DOI QR코드

DOI QR Code

초소형 바이너리 발전 플랜트를 위한 Neuro PID 제어

Neuro PID Control for Ultra-Compact Binary Power Generation Plant

  • 투고 : 2021.04.30
  • 심사 : 2021.05.18
  • 발행 : 2021.11.30

초록

초소형 바이너리 발전 플랜트는 열원과 냉각원 사이의 저온도차 열에너지를 이용하여 열에너지를 전력으로 변환한다. 실제 발전환경에서 플랜트의 특성치는 환경 조건이나 관련 장비의 부식과 같은 부정적인 영향으로 인해 변동하고, 플랜트 특성치의 변동은 PID 파라미터가 고정된 종래의 PID 제어시스템에서 불안정한 터빈 출력으로 이어진다. 본 논문에서는 플랜트의 특성치 변동에 따라 PID 파라미터를 적응적으로 조정하는 신경망 기반의 Neuro PID 제어시스템을 제안한다. 초소형 바이너리 발전 플랜트의 동작점 근방에서 동특성을 나타내는 이산시간 전달함수 모델을 도출하고, 제안된 제어시스템의 설계 전략을 기술한다. 제안된 Neuro PID 제어시스템을 종래의 PID 제어시스템과 비교하고, 시뮬레이션 결과를 통해 그 유효성을 보인다.

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.

키워드

참고문헌

  1. Deloitte Insights. Korea's Carbon Neutrality Roadmap and NDC forecast on strengthening [Internet]. Available: https://www2.deloitte.com/content/dam/Deloitte/kr/Documents/insights/deloitte-korea-review/19/kr_insights_deloittekorea-review-19_1_2.pdf.
  2. Joint Ministry concerned. The meaning of the Korean version of the New Deal [Internet]. Available: http://www.knewdeal.go.kr/front/view/newDealMean.do.
  3. H. Jung, "Organic Rankine(ORC) power generation system using low&medium-grade temperature waste heat," The proceedings of KIEE, vol. 65, no. 5, pp. 34-40, May. 2016.
  4. D. W. Lee, "Technology Market Prospect of Eco-friendly Organic Rankine Cycle Power Generation System," Korea Institute of Science and Technology Information, pp. 1-37, Nov. 2016.
  5. K. Y. Han and S. D Park, "Binary Power plant using unused thermal energy and Neural Network Controllers," Korea Institute of Information and Communication Engineering, vol. 25, no. 10, pp. 1302-1309, Oct. 2021.
  6. L. Y. Bronicki, "History of Organic Rankine Cycle systems," in Organic Rankine Cycle (ORC) Power systems: Technologies and Applications, ch. 2, pp. 25-66, 2017.
  7. M. D. Mirolli, "The Kalina cycle for cement kiln waste heat recovery power plants," IEEE Cement Industry Technical Conference, pp. 330-336, Oct. 2005.
  8. H. Uehara, Y. Ikegami, and T. Nishida, "performance Analysis of OTEC Using a Cycle with Absorption and Extraction Process," Transaction of the Japan Society of Mechanical Engineers. B, vol. 64, no. 624, pp. 2750-2755 Aug. 1998. https://doi.org/10.1299/kikaib.64.2750
  9. S. Quoilin, R. Aumann, A. Grill, A. Schuster, V. Lemort, and H. Spliethoff, "Dynamic modeling and optimal control strategy of waste heat recovery Organic Rankine Cycles," Applied Energy, vol. 88, no. 6, pp. 2183-2190, Jun. 2011. https://doi.org/10.1016/j.apenergy.2011.01.015
  10. S. Shikasho, K. Y. Han, J. S. Shin, C. Y. Chui, and H. H. Lee, "A learning control of unused energy power generation," Journal of Artificial Life and Robotics, vol. 15, no. 4, pp. 1987-1996, Dec. 2010.
  11. S. Yamada and H. Oyama, "Small Capacity Geothermal Binary Power Generation System," Fuji electric review, vol. 51, no. 3, pp. 86-89, 2005.
  12. K. Y. Han, M. Arita, Y. Ikegami, and H. H. Lee, "Non-Squared Decouple PID Control of Ultra-Compact Binary Power Generation Plant using Low Temperature Difference Thermal Energy," The Institute of Electrical Engineers of Japan, vol. 137, no. 10, pp. 1340-1352, Oct. 2017.
  13. Q. Que and M. Belkin, "Back to the Future: Radial Basis Function Network Revisted," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 42, no. 8, pp. 1856-1867, Aug. 2020. https://doi.org/10.1109/tpami.2019.2906594
  14. X. Meng, P. Rozycki, J. F. Qiao, and B. M. Wilamowski, "Nonlinear System Modeling Using RBF Networks for Industrial Application," IEEE Transaction on Industrial Informatics, vol. 14, no. 3, pp. 931-940, Mar. 2018. https://doi.org/10.1109/tii.2017.2734686
  15. H. H. Tack and M. G Kim, "Design of Adaptive Linearation Controller for Nonlinear System Using RBF Networks," The Journal of The Korean Institute of Maritime information & Communication Science, vol. 5, no. 2, pp. 525-531, Jun. 2001.