Forecast of Operational Downtime of the Generating Units for Sediment Cleaning in the Water Intakes: A Case of the Jirau Hydropower Plant

Lenio Prado (), Marcelo Fonseca, José V. Bernardes, Mateus G. Santos, Edson C. Bortoni and Guilherme S. Bastos
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Lenio Prado: Systems Engineering and Information Technology Institute, Itajubá Federal University, Itajubá 37500-903, Brazil
Marcelo Fonseca: JIRAU ENERGIA, Distrito de Jaci-Paraná, Porto Velho 76840-000, Brazil
José V. Bernardes: Electric and Energy Systems Institute, Itajubá Federal University, Itajubá 37500-903, Brazil
Mateus G. Santos: Systems Engineering and Information Technology Institute, Itajubá Federal University, Itajubá 37500-903, Brazil
Edson C. Bortoni: Electric and Energy Systems Institute, Itajubá Federal University, Itajubá 37500-903, Brazil
Guilherme S. Bastos: Systems Engineering and Information Technology Institute, Itajubá Federal University, Itajubá 37500-903, Brazil

Energies, 2023, vol. 16, issue 17, 1-20

Abstract: Hydropower plants (HPP) in the Amazon basin suffer from issues caused by trees and sediments carried by the river. The Jirau HPP, located in the occidental Amazon basin, is directly affected by high sediment transportation. These materials accumulate in the water intakes and obstruct the trash racks installed in the intake system to prevent the entry of materials. As a result, head losses negatively impact the efficiency of the generating units and the power production capacity. The HPP operation team must monitor these losses and take action timely to clear the intakes. One of the possible actions is to stop the GU to let the sediment settle down. Therefore, intelligent methods are required to predict the downtime for sediment settling and restoring operational functionality. Thus, this work proposes a technique that utilizes hidden Markov models and Bayesian networks to predict the fifty Jirau generation units’ downtime, thereby reducing their inactive time and providing methodologies for establishing operating rules. The model is based on accurate operational data extracted from the hydropower plant, which ensures greater fidelity to the daily operational reality of the plant. The results demonstrate the model’s effectiveness and indicate the extent of the impact on downtime under varying sediment levels and when neighboring units are generating or inactive.

Keywords: Bayesian networks; correlation techniques; hidden Markov models; hydropower generation units operational downtime; sediment decantation (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: 2023
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