Brazilian Hydroelectric Plants Research Articles

Test inferences and link function selection in dynamic beta modeling of seasonal hydro-environmental time series with temporary abnormal regimes

We consider the modeling and forecasting of hydro-environmental time series subject to seasonal fluctuations and prolonged droughts. Abnormally dry periods have become more frequent as a result of climate change. We use a class of dynamic beta models which is tailored for doubly-bounded data. We examine two important aspects of this class of models: the accuracy of hypothesis tests based on asymptotic approximations and the choice of link function. In particular, we show that two commonly used tests can yield inaccurate inferences if the estimation of the null model is done on the basis of the maximum number of individual conditional log-likelihoods, and we introduce a new model selection criterion for selecting the model’s link function. Based on tests and the new criterion, we model the useful volumes of three Brazilian hydroelectric power plant water reservoirs. These time series exhibit seasonal fluctuations and contain abnormally dry periods due to intense and prolonged droughts. In-sample predictions and out-of-sample forecasts are produced and compared to those obtained with well known alternative approaches.

Fish injuries resulting from transient operating conditions in a Brazilian hydropower plant: morphological, physiological and biochemical evaluation in Pimelodus maculatus (Siluriformes: Pimelodidae)

Abstract This study investigated injuries sustained by Pimelodus maculatus caused by transient operating conditions in a Brazilian hydroelectric power plant. Fish (n = 43) that inhabited the tailrace and appeared on the water surface after changes in the operating conditions were evaluated (IF). Additionally, fish (n = 10) captured using longline in the vicinity of the tailrace were used for comparisons (NF). Blood samples were evaluated for cortisol, glucose, and lactate concentrations. Tissue samples (gill, liver, brain, and muscle) were analyzed for oxidative stress biomarkers (TBARS, protein carbonyl content, and catalase activity) and histology (gill, liver, and spleen). The most prominent findings in IF were barotraumas with intra-abdominal hemorrhage and swim bladder rupture. Cortisol and glucose concentrations were lower for IF when compared to NF. IF also suffered from oxidative stress, as indicated by increased TBARS in the liver and brain, as well as increased protein carbonyl content in the gills and liver. The main histological alterations induced by the adverse conditions were circulatory disorders, such as telangiectasia and gill aneurysm, hepatic congested veins, and hemorrhage in the liver and spleen. The abrupt drop of pressure resulting from changes in the operating conditions of the turbines caused barotraumas and oxidative stress, which were the leading causes of fish mortality

Linear Models Applied to Monthly Seasonal Streamflow Series Prediction

Linear models are widely used to perform time series forecasting. The Autoregressive models stand out, due to their simplicity in the parameters adjustment based on close-form solution. The Autoregressive and Moving Average models (ARMA) and Infinite Impulse Response filters (IIR) are also good alternatives, since they are recurrent structures. However, their adjustment is more complex, since the problem has no analytical solution. This investigation performs linear models to predict monthly seasonal streamflow series, from to Brazilian hydroelectric plants. The goal is to reach the best achievable performance addressing linear approaches. We propose the application of recurrent models, estimating their parameters via an immune algorithm. To compare the optimization performance, the Least Mean Square (LMS) and Recursive Prediction Error (RPE) algorithms are utilized. Also, the AR model and the Holt-Winters method were performed. The results showed that the insertion of feedback loops increases the quality of the responses. The ARMA models optimized by the immune algorithms achieved the best overall performance.

Spatio-temporal dynamics of surface water quality of two large reservoirs of Brazilian hydroelectric plants

RESUMO A utilização de recursos hídricos na geração de energia, em geral, envolve a concepção de barragens e reservatórios artificiais, que podem estar associados a impactos socioambientais. O emprego de técnicas estatísticas e índices para a avaliação dos dados da qualidade de águas superficiais tem se mostrado eficiente na compreensão da dinâmica desses ambientes. O objetivo do trabalho foi avaliar a qualidade das águas de dois grandes reservatórios e seus principais tributários, as usinas hidrelétricas Emborcação e Nova Ponte, em Minas Gerais. Dados secundários foram avaliados por meio da técnica estatística multivariada análise de cluster (AC), seguida de testes não paramétricos, além do cálculo dos índices de conformidade ao enquadramento (ICE) e de estado trófico (IET). Para subsidiar a discussão dos resultados, os usos e coberturas da terra da área também foram analisados. Os resultados da AC indicaram a separação das estações de cada reservatório em grupos, conforme localizações e tipo de ambiente nos quais estão inseridas. Os resultados dos testes estatísticos sugeriram melhor qualidade da água nos grupos de estações localizadas no reservatório e próximas a ele, o que foi corroborado pelo ICE. Os resultados do ICE indicaram adequação da qualidade dessas águas em relação aos usos propostos para a classe 2 de enquadramento. Quanto ao IET, verificou-se presença de produtividade primária intermediária em níveis aceitáveis. A predominância do uso do solo para agropecuária pode justificar, em parte, as condições de degradação da qualidade da água dos cursos d’água, principalmente no que se refere aos parâmetros associados a sólidos e nutrientes.

Sustainability assessment model for Brazilian hydroelectric projects using multicriteria analysis

The Analytic Hierarchy Process (AHP) has not previously been applied to the sustainable implementation of hydroelectric plants in Brazil. Despite attempts to conduct environmental analyses in a less subjective way, the Brazilian method is still in an early stage of development. Therefore, this study presents a new approach to the AHP methodology, which is used worldwide to analyze the feasibility of hydroelectric plant projects. Specifically, we develop a model for assessing the sustainability of hydroelectric projects that considers environmental, social, and economic criteria. To prove the effectiveness of this model, we conduct a survey of the main criteria and sub-criteria adopted around the world and discuss their respective prioritization processes. Subsequently, we present case studies of three Brazilian hydroelectric plants. Our analysis suggests that the AHP model is satisfactory. We recommend using the proposed model as a basis to encourage and incite discussion regarding the multiple perspectives involved in a hydroelectric project. The AHP model combined with a sensitivity analysis is capable of describing the potential conflicts and impacts resulting from the implementation of a hydroelectric project.

Comparative study of forecasting approaches in monthly streamflow series from Brazilian hydroelectric plants using Extreme Learning Machines and Box & Jenkins models

Abstract Several activities regarding water resources management are dependent on accurate monthly streamflow forecasting, such as flood control, reservoir operation, water supply planning, hydropower generation, energy matrix planning, among others. Most of the literature is focused on propose, compare, and evaluate the forecasting models. However, the decision on forecasting approaches plays a significant role in such models’ performance. In this paper, we are focused on investigating and confront the following forecasting approaches: i) use of a single model for the whole series (annual approach) versus using 12 models, each one responsible for predicting each month (monthly approach); ii) for multistep forecasting, the use of direct and recursive methods. The forecasting models addressed are the linear Autoregressive (AR) and Periodic Autoregressive (PAR) models, from the Box & Jenkins family, and the Extreme Learning Machines (ELM), an artificial neural network architecture. The computational analysis involves 20 time series associated with hydroelectric plants indicated that the monthly approach with the direct multistep method achieved the best overall performances, except for the cases in which the coefficient of variation is higher than two. In this case, the recursive approach stood out. Also, the ELM overcame the linear models in most cases.

Geomats Used to Control Erosion on Reservoir Margins in Brazilian Hydroelectric Power Plants

Erosion on reservoir margins causes losses in the production of energy in Hydroelectric Power Plants (HPPs), making it necessary to implement control techniques that ensure the reduction in sediment deposits inside reservoirs. Among these techniques, geomats (geosynthetic erosion mats) are widely used to control erosion processes in slopes and watercourses. This material protects the margins both in the short and long term, contributing to strengthening the vegetation’s roots developed in the erosion areas. This paper studies the performance of geomats (with and without sack gabion) installed in six margin sections distributed in three experimental units of the Brazilian HPPs called Porto Colômbia and Volta Grande. The geomat performance was evaluated over four years of monitoring through a qualitative evaluation matrix and differential bathymetry. The influence of ultraviolet (UV) radiation on the degradation of geomats installed in each section (natural conditions), as well as on geomat ultraviolet-aging samples in the laboratory (accelerated conditions), was also evaluated using differential scanning calorimetry (DSC). The results showed no significant difference in performance between using the erosion control technique consisting only of geomats and the technique consisting of geomats and sack gabion. The highest bathymetric difference value obtained in the present study was 1.62 m in the experimental unit VG1, controlled with geomat without the application of sack gabion on the margin.

Semi-Rigid Erosion Control Techniques with Geotextiles Applied to Reservoir Margins in Hydroelectric Power Plants, Brazil

In Brazil, hydroelectricity represents close to 70% of the energy consumed in the country. However, hydroelectric plant operations may be affected by the deposit of sediments for erosive processes on reservoir margins. This study presents the results of implementing two semi-rigid erosion control techniques installed on reservoir margins of two Brazilian Hydroelectric Power Plants (HPPs). These techniques were the gabion and gabion mattress used as a mixed technique and geogrid mattress technique. This paper highlights the importance of implementing geotextiles in the construction process of these erosion control techniques, taking advantage of their separation properties. The performance of the techniques was evaluated using qualitative performance variables and by differential bathymetry studies performed in 2016 and 2020 in the experimental units installed in each HPP. Moreover, the degradation of the geotextiles in each installation was evaluated through thermal analysis. The erosion control techniques that showed the best results were gabion and gabion mattress. Regarding the exhumed geotextiles, thermal analyses have shown that the commercial geotextiles that were used can withstand temperatures of up to 200 °C leading to no changes to their structure.

Os Reservatórios e o Clima

Muito do conhecimento que possuímos atualmente sobre o comportamento do sistema terrestre é proveniente de simulação numérica. Isso se deve ao fato de soluções analíticas para as equações de governo do sistema serem inexistentes, para casos de interesse prático, e dos dados observacionais disponíveis serem esparsos e irregularmente distribuídos no planeta. Em conjunto, essas restrições praticamente impedem investigações por meios analíticos e observacionais nos tempos atuais, obrigando os pesquisadores a recorrer à modelagem computacional. Contudo, simulações numéricas são apenas tão boas quanto o modelo matemático e os dados de entrada utilizados. Por isso, é importante compreender o processo através do qual a dinâmica atmosférica é representada computacionalmente, quais seus limites e quais as suas restrições. Este é um texto introdutório destinado a revisar conceitos importantes de simulação numérica da atmosfera. Ele começa descrevendo resumidamente os programas de computador utilizados para este fim, depois prossegue apresentando o conhecimento atual sobre a influência de grandes corpos de água sobre a baixa atmosfera, parte dele baseados em simulação. Termina realizando um estudo de caso para ilustrar os limites atuais da simulação de alta resolução aplicada ao reservatório de uma grande usina hidroelétrica brasileira.

Recursive linear models optimized by bioinspired metaheuristics to streamflow time series prediction

AbstractTime series forecasting problems are often addressed using linear techniques, especially the autoregressive (AR) models, due to their simplicity combined with good performances. It is possible to generalize a linear predictor by allowing infinite impulse response (IIR) through the addition of feedback loops, as occurs in the autoregressive and moving average (ARMA) models and IIR filters. However, the calculation of the free coefficients of these structures is more complex, as the optimization problem has no closed‐form solution. This work conducts an extensive investigation on the use of linear models to predict monthly seasonal streamflow series associated with Brazilian hydroelectric plants. The main goal is to reach the best achievable performance with linear approaches. We propose the application of recursive models, estimating their parameters with the aid of bioinspired metaheuristics: particle swarm optimization (PSO), genetic algorithm (GA), differential evolution (DE), and two immune‐inspired algorithms, the CLONALG, and the artificial immune network for optimization (Opt‐aiNet). The AR model is also considered. The results to multistep ahead forecasting indicated that the insertion of feedback loops increased the performances, with ARMA being the best predictor. The DE, PSO, and GA led to the minimum values of mean‐squared error during the tests, while DE yielded the smallest dispersion.

Multi-objective ensembles of echo state networks and extreme learning machines for streamflow series forecasting

Streamflow series forecasting composes a fundamental step in planning electric energy production for hydroelectric plants. In Brazil, such plants produce almost 70% of the total energy. Therefore, it is of great importance to improve the quality of streamflow series forecasting by investigating state-of-the-art time series forecasting algorithms. To this end, this work proposes the development of ensembles of unorganized machines, namely Extreme Learning Machines (ELMs) and Echo State Networks (ESNs). Two primary contributions are proposed: (1) a new training logic for ESNs that enables the application of bootstrap aggregation (bagging); and (2) the employment of multi-objective optimization to select and adjust the weights of the ensemble’s base models, taking into account the trade-off between bias and variance. Experiments are conducted on streamflow series data from five real-world Brazilian hydroelectric plants, namely those in Sobradinho, Serra da Mesa, Jiraú, Furnas and Água Vermelha. The statistical results for four different prediction horizons (1, 3, 6, and 12 months ahead) indicate that the ensembles of unorganized machines achieve better results than autoregressive (AR) models in terms of the Nash–Sutcliffe model efficiency coefficient (NSE), root mean squared error (RMSE), coefficient of determination (R2), and RMSE-observations standard deviation ratio (RSR). In such results, the ensembles with ESNs and the multi-objective optimization design procedure achieve the best scores.

Numerical simulations of precipitation and streamflow in current climate and future projections to drainage areas of Brazilian hydroelectric plants

Hydroelectric sources are a major contributor to power generation in Brazil. The constant evaluation of climate change impacts is relevant for guiding Brazilian energy policy. This research presents a methodological framework composed of the calibration of a hydrological model and verification of a climate model in the ‘present’ climate (1961-1990), in addition to future scenarios (2011-2100) of precipitation and streamflow for 4 hydroelectric plants in Brazil. For future projections, data from the Eta regional climate model (20 km horizontal resolution) nested within the HadGEM2-ES and MIROC5 global climate models were used. Monthly linear bias correction was applied to the simulations. Future projections were based on IPCC RCP4.5 and 8.5 scenarios. The SMAP hydrological model was adopted on a monthly scale with the addition of a translation parameter that examines the level of dependence of the present streamflow on the previous month's streamflow. The climate and hydrological models satisfactorily capture the distribution of precipitation and streamflow in different Brazilian regions, and effectively represent seasonal variability. Future projections point to a reduction in rainfall and natural streamflow in central-northern Brazil and a slight increase in the southern region. These scenarios should be carefully considered and require constant improvement and research since there are uncertainties associated with atmospheric dynamics and the hydrological cycle.

Daily Water Flow Forecasting via Coupling Between SMAP and Deep Learning

Hydrological models are essential tools to forecast daily water resources' availability, which are used to plan the short-term electrical systems' operation. However, there is a trade-off when choosing a given model. Complex models may provide good results depending on very complicated analytical and optimization procedures beyond sophisticated data, whereas simpler models offer reasonable results with much more amenable tuning approaches. To improve the quality of simpler models this article proposes the coupling of the Soil Moisture Accounting Procedure (SMAP) hydrological model with a Deep Learning architecture based on Conv3D-LSTM. In the proposed methodology, the SMAP is first optimized to obtain general parameters of the hydrographic basin. This optimized model's output is used as input to the Conv3D-LSTM estimator to provide the final results. This gray estimator model can generate fast and accurate results. Studies whit the goal of forecast the natural flow seven days ahead are carried out for two large Brazilian hydroelectric plants to validate the method. The results obtained by the architecture are better than those obtained with decoupled techniques.

A Study of Fault Diagnosis Based on Electrical Signature Analysis for Synchronous Generators Predictive Maintenance in Bulk Electric Systems

The condition of synchronous generators (SGs) is a matter of great attention, because they can be seen as equipment and also as fundamental elements of power systems. Thus, there is a growing interest in new technologies to improve SG protection and maintenance schemes. In this context, electrical signature analysis (ESA) is a non-invasive technique that has been increasingly applied to the predictive maintenance of rotating electrical machines. However, in general, the works applying ESA to SGs are focused on isolated machines. Thus, this paper presents a study on the condition monitoring of SGs in bulk electric systems by using ESA. The main contribution of this work is the practical results of ESA for fault detection in in-service SGs interconnected to a power system. Two types of faults were detected in an SG at a Brazilian hydroelectric power plant by using ESA, including stator electrical unbalance and mechanical misalignment. This paper also addresses peculiarities in the ESA of wound rotor SGs, including recommendations for signal analysis, how to discriminate rotor faults on fault patterns, and the particularities of two-pole SGs.

Morphological identification of species of the Nuneztovari Complex of Anopheles (Diptera: Culicidae) from an area affected by a Brazilian hydroelectric plant.

The Nuneztovari Complex of Anopheles (Diptera: Culicidae) comprises four species: An. nuneztovari Gabaldon, An. goeldii Rozeboom Gabaldon, An. dunhami Causey and An. nuneztovari species A. This study aimed to identify morphologically the species of the Nuneztovari Complex that occur in the area of the Belo Monte hydroelectric dam. The morphological identification of adult males and male genitalia (aedeagus and ventral claspette) was performed. A statistical analysis of the difference in aedeagal leaflet length was done using the Mann-Whitney test. Of the 38 male genitalia of specimens of the Nuneztovari Complex examined, 33 were identified as An. goeldii/An. nuneztovari A and five as An. nuneztovari s.s. A statistically significant difference in aedeagal leaflet length was detected between the species: the mean length was 1.23 μm for An. goeldii/An. nuneztovari A and 9.18 μm for An. nuneztovari s.s. This is the first record of An. nuneztovari s.s.in areas of environmental modification in the Brazilian Amazon. This study provides a measurement tool that can identify and differentiate species of the complex in the region, which can be applied to the other species of the complex as well to other anopheline species; thus, fostering the acquisition of information about the role of each species in malaria transmission.

Climatological and hydrological patterns and verified trends in precipitation and streamflow in the basins of Brazilian hydroelectric plants

This study focuses on investigating possible changes in hydrometeorological behavior on important Brazilian river basins for power generation purposes. Thereby, this research analyzes the historical averages and observed trends regarding rainfall and streamflow and their impact on the hydrological regime. Ten river basins were selected for the assessment of alterations in the precipitation and streamflow series throughout descriptive measures and statistical significance tests (Mann-Kendall and Sen’s slope). These data are available over different time spans, but most of the records include information from 1961 to 2006. As long as these river basins are subjected to different climate types, their corresponding rainfall and streamflow patterns vary accordingly the basin location in the country, in addition to the season. Most of the country has a predominantly tropical climate, with a wet period concentrated between October and March and a dry period between April and September. Some exceptions are the Northeastern region, where the climate is semi-arid, besides the Southern region, where there is abundant precipitation throughout the year. According to the obtained results, it could be noted that the streamflow response to precipitation is faster in the smaller river basins, what can be due especially to the water travel time. It was found that Belo Monte basin, in Northern Brazil, presents a statistically significant reduction in the annual rainfall. Furthermore, a significant decrease in the annual streamflow was also identified in Xingo and Sobradinho basins, in the Northeastern region. In contrast, Itaipu basin, located in Southern Brazil, showed increasing statistically significant trends in annual rainfall and streamflow during the second half of the twentieth century. Relevant decreasing trends were also identified in the minimum streamflows of the Brazilian Northern and Northeastern basins and increasing ones in the maximum streamflows in the Southern region basins. The results obtained in this work will support the assessment of the impact of rainfall and streamflow future scenarios in regulating capacity of the hydroelectric power plant reservoirs.

Performance analysis of unorganized machines in streamflow forecasting of Brazilian plants

This work performs an extensive investigation about the application of unorganized machines – extreme learning machines and echo state networks – to predict monthly seasonal streamflow series, associated to three important Brazilian hydroelectric plants, for many forecasting horizons. The aforementioned models are neural network architectures which present efficient and simple training processes. Moreover, the selection of the best inputs of each model is carried out by the wrapper method, using three different evaluation criteria, and three filters, viz., those based on the partial autocorrelation function, the mutual information and the normalization of maximum relevance and minimum common redundancy method. This study also establishes a comparison between the unorganized machines and two classical models: the partial autoregressive model and the multilayer perceptron. The computational results demonstrate that the unorganized machines, especially the echo state networks, represent efficient alternatives to solve the task.

A periodic spatial vine copula model for multi-site streamflow simulation

Operational strategies of the Brazilian Electric Sector have direct impacts on operating costs, energy prices, planning the expansion of the system, etc. These decisions are taken under a high level of uncertainty, as the future availability of water for energy generation is a stochastic variable. Computational models, routinely based on stochastic optimization, support these decisions. Some of them make use of streamflow scenarios as entries. In this way, the aim of this paper is to develop a sophisticated statistical model for multi-site stochastic streamflow simulation. Our approach is based on the extension of vine copulas for high dimensional spatial applications. The proposed model copes with both temporal and spatial dependencies of streamflows. At the same time, it can simulate numerous sites concurrently. We tested our approach on streamflow data from 39 Brazilian hydroelectric power plants. The results indicate that the model can simulate streamflow scenarios largely preserving the features observed in the recorded streamflow data.

Methane–hydrogen fuel blends for SI engines in Brazilian public transport: Potential supply and environmental issues

In the last decades, the whole world has been feeling the effects of environmental pollution, especially air pollution in large cities, with the transport sector as a major contributor to this scenario. In this sense, Brazil presents 27 states with great potential for biogas generation from landfills through anaerobic digestion. Only in the year 2016, the calculations presented in this paper point to a methane production in landfills of 5.57E+09 m3. On the other hand, the high number of rivers and hydroelectric dams in the country makes possible the generation of electricity that reached 90.27 TWh in 2016. However, the water that is drained by the floodgates to control the level of the reservoirs becomes an interesting wasted energy source for other uses, such as the production of other clean fuel, hydrogen, through classical processes such as electrolysis. It is pointed out the possibility of producing 2.76E+06 tons of hydrogen only with the energy drained in these hydroelectric plants. These two fuels together can be used to fuel vehicles powered by the hydrogen–methane blends, like HBio95 and HBio60. The promise of harmful and CO2 emissions by focusing on mixture of gases has recently attracted the interest of vehicle manufacturers and transport operators. In this scenario, this work presents an analysis on the use of mixture of gases in the urban bus fleet of the 27 Brazilian states, using as energy source derived from secondary energy from Brazilian hydroelectric plants and biogas from sanitary landfills, comparing this scenario with the use of only methane from landfills or only H2 from hydroelectric plants, assessing issues such as pollutant emissions, engine performance and deployment facilities.

FUZZY INFERENCE SYSTEMS FOR MULTI-STEP AHEAD DAILY INFLOW FORECASTING

ABSTRACT This paper presents the evaluation of a daily inflow forecasting model using a tool that facilitates the analysis of mathematical models for hydroelectric plants. The model is based on a Fuzzy Inference System. An offline version of the Expectation Maximization algorithm is employed to adjust the model parameters. The tool integrates different inflow forecasting models into a single physical structure. It makes uniform and streamlines the management of data, prediction studies, and presentation of results. A case study is carried out using data from three Brazilian hydroelectric plants of the Parana basin, Tiete River, in southern Brazil. Their activities are coordinated by Operator of the National Electric System (ONS) and inspected by the National Agency for Electricity (ANEEL). The model is evaluated considering a multi-step ahead forecasting task. The graphs allow a comparison between observed and forecasted inflows. For statistical analysis, it is used the mean absolute percentage error, the root mean square error, the mean absolute error, and the mass curve coefficient. The results show an adequate performance of the model, leading to a promising alternative for daily inflow forecasting.