the mountain wind power plant, aiming at different seasons, different typical working days and different working periods, according to VstepCarrying out spatial wind speed distribution clustering of a wind power plant, and realizing grouping of wind turbine units through wind speed area division; for mountain wind farms, VstepCalculated according to the formulas (1) and (2);
n＝round(Nsum/Ngroup) (1)
wherein N is the number of unit groups, NsumAnd NgroupThe round () represents an integer function for the total number of the wind power plant units and the average number of the wind speed groups; vmaxAnd VminEstimated values or actual values of the maximum wind speed and the minimum wind speed of the wind power plant in a time period to be analyzed;
step 1.3, according to the clustering parameter VstepForm by VstepA wind field wind speed zone of gradient;
step 1.4, establishing equivalent wind turbine generators in n groups of wind speed areas according to the grouping number n of the clustering parameter generators;
step 1.5, combining the step 1.1 to the step 1.4 to obtain a spatial distribution model of the clustering wind speed in the expected event scene;
step 1.6, selecting a wind speed time distribution model which comprises a slope wind model, a gust model, a Mexico straw hat wind model and a 'custom piecewise linear function wind speed' model;
step 1.7, solving a wind energy utilization coefficient Cp;
Cp＝0.5(r-0.022β2-5.6)e-0.17r(3)
wherein β is the pitch angle, r satisfies the formula r 2.237 Vw/omega, VwIs wind speed, m/s; omega is angular speed of the fan rotor, rad/s;
step 1.8, calculating an air density reduction factor a in the wind power plant in the mountainous areaTM-H
In the formula: in the formula: rhoHIs the air density at altitude H, g/m3；ρ0The density of air is 1.225g/m at sea level and 15 deg.C under standard conditions of normal temperature and standard atmospheric pressure3(ii) a H is the altitude, unit m; t is0Taking 273 ℃ as absolute temperature; a is the air temperature gradient, and the temperature is taken to be 0.0065 ℃/m; a isHIs an altitude depreciation factor;
the relationship between density and temperature, relative humidity and atmospheric pressure is
Wherein t is air temperature, DEG C; p is atmospheric pressure, hPa；
Relative humidity,%; a isTMIs the air density reduction factor under temperature and relative humidity;
finally, the relationship between the air density and the altitude, the temperature, the relative humidity and the atmospheric pressure is obtained as follows:
ρ＝αTMαHρ0＝αTM-Hρ0(6)
step 1.9, wind power plant fan mechanical power PmechObtaining
Calculating the mechanical power of the n groups of fan units according to a standard aerodynamic model, wherein the formula is shown as (7)
Wherein S is the swept area of the wind wheel, and S is pi R2＝3770m2R is the radius of the wind wheel blade m;
rho is air density, g/m3；VwIs wind speed, m/s;
step 2, constructing a doubly-fed induction generator (DFIG) model in the n groups of wind speed areas;
step 3, constructing a box type step-up transformer model;
step 4, current collection circuit construction;
step 5, constructing a main booster transformer model;
step 6, building a reactive power compensation device model;
and 7, constructing a line model of the wind field access power system.
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Application publication date: 20170728 Assignee: Guizhou Hydrogen Energy Efficiency Energy Technology Co.,Ltd. Assignor: Guizhou University Contract record no.: X2023980043432 Denomination of invention: Electromechanical Transient Model and Modeling Method for Mountain Wind Farm with Weakly Consistent Wind Speed Distribution Granted publication date: 20200707 License type: Common License Record date: 20231017 |
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