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

Abstract

The time truncated plan for the Weibull distribution under the indeterminacy is presented. The plan parameters of the proposed plan are determined by fixing the indeterminacy parameter. The plan parameters are given for various values of indeterminacy parameters. From the results, it can be concluded that the values of sample size reduce as indeterminacy values increase. The application of the proposed plan is given using wind speed data. From the wind speed example, it is concluded that the proposed plan is helpful to test the average wind speed at smaller values of sample size as compared to existing sampling plan.

Inspection plan for COVID-19 patients for Weibull distribution using repetitive sampling under indeterminacy

“…The Weibull distribution under neutrosophic statistics is developed by [ 42 ] for the design of the sampling scheme plan for testing the average wind speed under an indeterminate environment.…”

“…In Section 2, we present an introduction of a repetitive acceptance sampling plan for Weibull distribution under indeterminacy. In Section 3, the proposed repetitive acceptance sampling plan under indeterminacy is compared with the single sampling plan proposed by [ 42 ]. The proposed sampling plan is illustrated using COVID-19 data belong to Italy, which was recorded from 1 April to 20 July 2020 in Section 4.…”

Wind energy potential assessment based on wind speed, its direction and power data

“…Therefore, set Eq. (37) equals to 1.8×10 6 and 0, we can get the rated output speed, v r = 9.74 m/s, and the cut-in speed, v in = 2 m/s, respectively, as shown in Fig. 8.…”

“…In this case, we can take wind speed as a random variable and describe it by a probability density function (pdf). Therefore, the pdf of wind speed becomes an important basis for evaluating wind energy potential and wind stochastic characteristics 6 . If the frequency distribution of wind speed is comprehensively expressed by an estimated pdf, the wind power density and wind energy output of wind turbines can be evaluated, which can help us make a reasonable decision whether to build a wind farm in the observed area or not, and reduce the uncertainties and the errors of wind power output estimation 7 .…”

“…The parametric model is also divided into single and mixture distribution models. At present, the most widely used single distribution models include Gamma, Raleigh, Inverse Gaussian distribution, lognormal and Weibull distribution models [2][3][4][5][6][7][8][9][10][11][12][13][14] , etc. Among them, the two-parameter Weibull distribution model is often recognized as an effective model and is widely used in the field of wind industry to estimate wind energy potential mainly due to its simplicity.…”