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
Abdur Rehman (Soongsil University) Woojin Choi (Soongsil University)
저널정보
전력전자학회 전력전자학회논문지 전력전자학회 논문지 제29권 제4호
발행연도
2024.8
수록면
253 - 262 (10page)
DOI
10.6113/TKPE.2024.29.4.253

이용수

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

초록· 키워드

오류제보하기
The extensive use of inverters and variable frequency drives (VFDs) introduce harmonics because of pulse width modulated signals. These harmonics reduce power quality, increase losses, and affect the safety of the motor drive system making harmonic detection inevitable. The recent surge in induction motor-based electric vehicles (EVs) also forces the need for a harmonic detection method for proper monitoring. However, the conventional methods used for harmonic extraction mostly relate to the Fourier transform, lock-in amplifier, and other modified versions. These methods show a deterioration in the component extraction performance in an environment with disturbances such as frequency drift, DC offset, and high distortion. In addition, these methods need high computational power and large storage memory. To rectify the limitations of the conventional method, this article proposes a novel frequency adaptive fundamental and harmonic detection method for motor drive systems with a digital lock-in amplifier-based frequency-locked loop (DLIA-FLL). This method, with the capability to track fundamental frequency to provide accurate results in an environment with DC offset, frequency drift, and high distortion, can help with efficient monitoring and safe operation of the motor drive system. Mathematical modeling of the proposed method along with experimental evaluation results are presented. ZERA COM3003 is used as a reference instrument for comparative analysis.

목차

Abstract
1. Introduction
2. Modeling of Proposed Method
3. Validation of Proposed DLIA-FLL
4. Conclusion
References

참고문헌 (0)

참고문헌 신청

함께 읽어보면 좋을 논문

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

최근 본 자료

전체보기

댓글(0)

0