scholarly journals Fitting Wind Speed to a 3-Parameter Distribution Using Maximum Likelihood Technique

2021 ◽  
Vol 7 (1) ◽  
pp. 1
Author(s):  
Okumu Otieno Kevin ◽  
Troon John Benedict ◽  
Samuel Muthiga Ngaga
Keyword(s):  
Maximum Likelihood ◽  
Wind Speed ◽  
Parameter Distribution ◽  
Maximum Likelihood Technique

scholarly journals Fitting Wind Speed to a 3-Parameter Distribution Using Maximum Likelihood Technique

2021 ◽  
Author(s):  
Benedict Troon
Keyword(s):  
Maximum Likelihood ◽  
Wind Speed ◽  
Gamma Distribution ◽  
Model Comparison ◽  
Information Criterion ◽  
Parameter Distribution ◽  
Wind Speed Data ◽  
Distribution Parameters ◽  
Total Capacity ◽  
Maximum Likelihood Technique

Kenya is one of the countries in the world with a good quantity of wind. This makes the country to work ontechnologies that can help in harnessing the wind with a vision of achieving a total capacity of 2GW of wind energy by 2030.The objective of this research is to find the best three-parameter wind speed distribution for examining wind speed using the maximum likelihood fitting technique. To achieve the objective, the study used hourly wind speed data collected for a period of three years (2016 – 2018) from five sites within Narok County. The study examines the best distributions that the data fits and then conducted a suitability test of the distributions using the Kolmogorov-Smirnov test. The distribution parameters were fitted using maximum likelihood technique and model comparison test conducted using Akaike’s Information Criterion (AIC) and the Bayesian Information Criterion (BIC) values with the decision rule that the best distribution relies on the distribution with the smaller AIC and BIC values. The research showed that the best distribution is the gamma distribution with the shape parameter of 2.071773, scale parameter of 1.120855, and threshold parameter of 0.1174. A conclusion that gamma distribution is the best three-parameter distribution for examining the Narok country wind speed data

Statistically optimal event detection using small array data

1990 ◽  
Vol 80 (6B) ◽  
pp. 1934-1950 ◽  
Author(s):  
A. F. Kushnir ◽  
V. M. Lapshin ◽  
V. I. Pinsky ◽  
J. Fyen
Keyword(s):  
Maximum Likelihood ◽  
Power Spectrum ◽  
Event Detection ◽  
Underground Explosion ◽  
Seismic Signals ◽  
Array Data ◽  
On Line ◽  
Small Array ◽  
Maximum Likelihood Technique ◽  
Channel Filter

Abstract A generalization of Capon's maximum-likelihood technique for detection and estimation of seismic signals is introduced. By using a multi-dimensional autoregressive approximation of seismic array noise, we have developed a technique to use Capon's multi-channel filter for on-line processing. Such autoregressive adaptation to the curent noise matrix power spectrum is shown to yield good suppression of mutually correlated array noise processes. As an example, this technique is applied to detection of a small Semipalatinsk underground explosion recorded at the ARCESS array.

SAMPLING SYSTEMS FOR THE BIRCH CASEBEARER (LEPIDOPTERA: COLEOPHORIDAE)

1979 ◽  
Vol 111 (8) ◽  
pp. 875-882
Author(s):  
A. G. Raske ◽  
M. Alvo
Keyword(s):  
Maximum Likelihood ◽  
Sampling Plan ◽  
New Method ◽  
Betula Papyrifera ◽  
White Birch ◽  
Destructive Sampling ◽  
Damage Level ◽  
Sampling Systems ◽  
Non Destructive ◽  
Maximum Likelihood Technique

AbstractSample sizes needed to measure population levels of the birch casebearer, Coleophora fuscedinella Zeller, and its damage to white birch, Betula papyrifera Marsh, were calculated for various degrees of confidence and assurance. Both a non-destructive and a destructive sampling plan are presented and a new method to classify the damage level of a stand. This method uses a maximum likelihood technique to estimate the proportion of trees of various damage classes.

scholarly journals On the characteristics of ASCAT wind direction ambiguities

2013 ◽  
Vol 6 (4) ◽  
pp. 1053-1060 ◽  
Author(s):  
W. Lin ◽  
M. Portabella ◽  
A. Stoffelen ◽  
A. Verhoef
Keyword(s):  
Quality Control ◽  
Maximum Likelihood ◽  
Wind Speed ◽  
Wind Direction ◽  
Maximum Likelihood Estimators ◽  
High Rank ◽  
Low Rank ◽  
The Third ◽  
Low Probability

Abstract. The inversion of the Advanced Scatterometer (ASCAT) backscatter measurement triplets generally leads to two wind ambiguities with similar wind speed values and opposite wind directions. However, for up-, down- and crosswind (with respect to the mid-beam azimuth direction) cases, the inversion often leads to three or four wind solutions. In most of such cases, the inversion residuals or maximum likelihood estimators (MLEs) of the third and fourth solutions (i.e. high-rank solutions) are substantially higher than those of the first two (low-rank) ambiguities. This indicates a low probability for the high-rank solutions and thus essentially dual ambiguity. This paper investigates the characteristics of ASCAT high-rank wind solutions under different conditions with the objective of developing a method for rejecting the spurious high-rank solutions. The implementation of this rejection procedure improves the effectiveness of the ASCAT wind quality control (QC) and ambiguity removal procedures.

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