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Hybrid Global Maximum Power Point Tracking Algorithm for a Thermoelectric Generation System
- Jang, Yohan;
- Lee, Chaeeun;
- Ji, Sanghyuk;
- Bae, Sungwoo
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2초록
This paper proposes a new hybrid global maximum power point (GMPP) tracking algorithm which is a linear extrapolation-based grey wolf optimization algorithm (LEGWO). The LEGWO combines the advantages of a grey wolf optimization algorithm (GWO) and a linear extrapolation-based maximum power point tracking algorithm. As a result, this algorithm enables fast and accurate tracking of the GMPP. The proposed algorithm is verified by comparison simulation results of a perturbation and observation algorithm and the GWO in MATLAB/Simulink. The results validate that the LEGWO does not converge at the local maximum power point and tracks the exact GMPP. Also, the tracking time of the LEGWO is 53.09% faster than the GWO.
키워드
Maximum power point tracking; Non-uniform temperature conditions; Thermoelectric generation system; Extrapolation; Global optimization; MATLAB; Tracking (position); Gray wolves; Linear extrapolation; Maximum power point; Maximum Power Point Tracking; Maximum Power Point Tracking algorithms; Non-uniform temperature condition; Nonuniform temperature; Optimization algorithms; Temperature conditions; Thermoelectric generation systems; Maximum power point trackers
- 제목
- Hybrid Global Maximum Power Point Tracking Algorithm for a Thermoelectric Generation System
- 저자
- Jang, Yohan; Lee, Chaeeun; Ji, Sanghyuk; Bae, Sungwoo
- 발행일
- 2021-12
- 유형
- Proceedings Paper
- 저널명
- 2021 24TH INTERNATIONAL CONFERENCE ON ELECTRICAL MACHINES AND SYSTEMS (ICEMS 2021)
- 페이지
- 267 ~ 271