Weather Resistance for Wind Turbine Operation

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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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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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 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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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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Wind turbine blades with an improved lightning protection system that reduces erosion and wear at the blade tip. The blade has a replaceable conductive blade tip module that fits over and protects the tip of the main blade. The module overlaps the blade tip to shield the junction from weathering and electrical heating during lightning strikes. This reduces erosion and damage at the critical junction between the blade and the lightning receptor. The module can be replaced if worn or damaged, extending the life of the blade.

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Protecting wind turbines from extreme and fatigue loads during high wind speed events by accurately estimating loads instead of relying solely on wind speed sensors. The method involves solving a control algorithm using turbine operating data to estimate wind speed and loads at the turbine. Corrective actions like shutdown are implemented when estimated loads exceed limits, instead of relying solely on wind speed sensors which can be inaccurate at high speeds. This reduces shutdowns during storms and prevents damage from excessive loads.

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Ice-resistant paint for wind turbine blades that prevents ice formation while maintaining durability. It contains hydrophobic functional nanoparticles dispersed in a high-solid polyurethane paint. The nanoparticles are functionalized silica that resist ice buildup. The paint is made by mixing the nanoparticles with the paint components. Applying this ice-resistant paint to wind turbine blades helps prevent ice formation during cold weather without sacrificing the protective properties of the paint.

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Protecting wind turbines from harsh operating conditions like extreme weather events when overrated to prevent premature aging and fatigue damage. The method involves calculating fatigue life consumption rates for components based on sensors and applying lifetime usage estimators. It reduces overrating when components are in harsh conditions with high fatigue rates. This prevents overrating from causing excessive fatigue damage when operating in environments like high turbulence or extreme temperatures. The turbine controller adjusts overrating based on component fatigue estimates to mitigate premature aging during overrating.

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Permanent magnet direct-drive wind power generator with internal airflow to dry and cool the generator components. The generator has air holes in the stator support, tooth pressing plates, and an airflow passage through the iron core. The holes and passage allow an internal air source to flow through the stator interior. This creates a positive pressure environment to resist external airflow intrusion and protect insulation. An internal air source system can be connected to the holes for drying and cooling.

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A wind turbine assessment system that uses monitoring data to predict and prevent issues with wind turbines. The system has components like noise sensors, cameras, humidity sensors, temperature sensors, and pressure sensors on the blades. It also has voltage, power, and speed sensors on the generator. This data is transmitted to a server for analysis using a modeling server. The predictive system can forecast turbine performance based on the environmental conditions and blade integrity. This allows proactive maintenance and control to optimize turbine operation.

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A method for controlling wind turbines using lifetime usage estimators (LUEs) to protect components from excessive wear and fatigue while maximizing power generation. The method involves determining target fatigue life consumption for components based on expected rates from seasonal variations and lifetime goals. It then compares the target consumption to measured consumption. If consumption is below target, over-rating can be used to increase power. If consumption is above target, over-rating is restricted. This intelligent control strategy avoids unnecessarily restricting power by allowing early over-rating in favorable conditions while preventing excessive fatigue.

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