Predicting the malfunction timing of wind turbines is essential for maintaining high profitability power generation business. Machine learning methods have been studied using condition monitoring system data, such as vibration and supervisory control data acquisition (SCADA) to detect predict anomalies in automatically. Autoencoder-based techniques attracted significant interest detection or pr...

Renewable energy sources, which are becoming increasingly popular, often use a voltage source converter (VSC) for connection to the power system. Assessing effects of connecting such system is essential ensure proper operation and connected source. For this purpose, it necessary know range active reactive generation by converter. The authors indicate that interaction between affects its availab...

Detection and alarm of leakage hydrogen (H2) gas is an extremely important issue needed to be addressed. In this study, we fabricated calibation portable devices based on the H2 sensor using SnO2 thin film with Pd nanoscale catalytic as a sensing layer. For practical application sensor, designed realized PCB circuit boards, microprocessors, data processing, output signals, storage, power source...

Due to the stochastic nature of source, wind turbines operate under non-stationary conditions and extracted power depends non-trivially on ambient working parameters. It is therefore difficult establish a normal behavior model for monitoring performance turbine most employed approach be driven by data. The curve relation between intensity widely performance. On grounds above considerations, rec...