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.
Brazilian hydroelectric power plants often use telemetry stations to extract information about the environment. These equipment are usually installed in several strategic spots of rivers that "feed" the reservoir, and are capable of providing important information such as precipitation, river level, and water flow. This paper presents an analysis of Machine Learning applied to the forecasting of spillage occurrences over a set amount of time in a Brazilian power plant. To achieve this goal, telemetry stations' data were utilized together with the plant's operations historical, which provides information about previous spillages, turbines' flows, among others. The Machine Learning approach has shown to be promising in this problem, and the developed model presented the potential to effectively support decisions by helping the operators prepare for significant incoming water flows.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations –citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.