• Energy Research
• 2025-2025close
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In areas where there is high humidity and freezing rain, there is a tendency of blade icing on wind turbines. It results in energy dissipation and mechanical abrasion and also creates a safety concern due to the risk of having falling ice. Real-time online detection of icing is crucial in the enhancement of power generation efficiency and in the safety of wind turbines. The current methods of icing detection that use ultrasound, optics, vibration, and electromagnetics are already studied. But these methods have their drawbacks, including small detection ranges, low accuracy, large size, and challenges in distributed installation, making it hard to capture the real-time dynamics of the icing and de-icing processes on the wind turbine blades. To this end, this paper presents a new blade surface icing detection technique using microstrip lines. This approach uses the impact of icing state and thickness on the effective dielectric constant of the microstrip line surface. This paper presents the analysis of time-domain features of microwave signals, which facilitates the identification of both the icing state and the corresponding thickness. Simulation and experimental measurement of linear and S-shaped microstrip sensors are used in this research in order to compare the response of the sensors to the variation in the thickness of the icing layer. It is seen that for icing thickness ranging from 0 mm to 6 mm, the imaginary part of the S21 parameter of the S-shaped microstrip line has a more significant change than that of the linear microstrip line. The above experiments also confirm that the phase shift value of the S-shaped microstrip line is always higher than that of the linear microstrip line for the same variation of icing thickness, which proves that the S-shaped microstrip line is more sensitive than the linear one. Also, it was possible to establish the relationship between the phase shift values and icing thickness, which makes it possible to predict the icing thickness. The developed microwave microstrip detection technology is intended for usage in the wind turbine blade icing and similar surface detection areas. This method saves the size and thickness of icing sensors, which makes it possible to conduct measurements at various points. This is especially beneficial for usage in wind turbine blades and can be further applied in aerospace, automotive, and construction, especially the bridges.

Wind turbine operation and maintenance (O&M) faces significant challenges due to the complexity of equipment, harsh operating environments, and the difficulty of real-time fault prediction. Traditional methods often fail to provide timely and accurate warnings, leading to increased downtime and maintenance costs. To address these issues, this study systematically explores an intelligent operation and maintenance method for wind turbines, utilizing digital twin technology and multi-source data fusion. Specifically, it proposes a remote intelligent operation and maintenance (O&M) framework for wind turbines based on digital twin technology. Furthermore, an algorithm model for multi-source operational data analysis of wind turbines is designed, leveraging a Whale Optimization Algorithm-optimized Temporal Convolutional Network with an Attention mechanism (WOA-TCN-Attention). The WOA is used to optimize the hyperparameters of the TCN-Attention model. Then, the gearbox fault alarm threshold and warning threshold are set using the statistical characteristics of the residual values, and the absolute value of the residuals is used to determine the abnormal operating state of the gearbox. Finally, the proposed method was validated using operational data from a wind farm in Xinjiang. With input data from multiple sources, including seven key parameters such as temperature, pressure, and power, the method was evaluated based on EMAE, ERMSE, and EMAPE. The results demonstrated that the proposed method achieved the smallest prediction error and provided effective early warnings 18 h and 33 min prior to actual failures, enabling real-time and efficient operation and maintenance management for wind turbines.

This study explores the potential effect of a cross-sectional shape with an arcuate protruding and depressed features on the performance. The geometric configurations include two feature types (protruding and depressed), each with six distinct perimeter arrangements and three depths per arrangement, yielding thirty-six different cross-sectional shapes for systematic evaluation. The aerodynamic characteristics and electrical performance are numerically analyzed, using a computational fluid dynamics model and a distributed parameter electromechanical coupling model, respectively. A smooth protruding feature on the front, top, or bottom side suppresses the electrical output; however, when located on the rear side, it significantly increases the slope of the power versus wind speed curve. Depressed features on the rear, top, or bottom side only reduce the critical wind speed and the power enhancement positively correlates with the feature depth. Compared to a square, a harvester with depressed feature on both top and bottom sides exhibits a significant jump in power at the critical wind speed, greatly improving the power. These findings provide important design guidelines for structural optimization of galloping piezoelectric energy harvesters, enabling them to match the wind energy distribution characteristics of specific regions with optimal performance.

To reduce the fuel consumption of ships’ oceanic voyages, this study incorporates the influence of ocean currents into the traditional level set algorithm and proposes a route planning algorithm capable of identifying energy-efficient routes in complex and variable sea conditions. The approach introduces the influence factor of ocean current to optimize routing in dynamically changing marie environments. First, models for the energy consumption of ships and flow fields are established. The level set curve is then evolved by integrating the flow environment and energy consumption gradient, solving the Hamilton–Jacobi equation with energy consumption parameters. The optimal path is subsequently determined through backtracking along the energy consumption gradient, enabling energy-efficient route planning from the starting point to the endpoint in complex ocean conditions. To verify the effectiveness of the proposed algorithm, its performance is evaluated through two case studies, comparing energy consumption under different environmental conditions. The experimental results demonstrate that, compared to the shortest path method based on the level set algorithm, the proposed approach achieves an energy saving rate of approximately 2.1% in obstacle-free environments and 1.4% in environments with obstacles.

The rapid proliferation of Internet of Things (IoT) devices and applications has led to an increasing demand for energy-efficient and secure communication in wireless sensor networks. In this article, we firstly propose an intelligent approach to maximize the energy efficiency of the UAV in a secure sensor network with wireless power transfer (WPT). All sensors harvest energy via downlink signal and use it to transmit uplink information to the UAV. To ensure secure data transmission, the UAV needs to optimize the transmission parameters to decode received information under malicious interference from an attacker. Code Division Multiple Access (CDMA) is adopted to improve uplink communication robustness. To maximize the UAV’s energy efficiency in data collection tasks, we formulate a constrained optimization problem that jointly optimizes charging power, charging duration, and data transmission duration. Applying Deep Deterministic Policy Gradient (DDPG) algorithm, we train an action policy to dynamically determine near-optimal transmission parameters in real time. Numerical results validate the superiority of proposed intelligent approach over exhaustive search and gradient ascent techniques. This work provides some important guidelines for the design of green secure wireless-powered sensor networks.