Spillage Forecast Models in Hydroelectric Power Plants Using Information from Telemetry Stations and Hydraulic Control

Pedro H. M. Nascimento, Vinícius A. Cabral, Ivo C. Silva Junior, Frederico F. Panoeiro, Leonardo M. Honório and André L. M. Marcato
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Pedro H. M. Nascimento: Electrical Engineering Postgraduate Program, Federal University of Juiz de Fora, Juiz de Fora 36036-900, Brazil
Vinícius A. Cabral: Electrical Engineering Postgraduate Program, Federal University of Juiz de Fora, Juiz de Fora 36036-900, Brazil
Ivo C. Silva Junior: Electrical Engineering Postgraduate Program, Federal University of Juiz de Fora, Juiz de Fora 36036-900, Brazil
Frederico F. Panoeiro: Electrical Engineering Postgraduate Program, Federal University of Juiz de Fora, Juiz de Fora 36036-900, Brazil
Leonardo M. Honório: Electrical Engineering Postgraduate Program, Federal University of Juiz de Fora, Juiz de Fora 36036-900, Brazil
André L. M. Marcato: Electrical Engineering Postgraduate Program, Federal University of Juiz de Fora, Juiz de Fora 36036-900, Brazil

Energies, 2021, vol. 14, issue 1, 1-16

Abstract: Hydroelectric power plants’ operational decisions are associated with several factors, such as generation planning, water availability and dam safety. One major challenge is to control the water spillage from the reservoir. Although this action represents a loss of energy production, it is a powerful strategy to regulate the reservoir level, ensuring the dam’s safety. The decision to use this strategy must be made in advance based on level and demand predictions. The present work applies supervised machine learning techniques to predict the operating condition of spillage in a hydroelectric plant for 5 h ahead. The use of this method, in real time, aims to assist the operator so that he can make more assertive and safer decisions, avoiding waste of energy resources and increasing the safety of dams. The Random Forest and Multilayer Perceptron methods were used to define the architecture compared to the forecasting capacity. The proposed methodology was applied to a 902.5 MW Hydroelectric Power Plant located on the Tocantins River, Brazil. The results demonstrate effective assistance to operators in the decision-making, presenting accuracy of up to 99.15% for the spill decision.

Keywords: forecasting; hydroelectric power; machine learning; resources managing; telemetry (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: 2021
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