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
Xingshuo Li Huiqing Wen (Xi’an Jiaotong University) Lin Jiang (University of Liverpool) Eng Gee Lim (Xi’an Jiaotong-Liverpool University) Yang Du (Xi’an Jiaotong-Liverpool University) Chenhao Zhao (Xi’an Jiaotong-Liverpool University)
저널정보
전력전자학회 JOURNAL OF POWER ELECTRONICS JOURNAL OF POWER ELECTRONICS Vol.16 No.1
발행연도
2016.1
수록면
9 - 17 (9page)

이용수

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

초록· 키워드

오류제보하기
Maximum power point tracking (MPPT) is necessary for photovoltaic (PV) power system application to extract the maximum possible power under changing irradiation and temperature conditions. The β-parameter-based method has many advantages over conventional MPPT methods; such advantages include fast tracking speed in the transient stage, small oscillations in the steady state, and moderate implementation complexity. However, a problem in the implementation of the conventional beta method is the choice of an appropriate scaling factor N, which greatly affects both the steady-state and transient performance. Therefore, this paper proposes a modified β-parameter-based method, and the determination of the N is discussed in detail. The study shows that the choice of the scaling factor N is determined by the changes of the value of β during changes in irradiation or temperature. The proposed method can respond accurately and quickly during changes in irradiation or temperature. To verify the proposed method, a photovoltaic power system with MPPT function was built in Matlab/Simulink, and an experimental prototype was constructed with a solar array emulator and dSPACE. Simulation and experimental results are illustrated to show the advantages of the improved β-parameter-based method with the optimized scaling factor.

목차

Abstract
I. INTRODUCTION
II. MODIFIED BETA METHOD
III. RESULTS AND DISCUSSION
IV. CONCLUSION
REFERENCES

참고문헌 (30)

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-0101-2016-560-002315155