NREL is a national laboratory of the U.S. Department of Energy, Office of Energy 

Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

Contract No. DE-AC36-08GO28308

Wind Power Plant Prediction 

by Using Neural Networks

Preprint

Z. Liu and W. Gao

University of Denver

Y.-H. Wan and E. Muljadi

National Renewable Energy Laboratory

To be presented at the IEEE Energy Conversion Conference 

and Exposition

Raleigh, North Carolina

September 15–20, 2012

Conference Paper

NREL/CP-5500-55871

August 2012
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1

Abstract--This paper introduces a method of short term wind 

power prediction for a wind power plant by training neural 

networks based on historical data of wind speed and wind 

direction. There are two steps in the process of wind power 

prediction. In the first step, raw data collected by plant 

information system is filtered by probabilistic neural network.

This step prepares valid data to be used for building a prediction 

model. In the second step, a complex-valued recurrent neural 

network is applied to build a model to predict wind power. The 

test results of the prediction model are presented and analyzed at 

the end of the paper. The model proposed is shown to achieve a 

high accuracy with respect to the measured data.

Index Terms--wind power plant, wind power prediction, 

probabilistic neural network, complex-valued recurrent neural 

network.

I. INTRODUCTION

IND plant has lower cost of energy compared to other

renewable energy sources for large scale application.

Due to the different geographical patterns, weather, and 

properties of the wind turbines, a wind turbine may have 

various performance given different situations. If the total 

output of a wind power plant (WPP) can be predicted with 

high accuracy, more useful information can be provided to the

power companies to help in scheduling power generation. This 

information will allow a more flexible and intelligent control 

of a WPP (e.g., improve the working schedule of wind 

turbines, reactive power control, etc). Methods for predicting 

wind power generation can be categorized into physical 

methods, statistical methods, methods based on neural 

networks, and hybrid methods [1]. The physical methods rely 

heavily on numeric whether prediction, which is confined by 

the sensors and monitoring devices placed within the WPP. 

The quality of hardware chosen, the parameter settings, the 

computation time, the time delays, and the sampling rates 

influence the accuracy of data collected from the WPP. It is 

easier to predict a single wind turbine's performance rather 

than a whole WPP's power generation. Statistical and neural 

network methods are based on the historical data and have a 

low prediction cost. The relationship between input data and 

 This work was supported in part by U.S. DOE Contract No. DE-AC36-08-

GO28308 and in part by U.S. NSF Grant 0844707.

Z. Liu and W. Gao are with Department of Electrical and Computer 

Engineering, University of Denver, Denver, CO, 80210 USA

(E-mail: Ziqiao.Liu@du.edu and Wenzhong.Gao@du.edu).

Y. Wan and E. Muljadi are with National Renewable Energy Laboratory, 

Golden, CO 80401, USA (e-mail: Yih-Huei.Wan@nrel.gov and 

Eduard.Muljadi@nrel.gov).

output data based on historical measured data is learned and 

then a nonlinear relationship model between them is built. But 

when new data not previously included in the training data set 

is used as input into this kind of model, the prediction error 

might be large, which is a disadvantage. Different prediction 

methods mentioned above can be combined as hybrid methods 

to achieve better prediction results. But this will increase the 

complexity of the model. In this paper, several neural network 

methods are applied to predict power generation of a WPP

located in northeast Colorado.

In this WPP, data of wind information, such as wind speed, 

wind direction, wind power generation, humidity and air 

pressure are collected by a Plant Information (PI) system, and 

the output of the entire WPP is monitored by the utility's 

supervisory control and data acquisition (SCADA) system. 

Raw data from the WPP is processed by a probabilistic neural 

network (PNN) and then a complex-valued recurrent neural 

network (CRNN) model is built to predict the total output of 

the WPP with the following considerations [3]:

• The raw data set will be screened by probabilistic neural 

network to prepare high quality data for building neural 

network models;

• The model's inputs do not rely on the data of wind speed 

and wind direction from all turbines; representative wind 

turbines can be found to compress the length of the input 

data;

• The inputs are expressed as complex-valued data (vector 

representation) which combine wind speed and wind 

direction; 

• The complex-valued recurrent neural network model's 

time series inputs are generated based on the historical 

data values of the WPP rather than the predicted values by

the model at the previous steps;

• The result to be predicted is the total power generation of 

the whole WPP rather than outputs of some single wind 

turbines;

• The models are trained based on the data collected within 

the year of 2010; the prediction accuracy of the model is 

tested by the data of 2011.

The rest of the paper are arranged as follows. In section II,

the data preparation process of wind power plant by using 

PNN is introduced. In section III, we show the process of wind 

power prediction model design by using complex-valued 

recurrent neural network. In section IV, prediction results 

using CRNN are discussed and analyzed. The conclusion of 

the paper is summarized in section V.

Wind Power Plant Prediction by Using Neural 

Networks

Ziqiao Liu, Student Member, IEEE, Wenzhong Gao, Senior Member, IEEE, 

Yih-Huei Wan, Senior Member, IEEE, Eduard Muljadi, Fellow, IEEE

W
2

II. DATA PREPARATION

A data preparation process is a very important step in 

mathematical modeling, since the quality of raw data acquired 

by PI system may contain errors.

A. Raw Data Description and Analysis

There are two kinds of wind turbines in this WPP. There are 

53 turbines in Group 1 with each turbine’s rated power at 1.5

MW; there are 221 turbines in Group 2 with each rated at

1MW. The rated power of the whole WPP is 300.5 MW. The 

layout of wind turbines and two meteorological towers 

(MET1&2) is shown in Fig. 1.

Fig.1. Wind power plant distribution

The data of wind speed (m/s), wind direction (degree, 

0o

~360o), total metered plant-output power (MW), temperature 

(

o

C) and air pressure is monitored by the sensors installed at

the two MET towers. From individual wind turbines, data of 

wind speed, wind direction and power output is also collected. 

The data of total metered WPP output power is recorded at the 

point of interconnection and is very useful to a utility 

company as a reference to compute power revenue. Following 

the IEC standard, all the data acquired except the turbine 

status is averaged over a 10-minute period for turbine power 

curve measurement [1]. Fig. 2 shows a raw scatter plot of 

WPP output and wind speed data from MET 1. The raw data 

set (8486 dots) contains some invalid data which is not useful 

for power prediction and has a minor effect on the power grid. 

The raw data can be classified into five types as shown in 

Table I.

TABLE I

RAW DATA CLASSIFICATION

Type Description

1 data points following the main power stream

2 data points in low wind speed period with high power generation

3 data points with negative value wind speed

4 data points with negative value power generation

5 data points with low power generation at high wind speed period

The existence of type 2 data might be due to some physical 

problems, disabled sensors or data distortion in 

communication channels. Type 3 data does not exist in reality

and may be caused by anemometer that needs to be calibrated. 

Type 4 data is due to the fact that sometimes the wind turbine 

cannot generate enough power to offset the electrical 

consumption of the turbine itself and was drawing 

(consuming) power from the grid. The existence of type 5 data 

might be due to the fact that not all turbines are always online 

during high wind speed period (especially near cut-off wind 

speed) and some wind turbines maybe disabled during that 

period. Another reason can be derived from [4], because a 

strong wind from wrong direction can make a turbine work at 

low efficiency. In sum, all types of data except type 1 data 

should be filtered out.

Fig.2. Scatter plot of total output of WPP and wind speed from MET1 (2010 

Jan-Mar)

B. Data Selection Process

Probabilistic neural network (PNN) is a feed-forward neural 

network with supervised learning using Bayes decision rule 

and Parzen window [3]. PNN can be used for data 

classification. The structure of PNN is usually a two-layer 

model as shown in Fig. 3. In the pattern units, the distance 

between the input vector and the target vector will be 

calculated. A new vector will be generated to indicate how 

close the input is to the target vector. The summation units add

these distances for each type of inputs to produce a vector of 

probabilities as the output of the network. The output unit

generates a 1 for the target class and a 0 for the other classes

with the use of a competing transfer function, which picks the 

maximum the vector of probabilities [4].

Fig.3. Structure of PNN
3

In this paper, PNN was applied to filter out invalid data in 

the raw data set. For example, data points in Fig.2 were 

classified into five types and the portion for each type of data 

is different based on statistical analysis. The order of 

proportion from the largest to the smallest is type 1, type 4, 

type 5, type 2, type 3. In the process of building PNN model, 

about 20% of the data points in Fig. 2 (1700 data points) were 

selected as training data set. The PNN model was trained 

using the sampled data.

Since only type 1 data is the useful information and should 

be kept, there are two strategies in training PNN model. 

Method 1 is simpler, for which the classification results of 

PNN are assumed to have only two types. PNN is trained 

based on two groups: the first group is type 1; the second 

group includes type 2-5. 1700 data points are selected, among 

which 1540 were randomly selected from type 1 data points, 

the rest 160 data points were from type 2-5. In the training 

data set, the input data vector includes data of wind speed and 

wind power generation, and the target vector has only two 

elements, which are 1 (group 1) and 2 (group 2). And then, the 

rest data points (about 80%) were used as testing data set as 

input to be classified by the PNN model already built. The 

number of neurons in the input layer is equal to that of the 

output layer, which is 2. The training results are shown in Fig.

4 and Fig. 5.

Fig.4. Classification results of data in Fig.2 by method 1

Fig.5. Filtered scatter plot of Group 1 data points classified by method 1 (2010 

Jan-Mar)

As shown in Fig. 5, the classification result using method 1

is not ideal; PNN model could not succeed in diagnosing all 

the unwanted data. And the classification accuracy is 92.7%, 

which means the number of the correctly classified data points 

versus the number of type 1 data as shown in Fig. 2.

In method 2, there are five classification results of PNN, 

which are type 1, type 2, type 3, type 4, type 5. PNN is trained 

based on five types of data points as shown in Table I. In the 

1700 selected data points, 1540 data points were sampled from 

type 1 data. For the rest 160 data points, according to the 

portion of each data type, number of data points sampled from 

type 2, 3, 4, 5 were 20, 10, 70 and 60 respectively. The 

number of neurons in the input layer is 2 and the number of 

neurons in the output layer is 5. And results done by testing 

the rest of the data set can be seen in Fig. 6 and Fig.7.

Fig.6. Classification results of data in Fig.2 by method 2

Fig.7. Filtered scatter plot of type 1 data points classified by method 2 (2010 

Jan-Mar)

In Fig. 6, type 1 data can be separated from the testing data 

set as shown in the classification result and were plotted in Fig.

7. And PNN model built by method 2 could succeed in 

screening the raw data even though the power curve is not 

totally smooth. The classification accuracy is 96.5%, which is 

higher than that of method 1. The classification result using 
4

method 2 has a better accuracy than the one performed using 

method 1, because simply combining type 2-5 data pints into a 

group will disturb the process of building PNN and it creates

confusions in dividing the line between type 1 data and type 2-

5 data.

Wrong classification data points will decrease the accuracy

of the prediction model. In the data preparation process of the 

WPP power prediction model, we adopted method 2 to train 

PNN model and the problem of wrong classification can be 

solved by improving the PNN's training data set. For example, 

after the PNN is built, data points with wrong classification 

results from testing data set can be added into the training data 

set. And then PNN model should be trained again with the 

expanded training data set in order to have more accurate 

classification ability.

C. Data for Building Models

In this paper, the power prediction result of the WPP is 

based on wind speed and wind direction. At first, wind speed 

factor is a key point in determining the available power 

generated from a single wind turbine with a certain crosssectional area [5]. The wind speed experienced by individual 

wind turbines is acquired by the anemometer and comes from 

the direction of horizontal axes of turbine’s hub. The hub is 

behind the blades, which has an effect of decreasing the 

natural wind speed. The wind speed acquired from the MET 

towers represents the natural wind speed at the location on the 

tower. Even though the height of the hub and MET tower are 

the same, they have different physical meanings. When we 

predict wind power generation, wind speed from turbine 

should be adopted as input information of the model.

 Secondly, wind direction (direction from which the wind 

blows) is another kind of useful information to predict wind 

power based on previous research results [6]. Wind can come 

from every direction when the wind speed is low. The higher 

the wind speed, the more uniform and more focused the wind 

direction. So during the same wind speed period, wind 

turbines can have different efficiencies due to different wind 

directions. But it is not convenient to predict the total power 

generation of the whole WPP by processing data information 

from all the turbines. It is better to find wind turbines from 

which the wind speed and wind direction can be most 

representative of the WPP area's wind situation. The wind 

speed situation (after data selection process) of the whole year 

of 2010 is shown in Table II. The data from 2010 Apr-Jun

covers a wide range and has the largest mean value of wind 

speed, which is suitable for training neural network model and 

was researched in this paper.

TABLE II 

2010 WIND SPEED DATA ANALYSIS

Wind Speed (m/s) Avg. Std. Maximum

2010 Jan-Mar 6.945 3.919 21.362

2010 Apr-Jun 8.812 3.927 22.635

2010 Jul-Sep 6.371 3.212 19.552

2010 Oct-Dec 7.330 4.117 21.912

Wind directions of the two groups of wind turbines at 

3/18/2010 10:00 pm and 4/10/2010 8:40 am are shown in Fig.

8 and Fig. 9 respectively. The arrows indicate the direction of 

the wind. The wind directions of Group 2 turbines are focused 

on a certain direction. The reason of the messy direction of 

Group 1 turbine is likely to be the data distortion due to the 

data transmission channel or bad performance sensors. The 

total output of WPP can be predicted according to only one or 

two turbine’s information [1]. Based on the filtered data set,

the average wind speed of all the wind turbines can be 

acquired. By correlation method, the wind turbine which has 

the highest correlation value with the average wind speed can 

be found (turbine A as indicated in Fig.1) and thus turbine A is 

the one which has the most representative wind speed. 

Following the same method, the turbine which has the most 

representative wind direction can also be found (turbine B as 

indicate in Fig.1). In this paper, data acquired from turbine A 

and B will be used to predict the total output of the WPP.

Fig.8. Wind direction at 3/18/2010 10:00 pm

Fig.9. Wind direction at 4/10/2010 8:40 am

III. MODEL DESIGN

A. Complex-valued Recurrent Neural Network Model 

Structure

Data of wind speed and direction from turbine A and 

turbine B can be combined and expressed as a vector on a 

two-dimensional complex coordinates as shown in Fig. 10.
5 

The wind vector can be expressed as equation (1). Paper [7] 

demonstrates that the prediction effect by using complexvalued neural network outperforms that of using real-valued 

neural network. 

vሬԦ ൌ vcosθ ൅ i vsinθ (1) 

Fig.10. Wind vector 

Inputs of recurrent neural network can be either a series of 

historical measured data or simulated data generated by the 

model as shown in Fig. 11. The advantage of this kind of 

model is that the output signal does not just rely on the current 

input signals of the system but it also has an internal memory 

in its training process. The disadvantage is that the training 

time of the recurrent neural network is longer than that of the 

static neural network. In this paper, we built a complex-valued 

recurrent neural network (CRNN) to predict the WPP's power 

generation. The CRNN can be trained under two kinds of 

modes— Parallel (P) mode and Series-Parallel (SP) mode, as 

seen in Fig.11. In the former mode, the simulated outputs 

p෤ሺnെ2ሻ, p෤ሺnെ1ሻ, p෤ሺnሻ are fed back as input signals. In the 

SP mode, actual outputs in the previous time step pሺn െ

2ሻ, pሺnെ1ሻ, pሺnሻ are used. Paper [8] demonstrates that 

prediction model with parallel mode inputs will result in 

accumulation of error if the previous prediction results are not 

accurate. 

Fig.11. Recurrent neural network training structure 

B. Basic Algorithm 

In this paper, ݌ indicates the power readings from MET 

tower, which represents the value of the effective power 

amount of the whole WPP transmitted to the grid. The ݑ

includes the wind speed vectors from representative wind 

turbines (turbine A and B). And, ݊ indicates the time step of 

10 minutes period. Usually a two-layer NN model can 

reasonably approximate any nonlinear function [9]. In this 

paper, a single hidden layer NN with fifteen neurons and one 

output was used. A bias of 1 was set initially. The longer the 

length of delay, the heavier the load of the training process 

has, which will also inevitably increase the training time of the 

model. In this paper, we trained the complex-valued recurrent 

neural network in 10-min, 20-min, 30-min, 40-min, 50-min, 

60-min time delay modes. For the transfer function, logsigmoid function was selected to be the hidden layer’s transfer 

function due to its efficiency; linear transfer function was used 

in the output layer as a convention. Levenberg-Marquardt 

back propagation algorithm is used as the training function for 

the whole recurrent neural network model. This method is 

typically used in minimization problems because it appears to 

be the fastest method in terms of convergence. The weights of 

each connection between neurons are adjusted in the training 

process until the errors are within the pre-determined range. 

To compare the performance of the two modes of recurrent 

neural network, the accuracy of the model can be evaluated by 

mean absolute error (MAE), as shown in (2), root mean 

squared error (RMSE), as shown in (3) and mean absolute 

percentage error (MAPE), as shown in (4). In (2) (3) (4), xi

and x෥ న are the ith component of the actual power and predicted 

wind power respectively. And, n is the length of the vector. 

MAE ൌ

1

n෍ |x୧ െ x෕న| ୬

୧ୀଵ

(2) 

RMSE ൌ ඩ1

n෍ሺx୧ െ x෥నሻଶ

୬

୧ୀଵ

(3) 

MAPE ൌ

1

n෍ ฬx୧ െ x෥న

x୧

ฬ ൈ 100 ୬

୧ୀଵ

% (4) 

IV. COMPARISON AND ANALYSIS OF PREDICTION RESULTS

Based on section II, data as shown in Table III was selected 

to finish the WPP's power prediction model. Data of each 

group consists of wind speed, wind direction and wind power 

generation. 

TABLE III 

DATA DESCRIPTION

Data 

Group 

Start time End time No. of data 

points 

Description 

A 4/1/2010 0:00 5/8/2010 

23:50 

5474 Training 

data set 

B 4/1/2011 0:10 5/8/2011 

23:50 

5362 Testing data 

set 

In the modeling process, the Group A's data is used for 

training the model; the Group B's data is used for testing and 

validation of the model. In the training process of neural 

network, according to the principle of the neural network, 

training set data will be divided into two parts randomly, one 

part is for learning the relationship between input data and 

output data and building the model, which occupies 60% of 

the total data, the rest 40% data is reserved for validation of 

the model and for further adjusting value of its weights. So 
6

models built by a same training data set could be different due 

to neural network's randomness in training. In order to get 

more accurate results, each model was built by Group A data 

repeatedly for three times and the prediction results were 

tested by Group B data repeatedly for three times and then 

average values are computed. Results from the proposed 

model were compared with the actual values of the historical 

data. The error statistics of the prediction results by different 

time series SP mode CRNN is shown in Table IV and Table 

V. Model 1 denotes SP mode CRNN with only wind speed as 

input, Model 2 denotes SP mode CRNN with wind vectors as 

inputs. Table VI shows the error analysis of prediction results 

by complex-valued neural network (CVNN) and real-valued 

neural network model (RVNN).

TABLE IV

ERROR ANALYSIS I

( MW) MAE RMSE

Input type Model 1 Model 2 Model 1 Model2

10min 9.901 7.874 13.613 9.408

20min 10.156 8.635 14.067 9.677

30min 11.205 9.258 16.268 10.502

40min 13.371 9.422 16.331 10.845

50min 13.931 9.423 18.256 11.111

60min 14.691 9.58 18.584 11.374

TABLE V

ERROR ANALYSIS II

MAPE (%) Std. of Error (MW)

Input type Model 1 Model 2 Model 1 Model2

10min 12.163 11.204 10.722 9.408

20min 14.549 12.091 11.801 11.221

30min 18.149 12.753 13.834 11.474

40min 22.777 13.872 15.362 12.385

50min 24.448 17.772 17.255 12.722

60min 29.192 18.091 19.522 13.044

TABLE VI

ERROR ANALYSIS OF CVNN

MAE RMSE Std. of Error MAPE

CVNN 14.149 14.994 14.635 14.867%

RVNN 16.210 17.086 30.672 22.535%

From Table IV, V and VI above, the results show that the 

10-min delay mode of Model 2 has the best performance in the 

CRNN models and can be adopted to build power prediction 

models for WPP. The accuracy suggested by MAPE is 

11.204%, which also outperforms the prediction results of 

CVNN and RVNN as shown in Table VI. In the CRNN 

models, the accuracy of CRNN's prediction results decreases 

with the increasing of the delay length in the model training 

process. The reason is that the wind is changing rapidly, so it 

is better to predict the wind power by referring the wind status 

in the nearest previous time. Apparently, the accuracy of 

prediction results and its consistency for different delay length

are improved when the direction of wind is combined into 

input signals of the neural network. The prediction results of

CVNN and RVNN models, which do not include time delay in 

their training data set, have worse prediction results even 

compared to Model 2 with 40-min delay. Fig. 12 shows the 

prediction results from 4/1/2011 1:20 am to 4/2/201110:40 am, 

where the predicted power generation points are very close to 

theose actual ones. Additionally, there are always some 

prediction data points with large relative errors, which are 

larger than 100%. The characteristic of those data points are 

always generated during low wind speed period (below 4m/s) 

which is not important for wind power integration and can be 

ignored.

The whole prediction is shown in Fig. 13. Overall, most of 

the prediction values are smaller than the actual values. 

According to the errors of the prediction results, the power 

company can compensate the errors by allocating proper 

power reserve and make some adjustment in scheduling the

wind power generation.

Fig.12 Prediction results by 10-min time delay SP mode RNN

Fig.13 Predicted wind power vs. actual wind power of 2011

V. CONCLUSION

This paper describes a procedure of predicting total output 

of wind power plant (WPP) by neural networks. Probabilistic 

neural network (PNN) was applied to classify and screen the 

raw wind data for the training of neural network prediction 

models. And then certain representative wind turbines were 

selected as an input data source for modeling and to simplify

the input signals to the model. In the last step, based on the 

previous wind power prediction experience [2-6], complexvalued recurrent neural network (CRNN) model was chosen to 

predict the total output of WPP with high accuracy.
7

VI. ACKNOWLEDGMENT

This work was partially supported by the U.S. Department 

of Energy under Contract No. DE-AC36-08-GO28308 with 

the National Renewable Energy Laboratory and was also

partially supported by U.S. NSF Grant 0844707.

VII. REFERENCES

[1] Yuan-Kang Wu, Jing-Shan Hong, “A literature review of wind 

forecasting technology in the world,” presented at 2007 IEEE Lausanne 

Power Tech Conf., pp.504-509.

[2] Yih-Huei Wan, Erik Ela, and Kirsten Orwig, “Development of an

Equivalent Wind Plant Power Curve,” NREL, Golden, CO, Tech. Rep.

NREL/CP-550-48146, Jun.2010.

[3] Kang Meei-Song, Chen Chao-Shun, Ke Yu-Lung and Lin Chia-Hung, “

Load Profile Synthesis and Wind Power Generation Prediction for an 

Isolated Power System,” IEEE Trans. on Industry Applications, vpl.43, 

pp.1459-1484, Nov. 2007.

[4] http://www.mathworks.com/help/toolbox/nnet/ug/bss38ji-1.html.

[5] Miller, A., Muljadi, E. and Zinger, D.S. “A Variable Wind Turbine 

Power Control,” IEEE Trans. Energy Conversion, vol. 12, no.2, 1997,

pp. 181-186.

[6] Shuhui Li, Donald C. Wunsch, Edgar A. O’Hair and Michael G. 

Giesselmann, “Using neural networks to estimate wind turbine power 

generation,” IEEE Trans. on Energy Conversion, vol. 16, pp.267-282, 

Sep.2001.

[7] Takahiro Kitajima, Takashi Yasuno, “Output Prediction of Wind Power 

Generation System Using Complex-valued Neural Network,” in Proc.

2010 SICE Annual Conf., pp. 3610-3613.

[8] Andrew Kusiak, Haiyang Zheng and Zhe Song, “Short-term prediction 

of wind farm power: a data mining approach,” IEEE Trans on Energy 

Conversion, vol.24, no.1, pp.125-135, Mar. 2009.

[9] Jagannathan Sarangapani, Neural Network Control of Nonlinear 

Discrete-Time Systems, Taylor and Francis Group, TJ213. S117, 2006.

VIII. BIOGRAPHIES

Ziqiao Liu received the M.S. degree from Beijing 

Institute of Technology, School of Automation, 

Beijing, China, in 2010. She is currently working 

toward the Ph.D. degree in the Department of 

Electrical and Computer Engineering, University of 

Denver, Colorado, USA. Her current research interests 

include wind power prediction, wind farm supervisory 

control.

Wenzhong Gao (S’00–M’02–SM’03) received the 

M.S. and Ph.D. degrees in electrical and computer 

engineering specializing in electric power engineering 

from Georgia Institute of Technology, Atlanta, in 1999 

and 2002, respectively. He is currently with the 

Department of Electrical and Computer Engineering, 

University of Denver, Colorado, USA. His current 

teaching and research interests include renewable 

energy and distributed generation, smart grid, power system protection, power 

electronics applications in power systems, power system modeling and 

simulation, and hybrid electric propulsion systems. He is an Editor for IEEE 

Transactions on Sustainable Energy. He is the General Chair for The IEEE 

Symposium on Power Electronics and Machines in Wind Applications 

(PEMWA 2012).

Yih-huie Wan received the B.S. degree from National

Cheng Kung University, Taiwan, R.O.C., and the M.S. 

degree from Southern Illinois University, Carbondale, 

IL, in 1973 and 1979, respectively, both in electrical 

engineering.

He is a Senior Engineer with the National Renewable

Energy Laboratory (NREL), Golden, CO. His main 

expertise is in electric power system engineering, 

planning, and operation. Before joining NREL, he 

worked as an Electrical Engineer for an electric power company in Oklahoma 

for ten years in substation and distribution feeder design, transmission system 

planning, and bulk power transaction analysis. His work at NREL includes 

renewable energy technology assessment and integration of renewable energy 

technologies with the electric utility grid. He has also done work in renewable 

energy policy analysis, renewable energy resource assessment, and distributed 

generation.

Mr. Wan is a Registered Professional Engineer in the State of Oklahoma.

Eduard Muljadi (M’82, SM’94, F’10) received 

his Ph.D. in electrical engineering from the University 

of Wisconsin at Madison. From 1988 to 1992, he 

taught at California State University at Fresno. In June 

1992, he joined the National Renewable Energy 

Laboratory in Golden, Colorado. His research interests 

are in the fields of electric machines, power 

electronics, and power systems, with an emphasis on 

renewable energy applications. He is a member of Eta Kappa Nu and Sigma 

X, and a fellow of the Institute of Electrical and Electronics Engineers (IEEE). 

He is involved in the activities of the IEEE Industry Application Society 

(IAS), Power Electronics Society, and Power and Energy Society (PES), and 

an editor of the IEEE Transactions on Energy Conversion.

Dr. Muljadi is a member of various committees of the IAS, as well as a 

member of the Working Group on Renewable Technologies and the Task 

Force on Dynamic Performance of Wind Power Generation, both of the PES. 

He holds two patents in power conversion for renewable energy.