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Testing average wind speed using sampling plan for Weibull distribution under indeterminacy

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  • 1.

    Ajayi, O. O., Fagbenle, R. O., Katende, J., Aasa, S. A. & Okeniyi, J. O. Wind profile characteristics and turbine performance analysis in Kano, north-western Nigeria. Int. J. Energy Environ. Eng. 4, 1–15 (2013).

    Article 

    Google Scholar 

  • 2.

    Yan, A., Liu, S. & Dong, X. Variables two stage sampling plans based on the coefficient of variation. J. Adv. Mech. Des. Syst. Manuf. 10, JAMDSM0002 (2016).

    Article 

    Google Scholar 

  • 3.

    Yen, C.-H., Lee, C.-C., Lo, K.-H., Shiue, Y.-R. & Li, S.-H. A rectifying acceptance sampling plan based on the process capability index. Mathematics 8, 141 (2020).

    Article 

    Google Scholar 

  • 4.

    Akpinar, E. K. & Akpinar, S. A statistical analysis of wind speed data used in installation of wind energy conversion systems. Energy Convers. Manag. 46, 515–532 (2005).

    Article 

    Google Scholar 

  • 5.

    Yilmaz, V. & Çelik, H. E. A statistical approach to estimate the wind speed distribution: the case of Gelibolu region. Doğuş Üniversitesi Dergisi 9, 122–132 (2011).

    Google Scholar 

  • 6.

    Ali, S., Lee, S.-M. & Jang, C.-M. Statistical analysis of wind characteristics using Weibull and Rayleigh distributions in Deokjeok-do Island-Incheon, South Korea. Renew. Energy 123, 652–663 (2018).

    Article 

    Google Scholar 

  • 7.

    Arias-Rosales, A. & Osorio-Gómez, G. Wind turbine selection method based on the statistical analysis of nominal specifications for estimating the cost of energy. Appl. Energy 228, 980–998 (2018).

    Article 

    Google Scholar 

  • 8.

    Akgül, F. G. & Şenoğlu, B. Comparison of wind speed distributions: a case study for Aegean coast of Turkey. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 1–18 (2019).

  • 9.

    Ozay, C. & Celiktas, M. S. Statistical analysis of wind speed using two-parameter Weibull distribution in Alaçatı region. Energy Convers. Manag. 121, 49–54 (2016).

    Article 

    Google Scholar 

  • 10.

    Qing, X. Statistical analysis of wind energy characteristics in Santiago island, Cape Verde. Renew. Energy 115, 448–461 (2018).

    Article 

    Google Scholar 

  • 11.

    Mahmood, F. H., Resen, A. K. & Khamees, A. B. Wind Characteristic Analysis Based on Weibull Distribution of Al-Salman site (Iraq, 2019).

    Google Scholar 

  • 12.

    Campisi-Pinto, S., Gianchandani, K. & Ashkenazy, Y. Statistical tests for the distribution of surface wind and current speeds across the globe. Renew. Energy 149, 861–876 (2020).

    Article 

    Google Scholar 

  • 13.

    ul Haq, M. A., Rao, G. S., Albassam, M. & Aslam, M. Marshall-Olkin Power Lomax distribution for modeling of wind speed data. Energy Rep. 6, 1118–1123 (2020).

    Article 

    Google Scholar 

  • 14.

    Bludszuweit, H., Domínguez-Navarro, J. A. & Llombart, A. Statistical analysis of wind power forecast error. IEEE Trans. Power Syst. 23, 983–991 (2008).

    ADS 
    Article 

    Google Scholar 

  • 15.

    Brano, V. L., Orioli, A., Ciulla, G. & Culotta, S. Quality of wind speed fitting distributions for the urban area of Palermo, Italy. Renew. Energy 36, 1026–1039 (2011).

    Article 

    Google Scholar 

  • 16.

    Katinas, V., Gecevicius, G. & Marciukaitis, M. An investigation of wind power density distribution at location with low and high wind speeds using statistical model. Appl. Energy 218, 442–451 (2018).

    Article 

    Google Scholar 

  • 17.

    Zaman, B., Lee, M. H. & Riaz, M. An improved process monitoring by mixed multivariate memory control charts: an application in wind turbine field. Comput. Ind. Eng. 142, 106343 (2020).

    Article 

    Google Scholar 

  • 18.

    Jamkhaneh, E. B., Sadeghpour-Gildeh, B. & Yari, G. Important criteria of rectifying inspection for single sampling plan with fuzzy parameter. Int. J. Contemp. Math. Sci. 4, 1791–1801 (2009).

    MATH 

    Google Scholar 

  • 19.

    Jamkhaneh, E. B., Sadeghpour-Gildeh, B. & Yari, G. Inspection error and its effects on single sampling plans with fuzzy parameters. Struct. Multidiscip. Optim. 43, 555–560 (2011).

    MATH 
    Article 

    Google Scholar 

  • 20.

    Sadeghpour Gildeh, B., Baloui Jamkhaneh, E. & Yari, G. Acceptance single sampling plan with fuzzy parameter. Iran. J. Fuzzy Syst. 8, 47–55 (2011).

    MathSciNet 
    MATH 

    Google Scholar 

  • 21.

    Afshari, R. & Sadeghpour Gildeh, B. Designing a multiple deferred state attribute sampling plan in a fuzzy environment. Am. J. Math. Manag. Sci. 36, 328–345 (2017).

    MATH 

    Google Scholar 

  • 22.

    Tong, X. & Wang, Z. Fuzzy acceptance sampling plans for inspection of geospatial data with ambiguity in quality characteristics. Comput. Geosci. 48, 256–266 (2012).

    ADS 
    Article 

    Google Scholar 

  • 23.

    Uma, G. & Ramya, K. Impact of fuzzy logic on acceptance sampling plans–a review. Autom. Auton. Syst. 7, 181–185 (2015).

    Google Scholar 

  • 24.

    Smarandache, F. Neutrosophy. Neutrosophic probability, set, and logic, proquest information & learning. Ann Arbor, Michigan, USA 105, 118–123 (1998).

  • 25.

    Smarandache, F. & Khalid, H. E. Neutrosophic Precalculus and Neutrosophic Calculus. (Infinite Study, 2015).

  • 26.

    Peng, X. & Dai, J. Approaches to single-valued neutrosophic MADM based on MABAC, TOPSIS and new similarity measure with score function. Neural Comput. Appl. 29, 939–954 (2018).

    Article 

    Google Scholar 

  • 27.

    Abdel-Basset, M., Mohamed, M., Elhoseny, M., Chiclana, F. & Zaied, A.E.-N.H. Cosine similarity measures of bipolar neutrosophic set for diagnosis of bipolar disorder diseases. Artif. Intell. Med. 101, 101735 (2019).

    PubMed 
    Article 

    Google Scholar 

  • 28.

    Nabeeh, N. A., Smarandache, F., Abdel-Basset, M., El-Ghareeb, H. A. & Aboelfetouh, A. An integrated neutrosophic-topsis approach and its application to personnel selection: a new trend in brain processing and analysis. IEEE Access 7, 29734–29744 (2019).

    Article 

    Google Scholar 

  • 29.

    Pratihar, J., Kumar, R., Dey, A. & Broumi, S. In Neutrosophic Graph Theory and Algorithms 180–212 (IGI Global, 2020).

  • 30.

    Pratihar, J., Kumar, R., Edalatpanah, S. & Dey, A. Modified Vogel’s approximation method for transportation problem under uncertain environment. Complex Intell. Syst. 7, 1–12 (2020).

    Google Scholar 

  • 31.

    Smarandache, F. Introduction to neutrosophic statistics. (Infinite Study, 2014).

  • 32.

    Chen, J., Ye, J. & Du, S. Scale effect and anisotropy analyzed for neutrosophic numbers of rock joint roughness coefficient based on neutrosophic statistics. Symmetry 9, 208 (2017).

    Article 

    Google Scholar 

  • 33.

    Chen, J., Ye, J., Du, S. & Yong, R. Expressions of rock joint roughness coefficient using neutrosophic interval statistical numbers. Symmetry 9, 123 (2017).

    Article 

    Google Scholar 

  • 34.

    Aslam, M. Introducing Kolmogorov–Smirnov tests under uncertainty: an application to radioactive data. ACS Omega 5, 9914–9917 (2019).

    Google Scholar 

  • 35.

    Aslam, M. A new sampling plan using neutrosophic process loss consideration. Symmetry 10, 132 (2018).

    Article 

    Google Scholar 

  • 36.

    Aslam, M. Design of sampling plan for exponential distribution under neutrosophic statistical interval method. IEEE Access 6, 64153–64158 (2018).

    Article 

    Google Scholar 

  • 37.

    Aslam, M. A new attribute sampling plan using neutrosophic statistical interval method. Complex Intell. Syst. 11, 1–6 (2019).

    Google Scholar 

  • 38.

    Aslam, M., Jeyadurga, P., Balamurali, S. & Marshadi, A. H. Time-Truncated Group Plan under a Weibull Distribution based on Neutrosophic Statistics. Mathematics 7, 905 (2019).

    Article 

    Google Scholar 

  • 39.

    Alhasan, K. F. H. & Smarandache, F. Neutrosophic Weibull distribution and Neutrosophic Family Weibull Distribution. (Infinite Study, 2019).

  • 40.

    Cheema, A. N., Aslam, M., Almanjahie, I. M. & Ahmad, I. Mixture modeling of exponentiated pareto distribution in bayesian framework with applications of wind-speed and tensile strength of carbon fiber. IEEE Access 8, 178514–178525 (2020).

    Article 

    Google Scholar 

  • 41.

    Deep, S., Sarkar, A., Ghawat, M. & Rajak, M. K. Estimation of the wind energy potential for coastal locations in India using the Weibull model. Renew. Energy 161, 319–339 (2020).

    Article 

    Google Scholar 

  • 42.

    Gugliani, G., Sarkar, A., Ley, C. & Mandal, S. New methods to assess wind resources in terms of wind speed, load, power and direction. Renew. Energy 129, 168–182 (2018).

    Article 

    Google Scholar 


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