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
Hyeongsik Park (Sungkyunkwan University (SKKU)) Yoo Jeong Lee (Korea Aerospace University (KAU)) Myunghun Shin (Korea Aerospace University (KAU)) Youn-Jung Lee (Sungkyunkwan University (SKKU)) Jaesung Lee (Korea Aerospace University (KAU)) Changkyun Park (JUSUNG Engineering) Junsin Yi (Sungkyunkwan University (SKKU))
저널정보
한국태양광발전학회 Current Photovoltaic Research Current Photovoltaic Research Vol.6 No.4
발행연도
2018.12
수록면
102 - 108 (7page)

이용수

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

초록· 키워드

오류제보하기
A glass-texturing technique was developed for photovoltaic (PV) module cover glass; periodic honeycomb textures were formed by using a conventional lithography technique and diluted hydrogen fluoride etching solutions. The etching conditions were optimized for three different types of textured structures. In contrast to a flat glass substrate, the textured glasses were structured with etched average surface angles of 31–57°, and large aspect ratios of 0.17–0.47; by using a finite difference time-domain simulation, we show that these textured surfaces increase the amount of scattered light and reduce reflectance on the glass surface. In addition, the optical transmittance of the textured glass was markedly improved by up to 95% for wavelengths ranging from 400 to 1100 nm. Furthermore, applying the textured structures to the cover glass of the PV module with heterojunction with intrinsic thin-layer crystalline silicon solar cells resulted in improvements in the short-circuit current density and module efficiency from 39 to 40.2 mA/㎠ and from 21.65% to 22.41%, respectively. Considering these results, the proposed method has the potential to further strengthen the industrial and technical competitiveness of crystalline silicon solar cells.

목차

ABSTRACT
1. Introduction
2. Experimental details
3. Results and Discussion
4. Conclusion
References

참고문헌 (27)

참고문헌 신청

함께 읽어보면 좋을 논문

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

이 논문의 저자 정보

이 논문과 함께 이용한 논문

최근 본 자료

전체보기

댓글(0)

0

UCI(KEPA) : I410-ECN-0101-2019-530-000339480