| Autor: |
D. R. R. K. Dissanayake, Janaka Ekanayake, D. B. Thilakarathna, M. P. B. Ekanayake, G. M. R. I. Godaliyadda, D. M. K. V. B. Dissanayake |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
2021 10th International Conference on Information and Automation for Sustainability (ICIAfS). |
| DOI: |
10.1109/iciafs52090.2021.9605827 |
| Popis: |
The Maximum Power Point Tracking (MPPT) enables Photovoltaic (PV) systems to operate efficiently in varying natural conditions such as irradiance and temperature. This paper presents a comparison between fuzzy logic and (Artificial Neural Network) ANN controllers for boost and (Single Ended Primary Inductor Convertor) SEPIC dc-dc convertors with traditional (Perturb and Observation) P&O boost configuration for MPPT applications. For this evaluation, a real irradiance pattern is used to simulate the environmental conditions and the temperature is kept constant. The output power is compared with the theoretical power in terms of Mean Square Error and Mean Absolute Error. The speeds of configurations are compared in terms of rise and settling times. The ANN controller and boost dc-dc convertor configuration performed best in terms of accuracy and speed. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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