Author name: Daniel Evans

In a groundbreaking study, researchers are tackling one of the most pressing challenges in renewable energy: the unpredictability of wind power. Led by Xi Wang from Beijing Information Science & Technology University, the team has proposed a novel ap

In a significant advancement for the energy sector, researchers from the State Key Laboratory of Power Grid Safety and Energy Conservation have published a study addressing the complexities of large-scale centralized new energy sending systems in Chi

As the global push for renewable energy intensifies, the integration of wind power has emerged as a critical component in achieving sustainable energy goals. In China, the rapid development of wind farms, particularly those utilizing Doubly-Fed Induc

As the global energy landscape shifts towards renewable sources, the integration of wind power into existing power grids presents both opportunities and challenges. A recent study led by Xindong Fan from the School of Automation at Beijing Informatio

In a significant advancement for the wind energy sector, researchers have proposed a novel control strategy aimed at maximizing revenue for wind farms during periods of wind abandonment. This innovative approach, led by Xiaoguang Liu from the China H

In a significant advancement for wind turbine technology, researchers have unveiled a novel fault diagnosis method for rolling bearings, which are critical components in ensuring the efficient operation of these renewable energy systems. The study, l

In a significant stride towards enhancing the efficiency and security of renewable energy generation, a recent study has proposed a sophisticated communication technology framework aimed at optimizing the coordinated operation of wind and hydropower

In a groundbreaking study published in ‘发电技术’ (Power Generation Technology), researchers have unveiled a novel approach to optimizing the coordination between wind and hydropower generation, utilizing the Grey Wolf algorithm. This innovative method a

In a groundbreaking study published in ‘FME Transactions’—translated as ‘FME Transactions’—researchers have harnessed the power of machine learning to tackle a pressing issue in the renewable energy sector: the reliability of wind turbine gearboxes.