• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2304-2308
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• 2003

Ocean thermal energy conversion (OTEC) is an effective method of power generation, which has a small impact on the environment and can be utilized semi-permanently. This paper describes a dynamic model for a pilot OTEC plant built by the Institute of Ocean Energy, Saga University, Japan. This plant is based on Uehara cycle, in which binary mixtures of ammonia and water is used as the working fluid. Some simulation results attained by this model and the analysis of the results are presented. The developed computer simulation can be used to actual practice effectively, such as stable control in a steady operation, optimal determination of the plant specifications for a higher thermal efficiency and evaluation of the economic prospects and off-line training for the operators of OTEC plant.

• Journal of IKEEE
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• v.23 no.2
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• pp.529-533
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• 2019

Combined cycle power plants are often used to produce power. These days prediction of power plant output based on operating parameters is a major concern. This paper presents an approach to using computational intelligence technique to predict the output power of combined cycle power plant. Computational intelligence techniques have been developed and applied to many real world problems. In this paper, tree architectures of fuzzy neural networks are considered to predict the output power. Tree architectures of fuzzy neural networks have an advantage of reducing the number of rules by selecting fuzzy neurons as nodes and relevant inputs as leaves optimally. For the optimization of the networks, two-step optimization method is used. Genetic algorithms optimize the binary structure of the networks by selecting the nodes and leaves as binary, and followed by random signal-based learning further refines the optimized binary connections in the unit interval. To verify the effectiveness of the proposed method, combined cycle power plant dataset obtained from the UCI Machine Learning Repository Database is considered.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.11
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• pp.1495-1504
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• 2021

An ultra-compact binary power generation plant converts thermal energy into electric power using temperature difference between heat source and cooling source. In the actual power generation environment, the characteristic value of the plant changes due to any negative effects such as environmental condition or corrosion of related equipment. If the characteristic value of the plant changes, it may lead to unstable output of the turbine in a conventional PID control system with fixed PID parameters. A Neuro PID control system based on Neural Network adaptively to adjust the PID parameters according to the change in the characteristic value of the plant is proposed in this paper. Discrete-time transfer function models to represent the dynamic characteristics near the operating point of the investigated plant are deduced, and a design strategy of the proposed control system is described. The proposed Neuro PID control system is compared with the conventional PID control system, and its effectiveness is demonstrated through the simulation results.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.557-560
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• 2021

In the face of the COVID-19 pandemic, the Korean Government announced the Korean New Deal as a national development strategy to overcome the economic recession from the pandemic crisis and lead the global action aginst sturctural changes. The Green New Deal related with the energy aims to achieve net-zero emissions and accelerates the transition towards a low-carbon and green economy. To this end, the government plans to promete an increased use of renewable energy in the the society at large. This paper introduces a compact-binary power plant using unused thermal energy and a control system based on Neural Network in order to accelerate the transition towards a low-carbon and green economy. It is expected that he compact-binary power plant accelerate introduction of renewable energy along with solar and wind power.

• Proceedings of the SAREK Conference
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• 2009.06a
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• pp.39-44
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• 2009

Geothermal energy is the heat contained in the earth and its internal fluids. Geothermal energy is stored as sensible or latent heat. Supplied by both internal and external sources, it represents a vast supply which is only started to be tapped for generation of electric power. In general, this is natural dry or wet medium to high enthalpy steam at temperatures above $150^{\circ}C$. For some time, binary systems employing substances with a lower boiling point than water in a secondary circuit have been used to generate vapor for driving turbines at a lower temperature level. The utilization of binary plants and the possibility of production from enhanced geothermal systems can expand its availability on a worldwide basis. The geothermal electricity installed capacity is approaching the 10,000GW threshold. Geothermal energy is not present everywhere, but its baseload capability is a very important factor for its success.

• 한국신재생에너지학회:학술대회논문집
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• 2011.05a
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• pp.197.1-197.1
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• 2011

Target generation capacity of geothermal power generation pilot plant project through the Enhanced Geothermal Systems (EGS) with a doublet system down to 5 km depth was estimated. Production and re-injection temperatures of geothermal fluid were assumed $160^{\circ}C$ and $60^{\circ}C$, respectively, based on reservoir temperature of $180^{\circ}C$ calculated from the geothermal gradient of $33^{\circ}C$ in Pohang area. In this temperature range, 0.11 of thermal efficiency of the binary generation cycle is a practical choice. Assuming flow rates of 40 kg/sec, which is possible in current EGS technology, gross power generation capacity is estimated to reach 1.848 MW. Net generation considering auxiliary power including pumping power for geothermal fluid and condensing (cooling) energy of working fluid can be 1.5 MW.

• The Journal of Information Systems
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• v.29 no.1
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• pp.75-91
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• 2020

Purpose Retrofit, defined to be addition of new technologies or features to the old system to increase efficiency or to abate GHG emissions, is considered as an important alternative for the old coal-fired power plant. The purpose of this study is to propose mathematical method to model multiple alternative retrofit in Generation Capacity Expansion Planning(GCEP) problem, and to get insight to the retrofit patterns from realistic case studies. Design/methodology/approach This study made a multi-alternative retrofit GECP model by adopting some new variables and equations to the existing GECP model. Added variables and equations are to ensure the retrofit feature that the life time of retrofitted plant is the remaining life time of the old power plant. We formulated such that multiple retrofit alternatives are simultaneously compared and the best retrofit alternative can be selected. And we found that old approach to model retrofit has a problem that old plant with long remaining life time is retrofitted earlier than the one with short remaining life time, fixed the problem by some constraints with some binary variables. Therefore, the proposed model is formulated into a mixed binary programming problem, and coded and run using the GAMS/cplex. Findings According to the empirical analysis result, we found that approach to model the multiple alternative retrofit proposed in this study is comparing simultaneously multiple retrofit alternatives and select the best retrofit satisfying the retrofit features related to the life time. And we found that retrofit order problem is cleared. In addition, the model is expected to be very useful in evaluating and developing the national policies concerning coal-fired power plant retrofit.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.10
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• pp.1302-1309
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• 2021

Recently, the Korean Government announced the Korean New Deal as a national development strategy to overcome the economic recession from the pandemic crisis and lead the global action against structural changes. In the Korean New Deal, the Green New Deal related with the energy aims to achieve net-zero emissions and accelerates the transition towards a low-carbon and green economy. To this end, the government plans to promote an increased use of renewable energy in the society at large. This paper introduces a binary power generation using unused low-grade thermal energy to accelerate the transition towards a low-carbon and green economy and examines a control system based on Neural Network which is capable maintenance at low-cost by an unmanned automated operation in actual power generation environment. It is expected that the realization of binary power generation accelerates introduction of renewable energy along with solar and wind power.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.6
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• pp.845-851
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• 2013

Because the existing control system has been operating for about 20 years, it is necessary to upgrade the system for stable and efficient operation. But, there is a difficulty in maintenance by difference of manufacturer of each main control systems for boiler, turbine and generator. This developed IMCS(Integrated Monitoring and Control System) consists of more than 10,000 inputs and outputs for large scale thermal power plant. This paper consists of the development journey of IMCS MBC(mill and burner control) ; core binary protection & monitoring logic including prevention circuit of boiler explosion & implosion. In this project, the IMCS for boiler, turbine and generator was developed on basis of one communications platform. In this paper, the whole journey of development of IMCS MBC is dealt with designing software and hardware, coding application software, and validating software and hardware.

• Nuclear Engineering and Technology
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• v.55 no.9
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• pp.3277-3290
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• 2023

Nuclear power plant (NPP) operations with multiple objectives and devices are still performed manually by operators despite the potential for human error. These operations could be automated to reduce the burden on operators; however, classical approaches may not be suitable for these multi-objective tasks. An alternative approach is deep reinforcement learning (DRL), which has been successful in automating various complex tasks and has been applied in automation of certain operations in NPPs. But despite the recent progress, previous studies using DRL for NPP operations have limitations to handle complex multi-objective operations with multiple devices efficiently. This study proposes a novel DRL-based approach that addresses these limitations by employing a continuous action space and straightforward binary rewards supported by the adoption of a soft actor-critic and hindsight experience replay. The feasibility of the proposed approach was evaluated for controlling the pressure and volume of the reactor coolant while heating the coolant during NPP startup. The results show that the proposed approach can train the agent with a proper strategy for effectively achieving multiple objectives through the control of multiple devices. Moreover, hands-on testing results demonstrate that the trained agent is capable of handling untrained objectives, such as cooldown, with substantial success.