The optimal dispatch of hydropower plants consists of the challenge of taking advantage of both available head and river flows. Despite the objective of delivering the maximum power to the grid, some variables are uncertain, dynamic, non-linear, and non-parametric. Nevertheless, some models may help hydropower generating players with computer science evolution, thus maximizing the hydropower plants’ power production. Over the years, several studies have explored Machine Learning (ML) techniques to optimize hydropower plants’ dispatch, being applied in the pre-operation, real-time and post-operation phases. Hence, this work consists of a systematic review to analyze how ML models are being used to optimize energy production from hydropower plants. The analysis focused on criteria that interfere with energy generation forecasts, operating policies, and performance evaluation. Our discussions aimed at ML techniques, schedule forecasts, river systems, and ML applications for hydropower optimization. The results showed that ML techniques have been more applied for river flow forecast and reservoir operation optimization. The long-term scheduling horizon is the most common application in the analyzed studies. Therefore, supervised learning was more applied as ML technique segment. Despite being a widely explored theme, new areas present opportunities for disruptive research, such as real-time schedule forecast, run-of-river system optimization and low-head hydropower plant operation.

Climate change and environmental degradation has resulted in a reduction in water inflow at hydropower plants, as well as a decrease in reservoir levels. Existing hydropower plants suffer from water head reduction, mainly with decrease in efficiency of energy conversion in hydro turbines. This paper showcases the benefits of operations with variable speed in existing hydropower plants, when working at a lower water head than the rated one. Theoretical analyses and tests were performed in a special constructed laboratorial setup aiming at evaluating the amount of efficiency recovery with variable speed operation. Connection alternatives for a constant frequency grid and applications of the learned concepts in an existent hydropower plant are presented. The investigations were applied to the Furnas hydropower plant. The results point out that economic feasibility of the application can be achieved.

This work presents an experiment proposal for continuous partial discharges analysis during an automated thermal cycle experiment for hydro generator stator coils/bars. It is an innovative approach because we propose to perform partial discharges test and thermal cycle experiment simultaneously, while they are usually performed individually. We developed an automated test bench that allows performing data acquisition throughout the experiments. We decided to force different coil failures during the tests to observe any changes in the partial discharges trend. First, we changed the thermal cycle temperature limits, and this led to coil overheating before an electrical failure occurred in the insulation. Later, we applied a voltage that was higher than the rated for the coil, which led to a faster electrical failure. The partial discharges trend changed moments before the electrical fault, and the system was able to identify such change. Our preliminary results show that the continuous analysis of partial discharges trends might lead to better maintenance planning by identifying the moment when the hydro generator aging gets critical.

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.

This paper presents a methodology for obtaining the parameters of the equivalent circuit for an induction motor from the information on the nameplate and manufacturer catalog data sheet. The proposed method is straight and noniterative. The variation in rotor parameters for deep bars or double squirrel-cage rotors is modeled and presented as a function of the slip. The results of remarkable points, such as the starting current, starting and breakdown torques, and rated conditions, are compared with the supplied data, and the comparison showed very good agreement. The methodology was applied to hundreds of motors. A statistical analysis is presented showing that the typical parameters of an equivalent circuit can be readily estimated in the absence of further data.

The operation of hydroelectric power plants is directly dependent on data from various systems. These systems are classified as Operational Technologies (OT) when focused on fieldlevel control and Information Technologies (IT) when aimed at enterprise-level applications. The efficient integration of these systems is a challenge for industries since each system type has different perspectives and objectives. However, this integration is crucial to achieving the best possible operational performance. In the hydroelectric power generation market, there is often a lack of applications that can successfully integrate and analyze data from IT and OT systems, transforming it into useful information for operation. This often results in manual activities to collect and analyze data, reducing their effectiveness. This article presents an architecture for the integration of IT/OT systems, focusing on the hydroelectric power generation market, and discusses its implementation and results at the Jirau Hydroelectric Plant.