Earticle

초록

영어

In transformer cooling systems, oil temperature is controlled through the use of a blower and oil pump. For this paper, set-point algorithms, a reset algorithm and control algorithms of the cooling system were developed by neural networks and fuzzy logics. The oil inlet temperature was set by a 2× 2× 1 neural network, and the oil temperature difference was set by a 2× 3× 1 neural network. Inputs used for these neural networks were the transformer operating ratio and the air inlet temperature. The inlet set temperature was reset by a fuzzy logic based on the transformer operating ratio and the oil outlet temperature. A blower was used to control the inlet oil temperature while the oil pump was used to control the oil temperature difference by fuzzy logics. In order to analysis the performance of these algorithms, the initial start-up test and the step change test were performed by using the dynamic model of a transformer cooling system. Test results showed that algorithms developed for this study were effective in controlling the oil temperature of a transformer cooling system.

목차

Abstract
 1. Introduction
 2. Mathematic model of cooling system
 3. System algorithm
  3.1 Setpoint algorithm
  3.2 Reset algorithm
  3.3 Control algorithm
 4. Efficiency analysis of algorithm
  4.1 Initial operating efficiency test
  4.2 Main transformer operation rate step variation efficiency test
 5. Conclusion
 References

키워드

저자

• Jong-Yong Lee [ Ingenium college of liberal arts, KwangWoon University, Seoul 01897, Korea ]
• Chul Lee [ Department of Mechatronics, Inhatechnical Collage, 100 Inha-Ro, Namgu, Incheon, 402-752 ] Corresponding author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Advanced Culture Technology(IJACT)

• 간기

  계간

• pISSN

  2288-7202

• eISSN

  2288-7318

• 수록기간

  2013~2025

• 등재여부

  KCI 등재

• 십진분류

  KDC 600 DDC 700