Scienceline Publications Journal of Civil Engineering and Urbanism 2252-0430 8 2 2018 March Extreme Value Analysis of Wind Speed Data using Maximum Likelihood Method of Six Probability Distributions 12 16 http://ojceu.com/main/attachments/article/63/J.%20Civil%20Eng.%20Urban.%208%20(2)%2012-16,%202018.pdf EN Vivekanandan N Central Water and Power Research Station, Pune, Maharashtra, India Assessment of wind speed at a region is a pre-requisite while designing tall structures viz. cooling towers, stacks, transmission line towers, etc. This can be achieved through Extreme Value Analysis (EVA) by fitting of probability distributions to the annual series of extreme wind speed (EWS) data that is derived from hourly maximum wind speed. This paper details the study on EVA of wind speed data recorded at India Meteorological Department Observatories of Delhi and Kanyakumari adopting six probability distributions such as Normal, Log Normal, Gamma, Pearson Type-3, Log Pearson Type-3 (LP3) and Extreme Value Type-1. Maximum likelihood method is applied for determination of parameters of the distributions. The adequacy of fitting of probability distributions to the series of recorded EWS data is evaluated by Goodness-of-Fit tests viz., Anderson-Darling and Kolmogorov-Smirnov and diagnostic test using D-index. Based on GoF and diagnostic tests results, the study suggests the LP3 distribution is better suited amongst six probability distributions adopted for EVA of wind speed data for Delhi ad Kanyakumari. Anderson-Darling test D-index, Kolmogorov-Smirnov test Log Pearson Type-3 Prevalence Risk factors Maximum likelihood method Wind speed