Wind Speed Distribution – A Theoretical Approach to Probability Density Function

2011 ◽  
pp. 67-122
Author(s):  
Xianguo Li
Keyword(s):  
Wind Speed ◽  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Theoretical Approach ◽  
Speed Distribution ◽  
Wind Speed Distribution

scholarly journals Statistical analysis of modulated codes for robot positioning: application to BeAMS

2019 ◽  
Author(s):  
Marc Van Droogenbroeck ◽  
Pierlot Vincent
Keyword(s):  
Performance Analysis ◽  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Theoretical Approach ◽  
Upper Bound ◽  
Angle Measurement ◽  
Exact Probability ◽  
Analysis And Design ◽  
Robot Positioning

<div>Positioning is a fundamental issue for mobile robots. Therefore, a performance analysis is suitable to determine the behavior of a system, and to optimize its working. Unfortunately, some systems are only evaluated experimentally, which makes the performance analysis and design decisions very unclear. </div><div>In [4], we have proposed a new angle measurement system, named BeAMS, that is the key element of an algorithm for mobile robot positioning. BeAMS introduces a new mechanism to measure angles: it detects a beacon when it enters and leaves an angular window. A theoretical framework for a thorough performance analysis of BeAMS has been provided to establish the upper bound of the variance, and to validate this bound through experiments and simulations. It has been shown that the estimator derived from the center of this angular window provides an unbiased estimate of the beacon angle. </div><div>This document complements our paper by going into further details related to the code statistics of modulated signals in general, with an emphasis on BeAMS. In particular, the probability density function of the measured angle has been previously established with the assumption that there is no correlation between the times a beacon enters the angular window or leaves it. This assumption is questionable and, in this document, we reconsider this assumption and establish the exact probability density function of the angle estimated by BeAMS (without this assumption). </div><div>The conclusion of this study is that the real variance of the estimator provided by BeAMS was slightly underestimated in our previous work. In addition to this specific result, we also provide a new and extensive theoretical approach that can be used to analyze the statistics of any angle measurement method with beacons whose signal has been modulated. To summarize, this technical document has four purposes: </div><div>(1) to establish the exact probability density function of the angle estimator of BeAMS, </div><div>(2) to calculate a practical upper bound of the variance of this estimator, which is of practical interest for calibration and tracking (see Table 1, on page 13, for a summary), </div><div>(3) to present a new theoretical approach to evaluate the performance of systems that use modulated (coded) signals, and </div><div>(4) to show how the variance evolves exactly as a function of the angular window (while remaining below the upper bound).</div>

scholarly journals Comparison Of Three Numerical Methods For Estimating Weibull Parameters Using Weibull Distribution Model In Nigeria

2020 ◽  
Vol 27 (2) ◽  
pp. 8-15
Author(s):  
J.A. Oyewole ◽  
F.O. Aweda ◽  
D. Oni
Keyword(s):  
Standard Deviation ◽  
Wind Speed ◽  
Probability Density Function ◽  
Weibull Distribution ◽  
Probability Density ◽  
Density Function ◽  
Accurate Estimation ◽  
Weibull Parameters ◽  
Factor Method ◽  
Deviation Method

There is a crucial need in Nigeria to enhance the development of wind technology in order to boost our energy supply. Adequate knowledge about the wind speed distribution becomes very essential in the establishment of Wind Energy Conversion Systems (WECS). Weibull Probability Density Function (PDF) with two parameters is widely accepted and is commonly used for modelling, characterizing and predicting wind resource and wind power, as well as assessing optimum performance of WECS. Therefore, it is paramount to precisely estimate the scale and shape parameters for all regions or sites of interest. Here, wind data from year 2000 to 2010 for four different locations (Port Harcourt, Ikeja, Kano and Jos) were analysed and the Weibull parameters was determined. The three methods employed are Mean Standard Deviation Method (MSDM), Energy Pattern Factor Method (EPFM) and Method of Moments (MOM) for estimating Weibull parameters. The method that gave the most accurate estimation of the wind speed was MSDM method, while Energy Pattern Factor Method (EPFM) is the most reliable and consistent method for estimating probability density function of wind. Keywords: Weibull Distribution, Method of Moment, Mean Standard Deviation Method, Energy Pattern Method

Weibull Probability Distribution of Wind Speed for Gaza Strip for 10 Years

2019 ◽  
Vol 892 ◽  
pp. 284-291
Author(s):  
Ahmed S.A. Badawi ◽  
Nurul Fadzlin Hasbullah ◽  
Siti Hajar Yusoff ◽  
Sheroz Khan ◽  
Aisha Hashim ◽  
...  
Keyword(s):  
Wind Speed ◽  
Probability Density Function ◽  
Probability Distribution ◽  
Wind Energy ◽  
Probability Density ◽  
Density Function ◽  
Gaza Strip ◽  
Actual Data ◽  
Average Wind Speed ◽  
Average Wind

The need of clean and renewable energy, as well as the power shortage in Gaza strip with few wind energy studies conducted in Palestine, provide the importance of this paper. Probability density function is commonly used to represent wind speed frequency distributions for the evaluation of wind energy potential in a specific area. This study shows the analysis of the climatology of the wind profile over the State of Palestine; the selections of the suitable probability density function decrease the wind power estimation error percentage. A selection of probability density function is used to model average daily wind speed data recorded at for 10 years in Gaza strip. Weibull probability distribution function has been estimated for Gaza based on average wind speed for 10 years. This assessment is done by analyzing wind data using Weibull probability function to find out the characteristics of wind energy conversion. The wind speed data measured from January 1996 to December 2005 in Gaza is used as a sample of actual data to this study. The main aim is to use the Weibull representative wind data for Gaza strip to show how statistical model for Gaza Strip over ten years. Weibull parameters determine by author depend on the pervious study using seven numerical methods, Weibull shape factor parameter is 1.7848, scale factor parameter is 4.3642 ms-1, average wind speed for Gaza strip based on 10 years actual data is 2.95 ms-1 per a day so the behavior of wind velocity based on probability density function show that we can produce energy in Gaza strip.

scholarly journals Probability of the average monthly wind speed in Mossoró-RN, Brazil, through the probability density function of beta distribution

2019 ◽  
Vol 6 (5) ◽  
pp. 66-73
Author(s):  
Janilson Pinheiro de Assis ◽  
Roberto pequeno de Sousa ◽  
Paulo César Ferreira Linhares ◽  
Eudes de Almeida Cardoso ◽  
José Roberto de Sá ◽  
...  
Keyword(s):  
Wind Speed ◽  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Beta Distribution
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