- Title
- Use of MPPT techniques to reduce the energy pay-back time in PV systems
- Creator
- Farayola, Adedayo M., Hasan, Ali N., Ali, Ahmed
- Subject
- Artificial Intelligence (AI), ANFIS, ANN
- Date
- 2018
- Type
- Conference proceedings
- Identifier
- http://hdl.handle.net/10210/274581
- Identifier
- uj:29302
- Identifier
- Citation: Farayola, A.M., Hasan, A.N. & Ali, A. 2018. Use of MPPT techniques to reduce the energy pay-back time in PV systems.
- Description
- Abstract: Photovoltaic (PV) energy is a free-energy that is used as an alternative to fossil fuel energy. However, PV system without maximum power point tracking (MPPT) produces a low, unstable power and with a long energy pay-back time. This paper presents an innovative artificial neuro-fuzzy inference system (ANFIS) MPPT technique that could extract maximum power from a complete PV system and with a lessened EPBT. To confirm the effectiveness of the ANFIS algorithm, its result was compared with the results of PV system using Perturb&Observe (P&O) technique, non-MPPT technique, combination of artificial neural network and support vector machine as ANN-SVM technique and using Pretoria city weather data as case studies. Results show that ANFIS-MPPT yielded the best result and with the lowest EPBT.
- Language
- English
- Rights
- ©2018, authors
- Full Text
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