Artificial Neural Network Model for Estimating the Pelton Turbine Shaft Power of a Micro-Hydropower Plant under Different Operating Conditions

• Published in: Energies (Basel) Vol. 17; no. 14; p. 3597
• Main Authors: Delgado-Currín, Raúl R., Calderón-Muñoz, Williams R., Elicer-Cortés, J. C.
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.07.2024
• Subjects: Accuracy; Algorithms; Analysis; Artificial intelligence; artificial neural networks; Data entry; Efficiency; Electric generators; Generators; Hydraulics; Hydroelectric plants; Hydroelectric power; Hydroelectric power plants; Machine learning; Mean square errors; micro-hydropower plant; Modernization; Neural networks; Neurons; Pelton turbine; Pelton turbine shaft power; Sensors; Turbine industry; Turbines; Water-power; Chile; Germany

The optimal performance of a hydroelectric power plant depends on accurate monitoring and well-functioning sensors for data acquisition. This study proposes the use of artificial neural networks (ANNs) to estimate the Pelton turbine shaft power of a 10 kW micro-hydropower plant. In the event of a fa...