메뉴 건너뛰기
.. 내서재 .. 알림
소속 기관/학교 인증
인증하면 논문, 학술자료 등을  무료로 열람할 수 있어요.
한국대학교, 누리자동차, 시립도서관 등 나의 기관을 확인해보세요
(국내 대학 90% 이상 구독 중)
로그인 회원가입 고객센터 ENG
주제분류

추천
검색
질문

논문 기본 정보

자료유형
학술저널
저자정보
Yun-Hwan Lee (Coontec) Seung-Chan Oh (Korea University) Hwan-Ik Lee (Korea University) Sang-Geon Park (SILLA University) Byong-Jun Lee (Korea University)
저널정보
대한전기학회 Journal of Electrical Engineering & Technology Journal of Electrical Engineering & Technology Vol.13 No.1
발행연도
2018.1
수록면
60 - 67 (8page)

이용수

표지
📌
연구주제
📖
연구배경
🔬
연구방법
🏆
연구결과
AI에게 요청하기
추천
검색
질문

초록· 키워드

오류제보하기
The presence of induction motor loads in a power system may cause the phenomenon of delayed voltage recovery after the occurrence of a severe fault. A high proportion of induction motor loads in the power system can be a significant influence on the voltage stability of the system. This problem referred to as FIDVR(Fault Induced Delayed Voltage Recovery) is commonly caused by stall of small HVAC unit(Heating, Ventilation, and Air Conditioner) after transmission or distribution system failure. This delayed voltage recovery arises from the dynamic characteristics associated with the kinetic energy of the induction motor load. This paper proposes the UVLS (Under Voltage Load Shedding) control strategy for dealing with FIDVR. UVLS based schemes prevent voltage instability by shedding the load and can help avoid major economic losses due to wide-ranging cascading outages. This paper review recent topic about under voltage load shedding and compare decentralized load shedding scheme with conventional load shedding scheme. The load shedding strategy is applied to an actual system in order to verify the proposed FIDVR mitigation solution. Simulations demonstrate the effectiveness of the proposed method in resolving the problem of delayed voltage recovery in the Korean Power System.

목차

Abstract
1. Introduction
2. Description of Fault Induced Delayed Voltage Recovery
3. UVLS Scheme in Korean Power System
4. Case Studies
5. Conclusion
References

참고문헌 (14)

참고문헌 신청

함께 읽어보면 좋을 논문

논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!

이 논문의 저자 정보

최근 본 자료

전체보기

댓글(0)

0

UCI(KEPA) : I410-ECN-0101-2018-560-001704485