Search Results (670)

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

Anthropogenic infrastructure, such as inland wind turbines commonly found in agricultural fields, has substantially increased subterranean vibratory noise in the past decades. Plants, being rooted in soil, are continuously exposed to these vibrations, yet we have little understanding of how vibrational noise affects plant development and, consequently, plant–insect interactions. Here, we examine the impact of windmill-like vibrational noise on the growth of Pisum sativum and its full-factorial interaction with the generalist herbivore Spodoptera exigua. Plants were exposed to either high or low vibrational noise from seed germination to the seed production stage. We recorded germination, flowering, fruiting time, and daily shoot length. Additionally, we measured herbivory intensity by Spodoptera exigua caterpillars placed on a subset of plants. Plants exposed to high vibrational noise grew significantly faster and taller than those in the low-noise treatment. Additionally, we found a marginally significant trend for earlier flowering in plants exposed to high noise. We did not find a significant effect of vibrational noise on herbivory. Our results suggest that underground vibrational noise can influence plant growth rates, which may potentially have ecological and agricultural implications. Faster growth may alter interspecific competition and shift trade-offs between growth and defense. Understanding these effects is important in assessing the broader ecological consequences of renewable energy infrastructure. Full article

The degradation process of HVOF-MCrAlY + APS-nanostructured YSZ (APS-nYSZ) thermal barrier coatings, produced using gas turbine OEM-approved MCrAlY powders, is investigated by studying the TGO growth and crack propagation behaviors in a thermal cycling environment. The TGO growth yields a parabolic mechanism on the surfaces of all HVOF-MCrAlYs, and the growth rate increases with the aluminum content in the “classical” MCrAlYs. The APS-nYSZ layer comprises micro-structured YSZ (mYSZ) and nanostructured YSZ (nYSZ) zones. Both mYSZ/mYSZ and mYSZ/nYSZ interfaces appear to be crack nucleation sites, resulting in crack propagation and consequent crack coalescence within the APS-nYSZ layer in the APS-nYSZ/HVOF-MCrAlY vicinity. Crack propagation in the TBCs can be characterized as a steady-state crack propagation stage, where crack length has a nearly linear relationship with TGO thickness, and an accelerating crack propagation stage, which is apparently a result of the coalescence of neighboring cracks. All TBCs fail in the same way as APS-/HVOF-MCrAlY + APS-conventional YSZ analogs, but the difference in thermal cycling lives is not substantial, although the HVOF-low Al-NiCrAlY encounters chemical failure in the early stage of thermal cycling. Full article

The work presents the design of an Oscillating Water Column (OWC) system with a nominal capacity of 16 kW, proposed as a contribution to reducing the energy gap in Ecuador, where electricity demand surpasses supply. The province of Santa Elena was selected as a promising site due to its favorable wave conditions and coastal location. The design process involved identifying areas with high wave energy potential, conducting a brief mathematical modeling analysis, and defining the parameters required for the system. Computational Fluid Dynamics (CFD) simulations were carried out in two stages: In the first stage, OpenFOAM was used to evaluate wave behavior, specifically flow velocity and pressure, before the water enters the generation chamber. In the second stage, a different CFD tool was used, incorporating the output data from OpenFOAM to simulate the energy conversion process inside the Wells turbine. This analysis focused on how the turbine captures and transforms the wave energy into usable power. The results show that, under ideal conditions, the system achieves an average power output of 11 kW. These findings suggest that implementing this type of system in coastal regions of Ecuador is both viable and beneficial for local energy development. Full article

After primary frequency regulation in large-scale wind farms is completed, the power dip phenomenon occurs during the rotor speed recovery phase. This phenomenon may induce a secondary frequency drop in power systems, which poses challenges to system frequency security. To address this issue, this paper proposes a frequency security-oriented optimal dispatch model for multi-regional power systems, taking into account the risks of secondary frequency drop. In the first stage, risk-averse day-ahead scheduling is conducted. It co-optimizes operational costs and risks under wind power uncertainty through stochastic programming. In the second stage, frequency security verification is carried out. The proposed dispatch scheme is validated against multi-regional frequency dynamic constraints under extreme wind scenarios. These two stages work in tandem to comprehensively address the frequency security issues related to wind power integration. The model innovatively decomposes system reserve power into three distinct components: wind fluctuation reserve, power dip reserve, and contingency reserve. This decomposition enables coordinated optimization between absorbing power oscillations during wind turbine speed recovery and satisfies multi-regional grid frequency security constraints. The column and constraint generation algorithm is employed to solve this two-stage optimization problem. Case studies demonstrate that the proposed model effectively mitigates frequency security risks caused by wind turbines’ operational state transitions after primary frequency regulation, while maintaining economic efficiency. The methodology provides theoretical support for the secure integration of high-penetration renewable energy in modern multi-regional power systems. Full article

Amid the global shift toward clean energy, wind power has emerged as a critical pillar of the modern energy matrix. To improve the reliability and maintainability of wind farms, this work proposes a novel hybrid fault detection approach that combines expert-driven diagnostic knowledge with data-driven modeling. The framework integrates autoencoder-based neural networks with Failure Mode and Symptoms Analysis, leveraging the strengths of both methodologies to enhance anomaly detection, feature selection, and fault localization. The methodology comprises five main stages: (i) the identification of failure modes and their observable symptoms using FMSA, (ii) the acquisition and preprocessing of SCADA monitoring data, (iii) the development of dedicated autoencoder models trained exclusively on healthy operational data, (iv) the implementation of an anomaly detection strategy based on the reconstruction error and a persistence-based rule to reduce false positives, and (v) evaluation using performance metrics. The approach adopts a fault-specific modeling strategy, in which each turbine and failure mode is associated with a customized autoencoder. The methodology was first validated using OpenFAST 3.5 simulated data with induced faults comprising normal conditions and a 1% mass imbalance fault on a blade, enabling the verification of its effectiveness under controlled conditions. Subsequently, the methodology was applied to a real-world SCADA data case study from wind turbines operated by EDP, employing historical operational data from turbines, including thermal measurements and operational variables such as wind speed and generated power. The proposed system achieved 99% classification accuracy on simulated data detect anomalies up to 60 days before reported failures in real operational conditions, successfully identifying degradations in components such as the transformer, gearbox, generator, and hydraulic group. The integration of FMSA improves feature selection and fault localization, enhancing both the interpretability and precision of the detection system. This hybrid approach demonstrates the potential to support predictive maintenance in complex industrial environments. Full article

GTD 111 has been employed in first-stage blades in different gas turbines. The study of microstructural evolution is essential for the lifetime assessment and development of turbine blades. The microstructural stability of a 130 MW gas turbine first-stage blade at 800 °C was studied. The microstructure’s evolution was analyzed using scanning electron microscopy (SEM), transmission electron microscopy (TEM), and thermodynamic calculation. As thermal exposure time increases, the shape of γ′ precipitates changes from square to spherical. During thermal exposure, MC particles formed and coarsened along the grain boundaries, and primary MC carbide decomposed into the η phase and M₂₃C₆. The stability of MC carbide at the grain boundaries was lower than that within the grains. MC carbide precipitated at the grain boundaries tends to grow along the boundaries and eventually forms elongated carbide. High-resolution transmission electron microscopy (HRTEM) images indicate that the orientation of the γ′ precipitate changes during the coarsening process. The GTD 111 alloy can be deformed through dislocation shearing at 800 °C. The hardness value initially increases, then decreases with further exposure, which is related to the reduced precipitation strengthening by γ′ precipitates and the reduction in the hardness of the γ matrix. Full article

To address traditional methods’ limitations in neglecting the interaction between random shock loads and progressive degradation, as well as failure correlations, this study proposes a dynamic reliability framework integrating Gamma processes, homogeneous Poisson processes (HPP), and mixed Copula functions. The framework develops a wind turbine gearbox reliability model under shock–degradation coupling while quantifying failure correlations. Gamma processes characterize continuous degradation, with parameters estimated from P-S-N curves. Based on stress–strength interference theory, random shocks within damage thresholds are integrated to form a coupled reliability model. A Gumbel–Clayton–Frank mixed Copula with a multi-layer nested algorithm quantifies failure correlations, with correlation parameters estimated via the RSS principle and genetic algorithms. Validation using a 2 MW gearbox’s planetary gear-stage system covers four scenarios: natural degradation, shock–degradation coupling, and both scenarios with failure correlations. The results show that compared to independent assumptions, the model accelerates reliability decline, increasing failure rates by >37%. Relative to natural degradation-only models, failure rates rise by >60%, validating the model’s effectiveness and alignment with real-world operational conditions. Full article

Timely and accurate detection of wind turbine blade surface defects is crucial for ensuring operational safety and improving maintenance efficiency with respect to large-scale wind farms. However, existing methods often suffer from poor generalization, background interference, and inadequate real-time performance. To overcome these limitations, we developed an end-to-end defect recognition framework, structured as a three-stage process: blade localization using YOLOv5, robust feature extraction via the large vision model DINOv2, and defect classification using a Stochastic Configuration Network (SCN). Unlike conventional CNN-based approaches, the use of DINOv2 significantly improves the capability for representation under complex textures. The experimental results reveal that the proposed method achieved a classification accuracy of 97.8% and an average inference time of 19.65 ms per image, satisfying real-time requirements. Compared to traditional methods, this framework provides a more scalable, accurate, and efficient solution for the intelligent inspection and maintenance of wind turbine blades. Full article

Gearboxes are critical mechanical components in various modern constructions, including wind turbines, making their real-time monitoring and the prevention of major failures essential. Machine learning (ML) offers a precise and robust method for early-stage failure detection and efficient gear monitoring during operation, with computational efficiency that allows for use on edge devices. This article presents a method for detecting surface damage on gear teeth using unsupervised machine learning. Using only experimentally measured vibrational signals from a healthy gearbox as a training set, novel neural network architectures, including convolutional and recurrent autoencoders, were employed and compared with a classical dense autoencoder. The study confirmed the effectiveness of these methods in gear transmission diagnostics and demonstrated the potential for achieving high-quality classification metrics using unsupervised learning. Full article

As designers push engine efficiency closer to thermodynamic limits, the analysis of flow instabilities developed in a high-pressure turbine (HPT) is crucial to minimizing aerodynamic losses and optimizing secondary air systems. Purge flow, while essential for protecting turbine components from thermal stress, significantly impacts the overall efficiency of the engine and is strictly connected to cavity modes and rim-seal instabilities. This paper presents an experimental investigation of these instabilities in an HPT stage, tested under engine-representative flow conditions in the short-duration turbine rig of the von Karman Institute. As operating conditions significantly influence instability behavior, this study provides valuable insight for future turbine design. Fast-response pressure measurements reveal asynchronous flow instabilities linked to ingress–egress mechanisms, with intensities modulated by the purge rate (PR). The maximum strength is reached at PR = 1.0%, with comparable intensities persisting for higher rates. For lower PRs, the instability diminishes as the cavity becomes unsealed. An analysis based on the cross-power spectral density is applied to quantify the characteristics of the rotating instabilities. The speed of the asynchronous structures exhibits minimal sensitivity to the PR, approximately 65% of the rotor speed. In contrast, the structures’ length scale shows considerable variation, ranging from 11–12 lobes at PR = 1.0% to 14 lobes for PR = 1.74%. The frequency domain analysis reveals a complex modulation of these instabilities and suggests a potential correlation with low-engine-order fluctuations. Full article

District heating systems in northern China predominantly rely on coal-fired heat sources, necessitating sustainable alternatives to reduce carbon emissions. This study investigates a biomass combined heat and power (CHP) system integrated with cascaded absorption heat pump (AHP) technology to recover waste heat from semi-dry flue gas desulfurization exhaust and turbine condenser cooling water. A multi-source operational framework is developed, coordinating biomass CHP units with coal-fired boilers for peak-load regulation. The proposed system employs a two-stage heat recovery methodology: preliminary sensible heat extraction from non-saturated flue gas (elevating primary heating loop (PHL) return water from 50 °C to 55 °C), followed by serial AHPs utilizing turbine extraction steam to upgrade waste heat from circulating cooling water (further heating PHL water to 85 °C). Parametric analyses demonstrate that the cascaded AHP system reduces turbine steam extraction by 4.4 to 8.8 t/h compared to conventional steam-driven heating, enabling 3235 MWh of annual additional power generation. Environmental benefits include an annual CO₂ reduction of 1821 tonnes, calculated using regional grid emission factors. The integration of waste heat recovery and multi-source coordination achieves synergistic improvements in energy efficiency and operational flexibility, advancing low-carbon transitions in district heating systems. Full article

The series-connected DRU-MMC hybrid converter, with its compact size and cost-effectiveness, presents an attractive solution for long-distance offshore wind power transmission. However, its application is limited by the DRU’s unidirectional power flow and the voltage mismatch between the auxiliary MMC and the onshore MMC during black-start operations. To overcome these challenges, a four-stage black-start strategy utilizing an auxiliary step-down transformer connected to the onshore MMC is proposed. The proposed strategy operates as follows: The onshore MMC first lowers its valve-side voltage via an auxiliary transformer, enabling reduced DC-side voltage. With the DRU bypassed, the offshore MMC draws startup power through the DC link, then switches to V/f mode with wind turbine curtailment to reduce DC current below the DRU bypass threshold. After stable, low-power operation, the DRU is integrated. The onshore MMC then restores rated DC voltage and disconnects the transformer, allowing gradual wind turbine reconnection to complete black-start. The simulation results confirm the approach’s feasibility under conditions where all wind turbines operate in grid-following mode. Full article

This study presents a comprehensive numerical investigation into the influence of blade profile geometry on the internal flow dynamics and hydraulic performance of Cross-Flow Turbines (CFTs) under varying runner speeds. Four blade configurations, flat, round, sharp, and aerodynamic, were systematically evaluated using steady-state, two-dimensional Computational Fluid Dynamics (CFD) simulations. The Shear Stress Transport (SST) k–ω turbulence model was employed to resolve the flow separation, recirculation, and turbulence across both energy conversion stages of the turbine. The simulations were performed across runner speeds ranging from 270 to 940 rpm under a constant head of 10 m. The performance metrics, including the torque, hydraulic efficiency, water volume fraction, pressure distribution, and velocity field characteristics, were analyzed in detail. The aerodynamic blade consistently outperformed the other geometries, achieving a peak efficiency of 83.5% at 800 rpm, with improved flow attachment, reduced vortex shedding, and lower exit pressure. Sharp blades also demonstrated competitive efficiency within a narrower optimal speed range. In contrast, the flat and round blades exhibited higher turbulence and recirculation, particularly at off-optimal speeds. The results underscore the pivotal role of blade edge geometry in enhancing energy recovery, suppressing flow instabilities, and optimizing the stage-wise performance in CFTs. These findings offer valuable insights for the design of high-efficiency, site-adapted turbines suitable for micro-hydropower applications. Full article

Wind turbine blades are prone to icing in cold environments, which leads to decreased aerodynamic performance, increased power loss, and even endangers the safe and stable operation of wind turbines. Electric heating anti-deicing method is the most effective solution because of its flexible control, rapid response, and high deicing efficiency. However, in the process of blade high-speed rotation, the complex flow field effect significantly affects the blade heat transfer performance, which leads to the problems of high energy consumption, low heat utilization, and uneven heating of traditional electric heating anti-icing/deicing methods, limiting their application effect in complex working conditions. Based on the physical mechanism and heat exchange characteristics of electric heating deicing of wind turbine blades, a coupled flow–heat transfer numerical model suitable for complex flow field conditions was constructed in this study, aiming to realize the dynamic simulation of the global temperature field and the phase transition process of ice sheets under different heating modes. Furthermore, the deicing efficiency characteristics of continuous heating and cyclic heating modes were compared and analyzed. The blade tip section of a Sinoma87.5 was taken as the experimental object, and the deicing experiment of blade by electric heating was carried out under artificial ice-covering laboratory conditions. The simulation and experimental results show that the deicing process by electric heating can be divided into three typical stages: initial temperature rise, stagnation, and rapid temperature rise. Under the influence of incoming flow conditions, the temperature rise of the front stagnation point region lags behind that of the windward side, and the steady-state peak temperature is lower. Compared with the cyclic heating mode, the continuous heating mode can enter and cross the stagnation period more quickly. The peak steady-state temperature of the continuous heating mode is 24.2 °C, and the deviation from the simulation result is only 2.8 °C, which is within the acceptable error range, effectively verifying the reliability of the numerical calculation model established. Full article