Core Insights - The development of wind energy in China has been characterized by the pursuit of certainty amidst uncertainty, particularly in the evolution of wind turbine technology [1] Group 1: Wind Turbine Evolution - The first generation of wind turbines focused on automation and power target tracking, but faced limitations due to the computational power of industrial PLCs, leading to issues in complex operational conditions [1] - The second generation introduced a shift from rule-based control to model-based control, significantly increasing the complexity of control models and algorithms, but still struggled with reliability in complex environments [2] - The 2.5 generation, represented by Envision Energy's "smart turbine," utilized load sensing and real data to enhance design boundaries and address key operational challenges, such as blade overload issues [2] Group 2: AI Integration in Wind Turbines - The third generation, exemplified by the Galileo AI turbine, incorporates AI for generalized learning and adaptive optimization, moving away from manually coded scenarios to an autonomous exploration of optimal operational strategies [3][4] - The AI turbine's learning process is likened to a child's development of muscle memory, showing rapid improvement over time and the potential for limitless advancement [4] - The integration of AI technology in large-scale turbines addresses uncertainties in component and system performance, as well as market fluctuations, necessitating a holistic upgrade in perception, planning, and control [4][5] Group 3: Performance and Market Impact - The Galileo AI turbine has demonstrated a 20.9% increase in revenue since its deployment in 2024 compared to the previous generation, highlighting the effectiveness of AI in enhancing operational efficiency [5] - Envision Energy's recent deployment of the first AI turbine for the Nullagine wind project in Australia showcases the adaptability of AI turbines to extreme environmental conditions and complex wind scenarios [6]