Hydroelectric Generation Research Articles

Water-Level Prediction Analysis for the Three Gorges Reservoir Area Based on a Hybrid Model of LSTM and Its Variants

The Three Gorges Hydropower Station, the largest in the world, plays a pivotal role in hydroelectric power generation, flood control, navigation, and ecological conservation. The water level of the Three Gorges Reservoir has a direct impact on these aspects. Accurate prediction of the reservoir’s water level, especially in the dam area, is of utmost importance for downstream regions’ safety and economic development. This study investigates the application and performance of four distinct deep-learning models in predicting water levels. The models evaluated include the Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BiLSTM), Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM), and Convolutional Neural Network–Attention–Long Short-Term Memory (CNN–Attention–LSTM). The performance of these models was assessed using several metrics, namely the Coefficient of Determination (R2), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The findings indicate that the CNN–Attention–LSTM model outperforms the others in all metrics, achieving an R2 value of 0.9940, MAE of 0.5296, RMSE of 0.6748, and MAPE of 0.0032. Moreover, the CNN–LSTM model exhibited exceptional predictive accuracy for lower water levels. These results underscore the potential of deep-learning models in water-level forecasting, particularly highlighting the efficacy of attention mechanisms in enhancing predictive accuracy. Precise water-level predictions are instrumental in optimizing hydropower generation and providing a scientific basis for effective flood control and water resource management.

THE DRIP IRRIGATION SYSTEM

Irrigation can be defined as the process of applying artificial water to soil or land in order to promote the growth of agricultural crops such as corn and wheat. Water irrigation has a number of advantages,including an increase in crop yield, protection against famine, revenue generation, and the avoidance of mixed cropping. It also has advantages in navigation, hydroelectric power generation, and the creation of employment opportunities. It should be noted that irrigation has both advantages and disadvantages, just like everything else. Irrigation has a number of disadvantages, including the waste of irrigation water, formation of marshy lands, damp weather, and the loss of valuable agricultural land. Drip irrigation is one of the surface and subsurface irrigation methods that we will discuss in this paper. We will also discuss the various effects and advancements in this technique that have occurred as a result of the research conducted. Last but not least, the significance and necessity of this system will be discussed. Key words:- Drip, Irrigation, Artificial Irrigation, Efficiency, Framers

A Review on Drip Irrigation System

Irrigation can be defined as the process of applying artificial water to soil or land in order to promote the growth of agricultural crops such as corn and wheat. Water irrigation has a number of advantages,including an increase in crop yield, protection against famine, revenue generation, and the avoidance of mixed cropping. It also has advantages in navigation, hydroelectric power generation, and the creation of employment opportunities. It should be noted that irrigation has both advantages and disadvantages, just like everything else. Irrigation has a number of disadvantages, including the waste of irrigation water, formation of marshy lands, damp weather, and the loss of valuable agricultural land. Drip irrigation is one of the surface and subsurface irrigation methods that we will discuss in this paper. We will also discuss the various effects and advancements in this technique that have occurred as a result of the research conducted. Last but not least, the significance and necessity of this system will be discussed. Key words:- Drip, Irrigation, Artificial Irrigation, Efficiency, Framers

Last-Century Forest Dynamics in a Highland Pyrenean National Park and Implications for Conservation.

Ecological records from before and after the creation of natural parks are valuable for informing conservation and management but are often unavailable. High-resolution paleoecological studies may bridge the gap and provide the required information. This paper presents a 20th-century subdecadal reconstruction of vegetation and landscape dynamics in a national park of the Pyrenean highlands. The park lands had traditionally been used for cultivation, extensive grazing, forest exploitation, and hydroelectricity generation following the damming of numerous glacial lakes. A significant finding is that forests have dominated the landscape, with negligible changes in composition, and only experienced fluctuations in forest cover, influenced by both climatic and anthropogenic factors. The creation of the park (1955) and the initial restrictions on forest exploitation did not significantly affect vegetation cover or composition. Major forest expansion did not occur until several decades later, 1980, when the park was enlarged and forest exploitation was further restricted. This expansion peaked in the 1990s, coinciding with a warming trend and a decrease in fire incidence, before declining due to warmer and drier climates. This decline was coeval with the ongoing global forest dieback and may be exacerbated by the predicted global warming in this century, which could also increase fire incidence due to dead-wood accumulation. Currently, the main threats are global warming/drying, fire, and tourism intensification. Similar high-resolution paleoecological records in protected areas are globally scarce and would be capable in providing the long-term ecological scope required to properly understand forest dynamics and optimize conservation measures.

Assessment of climate change impacts on rainfall and streamflow in the Alto Paranapanema Basin, Brazil

ABSTRACT Climate change has the potential to fundamentally transform landscapes on global scale. Leveraging advanced predictive modeling to enhance water resource management within the Alto Paranapanema Basin (Brazil), holds the potential to proactively anticipate challenges and alleviate the impacts and conflicts arising from this phenomenon. This is particularly important in a region boasting over 1,600 centerpivot irrigation systems. This study employs the Soil Moisture Accounting Procedure, a physical model, to simulate long-term climate datasets and flows. Future climate scenarios, rooted in the Representative Concentration Pathways, are developed through the downscaling of Global Climate Models. The findings reveal a temporal shift in rainfall patterns, characterized by a reduction during the wet season of up to 40% compared to the average historical rainfall, and an increase throughout the dry season up to 40% compared to the same historical, estimated by the Eta -BESM model. These changes present challenges regarding to water availability, hydroelectric generation, and agricultural. By fostering collaboration among different governmental entities responsible for the managements of basins and harnessing the potential of predictive models, this research advocates for the adoption of proactive strategies in management of water resources. These strategies are imperative to effectively counteract the far-reaching effects of climate change.

Fibrous Solar Evaporator with Tunable Water Flow for Efficient, Self‐Operating, and Sustainable Hydroelectricity Generation

AbstractConvenient and green hydroelectricity generation (HG) systems are widely studied to cope with the energy crisis. Electricity can be generated through streaming potential with merely water droplets. Nevertheless, the operational requirement of persistent water supply restricts the durability and practicality of HG. Solar‐driven steam generation with continuous and sufficient water flow has become a promising integration way to enhance HG sustainability. However, most evaporation‐accelerated hydroelectricity generators are threatened by excessive wetting decay and ionic erosion damage, which significantly impair charge accumulation and operational durability. To address this issue, a carbon black/polypyrrole decorated Tencel framework (CPF) with tunable water flow is fabricated through twisting and braiding technology. The modified water flow rate, height, and content ensure rapid ion migration, appropriate wetting boundary, and sufficient brine circulation, respectively. The aqueous flow and ion strength are optimized through the evaporation‐accelerated and desalination‐integrated HG. Hence, the CPF evaporator shows an efficient, self‐operating, and sustainable output of 0.73 V, 0.6 µA, and 2.38 kg m−2 h−1 in 3.5 wt.% brine under 1 sun radiation without salt deposition. The scalable, prolonged, and adaptable outdoor operation throughout the daytime ensures the clean production of electricity and freshwater.

Investigation of climate change impacts on daily streamflow extremes in Eastern Black Sea Basin, Turkey

This study was undertaken to evaluate how future uncertain climate-related hydrological responses and low and highflow frequencies affect local hydrology up to the 2100, depending on different Global Circulation Models (GCM) and different Concentration Scenarios (CS). To this end, daily total precipitation and daily mean temperature data were used for RCP4.5 and RCP8.5 CSs by employing GCMs (GFDL-ESM2M, HadGEM2-ES and MPI-ESM-MR). In the study, the years 1971–2000 were chosen as the baseline period. Future streamflow predictions were modeled by machine learning. The models were calibrated with baseline period data and future streamflow values were predicted by using the best Streamflow Prediction Model during the 2055s (2041–2070) and 2085s (2071–2100) periods. Flow Duration Curves from the streamflow predictions were obtained and by using them, discharges corresponding to 95% probability of exceedance for lowflow and 5% probability of exceedance for highflow were calculated. According to the findings, it is predicted that there will be decrease of approximately 21% in lowflow discharges and approximately 30% in highflow discharges. This prediction has suggested water resources managers to implement mitigation measures to climate change in Eastern Black Sea Basin and most probably in Türkiye, especially in the context of continuity of aquatic life and sustainable water supply. It is expected that the paper will take a very important place in estimating and evaluating the expected changes in basin hydrology in the coming years, especially in the highflows and lowflows, which are the key concepts in hydroelectric power generation and in flood management.

A novel additive regression model for streamflow forecasting in German rivers

Forecasting streamflows, essential for flood mitigation and the efficient management of water resources for drinking, agriculture and hydroelectric power generation, presents a formidable challenge in most real-world scenarios. In this study, two models, the first based on the Additive Regression of Radial Basis Function Neural Networks (AR-RBF) and the second based on the stacking with the Pace Regression of the Multilayer Perceptron and Random Forest (MLP-RF-PR), were compared for the prediction of short-term (1–3 days ahead) and medium-term (7 days ahead) daily streamflow rates of three different rivers in Germany: the Elbe River at Wittenberge, the Leine River at Herrenhausen, and the Saale River at Hof. The lagged values of streamflow rate, precipitation and temperature were considered for the modeling. Moreover, the Bayesian Optimization (BO) algorithm was used to assess the optimal number of lagged values and hyperparameters. Both models showed accurate predictions for short-term forecasting, with R2 for 1-day ahead predictions ranging from 0.939 to 0.998 for AR-RBF and from 0.930 to 0.996 for MLP-RF-PR, while MAPE ranged from 2.02 % to 8.99 % for AR-RBF and from 2.14 % to 9.68 % for MLP-RF-PR, when exogeneous variables were included. As the forecast horizon increased, a reduction in forecasting accuracy was observed. However, both models could still predict the overall flow pattern, even for 7-day-ahead predictions, with R2 ranging from 0.772 to 0.871 for AR-RBF and from 0.703 to 0.840 for MLP-RF-PR, while MAPE ranged from 10.60 % to 20.45 % for AR-RBF and from 10.44 % to 19.65 % for MLP-RF-PR. Overall, the outcomes of this study suggest that both AR-RBF and MLP-RF-PR can be reliable tools for the short- and medium-term streamflow rate prediction, requiring a short number of parameters to be optimized, making them easy to implement while reducing the calculation time required.

Smart hydropower management: utilizing machine learning and deep learning method to enhance dam’s energy generation efficiency

AbstractRenewable energy sources and hydroelectric power generation in large parts of the electricity market are crucial as environmental pollution worsens . Utilizing meteorological data from the region, where the Hirfanlı Dam is located, this study employs machine learning (ML) and introduces a novel hybrid Genetic Grey Wolf Optimizer (GGW0)-based Convolutional Neural Network/Recurrent Neural Network (CNN/RNN) regression technique to predict hydroelectric power production (PP). In the first section of the study, various ML techniques SVR (Support Vector Regression), ELM (Extreme Learning Machine), RFR (Random Forest Regression), ANN (Artificial Neural Networks) and WKNNR (Weighted K-Nearest Neighbor) are presented with the Principal Component Analysis (PCA) method and the minimum–maximum method in the normalization of the features. A novel GGWO and CNN/RNN model)-Long Short-Term Memory (LSTM) regression technique is introduced in the second section. GGWO is used to select features, while the proposed CNN/RNN-LSTM model is employed for feature extraction and prediction of PP. The study demonstrates that the ELM algorithm in Method I outperforms other ML models, achieving a correlation coefficient (r) of 0.977 and the mean absolute error (MAE) of 0.4 with the best feature subset. Additionally, the proposed CNN/RNN hybrid model in Method II yields even better results, with r and MAE values of 0.9802 and 0.314, respectively. The research contributes to the field of renewable energy prediction, and the results can aid in efficient decision making for electricity generation and resource management.

Pre-collapse spaceborne deformation monitoring of the Kakhovka dam, Ukraine, from 2017 to 2023

The Kakhovka Dam on the Dnieper River in Kherson Oblast, Ukraine, was completed in 1956 as the final dam in the Dnieper reservoir cascade. On the morning of June 6th, 2023, a substantial portion of the dam suffered a collapse while under Russian control. This incident was documented through satellite optical and radar images, providing valuable evidence of the dam’s condition. Here we present the results of multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR) monitoring of the Kakhovka dam. The dam is vital for water management and hydroelectric power generation. Utilizing multi-temporal InSAR (MT-InSAR) data, we assessed the dam deformations prior to the collapse. Our findings indicate movements of the south side, facing the Dniprovska Gulf, compatible with several possible damage mechanisms. This study highlights the significance of employing spaceborne advanced monitoring techniques to detect signs of distress and ensure the stability of critical infrastructure.

Development of Hydroelectric Power Generation from Water Pipelines

Abstract: Pico hydroelectricity is a good way to make clean energy. We're talking about making a system that gets water from the tanks in houses. The water moving through household pipes can make electricity that we can store. To make this system work even better, we've thought of some new ideas: an air bladder to keep the water pressure steady, U-tube piping, and a big nozzle pipe at the end. This system doesn't need any fuel and doesn't need much fixing to keep working. Our plan is to make a small hydro system that can use the energy stored in a water tank to make power. This power can then be used in homes for different things

Evaluation of Energy Complementarity Between Wind, Solar and Water Resources in the Municipality of Lages (Santa Catarina, Brazil)

Purpose: Evaluate the energy complementarity between hydro, wind, and solar resources, and size a wind system and a photovoltaic system to meet the same production of the CGH Caveiras, (Lages/SC). Methods: Wind and solar systems were sized in order to meet the production of CGH Caveiras, from commercial models of equipment and considering data from 2017 to 2019. To assess complementary, data from the same period were obtained from the river monitoring station of ANA and the meteorological station of INMET. Results and Conclusion: In order to meet the demand of CGH Caveiras, only 1 medium sized wind turbine or 915 photovoltaic modules of the chosen models for the study would be necessary, and both systems could be used to compensate for periods of low hydroelectric production. The complementarity was confirmed through Pearson coefficients, where -0.39 was obtained between hydro and wind, and -0.32 between hydro and solar. Temporal complementarity indices, in turn, indicate complementarity between hydro and solar (κhs = 0.778) and between hydro and wind (κhe = 0.611). Research Implications: Both wind and solar resources exhibit complementarity with the hydro patterns of the study area, and either of the proposed systems, wind or photovoltaic solar, could be used in a hybrid manner with the existing hydroelectric system, especially during periods of droughts. Originality/value: Considering the scarcity of energy complementarity studies in Santa Catarina, as well as the impact of climate change on the hydrological regime and hydroelectric power generation, research of this nature is essential for the region’s energy security.

Studies on high power laser cladding Stellite 6 alloy coatings: Metallurgical quality and mechanical performances

The present study systematically investigates the effect of powder feeding rates (Vp) on the quality of Stellite 6 alloy coatings with a 20 kW fiber laser. The results demonstrate that the hot cracks in the Stellite 6 coatings can be effectively eliminated by increasing the Vp without preheating substrate or adding buffer layers. It is possible to achieve a single-track coating with a thickness of 2.34 mm, width of 20 mm and dilution rate of 17.3 % with crack-free and densely microstructure when Vp is 90 g/min. Moreover, the build rate (172 mm3/s) surpasses the maximum build rate (133 mm3/s) of low power laser cladding. The heat accumulation of multi-track and double-layer (5.6 mm) under high power laser results in the formation of ε-Co through phase transformation from part of γ-Co. Consequently, the dendrite region primarily consists of γ-Co while a small amount of ε-Co and Cr23C6 dominate in the inter-dendritic region. Although the heat accumulation effect also tends to coarsen the grains in bottom region of the coatings, leading to a slight reduction in mechanical properties, both hardness (485 HV–523 HV) and tensile strength (1131 MPa–1250 MPa) are comparable to those achieved by low power laser cladding, and the break elongation (6.9 %–8.1 %) has significantly surpassed the current level of 3 %. High power laser cladding processes may play a significant role in enhancing the effectiveness of surface modification or remanufacturing for large workpieces, such as the runner of hydroelectric generator set.

Developments in an acoustic resonance test for the detection of manufacturing anomalies in hydroelectric generator stator windings

To meet the ever-increasing demand for electricity, Hydro-Québec (HQ) is seeking to simultaneously increase the power of its generating stations while improving its service quality. Thus, the company has tasked its research institute to investigate innovate methods to meet the aforementioned goals. Of interest in the current study is the development of an acoustic resonance test (ART) to improve the quality control (QC) in the manufacturing of hydroelectric generator stator windings. Since HQ and its suppliers are investigating new fabrication methods for stator windings to meet the required timelines and increased power requirements, QC is required to ensure the service quality of its new hydroelectric generators. Typical manufacturing anomalies found in stator windings are delamination and air pockets between insulation layers. Such anomalies can result in an acceleration in the degradation of the winding insulation, which reduces the service quality of hydroelectric generators. To benchmark the ART method, the results of the suspected locations of the anomalies along the stator windings were compared with an acoustic camera and the locations were dissected and inspected under microscope for validation. Ten different stator windings were tested and two metrics (variations in both force and frequency responses) were found to indicate the location of delamination sites in the stator windings and corroborated with the result of both the acoustic camera and the dissections.

Amazon savannization and climate change are projected to increase dry season length and temperature extremes over Brazil

Land use change and atmospheric composition, two drivers of climate change, can interact to affect both local and remote climate regimes. Previous works have considered the effects of greenhouse gas buildup in the atmosphere and the effects of Amazon deforestation in atmospheric general circulation models. In this study, we investigate the impacts of the Brazilian Amazon savannization and global warming in a fully coupled ocean-land-sea ice-atmosphere model simulation. We find that both savannization and global warming individually lengthen the dry season and reduce annual rainfall over large tracts of South America. The combined effects of land use change and global warming resulted in a mean annual rainfall reduction of 44% and a dry season length increase of 69%, when averaged over the Amazon basin, relative to the control run. Modulation of inland moisture transport due to savannization shows the largest signal to explain the rainfall reduction and increase in dry season length over the Amazon and Central-West. The combined effects of savannization and global warming resulted in maximum daily temperature anomalies, reaching values of up to 14 °C above the current climatic conditions over the Amazon. Also, as a consequence of both climate drivers, both soil moisture and surface runoff decrease over most of the country, suggesting cascading negative future impacts on both agriculture production and hydroelectricity generation.

Experimental and Numerical Investigation of Cavitation Assessment for Runner Blades in a Francis Turbine

Cavitation in hydro turbines causes component deterioration that requires continuous and costly maintenance in the hydroelectric power generation stations. The Tarbela Dam Hydel Project (TDHP) in Pakistan is facing a cavitation problem in the Francis Turbine components, such as the runner and draft tube. Simulation work has been performed to examine and quantify the cavitation rate as a function of the suction head (SH) and flow velocity (FV) by utilizing a homogeneous cavitation model. The result shows that pressure fluctuation is maximum for overcrowded conditions and minimum for rated load conditions. Moreover, a higher cavitation rate is found for the part and overcrowded conditions compared to the rated load condition. Additionally, the cavitation rate becomes 50% higher when the SH increases from 5.54 to 12.34 m. Moreover, SEM results have verified the CFD results that higher FV and SH enhance the cavitation rate. Furthermore, the numerical work is validated by simplified hydrofoil geometry. The computational fluid dynamics results presented a good arrangement with the experimental data.

Redispatch Model for Real-Time Operation with High Solar-Wind Penetration and Its Adaptation to the Ancillary Services Market

Modern electrical power systems integrate renewable generation, with solar generation being one of the pioneers worldwide. In Latin America, the greatest potential and development of solar generation is found in Chile through the National Electric System. However, its energy matrix faces a crisis of drought and reduction of emissions that limits hydroelectric generation and involves the definitive withdrawal of coal generation. The dispatch of these plants is carried out by the system operator, who uses a simplified mechanism, called “economic merit list” and which does not reflect the real costs of the plants to the damage of the operating and marginal cost of the system. This inefficient dispatch scheme fails to optimize the availability of stored gas and its use over time. Therefore, a real-time redispatch model is proposed that minimizes the operation cost function of the power plants, integrating the variable generation cost as a polynomial function of the net specific fuel consumption, adding gas volume stock restrictions and water reservoirs. In addition, the redispatch model uses an innovative “maximum dispatch power” restriction, which depends on the demand associated with the automatic load disconnection scheme due to low frequency. Finally, by testing real simulation cases, the redispatch model manages to optimize the operation and dispatch costs of power plants, allowing the technical barriers of the market to be broken down with the aim of integrating ancillary services in the short term, using the power reserves in primary (PFC), secondary (SCF), and tertiary (TCF) frequency control.

Assessing the Prospects and Challenges of Hydroelectric Power Generation in Malaysia: A Multidimensional Analysis

Assessing the Prospects and Challenges of Hydroelectric Power Generation in Malaysia: A Multidimensional Analysis

Mechanisms and global deployment of different types of hydroelectric power generation

Mechanisms and global deployment of different types of hydroelectric power generation

Assessing the nonlinear relationship between consumer goods and water pollution in different seasons with machine learning models: A case study in the Yangtze River Economic Belt

Consumer goods volume and their consumption structure significantly contribute to water pollution within economic zones. However, there remains a need to better comprehend the nuanced and seasonal nonlinear relationship between consumer goods and water pollution, which currently obstructs essential consumption adjustments aimed at reducing water pollution. This study selected an effective machine learning model Light Gradient Boosting Machine (LightGBM) with SHapley Additive Explanations (SHAP) to assess the nonlinear relationship between consumer goods and water pollution during flood and non-flood seasons within the Yangtze River Economic Belt from 2013 to 2022, Baidu Index of consumer goods were used to represent the potential consumption level (PCL). The findings revealed the following: (1) During flood seasons, hydroelectricity and the PCL of furniture and cultural goods emerged as factors driving water pollution reduction. Conversely, GDP, PCL of communication devices, and beverages played significant roles in exacerbating water pollution. During non-flood seasons, the drivers for reducing water pollution included GDP, income gap and PCL of cultural goods. Meanwhile, PCL of cars, western medicine and oil became pivotal drivers for exacerbating water pollution. (2) Concerning consumption structure during flood and non-flood seasons, maintaining consumption levels of costume industrial goods, cultural, sports and health goods and transportation and accommodation goods below 66.71%, 77.66% and 79.61% respectively of their average PCL appeared beneficial in reducing water pollution. During flood seasons, consumption levels of cars and furniture below 85.69% and 91.46% respectively of their average PCL were associated with reduced water pollution. During non-flood seasons, consumption levels of cars and oil below 82.95% and 71.28% respectively of their average PCL could help reduce water pollution. (3) The proportion of hydroelectricity generation exceeding 2% and 3% respectively during flood and non-flood seasons helped in reducing water pollution. Moreover, GDP per capita surpassing 2388.82 and 2075.78 respectively during flood and non-flood season was preferable for reducing water pollution. These findings deepened our understanding of the nonlinear relationship and identify promising consumer goods and their consumption structure that could effectively aid in reducing water pollution from a consumption perspective.