Wind power conversion system model identification using adaptive neuro-fuzzy inference systems: A case study. (15th January 2022)

• Record Type: Journal Article
• Authors: Bilal, Boudy; Adjallah, Kondo Hloindo; Sava, Alexandre; Yetilmezsoy, Kaan; Kıyan, Emel
• Is Part Of: Energy. Volume 239:Part B(2022)
• Publication Date: 2022-01-15
• Subjects: Wind turbine -- Model identification -- Climatic conditions -- Adaptive neuro-fuzzy inference system -- Model benchmarking -- Mauritania; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2021.122089
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: This study proposes an original adaptive neuro-fuzzy inference system modeling approach to predict the output power of a wind turbine. The model's input includes the wind speed, turbine rotational speed, and mechanical-to-electrical power converter's temperature. The structure of the adaptive neuro-fuzzy inference system-based model was first identified using the processed data gathered from wind turbine number 1 of a 30-MW wind farm in Nouakchott (Mauritania). Then, the proposed data-driven model was trained and validated according to two new scenarios based on the data set from four identical wind turbines operated in the same climatic conditions and the data set from the same wind turbines operated under different climatic conditions. Benchmarking involved the proposed model, existing approaches in the literature, and five adaptive neuro-fuzzy inference system-based models, including grid partition, subtractive clustering, fuzzy C-means clustering, genetic algorithm, and particle swarm optimization, on the same data set to validate their prediction performance. Compared with existing adaptive neuro-fuzzy inference system-based models, the proposed approach was proven to be a promising methodology with higher accuracy for estimating the output power of wind turbines operating in different climatic conditions. According to the results from two different scenarios, the lowest value of the fitting rate and the highest values of the normalized mean square error, normalized mean absolute error, and root mean square error for the validating period were 0.9977, 0.0047, 0.0473, and 46.5831 kW, respectively. Moreover, the proposed model showed superior forecasting performance and thus better accuracy in estimating wind power output compared to other adaptive neuro-fuzzy inference system-based models. Graphical abstract: Image 1 Highlights: Original ANFIS approach was developed for wind turbine (WT) output power prediction. Scenarios-based validation with real data from wind farm enhanced model building. Machine learning embedded fuzzy logic boosted the model identification strategy. Benchmarking of existing models corroborated the superiority of preprocessed ANFIS. Neuro-fuzzy tool accurately estimated WT output power in various climatic conditions.