Power Output Optimization in Wind Turbines

Optimizing wind turbine performance during noise reduced operation to maximize energy output without accelerating component degradation. The optimization involves dynamically adjusting turbine power based on grid conditions instead of fixed noise reduction modes. When component life is prioritized, the turbine limits power near synchronous speed based on grid parameters. When AEP is prioritized, the turbine operates harder to extract max power. A fatigue tracker calculates life consumption.

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Intelligent control method and system for wind turbines that optimizes power generation efficiency by using digital twin modeling, data monitoring, and strategy matching to mitigate the effects of variable wind conditions. The method involves: monitoring wind speed with synchronized sensors, generating predicted wind speed, fitting turbine efficiency using digital twin models, matching optimization results with calibrated wind speeds, and stable optimization using averaged wind speeds.

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A wind turbine control method that optimizes power generation during grid peak shaving to balance grid stability and turbine safety. The method involves finding the optimal wind speed for each turbine during grid peak shaving by solving an economic objective function with constraints for power generation. This optimal wind speed is then used to generate control instructions for the turbines. By optimizing power generation considering constraints like turbine limits, it allows meeting grid needs while preventing excessive turbine power or unsafe operation.

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Dual-mode control method and system for timely monitoring of wind turbines based on wind speed to improve wind power generation efficiency. The method involves using machine learning to predict wind speed trends, analyze factors affecting wind speed changes, and quantify wind speed variability. This allows timely adjustment of wind turbine operation to optimize power generation in response to changing wind conditions.

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Wind farm automatic power generation control strategy based on model predictive control to improve grid stability and wind farm unit performance when connecting large numbers of wind turbines. The strategy involves a wind farm control module that takes grid reference power as input and outputs optimal power adjustments to each wind turbine using model prediction and rolling optimization. This coordinated, unified approach ensures frequency stability, grid code compliance, and economic benefits for the wind farm and grid compared to individual turbine PI controllers.

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Intelligent control of wind turbines using cloud-edge collaboration to improve power generation efficiency and reliability. The method involves real-time collection of wind data and turbine parameters at the edge using sensors and data collectors. It trains a short-term prediction channel using historical data to forecast wind energy accurately. If the forecast accuracy exceeds a threshold, it uses the forecast for intelligent control of the turbine. This leverages local edge processing for real-time monitoring and cloud-based training for accurate wind forecasting.

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Wind turbine start-stop control method based on wind speed prediction to improve efficiency, accuracy, and safety of wind farms. The method involves using wind speed forecasts from the power plant and fan operation data to predict when to start/stop the target turbine. It accesses the wind power plant's operation and meteorological databases, constructs prediction models for wind speed and fan start/stop, and uses real-time data to make decisions. This allows coordinated fan control based on both local and global wind conditions.

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Wind farm power control system that optimizes power allocation considering individual turbine output status to improve reliability and reduce fatigue. The system involves a wind power prediction module, a wind farm power distribution module, a database, SCADA, and turbine control systems. The power distribution module combines power prediction with turbine output analysis to optimize allocation of power commands to turbines based on their current output levels. This better tracks power commands and reduces fatigue compared to uniform or proportional allocation.

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Wind turbine optimization control method to improve wind farm economic performance by dynamically adjusting individual turbine power generation based on wind conditions. The method involves predicting future wind speeds, estimating turbine power generation, and calculating wind farm-wide benefits to determine optimal turbine power curtailment levels. It balances turbine component fatigue loads with wind farm power generation benefits to reduce wind abandonment and maximize revenue.

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A wind turbine self-adjusting control system that optimizes wind turbine performance and grid stability by dynamically adjusting blade angle and storing/releasing energy based on wind conditions. The system calculates a wind energy representation value using wind speed and blade pressure, determines trend, and adjusts blade angle accordingly. If wind energy is increasing, it increases blade angle. If wind energy is decreasing or stable, it reduces blade angle. This improves wind capture for increasing wind, but reduces blade size for decreasing wind to match demand. If wind energy is below output, it reduces blade angle further. If wind energy is above output, it considers trend again. This balances wind capture vs grid stability while avoiding excessive blade sizes.

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A wind farm power control system that uses primary frequency modulation to optimize wind turbine power output. The system includes a sensor for measuring wind speed and turbine power, and a control module with two controllers: a primary frequency modulation controller calculates the maximum power based on wind speed, and a fuzzy logic controller adjusts turbine power using the calculated maximum. This allows precise real-time power control as wind speeds change.

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Intelligent wind power prediction and dispatching method and system using deep learning and reinforcement learning to improve wind farm output and grid stability. The method involves using a ConvLSTM model to accurately predict wind speed and direction from historical data and meteorological inputs. This is followed by intelligent wind turbine control using reinforcement learning to maximize power and life. Finally, wind farm dispatching using reinforcement learning to balance output with grid needs, reducing impact and losses.

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A wind farm control strategy that maximizes overall power generation by using a trained model to predict optimal operating conditions for a wind farm based on real-time wind data and current turbine conditions. The model takes incoming wind data, turbine restrictions, and current turbine settings to forecast the best settings for maximum power. This allows the wind farm to operate at the highest possible output by dynamically adjusting turbine settings in real-time based on the predicted optimal conditions.

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Wind power supply control method and system for wind turbines that optimizes wind power utilization and quality by intelligently managing the wind turbine operation based on real-time data and predictions. The method involves monitoring wind turbine power output, extracting relevant data, checking if the turbine is generating normally, predicting power demand from connected equipment, setting turbine operation instructions based on desired power receipt, and controlling the turbine accordingly. This allows maximizing wind energy capture while ensuring sufficient power output for connected devices.

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AI-based wind turbine speed optimization system that leverages machine learning to dynamically adjust wind turbine speeds based on real-time environmental conditions. The system has modules for monitoring environmental factors, analyzing historical data to train prediction models, and generating speed control instructions based on the models. This allows optimizing turbine speeds for maximum energy output and wear reduction in response to changing wind conditions.

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Power generation control method for wind turbines using a SCADA system to optimize performance and reduce failures. The method involves monitoring wind turbine equipment, generating expected power based on wind speed, comparing actual power, and adjusting parameters if needed. The comparison uses evaluation coefficients to determine when to correct. If the actual power deviates significantly from expected, a correction is generated. If the deviation is smaller, a trend analysis is done to decide if a correction is needed. This allows targeted adjustments to wind turbine parameters based on actual operating conditions.

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Refinement energy management method and system for wind farms to improve coordinated control of active and reactive power. The method involves establishing a three-phase power model and wind-force network model for the wind farm using collected data. This allows accurate calculation of wind turbine generator system voltages for coordinated control. The system divides into data acquisition, situational awareness, and coordination control modules for independent functionality. It addresses issues of incompatible data and lack of coordination in existing wind farm energy management systems.

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Wind turbine start-stop adjustment system that reduces equipment wear and grid instability by optimizing wind turbine startup and shutdown based on weather conditions. The system uses a central processor connected to weather monitoring and wind turbine control units. The processor analyzes weather data to determine optimal start/stop points for turbines in varying wind conditions. This pre-set logic is then applied by the turbine control unit to avoid excessive starts/stops in fluctuating winds.

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Self-adaptive active power control for hybrid wind farms that optimizes the configuration and operation of the wind farm's sources (current and voltage) to improve grid stability. The method involves predicting source load and grid frequency fluctuations to dynamically allocate power between the sources and use energy storage to supplement power during frequency events. It also considers wind turbine differences to optimally distribute active load. By adapting source configurations based on forecasts, the hybrid wind farm can provide more effective grid frequency regulation.

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Power control method for wind farms to safely adjust wind turbine power without excessive speed jumps or shutdowns. The method involves calculating adjustable power ranges for each turbine based on current output levels. When the farm is asked to adjust total power, it allocates within the ranges. This prevents turbines from being over-asked and potentially going unstable during speed transitions. It also prevents the farm controller from commanding turbines to shutdown due to speed limits. By limiting adjustment within turbine capabilities, it reduces sudden speed changes and ensures safe operation.

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