Enhancing wind direction prediction of South Africa wind energy hotspots with Bayesian mixture modeling

Abstract

Wind energy production depends not only on wind speed but also on wind direction. Thus, predicting and estimating the wind direction for sites accurately will enhance measuring the wind energy potential. The uncertain nature of wind direction can be presented through probability distributions and Bayesian analysis can improve the modeling of the wind direction using the contribution of the prior knowledge to update the empirical shreds of evidence. This must align with the nature of the empirical evidence as to whether the data are skew or multimodal or not. So far mixtures of von Mises within the directional statistics domain, are used for modeling wind direction to capture the multimodality nature present in the data. In this paper, due to the skewed and multimodal patterns of wind direction on different sites of the locations understudy, a mixture of multimodal skewed von Mises is proposed for wind direction. Furthermore, a Bayesian analysis is presented to take into account the uncertainty inherent in the proposed wind direction model. A simulation study is conducted to evaluate the performance of the proposed Bayesian model. This proposed model is fitted to datasets of wind direction of Marion island and two wind farms in South Africa and show the superiority of the approach. The posterior predictive distribution is applied to forecast the wind direction on a wind farm. It is concluded that the proposed model offers an accurate prediction by means of credible intervals. The mean wind direction of Marion island in 2017 obtained from 1079 observations was 5.0242 (in radian) while using our proposed method the predicted mean wind direction and its corresponding $$95\%$$ 95 % credible interval based on 100 generated samples from the posterior predictive distribution are obtained 5.0171 and (4.7442, 5.2900). Therefore, our results open a new approach for accurate prediction of wind direction implementing a Bayesian approach via mixture of skew circular distributions.

The Predictive Study on Soweto Wind Turbine Results and Port-Elizabeth

“…CONCLUSION The benefits of this research were to achieve possible application of low-cost small-scale wind turbine in South Africa for low wind speed areas, thereby providing low-cost electricity to households and inhabitants in urban as well as in rural areas for a very large region in South Africa. This was achieved by developing a test prototype for low wind speed condition in Soweto, Johannesburg, South Africa, and Moreover prediction case study using Soweto test results were shared in number (2). Finally, a predictive case study for the Eastern Cape, focused on the Gqeberha (PE) area, was conducted using the empirically obtained data for the Soweto and Gqeberha (PE) areas, and it was concluded that it would be feasible to implement the Soweto technology in Port Elizabeth due to the results emanating presumably from the conditions at lower altitude (higher density air), and much higher wind speed resources at or near the coastal region.…”

“…South Africa's climatology allows for significant wind energy production especially along the coastal areas of the Eastern and Western Capes. The first large-scale wind farm in South Africa became operational in 2014 and based on the SAWEA report, there are 33 wind farms: 22 fully operational and 11 in constructions [2]. Amidst rising demands for decarbonization and sustainability in the 3 rd world.…”

Robust Nonlinear Adaptive Control for Power Quality Enhancement of PMSG Wind Turbine: Experimental Control Validation

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