Author name: Daniel Evans

In a significant advancement for the energy sector, research on the Doubly Fed Cascaded Induction Machine (DFCIM) offers promising insights that could reshape the landscape of wind power generation and other industrial applications. Conducted by Fell

As the global energy landscape increasingly embraces renewable sources, wind power has emerged as a leading player. However, the integration of wind generation into existing power systems can introduce significant challenges, particularly concerning

In an era where renewable energy sources are becoming increasingly vital, a new power control scheme for doubly-fed induction generators (DFIGs) has emerged, promising to enhance the efficiency of variable speed wind power generation. This innovative

In a groundbreaking study, researchers have unveiled a novel approach to enhance voltage control in electric grids that rely heavily on intermittent energy sources, particularly wind generation. The research, led by Silvangela L. S. Lima from the Uni

In a groundbreaking study published in ‘Eletrônica de Potência’ (Power Electronics), researchers are paving the way for a smarter, more efficient approach to wind energy generation. Led by A. J. Sguarezi Filho from C

In an era where renewable energy is paramount, a recent study published in ‘Eletrônica de Potência’ sheds light on innovative advancements in wind power generation. The research, led by Carlos H. S. de Vasconcelos from CEFET-MG in Brazil, explores th

In a significant advancement for wind energy technology, researchers have introduced a state feedback controller for doubly-fed induction generators (DFIGs) that promises to enhance the efficiency and reliability of variable-speed wind generation. Th

Recent advancements in wind power technology are set to reshape the energy landscape, particularly through the innovative use of Brushless Doubly Fed Induction Generators (BDFIG). A study led by Hélio Voltolini from CEFET/PR has unveiled promising fi

In a groundbreaking study published in ‘Wind Energy’, researchers are harnessing the power of machine learning to enhance the operational efficiency of wind farms. Led by Coleman Moss from the Department of Mechanical Engineering at the Wind Fluids a