Giwhyun Lee - Academia.edu (original) (raw)

Papers by Giwhyun Lee

This is the dataset used in the paper, Lee, Ding, Genton, and Xie, 2015, "Power curve estima... more This is the dataset used in the paper, Lee, Ding, Genton, and Xie, 2015, "Power curve estimation with multivariate environmental factors for inland and offshore wind farms," <em>Journal of the American Statistical Association</em>, Vol. 110, pp. 56-67.

Wind Energy, 2014

Power curves are commonly estimated using the binning method recommended by the International Ele... more Power curves are commonly estimated using the binning method recommended by the International Electrotechnical Commission, which primarily incorporates wind speed information. When such power curves are used to quantify a turbine's upgrade, the results may not be accurate because many other environmental factors in addition to wind speed, such as temperature, air pressure, turbulence intensity, wind shear and humidity, all potentially affect the turbine's power output. Wind industry practitioners are aware of the need to filter out effects from environmental conditions. Toward that objective, we developed a kernel plus method that allows incorporation of multivariate environmental factors in a power curve model, thereby controlling the effects from environmental factors while comparing power outputs. We demonstrate that the kernel plus method can serve as a useful tool for quantifying a turbine's upgrade because it is sensitive to small and moderate changes caused by certain turbine upgrades. Although we demonstrate the utility of the kernel plus method in this specific application, the resulting method is a general, multivariate model that can connect other physical factors, as long as their measurements are available, with a turbine's power output, which may allow us to explore new physical properties associated with wind turbine performance.

The Annals of Applied Statistics, 2013

This is the dataset used in the paper, Lee, Ding, Xie, and Genton, 2015, "Kernel Plus method... more This is the dataset used in the paper, Lee, Ding, Xie, and Genton, 2015, "Kernel Plus method for quantifying wind turbine upgrades," <em>Wind Energy</em>, Vol. 18, pp. 1207-1219.

IISE Transactions, 2016

Modern utility-scale wind farms consist of a large number of wind turbines. In order to improve t... more Modern utility-scale wind farms consist of a large number of wind turbines. In order to improve the power generation efficiency of wind turbines, accurate quantification of power generation levels of multi-turbines is critical, in both wind farm design and operational controls. One challenging issue is that the power output levels of multiple wind turbines are different, due to complex interactions between turbines, known as wake effects. In general, upstream turbines in a wind farm absorb kinetic energy from wind. Therefore, downstream turbines tend to produce less power than upstream turbines. Moreover, depending on weather conditions, the power deficits of downstream turbines exhibit heterogeneous patterns. This study proposes a new statistical approach to characterize heterogeneous wake effects. The proposed approach decomposes the power outputs into the average pattern commonly exhibited by all turbines and the turbine-to-turbine variability caused by multi-turbine interactions. To capture the wake effects, turbine-specific regression parameters are modeled using a Gaussian Markov random field. A case study using actual wind farm data demonstrates the proposed approach's superior performance.

Ergonomics

Despite the prevalence of pre-obesity and obesity, the physical capabilities of pre-obese/obese i... more Despite the prevalence of pre-obesity and obesity, the physical capabilities of pre-obese/obese individuals are not well documented. As an effort to address this, this study investigated the pre-obesity and obesity impacts on joint range of motion (RoM) for twenty-two body joint motions. A publicly available passive RoM dataset was analysed. Three BMI groups (normal-weight, pre-obese, and obese [Class I]) were statistically compared in joint RoM. The pre-obese and obese groups were found to have significantly smaller RoM means than the normal-weight for elbow flexion and supination, hip extension and flexion, knee flexion and ankle plantar flexion. The pre-obese and obese groups exhibited no significant inter-group mean RoM differences except for knee flexion; for knee flexion, the obese group had significantly smaller RoM means than the pre-obese. The findings would be useful for designing work tasks and products/systems for high BMI individuals and developing digital human models representing differently sized individuals. Practitioner summary: This study investigated the pre-obesity and obesity impacts on joint range of motion (RoM) by comparing three participant groups: normal-weight; pre-obese and obese. The pre-obese and obese groups had significantly smaller RoM means than the normal-weight for elbow flexion and supination; hip extension and flexion; knee flexion and ankle plantar flexion. ANCOVA: Analysis of Covariance; BMI: Body Mass Index; CI: Confidence Interval; RoM: Range of Motion; SPSS: Statistical Package for the Social Sciences.

In this paper, we present a new statistical approach for evaluating the time-dependent effectiven... more In this paper, we present a new statistical approach for evaluating the time-dependent effectiveness of wearable robots without real work. In total, 10 subjects participated in three phases of the experiment; not equipped with a wearable robot without any load, not equipped with the wearable robot with a 15 kg load, equipped with the wearable robot with a 15 kg load. A higher limb wearable robot called LEXO-W was utilized. We measured the time taken to complete a 10 m round trip 10 times as a lap time, and each participant was measured multiple times under all conditions. An increasing number of round trips causes an increment in lap times. In particular, the load-carrying group showed a rapid upward trend in lap time over the number of round trips. However, the robot-assisted group showed a slightly upward trend of lap time over the number of round trips. This study statistically shows that the LEXO-W helps reduce physical fatigue by using repeated measure ANOVA analysis. Furthermo...

Journal of the American Statistical Association, 2015

In the wind industry, a power curve refers to the functional relationship between the power outpu... more In the wind industry, a power curve refers to the functional relationship between the power output generated by a wind turbine and the wind speed at the time of power generation. Power curves are used in practice for a number of important tasks including predicting wind power production and assessing a turbine's energy production efficiency. Nevertheless, actual wind power data indicate that the power output is affected by more than just wind speed. Several other environmental factors, such as wind direction, air density, humidity, turbulence intensity, and wind shears, have potential impact. Yet, in industry practice, as well as in the literature, current power curve models primarily consider wind speed and, sometimes, wind speed and direction. We propose an additive multivariate kernel method that can include the aforementioned environmental factors as a new power curve model. Our model provides, conditional on a given environmental condition, both the point estimation and density estimation of power output. It is able to capture the nonlinear relationships between environmental factors and the wind power output, as well as the high-order interaction effects among some of the environmental factors. Using operational data associated with four turbines in an inland wind farm and two turbines in an offshore wind farm, we demonstrate the improvement achieved by our kernel method.

This is the dataset used in the paper, Lee, Ding, Genton, and Xie, 2015, "Power curve estima... more This is the dataset used in the paper, Lee, Ding, Genton, and Xie, 2015, "Power curve estimation with multivariate environmental factors for inland and offshore wind farms," <em>Journal of the American Statistical Association</em>, Vol. 110, pp. 56-67.

Wind Energy, 2014

Power curves are commonly estimated using the binning method recommended by the International Ele... more Power curves are commonly estimated using the binning method recommended by the International Electrotechnical Commission, which primarily incorporates wind speed information. When such power curves are used to quantify a turbine's upgrade, the results may not be accurate because many other environmental factors in addition to wind speed, such as temperature, air pressure, turbulence intensity, wind shear and humidity, all potentially affect the turbine's power output. Wind industry practitioners are aware of the need to filter out effects from environmental conditions. Toward that objective, we developed a kernel plus method that allows incorporation of multivariate environmental factors in a power curve model, thereby controlling the effects from environmental factors while comparing power outputs. We demonstrate that the kernel plus method can serve as a useful tool for quantifying a turbine's upgrade because it is sensitive to small and moderate changes caused by certain turbine upgrades. Although we demonstrate the utility of the kernel plus method in this specific application, the resulting method is a general, multivariate model that can connect other physical factors, as long as their measurements are available, with a turbine's power output, which may allow us to explore new physical properties associated with wind turbine performance.

The Annals of Applied Statistics, 2013

This is the dataset used in the paper, Lee, Ding, Xie, and Genton, 2015, "Kernel Plus method... more This is the dataset used in the paper, Lee, Ding, Xie, and Genton, 2015, "Kernel Plus method for quantifying wind turbine upgrades," <em>Wind Energy</em>, Vol. 18, pp. 1207-1219.

IISE Transactions, 2016

Modern utility-scale wind farms consist of a large number of wind turbines. In order to improve t... more Modern utility-scale wind farms consist of a large number of wind turbines. In order to improve the power generation efficiency of wind turbines, accurate quantification of power generation levels of multi-turbines is critical, in both wind farm design and operational controls. One challenging issue is that the power output levels of multiple wind turbines are different, due to complex interactions between turbines, known as wake effects. In general, upstream turbines in a wind farm absorb kinetic energy from wind. Therefore, downstream turbines tend to produce less power than upstream turbines. Moreover, depending on weather conditions, the power deficits of downstream turbines exhibit heterogeneous patterns. This study proposes a new statistical approach to characterize heterogeneous wake effects. The proposed approach decomposes the power outputs into the average pattern commonly exhibited by all turbines and the turbine-to-turbine variability caused by multi-turbine interactions. To capture the wake effects, turbine-specific regression parameters are modeled using a Gaussian Markov random field. A case study using actual wind farm data demonstrates the proposed approach's superior performance.

Ergonomics

Despite the prevalence of pre-obesity and obesity, the physical capabilities of pre-obese/obese i... more Despite the prevalence of pre-obesity and obesity, the physical capabilities of pre-obese/obese individuals are not well documented. As an effort to address this, this study investigated the pre-obesity and obesity impacts on joint range of motion (RoM) for twenty-two body joint motions. A publicly available passive RoM dataset was analysed. Three BMI groups (normal-weight, pre-obese, and obese [Class I]) were statistically compared in joint RoM. The pre-obese and obese groups were found to have significantly smaller RoM means than the normal-weight for elbow flexion and supination, hip extension and flexion, knee flexion and ankle plantar flexion. The pre-obese and obese groups exhibited no significant inter-group mean RoM differences except for knee flexion; for knee flexion, the obese group had significantly smaller RoM means than the pre-obese. The findings would be useful for designing work tasks and products/systems for high BMI individuals and developing digital human models representing differently sized individuals. Practitioner summary: This study investigated the pre-obesity and obesity impacts on joint range of motion (RoM) by comparing three participant groups: normal-weight; pre-obese and obese. The pre-obese and obese groups had significantly smaller RoM means than the normal-weight for elbow flexion and supination; hip extension and flexion; knee flexion and ankle plantar flexion. ANCOVA: Analysis of Covariance; BMI: Body Mass Index; CI: Confidence Interval; RoM: Range of Motion; SPSS: Statistical Package for the Social Sciences.

In this paper, we present a new statistical approach for evaluating the time-dependent effectiven... more In this paper, we present a new statistical approach for evaluating the time-dependent effectiveness of wearable robots without real work. In total, 10 subjects participated in three phases of the experiment; not equipped with a wearable robot without any load, not equipped with the wearable robot with a 15 kg load, equipped with the wearable robot with a 15 kg load. A higher limb wearable robot called LEXO-W was utilized. We measured the time taken to complete a 10 m round trip 10 times as a lap time, and each participant was measured multiple times under all conditions. An increasing number of round trips causes an increment in lap times. In particular, the load-carrying group showed a rapid upward trend in lap time over the number of round trips. However, the robot-assisted group showed a slightly upward trend of lap time over the number of round trips. This study statistically shows that the LEXO-W helps reduce physical fatigue by using repeated measure ANOVA analysis. Furthermo...

Journal of the American Statistical Association, 2015

In the wind industry, a power curve refers to the functional relationship between the power outpu... more In the wind industry, a power curve refers to the functional relationship between the power output generated by a wind turbine and the wind speed at the time of power generation. Power curves are used in practice for a number of important tasks including predicting wind power production and assessing a turbine's energy production efficiency. Nevertheless, actual wind power data indicate that the power output is affected by more than just wind speed. Several other environmental factors, such as wind direction, air density, humidity, turbulence intensity, and wind shears, have potential impact. Yet, in industry practice, as well as in the literature, current power curve models primarily consider wind speed and, sometimes, wind speed and direction. We propose an additive multivariate kernel method that can include the aforementioned environmental factors as a new power curve model. Our model provides, conditional on a given environmental condition, both the point estimation and density estimation of power output. It is able to capture the nonlinear relationships between environmental factors and the wind power output, as well as the high-order interaction effects among some of the environmental factors. Using operational data associated with four turbines in an inland wind farm and two turbines in an offshore wind farm, we demonstrate the improvement achieved by our kernel method.