In the heart of China’s energy sector, a groundbreaking method is set to revolutionize wind power prediction, particularly under challenging low-temperature conditions. Researchers, led by ZHANG Yangfan from the North China Electric Power Research Institution, have developed a novel approach that could significantly enhance the accuracy of short-term wind power forecasts, providing crucial decision-making information for power system scheduling and operation.
The study, published in the journal *Power Technology*, addresses the pressing need for reliable wind power predictions in regions with high proportions of new energy sources. By leveraging the fuzzy C-means (FCM) clustering algorithm, the researchers cluster wind turbines based on their operation status and protection control information. This clustering allows for a more nuanced understanding of how individual turbines behave under low-temperature conditions.
“Our method not only predicts the power output but also accurately forecasts the critical shutdown time of wind turbines,” explains ZHANG Yangfan. This dual prediction capability is a game-changer for power system operators, enabling them to make more informed decisions and optimize grid stability.
The research employs a support vector machine to predict whether wind turbines are in normal operation status and uses the LightGBM algorithm in ensemble learning to forecast the power output of normally operating turbines. By integrating these predictions, the overall wind power output of the wind farm can be determined with unprecedented accuracy.
The effectiveness of the proposed method was validated through a case study of a wind farm in northern Hebei. The results were impressive, with the prediction accuracy of wind power exceeding 90%. This level of precision is a significant improvement over existing methods and holds substantial commercial implications for the energy sector.
“Accurate wind power prediction is crucial for power scheduling and control,” ZHANG Yangfan emphasizes. “Our method provides reliable prediction information that can help grid operators manage the intermittency of wind power more effectively, reducing the need for costly backup power sources and enhancing overall grid reliability.”
The implications of this research extend beyond low-temperature conditions. The proposed method can serve as a reference for short-term wind power prediction under other extreme weather conditions, such as strong winds. This versatility makes it a valuable tool for energy sectors worldwide, particularly in regions prone to diverse and challenging weather patterns.
As the world continues to transition towards renewable energy sources, the need for advanced prediction methods becomes increasingly critical. The work of ZHANG Yangfan and his team represents a significant step forward in this endeavor, offering a robust solution that can enhance the efficiency and reliability of wind power integration into the grid.
In an era where renewable energy is at the forefront of global energy strategies, this research provides a compelling example of how innovative technologies can address the challenges of integrating intermittent energy sources into the power grid. By improving the accuracy of wind power predictions, this method not only supports the stable operation of power systems but also contributes to the broader goal of achieving a sustainable energy future.