Abstract
This paper proposes an adaptive neural-fuzzy inference system (ANFIS) based maximum power point tracking (MPPT) system for a photovoltaic (PV) power generating system in standalone operation. To extract the maximum power, the DC-DC buck/boost converter is connected between the PV module and the load. The proposed ANFIS-based MPPT controller performs the fast dynamic response with high accuracy. With the test under varying load conditions, experimental results are provided to validate our approach.
| Original language | English |
|---|---|
| Title of host publication | 2015 18th International Conference on Electrical Machines and Systems, ICEMS 2015 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 692-697 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781479988044 |
| DOIs | |
| Publication status | Published - 2016 Jan 18 |
| Externally published | Yes |
| Event | 18th International Conference on Electrical Machines and Systems, ICEMS 2015 - Pattaya City, Thailand Duration: 2015 Oct 25 → 2015 Oct 28 |
Publication series
| Name | 2015 18th International Conference on Electrical Machines and Systems, ICEMS 2015 |
|---|
Conference
| Conference | 18th International Conference on Electrical Machines and Systems, ICEMS 2015 |
|---|---|
| Country/Territory | Thailand |
| City | Pattaya City |
| Period | 2015/10/25 → 2015/10/28 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
Keywords
- Photovoltaic (PV) systems
- artificial intelligence
- maximum power point tracking (MPPT)
- neural fuzzy systems
- solar power generation
ASJC Scopus subject areas
- Energy Engineering and Power Technology
- Electrical and Electronic Engineering
- Mechanical Engineering
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