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A hybrid statistical approach for performance optimization of micro-scale wind energy systems
Authors:
Dattu Balu Ghane1
, Vishnu D. Wakchaure1
1Amrutvahini College of Engineering, Sangamner- 422608, Dist. Ahilyanagar, India
Received: 8 September 2025
Revised: 4 November 2025
Accepted: 21 November 2025
Published: 15 December 2025
Abstract:
Micro wind turbines (MWTs) are becoming a promising source of electricity generation for decentralised electricity generation, especially in rural areas. The efficiency of MWTs depends on some design and operational factors, including the number of blades, blade radius and wind speed. This paper seeks to establish the effects of these parameters on the performance of the turbine and determine the best configuration that will yield the highest power and efficiency. The experimental design was done systematically using the Taguchi method with an L16 orthogonal array to reduce the number of experiments required for the analysis. Two dependent variables, namely power output and coefficient of performance (Cp), were recorded for each configuration tested. The results of the experiments were analyzed using Analysis of Variance (ANOVA) to test the significance of each input factor and the Weighted Sum Model (WSM) for multi-objective optimization. As for the WSM method, unequal weights were assigned to power (0.35) and Cp (0.65), with efficiency taking precedence over other factors. The optimization studies revealed that the highest performing turbine was the three-bladed turbine with a radius of 0.26 m and a wind speed of 12 m/s. Confirmation experiments under these conditions also showed the same results with little variability, thus confirming the experimental results. The present work offers a systematic, quantitative approach to improve MWT performance, useful for the design and implementation of small-scale wind energy systems in distributed energy applications.
Keywords:
Micro wind turbine, Performance evaluation, Taguchi method, ANOVA, WSM optimization
References:
[1] Q. Hassan, P. Viktor, T.J. Al-Musawi, B.M. Ali, S. Algburi, H.M. Alzoubi, A.K. Al-Jiboory, A. Zuhair Sameen, H.M. Salman, M. Jaszczur, The renewable energy role in the global energy Transformations. Renewable Energy Focus, 48 2024: 100545. https://doi.org/10.1016/j.ref.2024.100545
[2] P. Sadorsky, Wind energy for sustainable development: Driving factors and future outlook. Journal of Cleaner Production, 289 2021: 125779. https://doi.org/10.1016/j.jclepro.2020.125779
[3] A.R. Varne, S. Blouin, B.L.M. Williams, D. Denkenberger, The Impact of Abrupt Sunlight Reduction Scenarios on Renewable Energy Production. Energies, 17(20), 2024: 5147.
https://doi.org/10.3390/en17205147
[4] K. Nam, S. Hwangbo, C. Yoo, A deep learning-based forecasting model for renewable energy scenarios to guide sustainable energy policy: A case study of Korea. Renewable and Sustainable Energy Reviews, 122, 2020: 109725. https://doi.org/10.1016/j.rser.2020.109725
[5] M. Xiao, T. Junne, J. Haas, M. Klein, Plummeting costs of renewables – Are energy scenarios lagging?. Energy Strategy Review, 35, 2021: 100636. https://doi.org/10.1016/j.esr.2021.100636
[6] R.J. Barthelmie, S.C. Pryor, Climate Change Mitigation Potential of Wind Energy. Climate, 9(9), 2021: 136. https://doi.org/10.3390/cli9090136
[7] A. Kudelin, V. Kutcherov, Wind ENERGY in Russia: The current state and development trends. Energy Strategy Reviews, 34, 2021: 100627. https://doi.org/10.1016/j.esr.2021.100627
[8] D.X. He, Y. Li, Overview of Worldwide Wind Power Industry, in: Strategies of Sustainable Development in China’s Wind Power Industry. Springer, Singapore, 2020: 29-60. https://doi.org/10.1007/978-981-13-9516-1_2
[9] A. Martinez, G. Iglesias, Global wind energy resources decline under climate change. Energy, 288, 2024: 129765. https://doi.org/10.1016/j.energy.2023.129765
[10] S.H. Li, Impact of climate change on wind energy across North America under climate change scenario RCP8.5. Atmospheric Research, 288, 2023: 106722. https://doi.org/10.1016/j.atmosres.2023.106722
[11] D.L. Woodard, A. Snyder, J.R. Lamontagne, C. Tebaldi, J. Morris, K.V. Calvin, M. Binsted, P. Patel, Scenario Discovery Analysis of Drivers of Solar and Wind Energy Transitions Through 2050. Earth’s Future, 11(8), 2023: e2022EF003442. https://doi.org/10.1029/2022EF003442
[12] B. Rachunok, A. Staid, J.-P. Watson, D.L. Woodruff, Assessment of wind power scenario creation methods for stochastic power systems operations. Applied Energy, 268, 2020: 114986.
https://doi.org/10.1016/j.apenergy.2020.114986
[13] S. Baur, B.M. Sanderson, R. Séférian, L. Terray, Change in Wind Renewable Energy Potential Under Stratospheric Aerosol Injections. Earth’s Future, 12(10), 2024: e2024EF004575.
https://doi.org/10.1029/2024EF004575
[14] K. Abhishek, Scenario of Wind Energy in India – A Review. i-Manager’s Journal of Future Engineering and Technology, 18(2), 2023: 40-47. https://doi.org/10.26634/jfet.18.2.19017
[15] M. Glowik, W.A. Bhatti, A. Chwialkowska, A cluster analysis of the global wind power industry: Insights for renewable energy business stakeholders and environmental policy decision makers. Business Strategy and the Environment, 32(6), 2023: 2755–2766. https://doi.org/10.1002/bse.3268
[16] D. Ghane, V. Wakchaure, Parametric Analysis and Design Considerations for Micro Wind Turbines: A Comprehensive Review. Energy Engineering, 121(11), 2024: 3199–3220.
https://doi.org/10.32604/ee.2024.050952
[17] P. Arumugam, V. Ramalingam, K. Bhaganagar, A pathway towards sustainable development of small capacity horizontal axis wind turbines – Identification of influencing design parameters & their role on performance analysis. Sustainable Energy Technologies and Assessments, 44, 2021: 101019. https://doi.org/10.1016/j.seta.2021.101019
[18] R.M. Yonk, C. Clark, J. Rood, Regulatory Impediments to Micro-Wind Generation, in: Microgrids and Local Energy Systems. IntechOpen, 2021. https://doi.org/10.5772/intechopen.99688
[19] P. Chauhan, S. Pandey, Meeting future energy demands sustainably: assessing small wind turbine installations in urban built environments. ShodhKosh: Journal of Visual and Performing Arts, 5, 2024: 197-210. https://doi.org/10.29121/shodhkosh.v5.iICETDA24.2024.1305
[20] H.E.V. Donnou, G.K. N’Gobi, H. Kougbéagbédè, G. Hounmenou, A.B. Akpo, B.B. Kounouhewa, Study of the Decentralized Electrification by a Micro-Wind Power Plant: Case of Ahouandji Locality in Southern Benin. TH Wildau Engineering and Natural Sciences Proceedings, 1, 2021.
https://doi.org/10.52825/thwildauensp.v1i.7
[21] M.A. Qasim, V.I. Velkin, Experimental investigation of power generation in a microgrid hybrid network. Journal of Physics: Conference Series, 1706, 2020: 012065. https://doi.org/10.1088/1742-6596/1706/1/012065
[22] C. Veeramani, G. Mohan, Design of Fuzzy Gain Scheduled PI Controller for an Isolated Wind Energy Conversion System. International Journal of Simulation: Systems, Science and Technology, 2020: 64-71. https://doi.org/10.5013/IJSSST.a.15.01.09
[23] B. Maheswaran, A. Abd Aziz, E. Alexander, L. Brigandi, C. Branagan, Power Generation Through Small-scale Wind Turbine. 2020 ASEE Virtual Annual Conference Content Access, Virtual Online, 22 June 2020.
https://doi.org/10.18260/1-2–35064
[24] S. Vickram, S. Kaviya, H. Dhamodharan, M. Sivasubramanian, Micro-Hydro Systems: Empowering Rural Communities with Small-Scale Solutions. Nanotechnology Perceptions, 20, 2024: 108-138.
https://doi.org/10.62441/nano-ntp.v20iS6.10
[25] M. Pellegrini, A. Guzzini, C. Saccani, Experimental measurements of the performance of a micro-wind turbine located in an urban area. Energy Reports, 7, 2021: 3922–3934.
https://doi.org/10.1016/j.egyr.2021.05.081
[26] R. Banihabib, M. Assadi, The Role of Micro Gas Turbines in Energy Transition. Energies, 15(21), 2022: 8084. https://doi.org/10.3390/en15218084
[27] J. Vilà, N. Luo, L. Pacheco, T. Pujol, J.R. Gonzalez, I. Ferrer, A. Massaguer, E. Massaguer, Design of an active pitch control for small horizontal-axis wind turbine. Renewable Energy and Power Quality Journal, 19(2), 2021: 195-198. https://doi.org/10.24084/repqj19.253
[28] H. Muhsen, W. Al-Kouz, W. Khan, Small Wind Turbine Blade Design and Optimization. Symmetry, 12(1), 2019: 18. https://doi.org/10.3390/sym12010018
[29] A. Ashraf, I. Goda, M. M. Abdalla, A Simple Optimization Technique Using Matlab for Small Wind Turbine Blades. 2020 6th IEEE International Energy Conference (ENERGYCon), IEEE, Tunisia, 2020: 423–428.
[30] R.K. Gupta, V. Warudkar, R. Purohit, S.S. Rajpurohit, Modeling and Aerodynamic Analysis of Small Scale, Mixed Airfoil Horizontal Axis Wind Turbine Blade. Materials Today: Proceedings, 4(4), 2017: 5370–5384. https://doi.org/10.1016/j.matpr.2017.05.049
[31] L.T.T. Nhung, N. Van Y., D. Cong Truong, V. Dinh Quy, CFD analysis of key factors impacting the aerodynamic performance of the S830 wind turbine airfoil. International Journal of Renewable Energy Development, 13(6), 2024: 1068-1077. https://doi.org/10.61435/ijred.2024.60249
[32] S. Raut, S. Shrivas, R. Sanas, N. Sinnarkar, M.K. Chaudhar, Simulation of Micro Wind Turbine Blade in Q-Blade. International Journal For Applied Science and Engineering Technology, 5(IV), 2017: 256-262.
https://doi.org/10.22214/ijraset.2017.4048
[33] E. Tengs, F. Charrassier, M.R. Jordal, I. Iliev, Fully automated multidisciplinary design optimization of a variable speed turbine. IOP Conference Series: Earth and Environmental Science, 774, 2021: 012031.
https://doi.org/10.1088/1755-1315/774/1/012031
[34] D.A. Umar, C.T. Yaw, S.P. Koh, S.K. Tiong, A.A. Alkahtani, T. Yusaf, Design and Optimization of a Small-Scale Horizontal Axis Wind Turbine Blade for Energy Harvesting at Low Wind Profile Areas. Energies, 15(9), 2022: 3033. https://doi.org/10.3390/en15093033
[35] V. Akbari, M. Naghashzadegan, R. Kouhikamali, F. Afsharpanah, W. Yaïci, Multi-Objective Optimization of a Small Horizontal-Axis Wind Turbine Blade for Generating the Maximum Startup Torque at Low Wind Speeds. Machines, 10(9), 2022: 785. https://doi.org/10.3390/machines10090785
[36] A. Tahir, M. Elgabaili, Z. Rajab, N. Buaossa, A. Khalil, F. Mohamed, Optimization of small wind turbine blades using improved blade element momentum theory. Wind Engineering, 43(3), 2019: 299–310.
https://doi.org/10.1177/0309524X18791395
[37] H. Yang, J. Chen, X. Pang, Wind Turbine Optimization for Minimum Cost of Energy in Low Wind Speed Areas Considering Blade Length and Hub Height. Applied Sciences, 8(7), 2018: 1202.
https://doi.org/10.3390/app8071202
[38] K. Deghoum, M.T. Gherbi, M.J. Jweeg, H.S. Sultan, A.M. Abed, O.I. Abdullah, N. Djilani, Design optimization of small wind turbine blade based on structural and fatigue life analyses. International Journal of Energy for a Clean Environment, 24(5), 2023: 53–66.
https://doi.org/10.1615/InterJEnerCleanEnv.2022045867
[39] S. Arivalagan, R. Sappani, R. Čep, M.S. Kumar, Optimization and Experimental Investigation of 3D Printed Micro Wind Turbine Blade Made of PLA Material. Materials, 16(6), 2023: 2508.
https://doi.org/10.3390/ma16062508
[40] C.V. Rodriguez, C. Celis, Design optimization methodology of small horizontal axis wind turbine blades using a hybrid CFD/BEM/GA approach. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 44, 2022: 254. https://doi.org/10.1007/s40430-022-03561-4
[41] M. Jaszczur, M. Borowski, J. Halibart, K. Zwolińska-Glądys, P. Marczak, Optimization of the Small Wind Turbine Design—Performance Analysis. Computation, 12(11), 2024: 215.
https://doi.org/10.3390/computation12110215
[42] S.C. Vetal, D. Kamble, V. Deulgaonkar, T. Gadekar, S. Mahadik, Structural Topology Optimization of Micro Wind Turbine Components: Integrating Mass and Material Strength Criteria for Realistic Results. SSRN, 2025. https://doi.org/10.2139/ssrn.5118414
© 2025 by the authors. This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0)
Volume 10
Number 4
December 2025
Last Edition
Volume 10
Number 4
December 2025
How to Cite
D.B. Ghane, V.D. Wakchaure, A Hybrid Statistical Approach for Performance Optimization of Micro-Scale Wind Energy Systems. Applied Engineering Letters, 10(4), 2025: 211-221.
https://doi.org/10.46793/aeletters.2025.10.4.3
More Citation Formats
Ghane, D.B., & Wakchaure, V.D. (2025). A Hybrid Statistical Approach for Performance Optimization of Micro-Scale Wind Energy Systems. Applied Engineering Letters, 10(4), pp. 211-221. https://doi.org/10.46793/aeletters.2025.10.4.3
Ghane, Dattu Balu, and Vishnu D. Wakchaure. “A Hybrid Statistical Approach for Performance Optimization of Micro-Scale Wind Energy Systems.“ Applied Engineering Letters, vol. 10, no. 4, 2025, pp. 211-221. https://doi.org/10.46793/aeletters.2025.10.4.3
Ghane, Dattu Balu, and Vishnu D. Wakchaure. 2025. “A Hybrid Statistical Approach for Performance Optimization of Micro-Scale Wind Energy Systems.“ Applied Engineering Letters, 10 (4): 211-221. https://doi.org/10.46793/aeletters.2025.10.4.3
Ghane, D.B. and Wakchaure, V.D. (2025). A Hybrid Statistical Approach for Performance Optimization of Micro-Scale Wind Energy Systems. Applied Engineering Letters, 10(4), pp. 211-221.
doi: 10.46793/aeletters.2025.10.4.3.