Weather Resistance for Wind Turbine Operation

A method for analyzing and controlling the operating status of wind turbines to monitor, predict, and automatically regulate wind turbine performance in real time. The method involves regularly collecting blade strain, vibration, and inclination data at a preset frequency. This data is preprocessed to calculate load ranges, equivalent fatigue loads, and frequency bands. The target data is compared to health data to early warn of issues. Machine learning is used to calculate equivalent fatigue loads and frequency bands in real time. Blade sectors with excessive load or fatigue are reduced power, while sectors with lower load or fatigue are increased power. This proactive control strategy helps keep wind turbines operating safely and reliably by mitigating overloads and fatigue.

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A data fusion-based damage analysis system for large components of wind turbines that uses multi-source, multi-angle analysis to accurately locate the type and root cause of wind turbine component failures. The system integrates data from operating conditions, terrain, weather, wind measurements, UAVs, design, and simulation to provide a comprehensive understanding of component damage from macro to micro, external to internal. This helps identify targeted solutions to prevent future failures.

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Wind turbine damage prediction during typhoons using machine learning to improve accuracy and efficiency compared to physical modeling. The method involves calculating the probability of wind turbine damage using a stress interference model based on factors like wind speed, turbulence, corrosion, and aging. Then a machine learning algorithm corrects the damage probability using a random forest model trained on factors like wind speed, altitude, density, and shear index. This leverages machine learning to enhance wind turbine typhoon damage prediction by correcting the damage probability calculated from the stress model.

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Intelligent control method and system for wind turbines that reduces costs, improves performance, and enhances reliability by leveraging sensors and perception to intelligently manage wind turbine loads and power generation. The method involves using depth sensing, load sensing, distance sensing, sound perception, posture perception, image perception, and unit status perception to sense the environment around the wind turbine. This information is then sent to an intelligent control module and an intelligent protection module to enable intelligent control and protection features. Examples include gust load reduction, adaptive fuzzy overspeed suppression, load-based load reduction, high yaw error reduction, and dynamic rated speed adjustment. By sensing the turbine's environment and using intelligent control, the wind turbine can reduce ultimate loads, improve power generation, and enhance reliability compared to traditional fixed control methods.

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A method for assessing the robustness of wind turbine designs and providing early warnings of potential issues. The method involves creating multiple derivative designs with variations in key structural and aerodynamic parameters. These designs are simulated to determine stability boundaries. If the actual turbine parameters fall outside these boundaries, it indicates potential instability and early warning of potential issues. This allows proactive monitoring and mitigation of design robustness during the development phase.

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Device to improve the aerodynamic performance of large wind turbines in strong wind and rain environments. The device has four components: a rainfall monitoring system, a pressure monitoring system, an active blade blowing system, and a tower kinetic energy buffering and absorption system. The rainfall monitoring system detects rain impact on the turbine. The pressure monitoring system measures surface pressure. The active blade blowing system generates jets to repel raindrops. The tower buffering system absorbs rain impact energy. This allows the turbine to mitigate adverse aerodynamic effects from rain impact in strong wind conditions.

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Wind turbine safety monitoring using Beidou positioning to provide early warning and prevent failures. The monitoring involves analyzing wind turbine position offsets, force anomalies, weather conditions, and historical data to identify abnormalities and predict accidents. By tracking turbine movement, stress, and weather, it enables proactive maintenance and remediation planning. The system uses Beidou positioning to precisely locate turbines and monitor position shifts. This is combined with force analysis to diagnose causes of abnormalities. In severe weather, position and stress changes are analyzed for status and accident prediction. Weather data is checked for remediation options. Historical data is analyzed for wear and maintenance needs.

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Adaptive wind turbine control strategy that optimizes power production, operating costs, and fatigue life by dynamically modifying turbine operating parameters based on real-time fatigue loading estimates. The method involves comparing estimated fatigue loading during operation to expected fatigue based on the turbine's life. If the actual fatigue is lower, more aggressive operating modes can be selected for higher power. If actual fatigue is higher, less aggressive modes are chosen to reduce loading. This allows tailoring turbine operation to actual site conditions for better performance and fatigue management.

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A dynamic method to improve wind turbine performance and extend lifespan by using real-time data analysis and machine learning. The method involves continuously monitoring operating conditions, visual data, and acoustic signals from the turbine. This data is analyzed using machine learning algorithms to detect degradation patterns and predict remaining life. Based on the analysis, the turbine's performance can be dynamically adjusted to mitigate degradation and extend life. The method aims to provide timely, accurate, and automated performance optimization and life extension compared to manual diagnosis and static strategies.

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Disaster weather emergency prevention method for wind farms that accurately understands the impact of weather events on wind farms to save resources and reduce losses. The method involves collecting weather data, analyzing historical results, and adjusting wind turbine parameters to prevent damage during disasters. It provides targeted emergency measures based on the severity of the weather event and the specific wind turbine conditions. This prevents overreacting in areas not affected or underreacting in severely impacted areas.

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A method, device, and system for preventing and mitigating tower overturning in wind turbines to improve safety and reduce costs. The method involves collecting key parameters like tower displacement, frequency, and tilt angles, along with conventional parameters. It extracts features from the key parameters and uses them along with the conventional parameters to make decisions and provide recommendations when thresholds are exceeded. This allows proactive actions to prevent tower overturning, and subsequent health assessments and maintenance recommendations to avoid recurrence. The system uses sensors, data processing modules, and wind turbine control integration to enable this proactive overturning prevention and maintenance.

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A blade made of layered composite material is better resistant to delamination and detachment failures when exposed to fluid flows, especially fluctuating loads. The blade has skins with overlapping layers that extend from body portions between the skins towards the trailing edge. The internal layers have body portions and skin portions that form the skins. This integral layer arrangement prevents delamination by providing overlapping connections between the skins. The idea is that adjacent layers of the composite material overlap rather than join at the spar, skins, or leading/trailing edges. This keeps the layers connected along the blade instead of having detached sections.

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Damage warning system for wind turbines that allows remote monitoring of turbine components during severe weather events to quickly identify if damage has occurred. The system involves collecting key parameters like vibration, inclination, and load from the turbine during emergency conditions. A server analyzes these parameters to determine if the turbine components are at risk of damage. If so, it sends an alarm indicating potential damage. This allows proactive assessment without physically inspecting the turbine and prevents unnecessary turbine shutdowns.

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Real-time monitoring and early warning for wind turbines to improve reliability, efficiency, and safety. It involves collecting real-time vibration and operating data from wind turbines, storing historical data, and using machine learning to create a dynamic model of the turbine's performance. This model is updated in real-time with new data. By calling the model with future weather data, it can predict turbine health and warn of failures before they occur.

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Method to optimize maintenance of wind turbine blades by considering the combined effect of wind and rain on blade wear. The method involves calculating a co-invasion index based on hourly wind speed and precipitation. The index is used to predict blade wear over time. This allows targeted blade maintenance instead of fixed cycles. The idea is to avoid excessive wear from high wind-rain events while preventing premature deformation from extended exposure.

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A monitoring and analysis system for offshore wind power that allows adjusting the blade direction based on wind conditions and provides health monitoring of the wind turbine components to improve reliability and reduce maintenance costs. The system involves a tower connected to a placement groove with a sealing ring to prevent water ingress. The tower has a reducer, flange, rubber layer, shaft, and fastening bolt. The flange connects to the motor. A steering assembly on the blade can be adjusted based on wind direction detected by a sensor. This allows optimal blade orientation for wind conditions. The system also has components for monitoring vibration, temperature, inclination, etc. to diagnose faults and predict failures.

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A method for predicting the remaining life of key structural components in wind turbines using digital mirroring, simulation, and data analysis. The method involves creating a digital model of the wind turbine that accurately represents its condition and load. This model is used to extract simulation results for different operating conditions. Real-time wind data and historical operating data are combined to extract equivalent fatigue loads. These loads are used to calculate damage at key structural locations. Miner's linear cumulative damage theory is applied to determine remaining life. The method allows efficient online prediction of remaining life for wind turbine components using digital modeling and real-world data.

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A wind turbine blade with a lightning protection system that improves the protection of electronic components, especially communication devices, located within the blade from lightning strikes. The system includes receptors, down conductors, and grounding to conduct lightning current safely outside the blade. It also uses separate signal-carrying structures like coaxial cables and waveguides to isolate and protect the communication devices from lightning currents.

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Controlling a wind turbine to maximise efficient use of over-rating, whilst protecting against premature ageing and fatigue-damage accumulation when implementing such a control strategy. The control includes determining whether measures of fatigue life consumed by one or more turbine components exceed respective threshold values, and if so, sending a control signal to control the power output of the wind turbine to limit the total fatigue life consumed by the one or more turbine components prior to the one or more periods of time.

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Connection and fastening unit for lightning protection system components like receptors in wind turbine blades. The unit has a closable cavity integrated into the blade wall. Connectors inside the cavity are used to attach the lightning receptor and other components.

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