• 2025-04-25
• M.K. Agboola, K.A. Hassan, T.O. Ajewole, S. Oyelami
• Volume 1, Issue 1

Abstract

Integration of wind energy conversion systems (WECS) to the electric grid poses power quality problems due to the intermittent nature of wind flow. Power quality improvement has been addressed using conventional approaches for the adjustment of wind turbine’s tip speed ratio (TSR). However, low rotor speed and mechanical sensor failure are linked to the traditional classical control (TCC) methods. Therefore, this study employed an artificial neural network (ANN) technique in adjusting turbine’s TSR for power quality enhancement. Data on windspeed and turbine hub-speed were obtained from Shagari Wind Farm in Sokoto, Nigeria and a mathematical model of ANN-based TSR controller was develop. Using MATLAB/Simulink platform and Levenberg-Marquardt algorithm, a simulation model of the proposed method was developed. Impact of the TSR controller on the turbine blades was investigated and evaluated together with the quality of the microgrid’s output power, using mean square error. With implementation of the method on a 2.5 KW rated WECS and with TSR variations from 0 radians to 25 radians, the mechanical power output using the TCC method becomes stable at 240W level, while for the proposed method the stability level was achieved at 140W; indicating 58.33% improvement in the stability responses. In comparison with the TCC approach to TSR adjustment, the technique proposed in this study offers a better performance. Therefore, it is recommended for the improvement of power quality on grid-connected WECS.

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