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

Raúl R. Delgado-Currín (), Williams R. Calderón-Muñoz and J. C. Elicer-Cortés
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Raúl R. Delgado-Currín: Department of Mechanical Engineering, Universidad de La Frontera, Francisco Salazar 01145, Temuco 4811230, Chile
Williams R. Calderón-Muñoz: Department of Mechanical Engineering, Universidad de Chile, Beauchef 851, Santiago 8370456, Chile
J. C. Elicer-Cortés: Department of Mechanical Engineering, Universidad de Chile, Beauchef 851, Santiago 8370456, Chile

Energies, 2024, vol. 17, issue 14, 1-14

Abstract: 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 failure of the sensor measuring the torque and/or rotational speed of the Pelton turbine shaft, the synthetic turbine shaft power data generated by the ANN will allow the turbine output power to be determined. The experimental data were obtained by varying the operating conditions of the micro-hydropower plant, including the variation of the input power to the electric generator and the variation of the injector opening. These changes consequently affected the flow rate and the pressure head at the turbine inlet. The use of artificial neural networks (ANNs) was deemed appropriate due to their ability to model complex relationships between input and output variables. The ANN structure comprised five input variables, fifteen neurons in a hidden layer and an output variable estimating the Pelton turbine power. During the training phase, algorithms such as Levenberg–Marquardt (L–M), Scaled Conjugate Gradient (SCG) and Bayesian were employed. The results indicated an error of 0.39% with L–M and 7% with SCG, with the latter under high-flow and -energy consumption conditions. This study demonstrates the effectiveness of artificial neural networks (ANNs) trained with the Levenberg–Marquardt (L–M) algorithm in estimating turbine shaft power. This contributes to improved performance and decision making in the event of a torque sensor failure.

Keywords: artificial neural networks; micro-hydropower plant; Pelton turbine; Pelton turbine shaft power (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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