• Proceedings of the KIEE Conference
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• 2002.04a
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• pp.207-212
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• 2002

As is well-known, the maximum power point(MPP) of PV power generation system depends on array temperature and solar insolation, it is necessary to track MPP of solar array all the time. Among various MPP control algorithms, the constant voltage control method, the perturbation and observation(P&O) method and the incremental conductance method(IncCond) have drawn many attractions due to the usefulness of each system. In this paper, the effectiveness of above mentioned three different control algorithms are thoroughly investigated via simulations and proposed efficiency evaluation method on experiment. Both the steady-state and transient characteristics of each control algorithms along with measured efficiency are analyzed, respectively. Finally, a novel MPPT control algorithm combining the constant voltage control and IncCond method for low insolation condition is proposed to improve efficiency of the 3KW PV power generation system.

• Journal of Wind Energy
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• v.2 no.1
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• pp.82-89
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• 2011

his dissertation is on power control system for MW-class wind turbine. Especially, the control purpose is reduction in electrical power and rotor speed. The base control structure is power curve tracking control using variable speed variable pitch operational type. For the reduction of fluctuations, more control algorithm is needed in above rated wind conditions. Because general pitch control system is low dynamic response as compared with the wind speed change. So, this paper introduces about the pitch feed forward control to minimize fluctuations of the electrical power and rotor speed. To maintain rated electrical power, the algorithm of feed forward control adds feed forward pitch amount to the pitch command of power curve tracking control. The effectiveness of the feed forward control is verified through the simulation.

• Journal of Advanced Marine Engineering and Technology
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• v.39 no.6
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• pp.642-648
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• 2015

Recently, there have been many studies pertaining to reducing energy consumption on ships. As part of those studies, the energy consumption of ships can be reduced by understanding and controlling the varying loads, excluding fixed loads. In existing ships, engine room fans are usually operated based on the actual experience of the crew without any special guidelines. To realize energy reduction, we investigate the characteristics of engine-room fans, and we propose an energy-management system called the engine room fan control system (ERFCS) and the ERFCS algorithm. The ERFCS controls the fan speed depending on the temperature and pressure, where one to four fans are operated depending on changes in the load. In addition, the minimum rotation speed of the engine-room fan was limited to 50% to prevent the surging phenomenon, which is due to fan damage or low pressure resulting from mechanical friction or heating at low fan speeds. We develop a fan control system simulation model using LabVIEW that is based on the proposed algorithm and ISO 8861. Finally, we perform simulations to confirm that operation of the proposed fan control system is possible using only 46.4% of the power required by the existing method.

• International journal of advanced smart convergence
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• v.3 no.2
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• pp.1-5
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• 2014

Sensor nodes depend on batteries for energy source in Wireless Sensor Networks (WSNs). Low Energy Adaptive Clustering Hierarchy (LEACH) is a representative cluster-based routing protocol designed to ensure energy use efficiency whereas the virtual cluster exchange routing (VCR) information only with its closest node to build a network. In this paper, a protocol scheme was proposed wherein member nodes are designed to compare the currently sensed data with the previously sensed one and to switch to sleep mode when a match is achieved. The design is to help improve the transmission energy efficiency too.

• Structural Engineering and Mechanics
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• v.23 no.2
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• pp.177-193
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• 2006

Determining the hysteretic energy demand and dissipation capacity and level of damage of the structure to a predefined earthquake ground motion is a highly non-linear problem and is one of the questions involved in predicting the structure's response for low-performance levels (life safe, near collapse, collapse) in performance-based earthquake resistant design. Neural Network (NN) analysis offers an alternative approach for investigation of non-linear relationships in engineering problems. The results of NN yield a more realistic and accurate prediction. A NN model can help the engineer to predict the seismic performance of the structure and to design the structural elements, even when there is not adequate information at the early stages of the design process. The principal aim of this study is to develop and test multi-layered feedforward NNs trained with the back-propagation algorithm to model the non-linear relationship between the structural and ground motion parameters and the hysteretic energy demand in steel moment resisting frames. The approach adapted in this study was shown to be capable of providing accurate estimates of hysteretic energy demand by using the six design parameters.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.1
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• pp.191-196
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• 2014

For the large scale of electricity consumer, since the price for the electricity consumption depends on the peak power, the issue for peak power reduction have been being studied widely. Electric railway systems, which is one of the most representative large scale of loads, also has assignment to reduce the peak power since they have high peak power and low energy consumption load characteristics. In the aspect of the economic operation through reduction of peak power, this paper proposes a novel algorithm for power management system in electric railway systems using energy storage.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.8
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• pp.849-853
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• 2014

In micro-scale electromechanical systems, the power to perform accurate position sensing often greatly exceeds the power needed to generate motion. This paper explores the implications of sampling rate and amplifier noise density selection on the performance of a system identification algorithm using a capacitive sensing circuit. Specific performance objectives are to minimize or limit convergence rate and power consumption to identify the dynamics of a rotary micro-stage. A rearrangement of the conventional recursive least-squares identification algorithm is performed to make operating cost an explicit function of sensor design parameters. It is observed that there is a strong dependence of convergence rate and error on the sampling rate, while energy dependence is driven by error that may be tolerated in the final identified parameters.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.839-844
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• 2003

This paper presents a method to segment wafer ID marks on poor quality images under uncontrolled lighting conditions of the semiconductor process. The active multiple templates matching method is suggested to search ID areas on wafers and segment them into meaningful regions and it would have been impossible to recognize characters using general OCR algorithms. This active template model is designed by applying a snake model that is used for active contour tracking. Active multiple template model searches character areas and segments them into single characters optimally, tracking each character that can vary in a flexible manner according to string configurations. Applying active multiple templates, the optimization of the snake energy is done using Greedy algorithm, to maximize its efficiency by automatically controlling each template gap. These vary according to the configuration of character string. Experimental results using wafer images from real FA environment are presented.

• New & Renewable Energy
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• v.9 no.2
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• pp.51-57
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• 2013

The Grid code is being strengthen as increase of renewable energy ratio. Especially, the grid connection regulations are continuously being updated for stable operation of power grids. Static grid support and Dynamic grid support must make an accurate measure at Grid connected point because they needs control algorithm individually. It has to exactly measure voltage including switching ripple at the output of the inverter generating system. In addition, it is necessary to have an accurate voltage measurement when the situation rapidly changing the grid impedance is caused by the input of serial impedance of transformer and line impedance as well as Grid Fault Device. In this paper, We propose a new detection method of grid voltage to calculate accurately the r.m.s voltage of the grid connection point along the standard required by the low voltage regulation. We verified performance through simulation grid fault device.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.2
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• pp.107-114
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• 2024

Mobile ad hoc networks represent self-configuring networks of mobile devices that communicate without relying on a fixed infrastructure. However, traditional routing protocols in such networks encounter challenges in selecting efficient and reliable routes due to dynamic nature of these networks caused by unpredictable mobility of nodes. This often results in a failure to meet the low-delay and low-energy consumption requirements crucial for such networks. In order to overcome such challenges, our paper introduces a novel multi-objective and adaptive routing scheme based on the Q-learning reinforcement learning algorithm. The proposed routing scheme dynamically adjusts itself based on measured network states, such as traffic congestion and mobility. The proposed approach utilizes Q-learning to select routes in a decentralized manner, considering factors like energy consumption, load balancing, and the selection of stable links. We present a formulation of the multi-objective optimization problem and discuss adaptive adjustments of the Q-learning parameters to handle the dynamic nature of the network. To speed up the learning process, our scheme incorporates informative shaped rewards, providing additional guidance to the learning agents for better solutions. Implemented on the widely-used AODV routing protocol, our proposed approaches demonstrate better performance in terms of energy efficiency and improved message delivery delay, even in highly dynamic network environments, when compared to the traditional AODV. These findings show the potential of leveraging reinforcement learning for efficient routing in ad hoc networks, making the way for future advancements in the field of mobile ad hoc networking.