Earticle

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.

The Genetic Algorithm (GA), an optimization technique based on the theory of natural selection, has proven to be a relatively robust means of searching for global optimum. It converges to the global optimum point without auxiliary information such as differentiation of function. In the case of a complex problem, the GA involves a large population number and requires a lot of computing time. To improve the process, this research used parallel processing with several personal computers. Parallel process technique is classified into two methods according to subpopulation's size and number. One is the fine-grained method (FGM), and the other is the coarse-grained method (CGM). This study selected the CGM as a parallel process technique because the load is equally divided among several computers. The given design domain should be reduced according to the degree of feasibility, because mechanical system problems have constraints. The reduced domain is used as an initial design domain. It is consistent with the feasible domain and the infeasible domain around feasible domain boundary. This parallel process used the Message Passing Interface library.

본 논문은 OCV 추정기법을 이용한 전류적산법의 SOC Reset 알고리즘을 제안한다. 제안한 알고리즘은 배터리 상황에 따라 OCV 추정을 위한 휴지시간을 달리 설정한다. 이에 따라 짧은 휴지 시에도 SOC Reset Point를 늘려 전류적산법의 오차를 Reset함으로써 SOC 추정 능력을 향상한다. Li-ion 27 Ah/99.9 Wh 배터리의 충 방전 실험을 통해서 OCV 판별 시간을 도출하여 알고리즘을 구현한다. 전기자동차의 주행 패턴을 모사하여 기존의 전류적산법과 비교 실험을 통해 제안한 알고리즘을 검증한다.

본 논문은 배터리 모델 의존도를 최소화한 SOC 추정 알고리즘을 제안한다. 제안한 알고리즘은 전류 적산법을 기반으로, 100초 내외의 짧은 휴지시간 동안 측정된 휴지곡선을 함수최적화 기법으로 분석하여 OCV를 추정하고 추정 OCV를 이용하여 누적 SOC 오차를 리셋 한다. 또한 최적화 기법의 발산 위험을 저감하기 위한 초깃값 설정 과정을 포함한다. 제안하는 알고리즘의 정확성은 시뮬레이션과 단전지 실험을 통해 검증한다.

컨트롤러에 의해서 동작 및 운영되는 시스템들은 시스템의 내외부적인 요인으로 고장이 발생한 경우 정상적으로 작동할 수 없으며, 이 경우 2차 문제 발생 및 시스템 복구비용이 요구된다. 본 논문에서는 시스템 내부의 소프트웨어 오류 발생 시 컨트롤러 내의 감시 타이머를 사용하여 다운타임 시점 이전의 상태로 복구하는 알고리즘과, 하드웨어 오류의 경우 별도의 자동 리셋 기능을 통하여 시스템을 재설정하고 재 동작이 가능하도록 하는 기능을 구현한다. 제안 시스템은 시스템이 외부 지원 없이 자체적으로 반영구적인 동작이 가능하도록 함으로써, 시스템의 안정성과 신뢰성을 제공하고 운영 및 관리비용의 절감 효과를 제공한다.

In order to protect the environment from the refrigerant pollution, the $CO_2$ may be regarded as one of the most attractive alternative refrigerants for an automotive air-conditioning system. Control methods for a $CO_2$ system should be different because of the unique property of a $CO_2$ as a refrigerant. Especially, the high-side pressure of a $CO_2$ system should be controlled for the efficient operation. The high-side pressure algorithm being composed of the pressure setpoint algorithm and the pressure setpoint reset algorithm was developed. The pressure setpoint algorithm, by using a least square method, was developed. The pressure setpoint reset algorithm, by using a fuzzy logic and by using a proportional logic, was also developed and compared. Simulation results showed that a proportional logic was more practical than a fuzzy logic for the pressure setpoint reset algorithm.

This paper presents outdoor reset control based on fuzzy algorithm for radiant floor heating system. We construct fuzzy system under indoor temperature and outdoor temperature. Simulation is based on TRNSYS with MATLAB. MATLAB is calculating and decide heat source using fuzzy system. Energy efficiency of Fuzzy algorithm is analyzed in term of indoor by TRNSYS System.