The results on a testing data indicate that theANFIS model is found to be more successful than the ANN approach in estimating the power factor.
📌 3 Key Takeaways
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Objective & Hypothesis:This paper introduces an adaptive neuro-fuzzy inference system (ANFIS) model for predicting thepower factor of a wind turbine.
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Key Findings & Data:This model based on the parameters involved for NACA 4415 and LS- 1 profile types with 3 and 4 blades.
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Conclusion & Impact:The results on a testing data indicate that theANFIS model is found to be more successful than the ANN approach in estimating the power factor.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters
Abstract
This paper introduces an adaptive neuro-fuzzy inference system (ANFIS) model for predicting thepower factor of a wind turbine. This model based on the parameters involved for NACA 4415 and LS- 1 profile types with 3 and 4 blades. In model development, profile type, blade number, Schmitzcoefficient, end loss, profile type loss, and blade number loss were taken as input variables, while thepower factor was taken as output variable. After a successful learning and training process theproposed model produced reasonable mean errors. The results on a testing data indicate that theANFIS model is found to be more successful than the ANN approach in estimating the power factor.
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Cite This Article
ATA, R. (2022). An adaptive neuro-fuzzy inference system approach for prediction of power factor in wind turbines. Istanbul University Journal of Electrical and Electronics Engineering, Cilt: 9 - Sayı: 1, 905–911. https://ajindex.com/articles/543742
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