抄録
An ultra-compact binary power plant converts thermal energy into electric power using low temperature difference thermal energy between heat source and cooling source. In control of the binary power plant, the negative effects such as characteristic changes caused by environmental condition and corrosion of related equipment, and coupling between control loops are the main difficulties in designing a controller and fine-tuning its parameters. In order to realize the stable power generation it is necessary to consider a control system to keep control performance when characteristic change of the binary power plant, and to compensate coupling in Multi-Input and Multi-Output (MIMO) systems. A Multiply-Connected (MC) Neuro PID control system using a Neural Network architecture connected directly by neurons of each control loop is proposed to overcome above difficulties, and its strategy for design of the control system is introduced. The proposed MC Neuro PID control system is compared to traditional control systems to show the effectiveness of the MC Neuro PID control through simulations in this paper.
| 元の言語 | English |
|---|---|
| ページ（範囲） | 504-513 |
| ページ数 | 10 |
| ジャーナル | IEEJ Transactions on Electronics, Information and Systems |
| 巻 | 139 |
| 発行部数 | 4 |
| DOI | |
| 出版物ステータス | Published - 2019 1 1 |
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ASJC Scopus subject areas
- Electrical and Electronic Engineering
これを引用
Multiply-connected neuro PID control of ultra-compact binary power plant. / Han, Kun Young; Lee, HeeHyol.
：: IEEJ Transactions on Electronics, Information and Systems, 巻 139, 番号 4, 01.01.2019, p. 504-513.研究成果: Article
}
TY - JOUR
T1 - Multiply-connected neuro PID control of ultra-compact binary power plant
AU - Han, Kun Young
AU - Lee, HeeHyol
PY - 2019/1/1
Y1 - 2019/1/1
N2 - An ultra-compact binary power plant converts thermal energy into electric power using low temperature difference thermal energy between heat source and cooling source. In control of the binary power plant, the negative effects such as characteristic changes caused by environmental condition and corrosion of related equipment, and coupling between control loops are the main difficulties in designing a controller and fine-tuning its parameters. In order to realize the stable power generation it is necessary to consider a control system to keep control performance when characteristic change of the binary power plant, and to compensate coupling in Multi-Input and Multi-Output (MIMO) systems. A Multiply-Connected (MC) Neuro PID control system using a Neural Network architecture connected directly by neurons of each control loop is proposed to overcome above difficulties, and its strategy for design of the control system is introduced. The proposed MC Neuro PID control system is compared to traditional control systems to show the effectiveness of the MC Neuro PID control through simulations in this paper.
AB - An ultra-compact binary power plant converts thermal energy into electric power using low temperature difference thermal energy between heat source and cooling source. In control of the binary power plant, the negative effects such as characteristic changes caused by environmental condition and corrosion of related equipment, and coupling between control loops are the main difficulties in designing a controller and fine-tuning its parameters. In order to realize the stable power generation it is necessary to consider a control system to keep control performance when characteristic change of the binary power plant, and to compensate coupling in Multi-Input and Multi-Output (MIMO) systems. A Multiply-Connected (MC) Neuro PID control system using a Neural Network architecture connected directly by neurons of each control loop is proposed to overcome above difficulties, and its strategy for design of the control system is introduced. The proposed MC Neuro PID control system is compared to traditional control systems to show the effectiveness of the MC Neuro PID control through simulations in this paper.
KW - Binary power plant
KW - Low-temperature difference thermal energy
KW - Multiply-Connected Neuro PID
UR - http://www.scopus.com/inward/record.url?scp=85063724063&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85063724063&partnerID=8YFLogxK
U2 - 10.1541/ieejeiss.139.504
DO - 10.1541/ieejeiss.139.504
M3 - Article
AN - SCOPUS:85063724063
VL - 139
SP - 504
EP - 513
JO - IEEJ Transactions on Electronics, Information and Systems
JF - IEEJ Transactions on Electronics, Information and Systems
SN - 0385-4221
IS - 4
ER -