• International Journal of Ocean System Engineering
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• v.1 no.3
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• pp.144-147
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• 2011

The rotor that initially converts the flow energy into rotational energy is a very important component that affects the efficiency of the entire tidal current power system. Rotor performance is determined by various design variables. Power generation is strongly dependent on the incoming flow velocity and the size of the rotor. To extract a large quantity of power, a tidal current farm is necessary with a multi-arrangement of devices in the ocean. However, the interactions between devices also contribute significantly to the total power capacity. Therefore, rotor performance, considering the interaction problems, needs to be investigated to maximize the power generation in a limited available area. The downstream rotor efficiency is affected by the wake produced from the upstream rotor. This paper introduces the performance of a downstream rotor affected by wakes from an upstream rotor, demonstrating the interference affecting various gabs between devices.

• Proceedings of the KIPE Conference
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• 1998.11a
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• pp.49-54
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• 1998

This paper describes a simulation model to analyze the dynamic performance of Unified Power Flow Controller, which adjust flexibly the active and reactive power flow through the ac transmission line. The basic operation was analyzed in detail using equivalent circuits and the design of control system was developed using vector control method. A simulation model with EMTP code was conceived to evaluate the performance of the Unified power Flow Controller. The simulation results show that the developed simulation model is very effective to analyze the dynamic performance of the Unified Power Flow Controller.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.1C
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• pp.149-156
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• 2004

In this paper, novel power-aware motion estimation is proposed for low-power multimedia communication. In the video compression, motion estimation dominates the total power consumption, where better performance usually requires more power consumption. Among several motion estimation algorithms with different performance and power, the proposed motion estimation adaptively selects the optimal algorithm during run-time, considering the trade-off between performance and power. The proposed motion estimation can be easily applied to various motion estimation algorithms with negligible computation or hardware overhead. According to simulation results, the proposed motion estimation reduces the power consumption to 1/15.7~1/5.6 without performance degradation, when compared to the conventional algorithms.

• Journal of Electrical Engineering and Technology
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• v.13 no.1
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• pp.181-191
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• 2018

Rapid development of information technology makes our environment become smarter and massive high performance computers are providing powerful computing for that. Graphics Processing Unit (GPU) as a typical high performance component is being widely used for both graphics and general-purpose applications. Although it can greatly improve computing power, it also delivers significant power consumption and need sufficient power supplies. To make high performance computing more sustainable, the important step is to measure it. Current power technologies for GPU have some drawbacks, such as they are not applicable for power estimation at the early stage. In this article, we present a novel power technology to correlate power consumption and the characteristics at the programmer perspective, and then to estimate power consumption of source program without prerunning. We conduct experiments on Nvidia's GT740 platform; the results show that our power model is more accurately than regression model and has an average error of 2.34% and the maximum error of 9.65%.

• Journal of Advanced Marine Engineering and Technology
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• v.35 no.8
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• pp.1070-1077
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• 2011

This study includes field test results for performance evaluation of a tubular turbine in marine small hydro power plant. Minimum output power of the tested turbine generator is examined with using of the measured effective head, output power and efficiency. For the rated and maximum output power tests, corrected values from the result of turbine model test are used for the performance evaluation, because experimental conditions of field test at the rated and maximum output powers are restricted correctly. Performance of the test turbine shows good conformance with the suggested guarantee values of output power and efficiency at the measured points of minimum, rated and maximum output power.

• Nuclear Engineering and Technology
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• v.33 no.3
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• pp.339-348
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• 2001

NSSS Control Systems automatically mitigate transient conditions and leads to a stable plant condition without operator actions when a transient occurs during normal power operation. In this paper, the function and performance of NSSS control systems were examined and evaluated by comparing the predicted results with the measured data for the selected events. Loss of a Main Feedwater Pump and Load Rejection to House Load Operation events were selected for the evaluation among the transient tests peformed during the Power Ascension Test (PAT) of UCN unit 4. The overall schematic control actions of NSSS control systems can be evaluated easily through the observation of these two typical events. The selected events were analyzed by the KISPAC computer code[l] which had been used in developing the control logic and determining the control setpoints during the plant design. Additionally, the performance of FWCS during low power operation was evaluated. The result of evaluation showed that the NSSS control systems were designed properly and the performance of the NSSS control systems was excellent and also the computer code had a good prediction capability.

• International Journal of Fluid Machinery and Systems
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• v.6 no.1
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• pp.25-32
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• 2013

Data centers have been built with spread of cloud computing. Further, electric power consumption of it is growing rapidly. High power cooling small-sized fans; high pressure and large flow rate small-sized fan, are used for servers in the data centers and there is a strong demand to increase power of it because of increase of quantity of heat from the servers. Contra-rotating rotors have been adopted for some of high power cooling fans to meet the demand for high power. There is a limitation of space for servers and geometrical restriction for cooling fans because spokes to support fan motors, electrical power cables and so on should be installed in the cooling fans. It is important to clarify complicated internal flow condition and influence of a geometric shape of the cooling fans on performance to achieve high performance of the cooling fans. In the present paper, the performance and the flow condition of the high power contra-rotating small-sized axial fan with a 40mm square casing are shown by experimental and numerical results. Furthermore, influence of the geometrical shape of the small-sized cooling fan on the internal flow condition is clarified and design guideline to improve the performance is discussed.

• KEPCO Journal on Electric Power and Energy
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• v.7 no.2
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• pp.323-328
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• 2021

Diagnostic models are required. Data augmentation is one of the best ways to improve deep learning performance. Traditional augmentation techniques that modify image brightness or spatial information are difficult to achieve great results. To overcome this, a generative adversarial network (GAN) technology that generates virtual data to increase deep learning performance has emerged. GAN can create realistic-looking fake images by competitive learning two networks, a generator that creates fakes and a discriminator that determines whether images are real or fake made by the generator. GAN is being used in computer vision, IT solutions, and medical imaging fields. It is essential to secure additional learning data to advance deep learning-based fault diagnosis solutions in the power industry where facilities are strictly maintained more than other industries. In this paper, we propose a method for generating power facility images using GAN and a strategy for improving performance when only used a small amount of data. Finally, we analyze the performance of the augmented image to see if it could be utilized for the deep learning-based diagnosis system or not.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.5
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• pp.541-552
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• 2022

This research examines the effects of organizational learning and network involvement, as well as many contextual factors, on power generation businesses' innovation capability and operational success in Vietnam. This research also aims to attest to the moderating roles of top management support and company age, and firm possession type in the power generation industry. This study applied the exploratory factor analysis (EFA) and PLS-SEM approach for data analysis. In this research, we have tested hypotheses with data collected from 132 top managers and other key personnel from power generation companies in Vietnam. The results also attest to the moderating role of top management support on the two relationships between organizational learning - innovation capability and network involvement - innovation capability. Another important finding is that the company age has a negative impact on operational performance but shows a positive moderating role in the relationship between innovation capability and operational performance. This study highlights the central roles of organizational learning and innovation capability in impacting the organizational performance of power generation companies. These companies play a key role in supporting the development of industries in practice. This research also emphasizes the moderating roles of top management support and company age and possession type in practice.

• Journal of Wind Energy
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• v.15 no.3
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• pp.21-29
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• 2024

Wind power is highly variable due to the intermittent nature of wind. This can lead to power grid instability and decreased efficiency. Therefore, it is necessary to improve wind power prediction performance to minimize the negative impact on the power system. Recently, wind power prediction using machine learning has gained popularity, and ensemble models in machine learning have shown high prediction accuracy. RF, GB, XGB and LGBM are decision tree-based ensemble models and have high predictive performance in wind power, but these models have problems from over-fitting and strong dependence on certain variables. However, the stacking model can improve prediction performance by combining individual models and compensate for the shortcomings of each model. In this study, The MAE of RF, XGB and LGBM is 310.42 kWh, 217.07 kWh and 265.20 kWh, respectively, while the stacking model based on RF, XGB and LGBM is 202.33 kWh. Stacking models can improve prediction performance. Finally, it is expected to contribute to electricity supply and demand planning.