Search Results (66)

The efficiency decline of Francis turbines, a key component of hydroelectric power generation, presents a multifaceted challenge influenced by interconnected factors such as water quality, incidence angle, erosion, and runner wear. This paper is structured into two main sections to address these issues. The first section applies the Buckingham π theorem to establish a dimensional analysis (DA) framework, providing insights into the relationships among the operational variables and their impact on turbine wear and efficiency loss. Dimensional analysis offers a theoretical basis for understanding the relationships among operational variables and efficiency within the scope of this study. This understanding, in turn, informs the selection and interpretation of features for machine learning (ML) models aimed at the predictive maintenance of the target variable and important features for the next stage. The second section analyzes an extensive dataset collected from a Francis turbine in Colombia, a country that is heavily reliant on hydroelectric power. The dataset consisted of 60,501 samples recorded over 15 days, offering a robust basis for assessing turbine behavior under real-world operating conditions. An exploratory data analysis (EDA) was conducted by integrating linear regression and a time-series analysis to investigate efficiency dynamics. Key variables, including power output, water flow rate, and operational time, were extracted and analyzed to identify patterns and correlations affecting turbine performance. This study seeks to develop a comprehensive understanding of the factors driving Francis turbine efficiency loss and to propose strategies for mitigating wear-induced performance degradation. The synergy lies in DA’s ability to reduce dimensionality and identify meaningful features, which enhances the ML models’ interpretability, while ML leverages these features to model non-linear and time-dependent patterns that DA alone cannot address. This integrated approach results in a linear regression model with a performance (R²-Test = 0.994) and a time series using ARIMA with a performance (R²-Test = 0.999) that allows for the identification of better generalization, demonstrating the power of combining physical principles with advanced data analysis. The preliminary findings provide valuable insights into the dynamic interplay of operational parameters, contributing to the optimization of turbine operation, efficiency enhancement, and lifespan extension. Ultimately, this study supports the sustainability and economic viability of hydroelectric power generation by advancing tools for predictive maintenance and performance optimization. Full article

This study addresses the challenge of improving fault management in hydroelectric systems using Petri nets. The objective is to propose a novel methodology for efficient fault diagnosis and intervention in the Governor system, a critical component in regulating turbine speed. Traditional diagnostic approaches often rely on manual inspection and expert intuition, and they lack formal mechanisms to model concurrent or asynchronous system behavior—leading to delays and reduced accuracy in fault identification. Our approach introduces a structured modeling technique using Petri nets, enabling dynamic analysis of the system’s behavior and response to faults. A detailed methodology was developed, beginning with a thorough characterization of the system and its translation into a Petri net model. Simulation results demonstrate the model’s effectiveness in significantly reducing diagnostic and intervention times compared to traditional methods. Results show that using Petri nets improves fault detection accuracy, accelerates decision-making, and optimizes resource allocation. This research concludes that the proposed model offers a robust framework for enhancing fault management in hydroelectric plants, providing both operational efficiency and reduced downtime. Future work will focus on integrating real-time monitoring and further validating the model in live environments to ensure scalability and adaptability to other power generation systems. Full article

This article presents the simulation results of synchronization of a permanent magnet synchronous generator (PMSG) dedicated for a hydroelectric plant without power converter devices. The proposed machine design allows to connect a generator to the grid in two different ways. With the first method, the machine is connected to the grid in a similar way as in the case of an electrically excited synchronous generator. The second method is a direct line-start process based on asynchronous torque—similar to asynchronous motor start. Both methods can be used alternately. The advantages of the presented design are elimination of converter devices for starting the PMSG, possibility of use in small and medium hydroelectric power plants, operation with a high efficiency and high power factor in a wide range of generated power, and smaller dimensions in comparison to the generators currently used. The described rotor design allows for the elimination of capacitor batteries for compensation of reactive power drawn by induction generators commonly used in small hydroelectric plants. In addition, due to the high efficiency of the PMSG, high power factor, and appropriately selected design, the starting current during synchronization is smaller than in the case of an induction generator, which means that the structural elements wear out more slowly, and thus, the generator’s service life is increased. In this work, it is shown that PMSG with a rotor cage should have permanent magnets with an increased temperature class in order to avoid demagnetization of the magnets during asynchronous start-up. In addition, manufacturers of such generators should provide the number of start-up cycles from cold and warm states in order to avoid shortening the service life of the machine. The main objective of the article is to present the methods of synchronizing a generator of such a design (a rotor with permanent magnets and a starting cage) and their consequences on the behavior of the machine. The presented design allows synchronization of the generator with the network in two ways. The first method enables synchronization of the generator with the power system by asynchronous start-up, i.e., obtaining a starting torque exceeding the braking torque from the magnets. The second method of synchronization is similar to the method used in electromagnetically excited generators, i.e., before connecting, the rotor is accelerated to synchronous speed by means of a water turbine, and then, the machine is connected to the grid by switching on the circuit breaker. This paper presents electromagnetic phenomena occurring in both cases of synchronization and describes the influence of magnet temperature on physical quantities. Full article

The accuracy of hydro-turbine fault diagnosis directly impacts the safety and operational efficiency of hydroelectric power generation systems. This paper addresses the challenge of low diagnostic accuracy in traditional methods under complex environments. This is achieved by proposing a signal preprocessing method that combines complete ensemble empirical mode decomposition with adaptive noise and multiscale permutation entropy (CEEMDAN-MPE) and that is optimized with the crested porcupine optimizer algorithm for the bidirectional long- and short-term memory network (CPO-BILSTM) model for hydro-turbine fault diagnosis. The method performs signal denoising using CEEMDAN, while MPE extracts key features. Furthermore, the hyperparameters of the CPO-optimized BILSTM model are innovatively introduced. The extracted signal features are fed into the CPO-BILSTM model for fault diagnosis. A total of 150 sets of acoustic vibrational signals are collected for validation using the hydro-turbine test bench under different operating conditions. The experimental results demonstrate that the diagnostic accuracy of the method is 96.67%, representing improvements of 23.34%, 16.67%, and 6.67% over traditional models such as LSTM (73.33%), CNN (80%), and BILSTM (90%), respectively. In order to verify the effectiveness of the signal preprocessing method, in this paper, the original signal, the signal processed by CEEMDAN, CEEMDAN-PE, and CEEMDAN-MPE are input into the CPO-BILSTM model for controlled experiments. The results demonstrate that CEEMDAN-MPE effectively denoises hydro-turbine acoustic vibrational signals while preserving key features. The method in this paper integrates signal preprocessing and deep learning models and, with the help of intelligent optimization algorithms, significantly enhances the model’s adaptive ability, improves the model’s applicability under complex operating conditions, and provides a valuable supplement for hydro-turbine fault diagnosis. Full article

To meet the load requirements of the power grid, the hydroelectric power plants need to extend the operational load range of the turbine units, which are often operated under off-design operating conditions. This new challenge significantly changes the flow characteristics of the hydro turbine units. Strong vibrations and high stresses caused by pressure pulsations at various loads directly lead to severe damage to the runner blades, threatening the safe operation of the hydropower unit. In this study, the detailed flow dynamics analysis under three loading conditions of a large-scale Francis turbine, i.e., 33.3%, 66.6%, and 100% of the Francis turbine’s rated power, is investigated with computational fluid dynamics (CFD) calculations. The pressure files at different operating conditions are adopted to carry out the corresponding flow-induced strength analysis of the Francis runner prototype. The pressure distributions and flow velocity distributions at these three typical operating conditions are studied, and the maximum stress of the runner gradually increases with the power output of the turbine, but it is only around one-third of the yield stress of the runner material. It reveals that the runner is safe to operate in the extended operation range from a 33.3% to 100% of the rated power load. The analysis approach in this work can be applied to other hydraulic machinery including Francis turbines, pumps and pump–turbines. Full article

The Canary Archipelago, in general, and the island of Gran Canaria, in particular, operate with an independent energy system (SIE), which depends largely on local power generation. Today, its energy supply comes mainly from two sources: (a) Renewable energy, accounting for 19.90%, and (b) Fossil fuel combustion in thermal power plants, contributing the remaining 80.10%. The existing energy infrastructure faces challenges due to aging technology, requiring either modernization or replacement to prevent a potential energy crisis and ensure a sustainable production cycle. A transformative step to improve the system is the completion and commissioning in 2030 of the Chira-Soria pumped hydroelectric energy storage (PHES) plant. This installation will allow water to be transported to high altitudes by pumping, to be deposited until the right time and to be turbined to generate electricity in optimal conditions. To fully understand the impact of this integration, detailed analyses of annual energy production patterns, equipment performance, and real-time demand data (collected at five-minute intervals) will be conducted. These assessments will provide insights into how the Chira-Soria PHES can be seamlessly integrated into Gran Canaria’s energy network. Furthermore, they will help identify both the strengths and limitations of this storage solution, paving the way for a more resilient and efficient energy future for the island. Full article

In 2019, more than 16% of the globe’s total production of electricity was provided by hydroelectric power plants. The core of a typical hydroelectric power plant is the turbine. Turbines are subjected to high levels of pressure, vibration, high temperatures, and air gaps as water passes through them. Turbine blades weighing several tons break due to this surge, a tragic accident because of the massive damage they cause. This research aims to develop predictive models to accurately predict the status of hydroelectric power plants based on real stored data for all factors affecting the status of these plants. The importance of having a typical predictive model for the future status of these plants lies in avoiding turbine blade breakage and catastrophic accidents in power plants and the resulting damages, increasing the life of these plants, avoiding sudden shutdowns, and ensuring stability in the generation of electrical energy. In this study, artificial neural network algorithms (RNN and LSTM) are used to predict the condition of the hydropower station, identify the fault before it occurs, and avoid it. After testing, the LSTM algorithm achieved the greatest results with regard to the highest accuracy and least error. According to the findings, the LSTM model attained an accuracy of 99.55%, a mean square error (MSE) of 0.0072, and a mean absolute error (MAE) of 0.0053. Full article

Climate change has had an impact on the reduction in river flows in many places, affecting the hydroelectric production of several power plants, and this, together with the reduction in the economic retribution for this type of generation in several countries, has meant a substantial reduction in the income of companies. To offset these economic losses, the aim is to improve production efficiency in hydroelectric power plants. Therefore, it is proposed to innovate, firstly, by using doubly fed asynchronous electrical machines, DFIG; secondly, by using new construction criteria in the power plants; and lastly, by proposing new control and regulation variables. This improves the performance of low-flow water turbines and increases their efficiency. As a practical example, a particular study is presented for the Arenas de Iguña hydroelectric power plant (Hidroiguña) located in Cantabria, Spain, which allows a technical evaluation of the proposed action to be carried out in order to draw the corresponding conclusions. Full article

In light of Switzerland’s 2050 energy goals, the nation aims to boost its domestic hydroelectric output, notably focusing on small-scale hydroelectric power plants. Concurrently, there is an effort to renovate hydroelectric plants to make them more environmentally friendly, emphasizing ecological flow regulation to improve river conditions. This study explores the application of a non-proportional flow allocation method to better assess both ecological and economic outcomes. Unlike traditional fixed or proportional flow methods, this approach allows for a more dynamic balance between hydropower generation and riverine ecosystem health. This study focuses on two key species, brown trout and grayling. In particular, this work highlighted that trout are better suited for low-flow conditions (Weighted Usable Area, WUA, peaks below 1 m³/s), while grayling require significantly higher flows (WUA peaks over 4.5 m³/s). This disparity in habitat preferences raises concerns about the current reliance on single-species models, emphasizing the need for multi-species ecological assessment in future studies. When applied to a small hydropower plant in the Swiss Jura, the non-proportional flow method resulted in an improvement of ecological conditions of at least 37.7%, which consequently led to a reduction of the hydroelectric production of at least 10%. Through strategic upgrades to the facility (e.g., by minimizing hydraulic losses, implementing more efficient turbines, or incorporating photovoltaic panels over water channels), it is possible to simultaneously enhance both energy output and environmental sustainability. These findings suggest that non-proportional flow allocation holds significant potential for broader use in sustainable hydropower management, providing a pathway toward meeting both energy production and ecological conservation goals. Full article

In this study, a previously developed fully passive hydrokinetic turbine with two flapping foils was used to directly drive a reciprocating pump, and the performance of this system was investigated at an offshore site in Republic of Korea. The fully passive operation of the turbine worked effectively due to its coupling mechanism, and pumping was successfully carried out during flood tides when the pumping height was consistently maintained using a water level gauge and winch system. Pumping occurred at a height of approximately 9 m when the flow velocity reached 1.8 m/s, at which point the corresponding Reynolds number exceeded one million. In one case where a high pumping flow rate was achieved during offshore trials conducted over a period of time, the pumping efficiency reached up to 34% when the reduced frequency of the turbine was 0.126, falling within the known optimum range. The pump driven by the flapping-foil hydrokinetic turbine, which can be positioned near the shore or in shallow water, could provide a viable solution for off-grid communities needing to pump seawater or generate hydroelectric power. Full article

The operational efficiency, stability, and lifespan of hydroelectric power plants operating on sediment-laden rivers are affected by sediment erosion. A numerical simulation of the sand–water flow in the water-intake components of a turbine at a specific power station was conducted using the Euler–Lagrange method. Additionally, sediment erosion tests were carried out on the water-intake components coated with epoxy mortar material. The results indicate that sediment erosion on the stay vane surface mainly occurs on the front face, with the most severe erosion at the head, while sediment erosion on the stay ring surface primarily occurs near the stay vane head. The extent of erosion is mainly influenced by the distribution characteristics of sediment particles. The wear of epoxy mortar coating material is minimally affected by the spraying thickness. Adding 30% hardener to the epoxy mortar material can significantly improve the erosion resistance of the stay vane surface by about 30%. The erosion rate on the frontside of the stay vane is approximately 2.6 times that of the backside. Based on the sediment erosion tests and numerical simulation results of the sand–water flow, an estimation formula for the sediment erosion rate of the epoxy mortar erosion-resistant coating was established. This formula can be used to predict the anti-sediment erosion performance of epoxy mortar materials applied to the water-intake components of this turbine and similar river turbines. Full article

The widespread adoption of the power grid has led to increased attention to load frequency control (LFC) in power systems. The LFC strategy of multi-source hybrid power systems, including hydroelectric generators, Wind Turbine Generators (WTGs), and Photovoltaic Generators (PVGs), with thermal generators is more challenging. Existing methods for LFC tasks pose challenges in achieving satisfactory outcomes in hybrid power systems. In this paper, a novel method for the multi-source hybrid power system LFC task by using an optimal active disturbance rejection control (ADRC) strategy is proposed, which is based on the combination of the improved linear quadratic regulator (LQR) and the ADRC controller. Firstly, an established model of a hybrid power system is presented, which incorporates multiple regions and multiple sources. Secondly, utilizing the state space representation, a novel control strategy is developed by integrating improved LQR and ARDC. Finally, a series of comparative simulation experiments has been conducted using the Simulink model. Compared with the LQR with ESO, the maximum relative error of the maximum peaks of frequency deviation and tie-line exchanged power of the hybrid power system is reduced by 96% and 83%, respectively, by using the proposed strategy. The experimental results demonstrate that the strategy proposed in this paper exhibits a substantial enhancement in control performance. Full article

As part of the initiative to achieve Singapore’s Green Plan 2030, we propose to investigate the potential of utilizing micro-pumped hydroelectric energy storage (PHES) systems in multi-level carparks (MLCP: a stacked car park that has multiple levels, may be enclosed, and can be an independent building) as a more environmentally friendly alternative to traditional battery storage for a surplus of solar energy. This study focuses on an MLCP with a surface area of 3311 m² and a height of 12 m, considering design constraints such as a floor load capacity of 5 kN/m² and the requirement for a consistent energy discharge over a 12 h period. The research identifies a Turgo turbine as the optimal choice, providing a power output of 2.9 kW at a flow rate of 0.03 m³/s with an efficiency of 85%. This system, capable of storing 1655.5 m³ of water, can supply power to 289 light bulbs (each consuming 10 W) for 15.3 h, thus having the capacity to support up to three MLCPs. These results underscore the environmental advantages of PHES over conventional batteries, highlighting its potential for integration with solar panels to decrease carbon emissions. This approach not only aligns with Singapore’s green initiatives but also promotes the development of a more sustainable energy infrastructure. Full article

The optimal performance of a hydroelectric power plant depends on accurate monitoring and well-functioning sensors for data acquisition. This study proposes the use of artificial neural networks (ANNs) to estimate the Pelton turbine shaft power of a 10 kW micro-hydropower plant. In the event of a failure of the sensor measuring the torque and/or rotational speed of the Pelton turbine shaft, the synthetic turbine shaft power data generated by the ANN will allow the turbine output power to be determined. The experimental data were obtained by varying the operating conditions of the micro-hydropower plant, including the variation of the input power to the electric generator and the variation of the injector opening. These changes consequently affected the flow rate and the pressure head at the turbine inlet. The use of artificial neural networks (ANNs) was deemed appropriate due to their ability to model complex relationships between input and output variables. The ANN structure comprised five input variables, fifteen neurons in a hidden layer and an output variable estimating the Pelton turbine power. During the training phase, algorithms such as Levenberg–Marquardt (L–M), Scaled Conjugate Gradient (SCG) and Bayesian were employed. The results indicated an error of 0.39% with L–M and 7% with SCG, with the latter under high-flow and -energy consumption conditions. This study demonstrates the effectiveness of artificial neural networks (ANNs) trained with the Levenberg–Marquardt (L–M) algorithm in estimating turbine shaft power. This contributes to improved performance and decision making in the event of a torque sensor failure. Full article

The primary goal of enhancing automatic generation control (AGC) in interconnected multi-area power systems is to ensure high-quality power generation and reliable distribution during emergencies. These systems still struggle with consistent stability and effective response under dynamic load conditions despite technological advancements. This research introduces a secondary controller designed for load frequency control (LFC) to maintain stability during unexpected load changes by optimally tuning the parameters of a Proportional–Integral–Derivative (PID) controller using pelican optimization algorithm (POA). An interconnected power system for ith multi-area is modeled in this study; meanwhile, for determining the optimal PID gain settings, a four-area interconnected power system is developed consisting of thermal, reheat thermal, hydroelectric, and gas turbine units based on the ith area model. A sensitivity analysis was conducted to validate the proposed controller’s robustness under different load conditions (1%, 2%, and 10% step load perturbation) and adjusting nominal parameters (R, T_p, and T_ij) within a range of ±25% and ±50%. The performance response indicates that the POA-optimized PID controller achieves superior performance in frequency stabilization and oscillation reduction, with the lowest integral time absolute error (ITAE) value showing improvements of 7.01%, 7.31%, 45.97%, and 50.57% over gray wolf optimization (GWO), Moth Flame Optimization Algorithm (MFOA), Particle Swarm Optimization (PSO), and Harris Hawks Optimization (HHO), respectively. Full article