This paper presents a probabilistic methodology for small signal stability analysis of power system with correlated wind sources. The approach considers not only the stochastic characteristics of wind speeds which are treated as random variables with Weibull distributions, while also the wind speed spatial correlations which are characterized by a correlation matrix. The approach based on the 2
m
+1 point estimate method and Cornish Fisher expansion, the orthogonal transformation technique is used to deal with the correlation of wind farms. A case study is carried out on IEEE New England system and the probabilistic indexes for eigenvalue analysis are computed from the statistical processing of the obtained results. The accuracy and efficiency of the proposed method are confirmed by comparing with the results of Monte Carlo simulation. The numerical results indicate that the proposed method can actually capture the probabilistic characteristics of mode properties of the power systems with correlated wind sources and the consideration of spatial correlation has influence on the probability of system small signal stability.
1. Introduction
With the increase in penetration of wind power which is essentially intermittent and random, the dynamic performance of the power system will be affected
[1]
. It is necessary and imperative for the power system engineers to understand in essential how the wind power penetration affects an existing interconnected largescale power system, especially for the power system small signal stability
[2

4]
.
Deterministic strategies used for small signal stability analysis of power systems as affected by penetration of large scale wind generation are limited since they are carried out based on a specified operating condition (e.g. mean wind generation scenario). However, since the wind generation is primarily determined by wind speed and thus fluctuating constantly, the operating conditions of the system are stochastically uncertain. The results obtained by deterministic strategies are too conservative, in other words, though the system is stable deterministically, there exists the certain probability that the system can lose stability due to the stochastic fluctuations caused by wind generation
[5]
. The behavior of these probabilistic characteristics of the power system can be described only in statistical terms. Moreover, wind sources are spatially correlated within a given geographical area in a very significant manner, as they are influenced often by the same physical phenomena
[6]
. This correlation can have a significant impact on the power flow, voltage stability and reliability of power systems
[6

8]
. Thus, there is a clear need to develop an algorithm of probabilistic small signal stability analysis (PSSSA) which includes the uncertainty of wind generation and the dependencies of the wind sources.
The probabilistic analysis was firstly applied in power system by Borkowska in
[9]
for power flow study and was firstly introduced into investigating small signal stability of power system by Burchett and Heydt in
[10]
. In the previous published literatures, a number of different probabilistic theorybased approaches have been proposed to deal with the uncertainty problems in power system. These approaches could be divided into three main categories: Monte Carlo simulation, analytical methods, and approximate methods.
Monte Carlo simulation (MCS) which has been used in reliability assessment for many years is a repetitive procedure which generates a large number of random computational scenarios according to the distribution density of the input variables. In
[11

13]
, MCS is introduced to study the influence of uncertainties of wind generation on power system small signal stability. Although MCS can provide accurate results, thousands of simulations are usually required to attain convergence and high computational burden makes this method unattractive. Most of researchers only use it for comparison purpose.
The advantage of analytical methods such as fast Fourier transform (FFT), cumulantbased method is computational efficiency, but these methods rely on complex mathematical approximations and extensive modifications of the original model. The method of combined cumulants and GramCharlier expansion with considering the spatial correlations of wind generation is employed in
[5]
to determine the probabilistic small signal stability of power systems penetrated by multiple wind sources. This method utilizes first order eigenvalue sensitivity with respect to wind power generation, and the statement is made that this provides accurate results for the system under consideration. However, there are a number of situations where first order approximation may not be sufficient accurate
[14]
.
Approximate methods provide an approximate description of the statistical properties of output random variables with reduced computational efforts compared to MCS. Firstorder secondmoment method (FOSMM) and point estimate methods (PEMs) fit into the family of approximate methods. Since Rosenblueth’s two point estimate method
[15]
needs a large number of simulations if the number of input random variables is high and Harr’s 2
m
PEM
[16]
is constrained to symmetric variables, 2
m
+1 scheme based on Hong’s point estimate method
[17]
is used in this paper. The main advantages are as following:

1) Compared with MCS, PEMs can calculate the statistical properties of output random variables with satisfactory accuracy and much less computation effort.

2) As cumulantbased method, PEMs also utilize the statistical features of input random variables to provide (i.e., first few statistical moments), overcoming the difficulties associated with the lack of analytical expression of the probability functions of inputs; however different approaches are applied to obtain the statistical features of outputs, which is not based on the first order eigenvalue sensitivity terms.

3) Hong’s PEM can deal with the case in which the variables are skewed, since the wind speed is generally treated as a random variable assumed to have a Weibull distribution which is not symmetrical.

4) Among different schemes based on Hong’s PEM, the 2m+1 scheme has been shown to have good performance in terms of both accuracy and computational time and is the most efficient scheme in dealing with nonnormal distribution[18,19].
The aim of PEMs is to compute the first moments of a random variable
z
that is a function of m random input variables
x_{i}
, i.e.,
z
=
F
(
x
_{1}
, ···,
x_{i}
, ···,
x_{m}
) . Knowing the first few statistical moments, it is possible to obtain the probability density functions (PDFs) or the cumulative density functions (CDFs) of the output variables by using the analytical expressions, such as CornishFisher expansion method
[20]
, GramCharlier expansion series. However, the original Hong’s PEM can only be applied when the input random variables are uncorrelated. Thus, a suitable adjustment has to be introduced to deal with the correlation. The wellknown orthogonal (rotational) transformation technique is applied in this paper. This
This paper presents a probabilistic methodology based on 2
m
+1 PEM for small signal stability analysis of power system with correlated wind sources. The approach considers not only the stochastic characteristics of wind speeds which are treated as random variables with Weibull distributions, while also the wind speed spatial correlations which are characterized by a correlation matrix. The rest of the paper is organized as follows. Section 2 briefly describes wind speed correlation and its impact on power system. Section 3 gives a short review of the probabilistic small signal stability and presents the dynamic model of wind turbine. Section 4 explains the theoretical foundations of the 2
m
+1 PEM, together with Cornish Fisher expansion, and the orthogonal transformation technique. In section 5, a case study on IEEE New England system is carried out and the probabilistic indexes for PSSSA are computed from the statistical processing of the obtained results. The results are compared with those obtained by MCS to validate the accuracy and efficiency of the proposed method. Section 6 summarizes some relevant conclusions based on the numerical results.
2. Wind Speed Correlation
Wind is a highly variable and sitespecific energy source with instantaneous, hourly, diurnal and seasonal variations of wind speed. Wind speeds at different wind sites can be assumed to be independent if they are far away from each other. However, the wind farms are correlated to some degree if the distances between the wind sites are not very large. This correlation can have a significant impact on the power flow, voltage stability and reliability of power systems. Therefore, it should be considered in PSSSA of power system integrated with wind power generation.
The wind speed correlation between two wind sites can be calculated using crosscorrelation. The crosscorrelation coefficient
ρ_{xy}
is a measure of how well two time series follow each other
[21]
, as shown in (1)
where
x_{i}
and
y_{i}
are elements of the first and second time series, respectively, µ denotes the mean value, σ the standard deviation, and n the number of points of the time series. The value of
ρ_{xy}
is near the maximum value of 1.0, if the two time series totally dependent. The value is close to zero, if the two time series are basically uncorrelated.
The crosscorrelation coefficients were calculated for 89 wind sites in Nebraska of US for one year 2004. The wind speed data was collected from the wind integration datasets of National Renewable Energy Laboratory’s website
[22]
. The Results is presented in figure
Fig. 1
, which shows the relationship between the crosscorrelation coefficient and the distance of two wind sites. The coefficient was fitted by exponential function with a damping ratio of 0.002907 km
^{1}
. It can be seen from the figure that there is a decrease tendency for crosscorrelation of two different wind sites as the distance increases.
Crosscorrelation coefficients of the wind sites in Nebraska of US for one year
The wind speed time series during 1000 hour period for different windsitepairs with a maximum value (
ρ_{xy}
= 0.9920), middle value (
ρ_{xy}
=0.500) and minimum (
ρ_{xy}
= 0.1168) value of crosscorrelation coefficients are shown in
Fig. 2.
This figure shows that in the high correlation level scenario, the up and down movements of the two wind time series occur in the same direction, in the low correlation level scenario, the two wind time series do not follow each other and are complementary in most of the time. Thus, high level correlation of wind speed will strength the synchronization (increase and decrease simultaneously) of different wind farms’ power output and increase the fluctuation of aggregated wind power output and low level correlation will smooth out the wind power variation.
Fig. 3
shows the frequency distribution of hourly aggregated wind power of windsitepairs for one year with different correlation level. It can be seen in
Fig. 3
that high correlation level increase the occasions with near zero and peak power output.
Wind speed time series of windsitepairs with different correlation level
Frequency distribution of aggregated wind power of windsitepairs with different correlation level
Variable speed wind generators which are widely used today will not themselves cause electromechanical modes of oscillation. However, as illustrated above, the timevarying and correlated wind speeds will change the aggregated wind generation and hence have the potential to indirectly change the damping performance of the system by
[4
,
23]
: (i) significantly altering the dispatch of synchronous generation in order to accommodate wind generation; (ii) significantly altering the power flows in the transmission network; and (iii) interacting with synchronous machines to change the damping torques induced on their shafts. So it is necessary to consider the uncertainty and correlation of wind speeds in PSSSA of power system integrated with wind power generation.
3. Probabilistic Small Signal Stability Analysis
 3.1 Modal analysis
Small signal stability is the ability of a power system to maintain synchronism when subjected to small disturbances. The dynamic behavior of power system can be described by a set of nonlinear differential algebraic equations (DAEs):
where,
x
∈
R
^{n}
is the vector of state variables, i.e. synchronous and asynchronous machine rotor speeds, synchronous machine power angles, magnetic flux linkages, controller state variables, etc.
y
∈
R
^{m}
is the vector of algebraic variables, i.e. voltage amplitudes and phases at the network buses and all other algebraic variables such as generator field voltages, AVR reference voltages, etc.
f
are the vectors of nonlinear functions defining the states and g consists of the stator algebraic equations and the power flow equations in the powerbalance form. The most adequate tool to perform small signal stability studies is modal analysis which is performed by using the linearization of the DAEs (2) around a system operating point:
Eliminating the algebraic variables ∆
y
from (3), we get ∆
x
=
A
_{s}
∆
x
where
A
_{s}
is called state matrix implicitly assuming that
Gy
is nonsingular:
Analysis of the eigenproperties of
A_{s}
, such as eigenvalues, eigenvector, participation factor, provides valuable information regarding the stability of the system. According to Lyapunov’s first method, the small signal stability of a power system is given by the eigenvalues of
A_{s}
. If all eigenvalues have a negative real part, all oscillation modes (OM) decay with time and the system is said to be stable. The critical eigenvalues which determine the small signal stability of the power system are characterized by being complex and by being located near the imaginary axis of the complex plane. The damping ratio determines the rate of decay of the amplitude of the oscillation. The mode shape given by the right eigenvector helps to distinguish the various types of oscillation. Besides, the participation factor indicates the relative contribution of each state variable to a certain mode. The electromechanical (EM) oscillation mode is recognized according to the electromechanical relative coefficient ρ
_{EM}
and the frequency of oscillation
f
, i.e., ρ
_{EM}
> 1 and 0.2 <
f
< 2.5 Hz.
When power system uncertainties are considered, the system’s equilibrium is no longer deterministic. In particular, the DAEs will contain nondeterministic system parameters which have known statistics. The presence of these random variables will cause the eigenvalues of
A_{s}
to be nondeterministic. It is the purpose of PSSSA to determine the probability density of the real part of the eigenvalues of
A_{s}
and characterize the stochastic nature of power system stability.
In this paper, the wind speed is chosen as the uncertain parameter and other uncertainties are neglected. A widely used Matlabbased power system analysis and simulation tool — Power System Analysis Toolbox (PSAT)
[24]
, is used to run the power flow and calculate the eigenvalues and other eigenproperties of state matrix
A_{s}
of the investigated scenarios.
 3.2 DAEs of doublyfed induction generator system
Nowadays, wind turbines of variable speed type have become more common than traditional fixed speed turbines. Especially, the DoublyFed Induction Generator (DFIG)  based wind turbine is gaining prominence in the power industry due to its characteristics of high energy transfer efficiency, low investment and flexible control. Therefore, wind farms represented by DFIGbased wind turbines will be used in PSSSA in this paper.
The dynamic model of DFIG system contains the several components: driven train, pitch controller, generator, and converter controller. The DAEs of DFIG system is presented in Appendix A and the following assumptions and strategies are adopted.
The drive train comprising turbine, gearbox, shafts and other transmission components is represented by a two mass model.
The dynamic model of the gridside converter controller is neglected as it is noted that the dynamics of rotorside converter controller has more significant impact on the power system small signal stability than gridside converter controller. The decoupling control strategy developed in
[25]
is used for the active power and reactive power of rotorside converter. The stator voltageoriented control scheme is adopted, which makes the stator voltage line in accordance with
q
axis of
dq
reference frame, then
u_{ds}
becomes zero and
u_{qs}
is equal to the magnitude of the terminal voltage.
4. Solution Method
 4.1 2m+1 PEM
The 2
m
+1 PEM developed in
[17]
is applied in this paper to solve the problem of PSSSA, where
m
is the number of input random variable. This method uses deterministic routines for solving probabilistic problems; however, it generally requires a lower computational burden compared with MCS.
Random variables
z_{j}
is function
F_{j}
of
m
input random variables ( x
_{1}
, x
_{2}
, ···, x
_{m}
):
The 2
m
+1 PEM is used to obtain the first few moments of the output random variables of interest only required few statistical moments of the input random variables. To obtain these moments, the function
F_{j}
has to be calculated 2
m
+1 times. For each input random variable
x_{i}
, the function
F_{j}
is calculated using two input variable vectors
x_{i}
,
_{1}
,
x_{i}
,
_{2}
:
Where
is called the location of
x_{i}
,
µ_{xi}
is the means of the
m
1 remaining input variables.
After 2
m
calculations are carried out, one addition evaluation of the function
F
is required at the point
x_{µ}
constituted by the means of all of the input random variables:
Once the solution of the 2
m
+1 functions
F_{j}
(⋅) is known, the moments of the output random variables can be obtained by using the weighting factors ,
w_{i,k}
,
w
_{0}
associated with
x_{i,k}
and
x_{µ}
, respectively.
The pair (
,
w_{i,k}
) composed by a location ,
at which function
F_{j}
(⋅) is to be evaluated and a weighting factor
w_{i,k}
measuring the impact of this evaluation on the random behavior of output variable
z_{j}
is called the
k
th (
k
=1, 2) concentration of the random input variable
x_{i}
.
For each input random variable
x_{i}
, the location
is depend on the first four central moments and expressed as
where
µ_{xi}
and
σ_{xi}
are the mean and standard deviation of
x_{i}
, respectively,
ξ_{i,k}
is the standard location:
where
λ_{xi,3}
and
λ_{xi,4}
denote the third and fourth standardized central moments of
x_{i}
with probability density function
f_{i} (x)
, are also the skewness and kurtosis of
x_{i}
.
where
µ_{xi, r}
is the rth central moments of
x_{i}
.
Each location
is coupled with a weighting factor
w_{i,k}
computed as
Once all the concentrations of input random variables are determined by using (8)(13), the 2
m
+1 evaluations of function
F_{j}
(⋅) is then calculated as
The
n
th raw moment of the output random variable
z_{j}
, denoted by
m_{zj,n}
is estimated as
where
E
(⋅) denotes the expectation operator. The mean value and the standard deviation of
z_{j}
, denoted by
µ_{zj}
and
σ_{zj}
, can be estimated according to (16).
 4.2 Cornish fisher expansion
Knowing the statistical moments, it is possible to obtain the PDFs or the CDFs of the output variables by using the CornishFisher expansion method or GramCharlier expansion series
[20]
. Ref.
[26]
proved that CornishFisher expansion is more adequate for the problem conditions (nonGaussian PDF of the wind power uncertainties), instead of the GramCharlier expansion series. So in this paper, the CornishFisher expansion approach is applied to compute the PDFs and the CDFs of the output random variables. This approach provides the approximation of the normalized quantiles
α
of any cumulative distribution function
F
(
x
) in terms of the quantile of the standard normal N(0,1) distribution Φ and the cumulants of
F
(
x
).
Using the first five cumulants, the expansion is given by (20).
where
x(α)=F^{1}
(x).
k_{i}
is the ith cumlants, which can be obtained from its raw moments as follows:
 4.3 Managing the correlations of input variables
The procedure mentioned above can only be applied when the input random variables are uncorrelated. In the presence of correlation among random input variables, the wellknown orthogonal (rotational) transformation technique is used to transform the set of input correlated random variables
x
into an uncorrelated set of random variable
y
. Once the set of uncorrelated variables is obtained, the 2
m
+1 PEM described by (8)(13) can be applied and then the concentrations of
y
is determined. Finally, these points are untransformed to the original correlated variable space. The details about the procedure can be found in
[6]
.
 4.4 Computational procedure of PSSSA
The computational procedure to solve a PSSSA problem with correlated wind sources using the 2
m
+1 PEM is summarized below.

(1) The random wind speeds of m correlated wind farms are considered as the input random variablesx= (x1, x2, ⋯, xi, ⋯, xm)T. Known the PDF ofxiand the correlation coefficient matrixρ, determine the covariance matrixCxand calculate the first four central moment of each variablexi.

(2) Transform the first four central moments ofxinto uncorrelated space by applying the orthogonal transformation.

(3) Determine all the concentrations (,wi,k) according to (8)(13) and formed the 2m+1 transformed input pointsyi,k,yµ.

(4) Obtainxi,k,xµwhich are in the original space by using inverse transformation.

(5) Run the deterministic power flow for 2m+1 input point (wind speed)xi,kandxµ.

(6) Solvezjwhich denote the eigenvalues and the eigenvectors of state matrix, damping ratio and participation factors at each deterministic operating point determined by power flow. Estimate the raw moments ofzjas expressed in (16)(19).

(7) After obtaining the moments of the eigenvalues, find the CDFs of real part of critical modes(CMs) by applying the CornishFisher expansion as explained in (20), (21), and then determine the probability of power system small signal stability.
All the steps described above have been implemented in MATLAB with the help of PSAT.
5. Case Study
 5.1 Simulation conditions and assumptions
In this section, the proposed algorithm will be applied in the PSSSA of the IEEE New England (10machine 39bus) system
[27]
, modified to include two wind farms, to demonstrate its validity.
In this system, two 375MW wind farms having 250 1.5MW DFIGtype wind turbines are located at bus 40 and 41 which connected with bus 33 and 34 via transformers. Each wind farm is regarded as an aggregated wind turbine which is represented by the dynamic model of DFIG described in section 3.2 and the parameters of the DFIG system is presented in
[28]
. Synchronous generator G2 considered as the swing bus is modeled by the classic electromechanical model (the 2
^{nd}
order model). The other 6 synchronous generators are all modeled by the 4
^{th}
order models, with magnetic saturation neglected, and extended by 3
^{rd}
order exciter models.
The same Weibull distribution of wind speed
v_{w}
as stated in (22), with scale and shape parameters,
c
and
k
, equal to 7.65 and 2.06, respectively, is used to model wind speed at both sites.
 5.2 Performance evaluation
In order to demonstrate the accuracy and efficiency of the proposed method, a comparison of the results obtained by 2
m
+1 PEM are compared with those obtained by a MCS with 5000 trials. Inverse Nataf transformation is adopted in this paper to generate the random samples of correlated wind speed in the MCS.
Table 1
shows the mean value
µ
and standard deviation
σ
of the real part of critical eigenvalue
Eigreal
with different wind speed correlations (
ρ_{xy}
=0.1 to
ρ_{xy}
=0.9 with a step of 0.1). The results indicate that the proposed method provides a good approximation by comparing the results from MC method, both for the mean value and the standard deviation. However, MC requires 5000 simulations while the proposed 2
m
+1 PEM method requires only 5 simulations. Therefore the conclusion is made that 2
m
+1 PEM method can provide accurate results and is computationally much more efficient than MCS.
Mean and standard deviation ofEigrealwith different wind speed correlations
Mean and standard deviation of Eigreal with different wind speed correlations
 5.3 Impact of wind speed correlation on PSSSA
Two situations are presented using the proposed method to illustrate the importance of modeling wind farms with considering wind speed correlation: weak correlated situation (
ρ_{xy}
=0.1) and strong correlated situation (
ρ_{xy}
=0.9).
Fig. 5
shows the wind speed joint distribution using 2
m
+1 PEM and MCS respectively, under two situations. By comparing of (a) and (b), wind speed joint distribution tends to concentrate more on the diagonal in the strong correlated case than the weak correlated case, which will strength the synchronization of two wind farms’ power output and may change the probability of small signal stability.
Wind speed joint distribution
Based on the computational procedure described in section 4, the PSSSA of the power system concluding two wind farms is conducted and the statistic information of eigenvalues is then evaluated. By using CornishFisher expansion, the CDF of the real part of critical eigenvalue which determine the small signal stability of the system can be obtained, as shown in
Fig. 6
, assuming weak correlated and strong correlated respectively. Obviously, the results indicate a difference in the probability of small signal stability between the two situations. It can be observed from
Fig. 6
that the critical eigenvalue of the system has a probability of 98.90% to remain in the left halfplane in weak correlated situation, in other words, the system has a probability of 1.1% to be unstable. However, in the strong correlated situation, the probability of small signal stability of the system decreases to 96.98%.
The CDF comparison of the real part of critical eigenvalue
Furthermore, in order to study the impact of correlation level of wind speed on PSSSA, a set of wind speed correlation coefficients as shown in
Table 1
is used to calculate the probability of small signal stability of the test system. The results are shown in
Fig. 7
. It can be seen from
Fig. 7
that wind speed correlation has a negative impact on the small signal stability as the degree of correlation between the two wind farms increases. When the correlation coefficient changes from 0.1 to 0.9 (weak independent caseec to strong independent case), the probability of small signal stability is decreased from 98.90% to 96.98%.
The probability of small signal stability of the power system with different wind speed correlation coefficient
In addition to the real part of critical mode
Eigreal
, the mean and standard deviation of other important properties: the oscillation frequency
f
, damping ratio
ξ
and the electromechanical relative coefficient
ρ_{EM}
are also shown in
Fig. 8.
Fig. 8
shows that along with the increase of correlation coefficient,
µ_{Eigreal}
and
µ_{ρEM}
increase with percentage change of 4.78%, 9.29% respectively,
µ_{f}
and
µ_{ξ}
decreases with percentage change of 0.77%, 19.17% respectively. The standard deviations of all properties increase, the percentage change corresponding to
σ_{Eigreal}
,
σ_{f}
,
σ_{ξ}
and
σ_{ρEM}
are 75%, 40.49%, 75.65% and 27.94%. The standard deviations can be seen as a measure of the uncertainty level affecting the power system, and therefore the smaller they are, the better. So the conclusion can be drawn that the damping performance of the test system tends to deteriorate and the variation of critical mode properties becomes larger as the correlation level of two wind farms increases.
The mean and standard deviation of critical mode with different wind speed correlation coefficient
 5.4 EM mode analysis
EM oscillation modes of moderate correlated case (
ρ_{xy}
=0.5) are recognized by using the criterion of
ρ_{EM}
>1 and 0.2 <
f
< 2.5 Hz. There are nine EM oscillation modes in the modified 10machine 39bus system, due to the two DFIG based wind turbines do not engage in power system oscillation
[2
,
4]
. The mean and standard deviation of mode properties are depicted in
Fig. 9.
It can be seen from
Fig. 9
that the damping performances of 8
^{th}
EM (local oscillation) and 9
^{th}
EM (interarea oscillation) are greatly influenced by the fluctuation of wind power output.
The mean and standard deviation of EM mode properties
6. Conclusion
In this paper, a methodology based on the 2
m
+1 PEM combined with CornishFisher expansion and modal analysis is applied to solve the problem of probabilistic small signal stability of power systems with correlated wind sources.
A case study is carried out on the modified IEEE New England system with two gridconnected DFIG wind farms and the wind speeds with Weibull distribution are regarded as the input random variables. The accuracy and efficiency of the proposed method are confirmed by comparing with the results of MCS. The stable probability is assessed based on the statistical information of critical eigenvalues obtained by conducting the proposed method. Comparison of different correlation situations shows that wind speed correlation has a negative impact on the small signal stability as the degree of correlation between the two wind farms increases. Finally, electromechanical oscillation modes of the system are picked out; the numerical results indicate that the proposed method can actually capture the probabilistic characteristics of mode properties of the power systems with correlated wind sources.
Acknowledgements
This work was supported in part by the Major Program of the National Natural Science Foundation of China (Grant No. 51190103), the National High Technology Research and Development Program of China (Grant No. 2012AA050208) and the National Natural Science Foundation of China (Grant No. 51207052).
BIO
Hao Yue He received B.S. and M.S. degrees in electrical engineering from North China Electric Power University (NCEPU), P. R. China. Currently, he is pursuing the Ph.D. degree in NCEPU. His research interests are power system stability analysis and control including large wind farm grid connections.
Gengyin Li He received the B.S., M.S. and Ph.D. degrees, all in Electrical Engineering, from NCEPU in 1984, 1987 and 1996, respectively. Since 1987, he has been with the School of Electrical and Electronic Engineering at NCEPU, where he is currently a professor and executive vice dean of the School. His research interests are power system analysis and reliability, power quality analysis and control, HVDC and VSCHVDC transmission technology, and power systems economics.
Ming Zhou She received the B.S., M.S. and Ph. D. degrees in Electrical Engineering from NCEPU in 1989, 1992, and 2006, respectively. Since 1992, she has been with the School of Electrical and Electronic Engineering at NCEPU, where she is currently a professor. Since 2006, she is working as post doctorate in the School of Business Management, NCEPU. Her research interests are power system analysis and reliability, power systems economics, and power quality analysis.
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