CN110212570B - Wind power plant equivalent model based on MMSE mining and construction method and application thereof - Google Patents

Abstract

The invention discloses a wind farm equivalent model based on MMSE mining and its construction method and application, belonging to the field of wind farm equivalent model research, wherein the construction method includes: using the random key factors of each fan in the wind farm to construct The time series of the dynamic process of the wind turbine is used to calculate the multi-scale entropy value of the dynamic process of the wind turbine; the multi-scale entropy value of the dynamic process of the wind turbine is used as a clustering index to construct the equivalent model of the wind farm. The invention takes the time series of the dynamic process of the wind farm as the data mining object, extracts its multi-scale entropy value, and constructs an equivalent model of the wind farm using this as a clustering index. Compared with the traditional wind farm equivalent model, the model of the present invention can better simulate the dynamic process of the wind farm in various fault scenarios of the power system, and greatly reduces the number of wind farm equivalents. This solves the technical problem that the current wind power equivalent model is applicable to a single scenario and it is difficult to effectively reflect the dynamic process of the wind farm.

Description

Wind farm equivalent model based on MMSE mining and its construction method and application

Technical Field

The present invention belongs to the research field of wind farm equivalent models, and more specifically, relates to a wind farm equivalent model mined based on MMSE (multivariate multiscale entropy theory) and a construction method and application thereof.

Background Art

According to the 13th Five-Year Plan for Power Development issued by the National Development and Reform Commission and the National Energy Administration, the installed capacity of wind power in China will reach more than 210 million kilowatts by 2020. With the annual increase in installed capacity of wind power, the scale of wind farms is also getting larger and larger. Wind farms are often composed of dozens or even hundreds of wind turbines. If a detailed model is established, it will inevitably cause dimensionality disaster and it will be difficult to meet the research needs of simulation accuracy and efficiency.

Therefore, when simulating and analyzing a wind farm, it is necessary to calculate its equivalent value. The single-machine equivalent error of a wind farm is relatively large, so the equivalent research of wind farms in recent years has mainly focused on multi-machine equivalents, including the construction of a wind farm equivalent index system and the optimization of clustering algorithms in the equivalent process. The research on equivalent indexes started early, and the technology is relatively mature now; clustering algorithm optimization focuses on improving clustering accuracy.

Although the above studies study wind turbine equivalence from different perspectives, the equivalent model is generally only applicable to a relatively single scenario. As the simulation time or operating conditions of the wind farm change, the equivalent method must change, and the applicability of the equivalent model is greatly limited.

It can be seen that the current wind power equivalent model is applicable to a single scenario and is difficult to effectively reflect the dynamic process of wind farms.

Summary of the invention

In view of the above defects or improvement needs of the prior art, the present invention provides a wind farm equivalent model based on MMSE mining and its construction method and application, thereby solving the technical problems that the current wind power equivalent model has a single applicable scenario and is difficult to effectively reflect the dynamic process of the wind farm.

To achieve the above object, according to one aspect of the present invention, a method for constructing a wind farm equivalent model based on MMSE mining is provided, comprising the following steps:

(1) The dynamic process time series of each wind turbine in the wind farm is constructed using the random key factors of each wind turbine in the wind farm, and the multi-scale entropy value of the wind turbine dynamic process is calculated using the dynamic process time series of the wind turbine;

(2) The wind farm equivalent model is constructed using the multi-scale entropy value of the wind turbine dynamic process as the clustering indicator.

Furthermore, the random key factors include: wind turbine control mode, wind speed, wind direction and grid short circuit fault location.

Further, step (1) comprises:

(11) For the random key factors of each wind turbine in the wind farm, the probability characteristics of the random key factors are statistically analyzed based on historical data, and the random key factors are sampled using a random variable generator and input into the wind turbine to obtain the dynamic process time series of the wind turbine;

(12) The time series of the dynamic process of the wind turbine is coarse-grained to obtain a coarse-grained time series, and the multi-scale entropy value of the dynamic process of the wind turbine is calculated using the coarse-grained time series.

Further, step (2) comprises:

The multi-scale entropy value of the wind turbine dynamic process is used as the clustering index. The number of clusters is set to K, and K equivalent clusters are obtained. The relevant parameters in each equivalent cluster are calculated to construct the equivalent model of the wind farm.

Furthermore, the relevant parameters include relevant parameters of equivalent wind turbines, equivalent parameters of transformers and equivalent parameters of line impedances.

Furthermore, the wind turbines in the wind farm are of the same model and capacity, and the relevant parameters of the equivalent wind turbines include:

Among them, S _eq is the equivalent parameter of the capacity of the wind turbine, m represents the equivalent number of wind turbines in the equivalent group, S _i is the capacity of the i-th wind turbine, P _eq is the equivalent parameter of the active power of the wind turbine, P _i is the active power of the i-th wind turbine, Q _eq is the equivalent parameter of the reactive power of the wind turbine, _Qi is the reactive power of the i-th wind turbine, x _m-eq is the equivalent parameter of the excitation branch reactance, x _m is the excitation branch reactance, x _s-eq is the equivalent parameter of the stator winding reactance, x _s is the stator winding reactance, x _r-eq is the equivalent parameter of the rotor winding reactance, x _r is the rotor winding reactance, r _s-eq is the equivalent parameter of the stator winding resistance, r _s is the stator winding resistance, r _r-eq is the equivalent parameter of the rotor winding resistance, r _r is the rotor winding resistance, He _eq is the equivalent parameter of the shaft inertia time constant, H _i is the shaft inertia time constant of the i-th fan, _Keq is the equivalent parameter of the shaft stiffness coefficient, _Ki is the shaft stiffness coefficient of the i-th fan, _Deq is the equivalent parameter of the shaft damping coefficient, _Di is the shaft damping coefficient of the i-th fan, 1≤i≤m.

Furthermore, the equivalent parameters of the transformer include:

Among them, S _T represents the transformer capacity, x _T represents the transformer reactance, S _T-eq represents the equivalent parameter of the transformer capacity, x _T-eq represents the equivalent parameter of the transformer reactance, and m represents the equivalent number of wind turbines in the equivalent group.

Furthermore, equivalent parameters of line impedance include:

Among them, _Zeq is the equivalent parameter of branch impedance, _Zk is the branch impedance of the kth trunk cable, _Pj is the output power of the jth wind turbine, _Pi is the output power of the ith wind turbine, _Yeq is the equivalent parameter of ground admittance, _Yi is the ground admittance of the trunk cable in the ith wind turbine, m represents the number of equivalent wind turbines in the equivalent machine group, 1≤i≤m, n is the number of wind turbines in the trunk wind turbine branch, 1≤k≤i, k≤j≤n.

According to another aspect of the present invention, a wind farm equivalent model based on MMSE mining is provided. The wind farm equivalent model is constructed by a method for constructing a wind farm equivalent model based on MMSE mining.

According to another aspect of the present invention, an application of a wind farm equivalent model based on MMSE mining is provided, wherein the wind farm equivalent model is applied to dynamic process simulation in various scenarios of a power system.

In general, the above technical solutions conceived by the present invention can achieve the following beneficial effects compared with the prior art:

(1) The present invention uses the time series of the dynamic process of the wind farm as the data mining object, extracts its multi-scale entropy value, and uses it as a clustering indicator to construct a wind farm equivalent model. Compared with the traditional wind farm equivalent model, the model of the present invention can better simulate the dynamic process of the wind farm in various fault scenarios of the power system, greatly reducing the number of wind farm equivalents. This solves the technical problem that the current wind power equivalent model has a single applicable scenario and is difficult to effectively reflect the dynamic process of the wind farm.

(2) In terms of equivalent effect, the active output characteristics of the wind farm equivalent model of the present invention are highly consistent with the detailed model. Especially after a power system failure, the wind farm equivalent model of the present invention can well reflect the dynamic characteristics of the wind turbine, thereby verifying the accuracy of the wind farm equivalent model of the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is an overall flow chart of a method for constructing a wind farm equivalent model based on MMSE mining provided by an embodiment of the present invention;

FIG2 is a diagram of a process for extracting wind turbine clustering indices according to an embodiment of the present invention;

FIG3 is a diagram of a simulation system provided by Embodiment 1 of the present invention;

FIG4 is a multi-scale entropy value of a fan provided by Example 1 of the present invention;

FIG. 5 is a disturbance curve at the PCC under different models provided in Example 1 of the present invention.

DETAILED DESCRIPTION

In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

As shown in FIG1 , a method for constructing a wind farm equivalent model based on MMSE mining includes the following steps:

(1) The dynamic process time series of each wind turbine in the wind farm is constructed using the random key factors of each wind turbine in the wind farm, and the multi-scale entropy value of the wind turbine dynamic process is calculated using the dynamic process time series of the wind turbine;

(2) The wind farm equivalent model is constructed using the multi-scale entropy value of the wind turbine dynamic process as the clustering indicator.

The control of a doubly-fed wind turbine usually includes rotor-side control and grid-side control. The goal of grid-side control is usually to keep the DC capacitor voltage constant and control the power factor at the wind turbine access point; while the goal of rotor-side control is usually to achieve wind turbine speed tracking wind speed, thereby achieving variable speed constant frequency. In terms of the external dynamic characteristics of the wind turbine, the control methods of the wind turbine are mainly divided into constant voltage control and constant power factor control. Constant voltage control can realize the reactive power regulation function of the wind turbine. After a grid fault occurs, the active power fluctuation at the wind turbine grid connection point is relatively large, the process of entering a steady state is slow, and the fault ride-through capability is low; while the unit under constant power factor control has good dynamic characteristics and can enter a steady state quickly after a grid fault occurs, but the active power recovery value is usually too large.

Wind speed is the most important factor affecting the active power output of wind turbines. In addition, in order to achieve maximum wind energy tracking while taking into account the safety of wind turbine operation, the wind turbine speed and pitch angle of the doubly fed wind turbine at different wind speeds are different, and the wind turbine output also has a relatively complex mathematical relationship with the wind speed. Therefore, the difference in wind speed increases the complexity of the dynamic process of the wind turbine's external output.

A wind farm usually consists of multiple wind turbines. The input wind speed of the downwind wind turbine is lower than that of the upwind wind turbine due to the shielding effect of the upwind wind turbine. The wind turbine wake effect model considering terrain factors is used to obtain the expression of the wind speed correlation between wind turbines. Due to the existence of the wake effect, the dynamic characteristics of different wind turbines, especially the dynamic characteristics of the active power output by the wind turbines, are no longer independent of each other, but have a certain correlation.

When the wind direction changes, the wind turbine, under the control of the yaw system, keeps the windward surface of the rotor blades perpendicular to the wind direction. As the wind direction changes, the overlap area between the wind turbines will change, which will in turn affect the wake effect between the wind turbines. The change in the wake effect between the wind turbines will cause changes in the wind turbine speed and pitch angle, and at the same time change the overall active power output of the wind farm, thereby affecting the dynamic process of the wind farm.

At present, doubly-fed wind turbines are generally equipped with crowbar protection. When a grid fault occurs, the access of the crowbar protection will lock the wind turbine rotor-side converter, causing the wind turbine to operate asynchronously and absorb a large amount of reactive power from the grid, thereby changing the reactive power dynamic process of the doubly-fed wind turbine during low voltage ride-through. In order to effectively suppress the rotor fault current, the resistance of the crowbar protection resistor needs to be large enough; however, too large a resistance will cause overvoltage on the rotor side, charge the DC side capacitor, cause overvoltage on the DC side bus, and affect the dynamic process of the wind turbine.

After a power system fault occurs, the wind turbine port voltage is mainly related to the short-circuit current at the short-circuit point and the mutual impedance between the wind turbine port and the short-circuit point, while the short-circuit current at the short-circuit point is related to the self-impedance of the short-circuit point. Therefore, changes in the location of the fault (which is directly related to the degree of voltage drop at the wind turbine port) will cause differences in the dynamic voltage at the wind turbine port, which in turn affects the input of the wind turbine crowbar protection and the current on the wind turbine rotor side. The dynamic process of the wind turbine is closely related to the location of the grid short-circuit fault.

From the above analysis, it can be seen that there are many factors that affect the dynamic process of wind farms. The present invention aims to obtain a mathematical expression of the dynamic characteristics of wind turbines by means of data mining, that is, to obtain effective information that can describe the dynamic characteristics of wind turbines from a large number of wind turbine dynamic processes, and use this as a clustering index to perform wind farm equalization. Among the key factors affecting the dynamic characteristics of wind turbines, some factors are constant for a certain wind turbine, such as the resistance of the crowbar protection resistor; while more factors are variable, such as wind speed, and the different dynamic processes of a certain wind turbine are mainly caused by the changes in these random key factors. Therefore, by adjusting the values of random key factors and using them as the input of the wind turbine, a large number of wind turbine dynamic processes can be obtained, providing the necessary basis for the subsequent data mining work. Among the key factors affecting the dynamic process of wind turbines, the wake effect in the wind direction and the resistance of the crowbar protection resistor may be different in different wind turbines, but they are fixed for a single wind turbine; while the wind turbine control mode, wind speed, wind direction, and grid short-circuit fault location are random and cannot be exhaustively enumerated.

In summary, the random key factors include: wind turbine control mode, wind speed, wind direction and grid short circuit fault location.

As shown in FIG2 , step (1) includes:

(11) For the random key factors of each wind turbine in the wind farm, the probability characteristics of the random key factors are statistically analyzed based on historical data, and the random key factors are sampled using a random variable generator and input into the wind turbine to obtain the dynamic process time series of the wind turbine;

(12) The time series of the dynamic process of the wind turbine is coarse-grained to obtain a coarse-grained time series, and the multi-scale entropy value of the dynamic process of the wind turbine is calculated using the coarse-grained time series.

Further, step (2) comprises:

The multi-scale entropy value of the wind turbine dynamic process is used as the clustering index. The number of clusters is set to K. The k-means algorithm is used to perform cluster analysis on wind turbines to obtain K equivalent machine groups. The relevant parameters in each equivalent machine group are calculated to construct the equivalent model of the wind farm.

Furthermore, the relevant parameters include relevant parameters of equivalent wind turbines, equivalent parameters of transformers and equivalent parameters of line impedances.

Furthermore, the wind turbines in the wind farm have the same model and capacity, so the equivalent wind turbine parameters are related to the number of wind turbines. The relevant parameters of the equivalent wind turbines include:

Among them, S _eq is the equivalent parameter of the capacity of the wind turbine, m represents the equivalent number of wind turbines in the equivalent group, S _i is the capacity of the i-th wind turbine, P _eq is the equivalent parameter of the active power of the wind turbine, P _i is the active power of the i-th wind turbine, Q _eq is the equivalent parameter of the reactive power of the wind turbine, _Qi is the reactive power of the i-th wind turbine, x _m-eq is the equivalent parameter of the excitation branch reactance, x _m is the excitation branch reactance, x _s-eq is the equivalent parameter of the stator winding reactance, x _s is the stator winding reactance, x _r-eq is the equivalent parameter of the rotor winding reactance, x _r is the rotor winding reactance, r _s-eq is the equivalent parameter of the stator winding resistance, r _s is the stator winding resistance, r _r-eq is the equivalent parameter of the rotor winding resistance, r _r is the rotor winding resistance, He _eq is the equivalent parameter of the shaft system inertia time constant, H _i is the shaft inertia time constant of the i-th fan, _Keq is the equivalent parameter of the shaft stiffness coefficient, _Ki is the shaft stiffness coefficient of the i-th fan, _Deq is the equivalent parameter of the shaft damping coefficient, _Di is the shaft damping coefficient of the i-th fan, 1≤i≤m.

Furthermore, the wind turbines are connected to the common connection point through step-up transformers. Since the wind turbine capacities are the same, it can be assumed that the step-up transformer capacities are also the same. The equivalent parameters of the transformers include:

Among them, S _T represents the transformer capacity, x _T represents the transformer reactance, S _T-eq represents the equivalent parameter of the transformer capacity, x _T-eq represents the equivalent parameter of the transformer reactance, and m represents the equivalent number of wind turbines in the equivalent group.

Furthermore, based on the principle that the voltage loss before and after the equalization is unchanged, the line impedance is equalized, and the equivalent parameters of the line impedance include:

Among them, _Zeq is the equivalent parameter of branch impedance, _Zk is the branch impedance of the kth trunk cable, _Pj is the output power of the jth wind turbine, _Pi is the output power of the ith wind turbine, _Yeq is the equivalent parameter of ground admittance, _Yi is the ground admittance of the trunk cable in the ith wind turbine, m represents the number of equivalent wind turbines in the equivalent machine group, 1≤i≤m, n is the number of wind turbines in the trunk wind turbine branch, 1≤k≤i, k≤j≤n.

Example 1

Embodiment 1 of the present invention adopts PSCAD simulation software to build the IEEE14 node model containing a wind farm shown in Figure 3. The wind farm consists of 16 wind turbines, numbered W1-W16 in sequence, with a single wind turbine capacity of 1.5MW, connected to the common connection point PCC through a machine-end transformer (660V/35kV) and a collector line, and then connected to the power system through a main transformer (35kV/110kV). Assume that the wind speed obeys a Weibull distribution with a scale coefficient of 10.7 and a shape coefficient of 3.97, the wind turbine control mode obeys a two-point distribution, and the location where the fault occurs in each line obeys a uniform distribution. For wind direction, 0°~360° is divided into 16 wind direction zones, and the span of each wind direction zone is 22.5°, wherein the calculation results of the wake effect of the wind direction in Figure 3 are shown in Table 1.

Table 1

The numbers in the table represent the ratio of the wind speed of the input fan to the natural wind speed. The crowbar protection resistance of fans W1-W8 is 0.14Ω, and the crowbar protection resistance of fans W9-W16 is 0.12Ω. The system configuration used in the simulation is Intel(R) Core(TM) i7-7700CPU 3.60GHz, 16GB memory.

The simulation system shown in Figure 3 is simulated and analyzed by random combinations of wind turbine control mode, wind speed, wind direction, and grid short-circuit fault location. It is assumed that the fault occurs at t=3s and the fault duration is 0.15s. The active power curve at the outlet of the wind turbine is taken as the analysis object, and the active power curve at the outlet of the wind turbine under various random combinations is obtained. The multi-scale entropy values of W1-W16 are calculated using the wind turbine clustering index extraction method, and the results are shown in Figure 4. The difference in entropy values of different wind turbines at multiple scales reflects the difference in the dynamic process of the wind turbine. The multi-scale entropy value of the wind turbine is used as the clustering index, and the k-means algorithm is used for clustering analysis, where the number of clusters is 4, and the clustering results are shown in Table 2.

Table 2

Cluster Fan Equivalent Cluster 1 W1, W5, W6, W10, W11, W12, W15 Equivalent Cluster 2 W4, W9 Equivalent Cluster 3 W3, W7, W8, W13, W16 Equivalent Cluster 4 W2, W14

In order to verify the accuracy of the wind farm equivalent model, different operating conditions of the wind farm are set, as shown in Table 3. The fault is also set to occur at t = 3s and the fault duration is 0.15s. Under the operating conditions in Table 3, the active power and reactive power curves of the equivalent model and the detailed model at the PCC are shown in Figure 5.

Table 3

condition Wind speed (m/s) wind direction Fault location 1 95 North Wind Bus9 2 10 south wind Bus4-Bus7 middle 3 105 Southeast Wind Bus10-Bus11 4 11 Dongfeng Bus4-Bus5 middle 5 115 Northwest Wind Bus2-Bus3 middle 6 12 Northeast Wind Bus13-Bus14

As can be seen from FIG5 , in terms of equivalent effect, the active output characteristics of the dynamic equivalent model of the present invention are highly consistent with the detailed model, especially after a fault occurs in the power system, the dynamic equivalent model of the present invention can well reflect the dynamic characteristics of the wind turbine, thereby verifying the accuracy of the wind farm equivalent model of the present invention.

It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims (7)

1. A method for constructing a wind power plant equivalent model based on MMSE mining is characterized by comprising the following steps:

(1) Constructing a dynamic process time sequence of the wind turbine by using random key factors of each wind turbine in the wind power plant, and calculating a multi-scale entropy value of the dynamic process of the wind turbine by using the dynamic process time sequence of the wind turbine;

(2) Constructing a wind power plant equivalent model by taking the multi-scale entropy of the dynamic process of the fan as a clustering index;

the random key factors include: the control mode, the wind speed, the wind direction and the power grid short-circuit fault position of the fan;

the step (1) comprises the following steps:

(11) Counting the probability characteristics of the random key factors according to historical data for the random key factors of each fan in the wind power plant, sampling the random key factors by using a random variable generator, and inputting the sampled random key factors into the fans to obtain a dynamic process time sequence of the fans;

(12) Carrying out time series coarse graining treatment on the dynamic process time series of the fan to obtain a time series after coarse graining, and calculating a multi-scale entropy value of the dynamic process of the fan by using the time series after coarse graining;

the step (2) comprises the following steps:

the method comprises the steps of setting the clustering number to be K by taking the multi-scale entropy of the dynamic process of the fan as a clustering index, obtaining K equivalent clusters, calculating relevant parameters in each equivalent cluster to construct a wind power plant equivalent model, and reflecting the dynamic characteristics of the fan by the wind power plant equivalent model after the power system fails.

2. The method for constructing the equivalent wind power plant model based on MMSE mining is characterized in that the relevant parameters comprise relevant parameters of an equivalent wind turbine, equivalent parameters of a transformer and equivalent parameters of line impedance.

3. The method for constructing the equivalent wind farm model based on MMSE mining according to claim 2, wherein the types and the capacities of the wind turbines in the wind farm are the same, and the relevant parameters of the equivalent wind turbines comprise:

wherein S is _eq Is an equivalent parameter of the capacity of the fan, m represents the number of equivalent fans in an equivalent cluster, S _i Is the capacity of the ith fan, P _eq Is an equivalent parameter of the active power of the fan, P _i Active power, Q, of the ith fan _eq Is an equivalent parameter, Q, of the reactive power of the fan _i Is the reactive power of the ith fan, x _m-eq Being equivalent parameter of reactance of excitation branch, x _m Is the reactance of the excitation branch, x _s-eq Is an equivalent parameter of the stator winding reactance, x _s Is the stator winding reactance, x _r-eq Is an equivalent parameter of the rotor winding reactance, x _r Is the reactance of the rotor winding, r _s-eq Is an equivalent parameter of the stator winding resistance, r _s Is stator winding resistance, r _r-eq Is an equivalent parameter of the rotor winding resistance, r _r Is the rotor winding resistance, H _eq Is an equivalent parameter of the inertial time constant of the shafting, H _i Is the shafting inertia time constant, K, of the ith fan _eq Is an equivalent parameter of the shafting stiffness coefficient, K _i Is the shafting stiffness coefficient of the ith fan, D _eq Equivalent parameters, D, of damping coefficients of shafting _i The damping coefficient of the shafting of the ith fan is more than or equal to 1 and less than or equal to m.

4. The method for constructing the wind farm equivalent model based on MMSE mining according to claim 2, wherein the equivalent parameters of the transformer comprise:

wherein S is _T Representing the transformer capacity, x _T Representing the reactance, S, of the transformer _T-eq Equivalent parameter, x, representing the capacity of a transformer _T-eq And (3) representing equivalent parameters of the reactance of the transformer, and m representing the number of equivalent fans in an equivalent cluster.

5. The method for constructing the wind farm equivalent model based on MMSE mining according to claim 2, wherein the equivalent parameters of the line impedance comprise:

wherein, Z _eq Is an equivalent parameter of the branch impedance, Z _k Is the branch impedance, P, of the kth trunk cable _j Is the output power of the jth fan, P _i Is the output power of the ith fan, Y _eq Equivalent parameters, Y, for admittance to ground _i The number of the trunk line type cables in the ith fan is the ground admittance, m represents the number of the equivalent fans in the equivalent cluster, i is more than or equal to 1 and less than or equal to m, n is the number of the fans in the branch lines of the trunk line type fans, k is more than or equal to 1 and less than or equal to i, and j is more than or equal to k and less than or equal to n.

6. An MMSE mining-based wind power plant equivalent model is characterized by being constructed by the method for constructing the MMSE mining-based wind power plant equivalent model according to any one of claims 1-5.

7. The application of the wind farm equivalent model based on MMSE mining according to claim 6, wherein the wind farm equivalent model is applied to dynamic process simulation under various scenes of a power system.

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