Applied machine learning in wind speed prediction and loss minimization in unbalanced radial distribution system

Environmental parameter consideration has always prompted wind power to be used as renewable energy. However, the biggest challenge lies in wind energy integration to the power grid due to wind intermittency. Wind speed or wind power forecasting is one of the approach to manage this intermittency. Numerous prediction methods have been reported in previous literatures over few years. In this work, multivariate wind speed forecasting using Machine Learning framework in a python environment is executed. Several statistical models and neural network models are examined to best predict the wind speed of Surat, India [22.2587 degrees N, 71.1924 degrees E]. The model efficiency is tested in terms of measurements of correlation factors and Mean Absolute Error values. The predicted wind speed value is further considered for the power generation from the wind farm and integrated to the distribution system. Load Impedance Matrix method is implemented for Distribution System Load Flow analysis for being robust and simple with single-step computation. IEEE-19 bus and IEEE-25 bus-unbalanced radial distribution systems are considered for finding the power losses in the branches with wind power as Distributed Generation. An efficient and effective optimization technique, Teaching Learning-Based Optimization, is used to obtain the optimal location and capacity of Distributed Generation to minimize the power loss in the distribution lines.