CN106992545B - Electromechanical transient model of weakly-consistent wind speed distribution mountain wind power plant and modeling method - Google Patents

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 P_mechConstructing; 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

Electromechanical transient model and modeling method for mountain wind farm with weakly consistent wind speed distribution

technical field

The invention belongs to the technical field of wind power generation and grid connection, and in particular relates to an electromechanical transient model and a modeling method of a mountain wind farm with weakly consistent wind speed distribution.

Background technique

As of 2015, China has installed a total of 92,981 wind turbines, with a cumulative installed capacity of 145,362MW. The installed capacity of wind power has reached a new high, with a year-on-year increase of 26.8%. It can be said that wind power has become the main form of new energy power generation in my country, and the proportion of wind energy is gradually increasing. However, the output of wind power has the characteristics of randomness, intermittency and anti-peak regulation, which poses a series of new problems for the safe and stable operation of the power system, which has attracted extensive attention and in-depth research by industrial experts and academic scholars in the field of power and energy at home and abroad. .

Recently, the key development areas of inland wind power in China have gradually shifted from the “Three North Areas” with severe power curtailment to South China, Southwest China, and East China. Such areas have high altitudes, complex terrain and meteorological conditions, and the operating characteristics of mountain wind farms are quite different. sexual characteristics. The unique meteorological performance of plateau and mountainous areas in Guizhou determines that the environment where wind energy is located has the characteristics of high altitude and high humidity. Mountain wind power is obviously different from plain and offshore wind power in other regions of my country. With the gradual increase in the proportion of wind power installed capacity, it will bring new challenges to the safe and stable operation of the AC and DC transmission side power systems represented by Guizhou Power Grid. Therefore, it is urgent to carry out research on the electromechanical transient model and its modeling method of plateau mountain wind farms based on the geographical and climatic characteristics of the Yunnan-Guizhou Plateau, so as to better grasp the dynamic characteristics of a high proportion of wind energy connected to the power system, so as to provide information for the southwest and similar environments. Lay the foundation for the scientific improvement of regional power grid planning, construction and operation scheduling.

Traditionally, wind farm modeling mainly considers the internal relationship of the electromechanical transient model of wind turbines based on Double Fed Induction Generator (DFIG). Flexible AC transmission devices such as Reactive Power Compensator (Static Var Compensator, SVC), SVG (StaticVar Generator), etc. However, when wind farms are connected to the grid, the reactive power to emit or absorb is limited, and it is difficult to undertake grid voltage adjustment. In particular, the wind power characteristics of wind farms in plateau mountains are more likely to cause voltage and frequency fluctuations at the point of common coupling (PCC). Therefore, in the electromechanical transient analysis when the wind farm is connected to the regional power grid, the connection of models such as SVC and SVG should be considered. In addition, more importantly, most literatures often use the average wind speed in the wind speed simulation of the wind farm prime mover model. However, due to the influence of the mountainous landforms, acceleration effects, and time delay effects, the wind speed variation of each fan in the plateau mountain wind farm is relatively different. big. The wind speed increases with the elevation of the mountain. The wind speed is high at the top of the mountain, the ridge and the canyon tuyere, and the wind speed is small at the basin, valley bottom and leeward. The wind speed on the high mountains is generally high at night, small during the day, and the smallest in the afternoon, while the opposite is true in the foothills and valleys. Mountains can also produce some local circulation, such as valley wind (due to the thermal difference between the top of the mountain and the air near the bottom of the valley, the wind blows from the valley bottom to the top of the mountain during the day, this wind is called "valley breeze"; at night, The wind blowing from the top of the mountain to the bottom of the valley is called "mountain breeze". The mountain wind and the valley wind are collectively called the valley wind. The form of air movement in Sexual wind. During the day, it is "anabatic wind", and at night it is "downslope wind".) and so on. Therefore, the traditional wind farm modeling method cannot reflect the weak consistency of the temporal and spatial distribution of wind in the plateau and mountainous areas, and it is difficult to adapt to technical problems such as the analysis of the dynamic characteristics of the wind farm connection system in the southwest mountainous area.

SUMMARY OF THE INVENTION

The technical problem to be solved by the present invention is to provide an electromechanical transient model and a modeling method for a mountain wind farm with weakly consistent wind speed distribution, so as to solve the problem that the traditional wind farm modeling method in the prior art cannot reflect the weak temporal and spatial distribution of wind in plateau mountains. Due to the characteristics of consistency, it is difficult to adapt to technical problems such as dynamic characteristic analysis of the wind farm connection system in the southwest mountainous area.

Technical scheme of the present invention:

An electromechanical transient model and modeling method for a mountain wind farm with weakly consistent wind speed distribution, comprising:

Step 1. The fans in the wind farm in a specific area are grouped according to the wind speed gradient and constructed by the aerodynamic model P _mech ;

Step 2. Doubly-fed induction generator (DFIG) model construction in n groups of wind speed regions;

Step 3. Build the box-type step-up transformer model;

Step 4. Construction of the collector circuit;

Step 5. Construction of the main step-up transformer model;

Step 6. Model construction of reactive power compensation device;

Step 7, the wind farm is connected to the power system circuit model construction.

The fans in the wind farm in the specific area described in step 1 are constructed according to the wind speed gradient and the aerodynamic model P _mech , which includes the temporal and spatial distribution model of wind speed in the mountain wind farm and the construction of the aerodynamic model of the mountain wind farm. The specific steps include:

Step 1.1. Wind data collection and wind database construction of wind farms in specific regions;

Force distribution evaluation data, carry out data cleaning, and build the wind database of the mountain wind farm in the micro-meteorological environment;

Step 1.2. Clustering of spatial wind speed distribution of wind farms in different time periods;

According to the design wind power or the evaluation wind database of the aforementioned mountain wind farm, for different seasons, different typical working days, and different working hours, the spatial wind speed distribution clustering of the wind farm is carried out according to V _step , and the wind speed zone is divided to realize the grouping of wind turbines; For wind farms, V _step is calculated according to equations (1) and (2);

n=round(N _sum /N _group ) (1)

In the formula, n is the number of turbine groups, N _sum and N _group are the total number of wind farm units and the average number of wind speed groups, and round() represents the rounding function; V _max and V _min are the maximum wind speed of the wind farm during the period to be analyzed. and the estimated or actual value of the minimum wind speed;

Step 1.3, according to the clustering parameter V _step , form the wind speed area of the wind field with V _step as the gradient;

Step 1.4. According to the clustering parameter, the number of groups of wind turbines is n, and the equivalent wind turbines in the wind speed area of n groups are established;

Step 1.5, combining steps 1.1 to 1.4, to obtain the spatial distribution model of the clustered wind speed under the expected event scenario;

Step 1.6. Select the time distribution model of wind speed, including slope wind model, gust model, Mexican straw hat wind model, and "custom piecewise linear function wind speed" model.

Step 1.7, the calculation of wind energy utilization coefficient Cp;

C _p =0.5(r ^- 0.022β2-5.6)e- ^0.17r (3)

Where: β is the pitch angle; r satisfies the formula r= _2.237Vw /ω; Vw is the wind speed, m/s. ω is the angular speed of the fan rotor, rad/s.

Step 1.8. Calculate the air density reduction factor a _TM-H in the mountain wind farm

In the formula: where: ρ _H is the air density when the altitude is H, g/m ³ ; ρ ₀ is the air density under normal temperature and standard atmospheric pressure, and the air density at sea level and 15°C is 1.225g /m ³ ; H is the altitude, in m; T ₀ is the absolute temperature, which is 273°C; a is the air temperature gradient, which is 0.0065°C/m; a _H is the altitude reduction factor;

The relationship between density and temperature, relative humidity, and atmospheric pressure is

为相对湿度，％；a_TM为温度、相对湿度下的空气密度折减因子；Among them, t is air temperature, °C; P is atmospheric pressure, hPa _;

is the relative humidity, %; a _TM is the air density reduction factor under temperature and relative humidity;

Finally, the relationship between air density and altitude, temperature, relative humidity, and atmospheric pressure is:

ρ=α _TM α _H ρ ₀ =α _TM-H ρ ₀ (6)

Step 1.9. Obtaining the mechanical power P _mech of the wind farm fan

According to the standard aerodynamic model, calculate the mechanical power of n groups of fan units, as shown in formula (3-5)

In the formula, S is the sweeping area of the wind rotor (S=πR ² =3770m ² ), R is the radius of the rotor blade, m; ρ is the air density, g/m ³ ; V _w is the wind speed, m/s.

Beneficial effects of the present invention:

Aiming at the weak consistency of wind speed in mountain wind farms, the invention obtains an equivalent wind speed area by clustering the wind speeds of each fan, and then introduces a wind speed value reflecting the weakly consistent distribution of mountain wind farms by means of wind speed and aerodynamic model customization methods. Compared with the prior art, it simulates the time-domain dynamic characteristics of mountain wind farms more accurately;

Overall, compared with the prior art, the present invention has the following obvious beneficial effects:

1) The model uses wind speed and aerodynamic custom methods to introduce wind speed values that reflect weakly consistent distribution to accurately simulate the wind speed conditions at different locations in the wind farm; it avoids the large error in the analysis of mountain wind farms due to only considering the average wind speed of mountain wind farms The problem.

2) The model includes a complete wind farm model. Combined with the historical wind measurement data of the wind farm, a wind speed area of V _step wind speed gradient is established, and a wind speed model based on spatial distribution clustering is constructed, and the fans in each wind speed area are clustered into one group. Then, according to the wind energy distribution evaluation data of the wind farm, the temporal and spatial distribution model of wind speed in a specific scenario is set, the equivalent wind speed area is established, and the classification unit is formed. Wind turbines in mountain wind farms can generally be clustered into 3 to 5 groups. It effectively solves the problem of the lack of the plateau mountain wind farm model.

The invention solves the technical problems that the traditional wind farm modeling method cannot reflect the weak consistency of the temporal and spatial distribution of wind power in the plateau and mountain areas, and it is difficult to adapt to the dynamic characteristic analysis of the wind farm connection system in the southwest mountainous area of my country.

Description of drawings:

Fig. 1 is the overall calculation flow chart involved in the present invention;

Fig. 2 is the calculation flow chart of the aerodynamic module involved in the present invention;

3 is a schematic diagram of the geographical distribution of a certain mountain wind farm in the present invention;

Figure 4 is a schematic diagram of the DFIG model in the PSS/E software;

Fig. 5 is the schematic diagram of SVC model in PSS/E software;

6 is a detailed connection diagram of a wind turbine in a mountain wind farm in the present invention;

Fig. 7 is the traditional simplified model block diagram in the present invention;

8 is a schematic diagram showing the comparison of the simulation results of active power and reactive power between the detailed model proposed by the present invention and the traditional simplified model under the situation of cutting into the wind speed;

9 is a schematic diagram showing the comparison of the active power simulation results of the sixth group of units in the detailed model proposed by the present invention and the traditional simplified model under the condition of low wind speed;

Figure 10 is a schematic diagram of the Mexican "straw hat wind" waveform;

Figure 11 shows the comparison of active power under the rated wind speed scenario of "Straw Hat Wind" in Mexico;

Figure 12 shows the comparison of reactive power under the rated wind speed scenario of "Straw Hat Wind" in Mexico;

Fig. 13 is the value diagram of wind speed of each fan in the present invention;

Fig. 14 is the value diagram of the wind speed of each group of fans in the traditional 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 proposed by the present invention and the traditional simplified model under the condition of weakly consistent distributed wind speed.

Detailed ways

An electromechanical transient model and modeling method for a mountain wind farm with weakly consistent wind speed distribution, including (see Figure 1):

Step 1. The fans in the wind farm in a specific area are grouped according to the wind speed gradient and constructed by the aerodynamic model P _mech ;

The fans in the wind farm in the specific area described in step 1 are constructed according to the wind speed gradient and the aerodynamic model P _mech , which includes the temporal and spatial distribution model of the wind speed of the mountain wind farm and the construction of the aerodynamic model of the mountain wind farm. The specific steps include (see Figure 2):

Step 1.1. Wind data collection of wind farms in a specific area and construction of a wind database; according to the wind resource assessment data of the wind farm and the wind power distribution assessment data of the wind farm, data cleaning is carried out, and the wind database of the mountain wind farm under the micro-meteorological environment is constructed;

Step 1.2. Clustering of spatial wind speed distribution of wind farms in different time periods;

According to the design wind power or the evaluation wind database of the aforementioned mountain wind farm, for different seasons, different typical working days, and different working hours, the spatial wind speed distribution clustering of the wind farm is carried out according to V _step , and the wind speed zone is divided to realize the grouping of wind turbines; For wind farms, V _step is calculated according to equations (1) and (2);

n=round(N _sum /N _group ) (1)

In the formula, n is the number of turbine groups, N _sum and N _group are the total number of wind farm units and the average number of wind speed groups, and round() represents the rounding function; V _max and V _min are the maximum wind speed of the wind farm during the period to be analyzed. and the estimated or actual value of the minimum wind speed;

Step 1.3, according to the clustering parameter V _step , form the wind speed area of the wind field with V _step as the gradient;

Step 1.4. According to the clustering parameter, the number of groups of wind turbines is n, and the equivalent wind turbines in the wind speed area of n groups are established;

Step 1.5, according to step 1.1 to step 1.4, the spatial distribution model of clustered wind speed under the expected event scenario can be obtained;

Step 1.6, select a wind speed time distribution model, the wind speed time distribution model includes a slope wind model, a gust model, a Mexican straw hat wind model, and a "custom piecewise linear function wind speed" model.

Step 1.7, the calculation of wind energy utilization coefficient C _p ;

C _p =0.5(r ^- 0.022β2-5.6)e- ^0.17r (3)

Where: β is the pitch angle; r satisfies the formula r=2.237V _w /ω; V _w is the wind speed, m/s; ω is the fan rotor angular speed, rad/s; taking 9.6m/s as an example, r=14.362 ; therefore C _p =0.381.

Step 1.8. Calculate the air density reduction factor a _TM-H in the mountain wind farm

In the formula: where: ρ _H is the air density when the altitude is H, g/m ³ ; ρ ₀ is the air density under normal temperature and standard atmospheric pressure, and the air density at sea level and 15°C is 1.225g /m ³ ; H is the altitude, in m; T ₀ is the absolute temperature, which is 273°C; a is the air temperature gradient, which is 0.0065°C/m; a _H is the altitude reduction factor;

The relationship between air density and temperature, relative humidity, and atmospheric pressure is

为相对湿度，％；a_TM为温度、相对湿度下的空气密度折减因子；Among them, t is air temperature, °C; P is atmospheric pressure, hPa _;

is the relative humidity, %; a _TM is the air density reduction factor under temperature and relative humidity;

Finally, the relationship between air density and altitude, temperature, relative humidity, and atmospheric pressure is:

ρ=α _TM α _H ρ ₀ =α _TM-H ρ ₀ (6)

Step 1.9. Obtaining the mechanical power P _mech of the wind farm fan

According to the standard aerodynamic model, calculate the mechanical power of n groups of fan units, as shown in formula (3-5)

In the formula, S is the sweeping area of the wind rotor (S=πR2=3770m ² ), R is the radius of the rotor blade, m; ρ is the air density; V _w is the wind speed.

Step 2. Doubly-fed induction generator (DFIG) model construction in the n-group wind speed region

It includes generator/inverter model, electrical control model, shafting model, and pitch angle control model. Generally, the terminal voltage of the DFIG wind turbine in the mountain wind farm is 0.69kV.

Step 3. Construction of the box-type step-up transformer model

The present invention adopts the model of oil-immersed box-type transformer with model S11-2200/35. Generally, wind turbines in mountain wind farms use 0.69/38.5kV box-type step-up transformers to step up to 38.5kV.

Step 4. Construction of the collector circuit

Generally, according to the installed capacity and environmental conditions, 6 to 12 wind turbines are connected to the 35kV bus of the wind farm after being connected by the collector line in the mountain wind farm. The collector line usually includes two forms of power cable and overhead line. Here, the model of the collector line is constructed using the cable model YJLY23-3×240.

Step 5. Construction of the main step-up transformer model

The model is SZ11-100000/110 three-phase, double-winding, self-cooling oil-immersed low-loss on-load voltage regulating power transformer. Generally, mountain wind farms use 35/121kV (or 35/230kV) to increase the voltage to 121kV (or 230kV), and connect to the grid nearby through the access system line.

Step 6. Select the reactive power compensation device model

Generally, the step-up substation of the mountain wind farm adopts the SVC model, the SVG model or the FC and SVG joint compensation model as the reactive power compensation device of the wind farm, and the capacity is generally about 20% of the total capacity of the wind farm.

Step 7. The wind farm is connected to the power system line model construction. Here, the power cable model YJY23-3×240 is used to construct the line model of the wind farm connected to the power system.

The results of the above 7 steps finally form the electromechanical transient model of the mountain wind farm with weakly consistent wind speed distribution.

The technical solution of the present invention is further described below by taking a wind farm in a mountainous area of Guizhou as an example. The mountain belongs to the category of low wind speed, and the wind speed is easily affected by the terrain, and the wind speed varies greatly in different locations. The wind speed is mainly distributed in the 2-10m/s wind speed section. The cut-in wind speed of the wind farm fleet 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 20m/s. The distribution of fans in the wind farm is shown in Figure 3.

1. First build the aerodynamic model P _mech . Including the spatiotemporal distribution model of wind speed in mountain wind farms and the aerodynamic model of mountain wind farms. According to formulas (1) and (2), the value of n is 6, and the value of V _step is 1.1 m/s.

n=round(N _sum /N _group ) (1)

Among them, the N _sum value is 50, and the N _group is set to 9. In formula (2), the value of V _max is 9.5 m/s, and the value of V _min is 3 m/s.

Secondly, a wind speed area with a wind speed gradient of 1.1 m/s is formed and a total of 6 groups of equivalent wind turbines in the wind speed area are established. The grouping of wind farms according to the wind speed gradient is shown in Table 1.

Table 1 Grouping of wind farms according to wind speed distribution

Thirdly, according to step 1.5, the spatial distribution model of the clustered wind speed under the expected event scenario is established. Combined with the evolution characteristics of wind speed in time dimension, four classical wind speed models (respectively, basic wind, slope wind, gust and random wind) are added, and time constraints are added to obtain "Mexican straw hat wind" and "custom piecewise linear function wind speed", And according to step 1.6, select a wind speed time distribution model from the slope wind model, the gust model, the "Mexican straw hat wind" model, and the "custom piecewise linear function wind speed" model. Combined with specific wind farm operation scenarios, carry out time dimension matching in different seasons, different typical working days, and different working hours, and build a spatiotemporal distribution model of clustered wind speeds under the required operating scenarios according to steps 1.5 and 1.6.

Among them, the slope wind model, the gust model, the "Mexican straw hat wind" model, and the "custom piecewise linear function wind speed" model are shown below.

Step 1.6. Select the time distribution model of wind speed, including slope wind model, gust model, Mexican straw hat wind model, and "custom piecewise linear function wind speed" model;

The slope wind model is mainly described by the following three parameters: start time _{t sr} , end time _ter , and wind speed increase amplitude Ar . The mathematical expression for slope wind is:

In the formula, D _r = _ter -t _sr .

The gust model is mainly described by the following three parameters: start time t _sg , end time t _eg , and maximum wind speed V _max . The mathematical expression for gust is:

In the formula, A _g =(V _max -V ₀ )/2, and D _g =t _eg -t _sg .

The "Mexican straw hat wind" model is mainly described by the following parameters: V ₀ is the initial wind speed, V _max is the maximum wind speed, and V _min is the minimum wind speed.

The "custom piecewise linear function wind speed" model is described by the following parameters: V ₀ is the initial wind speed, and V ₁ , V ₂ , V ₃ , V ₄ , and V ₅ are the wind speeds at each time point.

Finally, the mechanical power P _mech of the wind farm fan is obtained. Including wind energy utilization coefficient C _p , air density reduction factor a _TM-H .

2. The DFIG model is constructed in the n-group wind speed area. Including generator/converter model, electrical control model, shafting model, pitch angle control model. Generally, the terminal voltage of the DFIG wind turbine in the mountain wind farm is 0.69kV. The DFIG model in PSS/E software is shown in Figure 4. The DFIG model consists of generator/frequency converter model, electrical control model, shafting model, pitch angle control model, wind turbine aerodynamic model and wind speed model. The shafting model of the wind turbine and the doubly-fed asynchronous motor model can be expressed by equations (7) and (8) respectively:

In the formula, T _M and T _G are the inertia time constants of the wind turbine and generator respectively; K _S is the stiffness coefficient of the shaft; D _M and D _G are the damping coefficients of the rotor of the wind turbine and the rotor of the generator, respectively; θ _S is the two The relative angular displacement between the masses; M _M and M _E are the mechanical torque of the wind turbine and the electromagnetic torque of the generator, respectively; ω _M and ω _G are the rotational speeds of the wind turbine and the generator rotor, respectively, and ω ₀ is the synchronous rotational speed ; X _r is the rotor leakage reactance; X _m is the excitation reactance; p is the differential operator; _s is the slip; X _s is the synchronous reactance _; ; u _ds , u _qs are the d and q axis components of the stator voltage; u _dr , u _qr are the d and q axis components of the rotor voltage; u' _dr = ud _dr X _m /(X _r +X _m ), u' _qr =u _qr X _m /(X _r +X _m ) is an intermediate variable; i _ds , i _qs are the d and q axis components of the stator current; E' _d , E' _q are the d and q axis components of the transient potential ;ω _s is the rotational angular velocity of the coordinate system, and ω _s =ω ₀

3. Construction of the box-type step-up transformer model. An oil-immersed box-type transformer model S11-2200/35 is used. Generally, wind turbines in mountain wind farms use 0.69/38.5kV box-type step-up transformers to step up to 38.5kV.

4. Construction of collecting circuit. Generally, according to the installed capacity and environmental conditions, 6 to 12 wind turbines are connected to the 35kV bus of the wind farm after being connected by the collector line in the mountain wind farm. The collector line usually includes two forms of power cable and overhead line. Here, the model of the collector line is constructed using the cable model YJLY23-3×240.

5. Construction of the main step-up transformer model. The main step-up transformer model is constructed; the three-phase, double-winding, self-cooling oil-immersed low-loss on-load voltage regulating power transformer with model SZ11-100000/110 is used. Generally, mountain wind farms use 35/121kV (or 35/230kV) to increase the voltage to 121kV (or 230kV), and connect to the grid nearby through the access system line.

6. Model construction of reactive power compensation device. Generally, the mountain wind farm adopts the SVC model, the SVG model or the FC and SVG joint compensation model as the reactive power compensation device of the wind farm, and the capacity is about 20% of the total capacity of the wind farm. The SVG model in the PSS/E software is shown in Figure 5, and the corresponding model of the SVG model is the CSVGN5 model. In the SVG model, after the bus voltage is collected, it is filtered and subtracted from the voltage setting value to generate a voltage error value. After the voltage error value is subtracted from the auxiliary signal, the susceptance error value is generated through the regulator, and the final voltage error value is generated. , After the susceptance error value is transformed by the quick judgment logic and susceptance-reactance, the compensation reactance is generated.

7. Construction of the line model of the wind farm connected to the power system. Here, the power cable model YJY23-3×240 is used to construct the line model of the wind farm connected to the power system

The wind farm grouping and simplified system obtained from steps 1 to 7 are shown in Figures 6 and 7 .

Scenario 1 Low wind speed fan cut in

In Step 1, select "Custom Piecewise Linear Function Wind Speed", and the wind speed gradually increases from 0.5m/s to 7m/s. When the wind speed increases to the cut-in wind speed, the fan is put into operation. The power output of the wind farm PCC bus is shown in Figure 8.

It can be seen from Figure 8 that the maximum deviation of the instantaneous active power output of the simplified model is 17.5MW, and the maximum deviation of the instantaneous reactive power output is 3.75Mvar. In the simplified model, the injected active power at the PCC bus has a large fluctuation. This scenario shows that when the wind speed gradually increases from 0.5m/s, the dynamic simulation of the wind power system with the simplified model will produce large deviations.

Scenario 2 Low wind speed fan out of service

The location of the sixth group of wind farms is relatively scattered. Therefore, select "Custom Piecewise Linear Function Wind Speed" in step 1, and in the sixth group, the wind speeds of #1~#7 fans gradually decrease from the rated wind speed to around 3m/s, while the #12 and #13 fans are at rated wind speeds. near wind speed. At this time, the output power of the sixth group of units is shown in Figure 9.

It can be seen from Figure 9 that when the wind speed of the wind farm gradually decreases, the instantaneous active power output error of the simplified model is relatively large. This scenario shows that in the process of gradually decreasing wind speed, using the simplified model to carry out the dynamic simulation of the wind power system will produce large deviations.

Scenario 3 Rated wind speed of Mexico "Straw Hat Wind"

In step 1, select "Mexican straw hat wind", and select the rated wind speed (9.5m/s) as the initial wind speed value. The schematic diagram of "Mexican straw hat wind" is shown in Figure 10. In the simplified model and the detailed model, the power output comparison of the wind farm is shown in Figure 11 and Figure 12. It can be seen from Figure 11 that the deviation of the active power in the simplified model relative to the detailed model is about 2.5MW, and the relative deviation is about 3.45%. It can be seen from Figure 12 that the deviation of reactive power in the simplified model relative to the detailed model is about 1MW, and the relative deviation is about 7.55%. Therefore, in the Mexican "straw hat wind" scenario, the relative deviation of the active power and reactive power of the simplified model is relatively large.

Scenario 4 "Custom Piecewise Linear Function Wind Speed"

In order to highlight the wind speed changes at different positions of the fans, select "Customize piecewise linear function wind speed" in step 1, select the rated wind speed (9.5m/s) for the wind speed value, and use the PSS/E software to customize the function according to the formula (2). ) for the definition of wind speed, and established the average value of different wind speeds at 50 fan locations, as shown in Figure 13. According to equation (1), the wind speed of the average unit in the simplified model is obtained by fitting the wind speed of each group in the detailed model, as shown in Figure 14. A comparison of variables related to the detailed model and the simplified model is shown in Figure 15. As a result, the relative deviation of the "custom piecewise linear function wind speed" is small, which is different from the Mexican "straw hat wind" scene. However, the relative deviations of the simplified models of both are within a reasonable range. It can be seen from Figure 15 that the maximum deviation of the instantaneous active power of the simplified model is 2.1MW, and the maximum deviation of the instantaneous reactive power is 1.9Mvar.

In order to illustrate the rationality of the simplified model, the average deviation of active power _EP and the average deviation of reactive power _EQ are defined as evaluation indicators, and the calculation formula is as follows:

In the formula, P _in and Q _in represent the active and reactive power at the PCC of the simplified wind farm, respectively; P _n and Q _n represent the active and reactive power at the PCC of the detailed wind farm, respectively; n is the sequence of integral calculation for each simulation step size No.

The relative deviations of the simplified models under different scenarios are shown in Table 2. Among them, the fault disturbance is a three-phase short circuit of the connecting line between the PCC bus and the grid-side bus, and the disturbance time is set to 0.25s.

Table 2 Deviations between simplified and detailed models

It can be seen from Table 2 that when the wind speed is the same, the output active power deviation of the wind farm is small. When the wind speed distribution changes, the output active power deviation increases to 1.32%. When the system fails, the reactive power deviation changes greatly, for the same wind speed, it increases to 5.77%, and for different wind speeds, it increases to 5.42%. The power deviations are all within a reasonable range. It can be seen that the simplified model can more accurately reflect the power output characteristics of the wind farm when the wind speed fluctuates around the rated value.

From the above analysis of the temporal and spatial distribution of wind speed in different wind farms, it can be seen that when the wind speed fluctuates around the rated value, the relative deviation of the simplified model is within a reasonable range, which can be used to analyze the dynamic characteristics of wind power access to regional systems. When the wind speed in the wind farm is small or some units are running at a low wind speed, a simplified model is used to analyze the dynamic response of the wind farm connection system, which will cause a large deviation. In addition, the simplified model cannot reflect the outage of wind turbines in the wind farm. In this case, a detailed model should be used to analyze the dynamic response of wind power connected to the local power grid.

To sum up, the present invention proposes an electromechanical transient model and a modeling method for a mountain wind farm with weakly consistent wind speed distribution. The calculation results of the above implementation case show that:

1) The validity and practicability of the electromechanical transient model and the modeling method of the weakly consistent wind speed distribution mountain wind farm;

2) When some units are cut in or out of operation due to the change of wind speed in the mountain wind farm, the relative deviation of the simplified model is relatively large. At this time, the simplified model is not suitable for the electromechanical transient analysis of the wind power connection local system.

3) The detailed model can reflect the weakly consistent wind speed distribution characteristics of mountain wind farms, and is suitable for low wind speed variation scenario analysis, internal fault disturbance analysis and dynamic characteristic analysis of high-density wind power access systems in mountain wind farms.

The above is only an example of an embodiment of the present invention, and does not limit the present invention in any form. Anything that does not depart from the content of the technical solution of the present invention still belongs to the scope of the technical solution of the present invention.

Claims (1)

1. A modeling method for the electromechanical transient model of a weakly consistent wind speed distribution mountain wind farm, the modeling method comprising: Step 1. The fans in the wind farm in a specific area are grouped according to the wind speed gradient and constructed by the aerodynamic model P _mech ; including the temporal and spatial distribution model of wind speed in the mountain wind farm and the construction of the aerodynamic model of the mountain wind farm. The specific steps include: Step 1.1. Wind data collection of wind farms in a specific area and construction of a wind database; according to the wind resource assessment data of the wind farm and the wind power distribution assessment data of the wind farm, data cleaning is carried out, and the wind database of the mountain wind farm under the micro-meteorological environment is constructed; Step 1.2. Clustering of spatial wind speed distribution of wind farms in different time periods; According to the design wind power or the evaluation wind database of the aforementioned mountain wind farm, for different seasons, different typical working days, and different working hours, the spatial wind speed distribution clustering of the wind farm is carried out according to V _step , and the wind speed zone is divided to realize the grouping of wind turbines; For wind farms, V _step is calculated according to equations (1) and (2); n=round(N _sum /N _group ) (1)

In the formula, n is the number of turbine groups, N _sum and N _group are the total number of wind farm units and the average number of wind speed groups, and round() represents the rounding function; V _max and V _min are the maximum wind speed of the wind farm during the period to be analyzed. and the estimated or actual value of the minimum wind speed; Step 1.3, according to the clustering parameter V _step , form the wind speed area of the wind field with V _step as the gradient; Step 1.4. According to the clustering parameter, the number of groups of wind turbines is n, and the equivalent wind turbines in the wind speed area of n groups are established; Step 1.5, combining steps 1.1 to 1.4, to obtain the spatial distribution model of the clustered wind speed under the expected event scenario; Step 1.6. Select the time distribution model of wind speed, including slope wind model, gust model, Mexican straw hat wind model, and "custom piecewise linear function wind speed" model; Step 1.7, the calculation of wind energy utilization coefficient Cp; C _p =0.5(r ^- 0.022β2-5.6)e- ^0.17r (3) In the formula: β is the pitch angle; r satisfies the formula r=2.237Vw/ω; V _w is the wind speed, m/s; ω is the fan rotor angular speed, rad/s; Step 1.8. Calculate the air density reduction factor a _TM-H in the mountain wind farm

In the formula: where: ρ _H is the air density when the altitude is H, g/m ³ ; ρ ₀ is the air density under normal temperature and standard atmospheric pressure, and the air density at sea level and 15°C is 1.225g /m ³ ; H is the altitude, in m; T ₀ is the absolute temperature, which is 273°C; a is the air temperature gradient, which is 0.0065°C/m; a _H is the altitude reduction factor; The relationship between density and temperature, relative humidity, and atmospheric pressure is

Among them, t is air temperature, °C; P is atmospheric pressure, hPa _;

is the relative humidity, %; a _TM is the air density reduction factor under temperature and relative humidity; Finally, the relationship between air density and altitude, temperature, relative humidity, and atmospheric pressure is: ρ=α _TM α _H ρ ₀ =α _TM-H ρ ₀ (6) Step 1.9. Obtaining the mechanical power P _mech of the wind farm fan According to the standard aerodynamic model, calculate the mechanical power of n groups of fan units, as shown in formula (7)

where S is the swept area of the wind rotor, S=πR ² =3770m ² , R is the radius of the rotor blade, m; ρ is air density, g/m ³ ; V _w is wind speed, m/s; Step 2. Doubly-fed induction generator (DFIG) model construction in n groups of wind speed regions; Step 3. Build the box-type step-up transformer model; Step 4. Construction of the collector circuit; Step 5. Construction of the main step-up transformer model; Step 6. Model construction of reactive power compensation device; Step 7, the wind farm is connected to the power system circuit model construction.

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