Wind turbines are becoming increasingly important in the generation of clean, renewable
energy worldwide. To ensure their dependable and accessible operation, advanced real …

Artificial neural networks (ANN), machine learning (ML), deep learning (DL), and ensemble
learning (EL) are four outstanding approaches that enable algorithms to extract information …

Solar energy is a very efficient alternative for generating clean electric energy. However,
pollution on the surface of solar panels reduces solar radiation, increases surface …

The reliable operation of power transmission networks depends on the timely detection and
localization of faults. Fault classification and localization in electricity transmission networks …

The field of aquaculture is one of the numerous scientific disciplines that benefit greatly from
machine learning (ML). The amount of dissolved oxygen (DO), an important indicator of …

Wind turbine blade maintenance is expensive, dangerous, time-consuming, and prone to
misdiagnosis. A potential solution to aid preventative maintenance is using deep learning …

Fault alarm time lag is one of the difficulties in fault diagnosis of wind turbine generators
(WTGs), and the existing methods are insufficient to achieve accurate and rapid fault …

Anomalies in wind turbines pose significant risks of costly downtime and maintenance,
underscoring the importance of early detection for reliable operation. However, conventional …

Wind power capacity is rapidly expanding across the world. In many nations, however, wind
energy profit margins are being reduced. As a result, many wind farm operators are looking …

Accurate identification of smart meter (SM) fault types is crucial for enhancing the efficiency
of Operation and Maintenance (O&M) and the reliability of power collection systems …