Estimation of Weibull parameters using graphical method for wind energy applications

Abstract

Wind power resources are abundant in India; as a result the wind power industry has entered a period of rapid growth, and has been facing new challenges currently. Wind power is environment friendly and renewable in nature. The wind is caused by an air in motion and is produced by the uneven heating of the earth's surface by the sun. The wind speed has been one of the most significant parameter in the design and investigation of wind energy conversion systems (WECS). The objective of the present research paper is to assess the feasibility of wind power generation for a new site in Himachal Pradesh using micro-turbines. The major contribution of this paper, envisages two parameters, i.e., shape and scale parameter based Weibull distribution model for investigating wind speed variations and Weibull parameters calculation by Least Squares Fit Method (LSM) also known as Linear regression model using graphical method. This research work shows that the hilly site of Hamirpur in Himachal Pradesh has good wind power potential and large magnitude of winds have been available for power generation during the months of March to June suitable for low wind power generation.

Estimation of Weibull parameters using artificial neural network

“…[6] used least square technique to compare the assessment of a two-parameter Weibull distribution. Murthy et al [7] used least square fit technique for estimating Weibull parameters to investigate wind speed difference.…”

Tide level in time and frequency domains at Dili port: Characteristic feature of a Lorentz oscillator

“…A popular technique for analyzing tides is the harmonic analysis (HA) in time domain (TD), in which the series of N harmonic constituents (sinusoids) are assumed (Pawlowicz et al 2002;Ro et al 2007;Stephenson 2016). Constructing a histogram for the tide level should also be useful to know the probability density at a particular level (Murthy, Rahi 2014;Tomaselli et al 2011). Another approach is to obtain a frequency spectrum (FS) in frequency domain (FD) (Franco 1997;Marone et al 2013).…”

Statistical and spectral analysis of wind power: Fractional oscillation dynamics

“…Wind turbines are often used in large-scale wind farms in the countryside or in coastal regions. However, there has been a growing market for small-scale local farms with large numbers, evolving commercially helpful (Murthy, Rahi 2014;Norheim, Pudjianto 2008).…”

“…In the time domain, one simple way is to construct statistically a histogram of the step size (wind speed etc.) in output over time (Murthy, Rahi 2014;Wan, Bucaneg 2002). To understand the nature of wind quality, introducing the autocorrelation function (AF) can be useful.…”