Use of Birnbaum-Saunders distribution for estimating wind speed and wind power probability distributions: A review

• Published in: Energy conversion and management Vol. 143; pp. 109 - 122
• Main Authors: Mohammadi, Kasra, Alavi, Omid, McGowan, Jon G.
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.07.2017; Elsevier Science Ltd
• Subjects: Birnbaum-Saunders distribution; Case studies; Data processing; Density distribution; Electric power distribution; Maximum likelihood estimator; Probability density function; Probability distribution; Renewable energy; Stations; Statistical analysis; Statistical indicators; Velocity; Weibull distribution; Wind farms; Wind measurement; Wind power; Wind power density distribution; Wind speed; Wind speed distribution; Wind speed distribution; Maximum likelihood estimator; Probability density function; Wind power density distribution; Statistical indicators; Birnbaum-Saunders distribution
• ISSN: 0196-8904; 1879-2227
• DOI: 10.1016/j.enconman.2017.03.083

•A new application of Birnbaum-Saunders (BS) distribution is reviewed.•BS distribution is utilized to estimate wind speed and power distributions.•Performance of BS distribution is compared against 9 previously used distributions.•10 stations distributed in Ontario province of Canada are considered as case study.•Results show the generalization capability, accuracy and effectiveness of BS distribution. A knowledge of wind speed and wind power probability distributions is important for several reasons such as assessing wind resources, planning wind farms and minimizing the uncertainties for wind power development. In this study, application of the two parameter Birnbaum-Saunders (BS) distribution is introduced and reviewed for characterizing the wind speed and wind power density distributions. For this work, a long-term time series of measured wind speed data for ten stations distributed in the Ontario province of Canada has been utilized for a case study. The suitability of the BS distribution was evaluated against nine earlier used one-component distributions. The results showed that BS distribution performed very favorably at all selected stations. It outperformed the other distributions at six stations while in the remaining four stations ranked either third or fourth. It was also found that, although the Weibull distribution was the second best function to estimate the wind power density distribution, it was not able to give adequate description of the frequency of wind speed data at all sites. In summary, the study results demonstrated the generalization capability, precision, and effectiveness of the BS distribution for characterizing wind speed and wind power distribution.