Search Results (47)

This paper presents a dynamic model for full-power converter permanent magnet synchronous wind turbines based on Physics-Informed Neural Networks (PINNs). The model integrates the physical dynamics of the wind turbine directly into the loss function, enabling high-accuracy equivalent modeling with limited data and overcoming the typical “black-box” constraints and large data requirements of traditional data-driven approaches. To enhance the model’s real-time adaptability, we introduce an online update mechanism leveraging Bayesian Neural Networks (BNNs) combined with a clustering-guided strategy. This mechanism estimates uncertainty in the neural network weights in real-time, accurately identifies error sources, and performs local fine-tuning on clustered data. This improves the model’s ability to track real-time errors and addresses the challenge of parameter-specific adjustments. Finally, the data-driven model is integrated into the CloudPSS platform, and its multi-scenario modeling accuracy is validated across various typical cases, demonstrating the robustness of the proposed approach. Full article

Ice formation on critical infrastructure such as wind turbine blades can lead to severe performance degradation and safety hazards. This study investigates the use of hyperspectral imaging (HSI) combined with machine learning to detect and classify ice on various coated and uncoated surfaces. Hyperspectral reflectance data were acquired using a push-broom HSI system under controlled laboratory conditions, with ice and rime ice generated using a thermoelectric cooling setup. Support Vector Machine (SVM) and Random Forest (RF) classifiers were trained on uncoated aluminum samples and evaluated on surfaces with different coatings to assess model generalization. Both models achieved high classification accuracy, though performance declined on black-coated surfaces due to increased absorbance by the coating. The study further examined the impact of spectral band reduction to simulate different sensor types (e.g., NIR vs. SWIR), revealing that model performance is sensitive to wavelength range, with SVM performing optimally in a reduced band set and RF benefiting from the full spectral range. A multiclass classification approach using RF successfully distinguished between glaze and rime ice, offering insights into more targeted mitigation strategies. The results confirm the potential of HSI and machine learning as robust tools for surface ice monitoring in safety-critical environments. Full article

With the increasing proportion of grid-connected capacity of new energy units, such as wind power and photovoltaics, accurately constructing simulation models of these units is of great significance to the study of new power systems. However, the actual control strategies and parameters of many new energy units are often unavailable due to factors like outdated equipment or commercial confidentiality. This unavailability creates modeling challenges that compromise accuracy, ultimately affecting grid-connected power generation performance. Aiming at the problem of accurate modeling of fault ride-through control of new energy turbine “black box” controllers, this paper proposes an accurate identification method of fault ride-through control characteristics of doubly fed wind turbines based on hardware-in-the-loop testing. Firstly, according to the domestic and international new energy turbine fault ride-through standards, the fault ride-through segmentation control characteristics are summarized, and a general structured model for fault ride-through segmentation control of doubly fed wind turbines is constructed; Secondly, based on the measured hardware-in-the-loop data of the doubly fed wind turbine black box controller, the method of data segmentation preprocessing and structured model identification of the doubly fed wind turbine is proposed by utilizing statistical modal features and genetic Newton’s algorithm, and a set of generalized software simulation platforms for parameter identification is developed by combining Matlab and BPA; lastly, using the measured data of the doubly fed wind turbine in the black box and the software platform, the validity and accuracy of the proposed parameter identification method and software are tested in the simulation. Finally, the effectiveness and accuracy of the proposed parameter identification method and software are simulated and tested by using the measured data of black box doubly fed wind turbine and the software platform. The results show that the method proposed in this paper has higher recognition accuracy and stronger robustness, and the recognition error is reduced by 2.89% compared with the traditional method, which is of high value for engineering applications. 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

Local scour is a phenomenon of sediment erosion and transport caused by the dynamic interaction between water flow and seabed sediment, posing a serious threat to the safety of marine engineering structures such as cross-sea bridges and offshore wind turbines. To improve scour prediction and prevention capabilities, this review systematically analyzes the influence mechanisms of factors such as hydrodynamic conditions, sediment characteristics, and structural geometry, and discusses scour protection measures. Based on this, a comprehensive evaluation of the applicability of different prediction methods, including traditional empirical formulas, numerical simulations, probabilistic prediction models, and machine learning (ML) methods, was conducted. The study focuses on analyzing the limitations of existing methods: empirical formulas lack adaptability under complex field conditions, numerical simulation still faces challenges in validating real marine environments, and data-driven models suffer from “black box” issues and insufficient generalization capabilities. Based on the current research progress, this review presents prospects for future development, emphasizing the need to deepen the study of scouring mechanisms in complex real marine environments, develop efficient numerical models for engineering applications, and explore intelligent prediction methods that integrate data-driven approaches with physical mechanisms. This aims to provide more reliable theoretical support for the safe design, risk prevention, and scouring mitigation measures in marine engineering. Full article

The aim of the present work is to assess the wind and hydrogen production capacity of the Black Sea basin from a spatial point of view, by using reanalysis data that covers a 10-year interval (2015–2024). Based on the ERA5 data it was possible to highlight the general distribution of the wind resources at 100 m height, with more consistent resources being noticed in the region of the Azov Sea or in the north-western sector of the Black Sea, where average values of 8.3 m/s are expected. Taking into account that at this moment in the Black Sea area there are no operational offshore wind farms, several generators ranging from 3 to 15 MW were considered for assessment. In this case, from a single turbine, we can expect values in the range of 11.04 GWh (3 MW system) and 89 GWh (15 MW system), respectively. As a next step, the electricity generated from each wind turbine was used to highlight the hydrogen production of several electrolysers systems (or PEMs). The equivalent number of PEMs was identified, and in some cases it was noticed that some devices will not reach their full capacity, while for smaller PEMs a single 10 MW wind turbine could support the operation of almost four modules. Regarding hydrogen output, a maximum of 1560 tons/year can be expected from the PEMs connected to a 15 MW wind turbine. Full article

Acute invasive fungal rhinosinusitis (AIFR) is a rare, severe, and life-threatening opportunistic infection associated with high mortality and morbidity. Rapid and accurate diagnosis and treatment are crucial for survival and effective disease management. Diagnosing AIFR is challenging because no single pathognomonic feature exists other than surgical biopsy showing fungal angioinvasion and necrosis. This narrative review focuses on the diagnostic challenges and pitfalls, emphasizing the critical clinical value of magnetic resonance imaging (MRI) for early diagnosis of AIFR. It includes selected cases that illustrate the significance of MRI. When AIFR is suspected, clinical symptoms, nasal endoscopy, blood samples, and facial computed tomography all provide non-specific information. In contrast, MRI can identify signs of devitalized sinonasal mucosa consistent with AIFR. The absence of mucosal enhancement on T1-weighted images, combined with restricted diffusivity, are characteristic MRI features of AIFR. The cases presented underscore the usefulness of MRI in supporting clinical suspicion of AIFR and accurately determining its topography, thereby guiding early surgical biopsies and debridement. In suspected cases of AIFR, MRI serves as a valuable supplementary, non-invasive tool to help determine whether prompt surgical biopsy or debridement is necessary, thereby enhancing early diagnosis and improving survival rates. Therefore, the threshold for conducting an MRI in these cases should be low. Full article

The study applies the black kite algorithm (BKA), equilibrium optimizer (EO), and secretary bird optimization algorithm (SBOA) to optimize the placement of electric vehicle charge stations (EVCSs), wind turbine stations (WTSs), photovoltaic units (PVUs), and capacitor banks (CAPBs) in the IEEE 69-node distribution power grid. Three single objectives, including power loss minimization, grid power minimization, and total voltage deviation improvement, are considered. For each objective function, five scenarios are simulated under one single operation hour, including (1) place-only EVCSs; (2) place EVCSs and PVUs; (3) place EVCSs, PVUs, and CAPBs; (4) EVCSs and WTSs; and (5) EVCSs, PVUs, WTSs, and CAPBs. The results indicate that the EO can find the best solutions for the five scenarios. The results indicate that the EO and SBOA are the two powerful algorithms that can find optimal solutions for simulation cases. For one operating day, the total grid energy that is supplied to base loads and charge stations is 80,153.1 kWh, and many nodes at high load factors violate the lower limit of 0.95 pu. As for installing more renewable power sources, the energy that the base loads and charge stations need to supply from the grid is 39,713.4 kWh. As more capacitor banks are installed, the energy demand continues to be reduced to 39,578.9 kWh. The energy reduction is greater than 50% of the demand of all base loads and charge stations. Furthermore, the voltage can be significantly improved up to higher than 0.95 pu, and a few nodes at a few hours fall into the lowest range. Thus, the study concludes that the economic and technical aspects can be guaranteed for DPGs with additional installation of EVCSs. Full article

One of the most important and efficient sources of green electricity is catching air currents through wind turbine technology. Wind power plants are located in areas where the energy potential of the wind is high but it varies. The time variation of the wind generates fluctuations in the power produced by the wind farms that is injected into the grid. This elevates, depending on the intensity, problems of network stability and the need for balancing energy, thus raising both technical and cost issues. The present paper analyzes the behavior of a wind turbine (WT) over time in varying wind speed conditions, highlighting that without automation algorithms, a WT is far from the operation at the maximum power point (MPP). However, even when it is brought to operate at MPP, there are still significant variations in the power injected into the network. These power variations can be compensated if the wind system has energy storage facilities for the captured wind. All of these assumptions are analyzed using improved mathematical models and processed in simulations, with experimental data used as input from a wind turbine with an installed power of 2.5 [MW] in operation on the Romanian Black Sea coastal area. Consequently, the paper demonstrates that during an operation in the optimal area, from an energy perspective, the wind turbine’s maximum power point requires a storage system for the captured wind energy. Full article

Synanthropic wild rodents associated with agricultural operations may represent a risk path for transmission of high pathogenicity avian influenza viruses (HPAIVs) from wild birds to poultry birds. However, their susceptibility to HPAIVs remains unclear. In the present study, house mice (Mus musculus), brown rats (Rattus norvegicus), and black rats (Rattus rattus) were experimentally exposed to H5N1 subtype HPAIVs to evaluate their vulnerability to infection. After intranasal inoculation with HA clade 2.2 and 2.3.2.1 H5N1 subtype HPAIVs, wild rodents did not show any clinical signs and survived for 10- and 12-day observation periods. Viruses were isolated from oral swabs for several days after inoculation, while little or no virus was detected in their feces or rectal swabs. In euthanized animals at 3 days post-inoculation, HPAIVs were primarily detected in respiratory tract tissues such as the nasal turbinates, trachea, and lungs. Serum HI antibodies were detected in HA clade 2.2 HPAIV-inoculated rodents. These results strongly suggest that synanthropic wild rodents are susceptible to infection of avian-origin H5N1 subtype HPAIVs and contribute to the virus ecosystem as replication-competent hosts. Detection of infectious viruses in oral swabs indicates that wild rodents exposed to HPAIVs could contaminate food, water, and the environment in poultry houses and play roles in the introduction and spread of HPAIVs in farms. Full article

Acoustic detection of machinery defaults from in-duct measurements is of practical importance in many areas, such as the health assessment of turbines in ventilation systems or engine testing in the surface and air transport sectors. This approach is, however, impeded by the low signal-to-noise ratio (SNR) observed in such environments. In this study, it is proposed to exploit the slow sound effect of Sonic Black Hole (SBH) ducted silencers to enhance the sensing of incident pulse acoustic signals with low SNR. It is found from transfer matrix and finite element modelling that fully opened SBH silencers with perforated skin interfaces are able to substantially enhance an incident pulse amplitude while channeling an air flow. We demonstrate that the graded depths of the SBH cavities provide rainbow spectral decomposition and amplification of the incident pulse frequency components, provided that impedance matching, slow sound, and critically coupled conditions are met. In-duct experiments showed the ability of a 3D printed SBH silencer to simultaneously enhance acoustic sensing and fully trap the pulse spectral components in the SBH cavities in the presence of a low-speed flow. This study opens up new avenues for the development of dual-purpose silencers designed for acoustic monitoring and noise control in duct systems without obstructing the air flow. Full article

There has been a notable increase in rhino-orbito-cerebral mucormycosis (ROCM) post-coronavirus disease 2019 (COVID-19), which is an invasive fungal infection with a fatal outcome. Magnetic resonance imaging (MRI) is a valuable tool for early diagnosis of ROCM and assists in the proper management of these cases. This study aimed to describe the characteristic MRI findings of ROCM in post-COVID-19 patients to help in the early diagnosis and management of these patients. This retrospective descriptive study was conducted at a single hospital and included 52 patients with COVID-19 and a histopathologically proven ROCM infection who were referred for an MRI of the paranasal sinuses (PNS) due to sino-orbital manifestations. Two radiologists reviewed all the MR images in consensus. The diagnosis was confirmed by histopathological examination. The maxillary sinus was the most commonly affected PNS (96.2%). In most patients (57.7%), multiple sinuses were involved with the black turbinate sign on postcontrast images. Extrasinus was evident in 43 patients with orbital involvement. The pterygopalatine fossa was involved in four patients. Three patients had cavernous sinus extension, two had pachymeningeal enhancement, and one had epidural collection. The alveolar margin was affected in two patients, and five patients had an extension to the cheek. The awareness of radiologists by the characteristic MRI features of ROCM in post-COVID-19 patients helps in early detection, early proper management, and prevention of morbid complications. Full article

For remote offshore wind farms, transmitting power to the main onshore grid via a Voltage Source Converter High Voltage Direct Current (VSC-HVDC) system is the mainstream of power transmission. It is not only cost-effective in long-distance transmission, but also can fully meet the grid side requirements such as black start, voltage support, fault ride through and frequency support. However, it still has some problems, such as the possible impact on the power grid needing to be paid attention to. In this paper, its impact on the torsional responses of turbine generator units neighboring to the onshore side of AC bus is studied by using the DIgSILENT PowerFactory software. It is found that the effects of the Sub-Synchronous Torsional Interaction (SSTI) with onshore controls and the generator de-rating operations can significantly affect the damping ratio of turbine torsional modes, whereas the effects of the machine configurations and the amount of wind farm power integrated can affect the electrical torque disturbance. The most noteworthy is that their effects can be superimposed on each other if these factors act simultaneously, which would lead to increased vibrations and reduce the turbine shaft’ s life. The findings will be helpful for avoiding accidents caused by torsional vibrations when it is going to integrate a VSC-HVDC connected wind farm into a power grid. Full article

The conventional way of studying corrosion in marine environments is by installing corrosion coupons. Instead, this paper presents an experimental field study using an unattended corrosion sensor developed on the basis of ultrasound (US) technology to assess the thickness loss caused by general atmospheric corrosion on land close to the sea (coastal region). The system described here uses FPGA, low-power microcontroller, analog front-end devices in the sensor node, and a Beaglebone black wireless board for posting data to a server. The overall system is small, operates at low power, and was deployed at Gran Canaria to detect the thickness loss of an S355 steel sample and consequently estimate the corrosion rate. This experiment aims to demonstrate the system’s viability in marine environments and its potential to monitor corrosion in offshore wind turbines. In a day, the system takes four sets of measurements in 6 hour intervals, and each set consists of 5 consecutive measurements. Over the course of 5 months, the proposed experiment allowed for us to continuously monitor the corrosion rate in an equivalent corrosion process to an average thickness loss rate of 0.134 mm/year. Full article

Dry-low emission (DLE) is one of the cleanest combustion types used in a gas turbine. DLE gas turbines have become popular due to their ability to reduce emissions by operating in lean-burn operation. However, this technology leads to challenges that sometimes interrupt regular operations. Therefore, this paper extensively reviews the development of the DLE gas turbine and its challenges. Numerous online publications from various databases, including IEEE Xplore, Scopus, and Web of Science, are compiled to describe the evolution of gas turbine technology based on emissions, fuel flexibility, and drawbacks. Various gas turbine models, including physical and black box models, are further discussed in detail. Working principles, fuel staging mechanisms, and advantages of DLE gas turbines followed by common faults that lead to gas turbine tripping are specifically discussed. A detailed evaluation of lean blow-out (LBO) as the major fault is subsequently highlighted, followed by the current methods in LBO prediction. The literature confirms that the DLE gas turbine has the most profitable features against other clean combustion methods. Simulation using Rowen’s model significantly imitates the actual behavior of the DLE gas turbine that can be used to develop a control strategy to maintain combustion stability. Lastly, the data-driven LBO prediction method helps minimize the flame’s probability of a blow-out. Full article