新型电力系统中人工智能应用与扩展

Investment Rating - The report does not explicitly provide an investment rating for the industry. Core Insights - The new generation of artificial intelligence (AI) is built on big data, high-performance computing, and machine learning, significantly advancing AI technology [13][160]. - AI applications in power systems include load forecasting, renewable energy output prediction, fault diagnosis, and scenario generation, indicating a strong trend towards digitalization and intelligent management in the energy sector [61][160]. - The integration of AI with blockchain and digital twin technologies is expected to enhance operational efficiency and decision-making in power systems [94][160]. Summary by Sections Artificial Intelligence Overview - AI is defined as a system that combines theories, technologies, and methods inspired by neuroscience, focusing on high-performance computing, big data, and machine learning [13][12]. AI Models - Various machine learning algorithms, including Support Vector Machines (SVM) and Decision Trees (DT), are widely used for predictive analytics in different applications [23][28]. AI Applications in Power Systems - AI is utilized for load forecasting, renewable energy output prediction, and fault diagnosis, employing models like LSTM and GAN for enhanced accuracy and efficiency [61][65][74]. - The report highlights the use of deep learning techniques for diagnosing faults in power distribution networks, particularly in complex scenarios like single-phase grounding faults [69][148]. AI Extensions - The report discusses the potential of federated learning in addressing data privacy issues in power systems, allowing for collaborative model training without compromising sensitive information [44][55]. - The application of blockchain technology in virtual power plants is explored, emphasizing the need for transparency and efficiency in energy trading [94][96]. Digital Twin Technology - Digital twin technology is presented as a means to create a virtual representation of physical systems, facilitating real-time monitoring and predictive maintenance in power systems [101][108]. Conclusion - The report concludes that the advancements in AI, combined with emerging technologies like blockchain and digital twins, will play a crucial role in the future development of intelligent power systems, enhancing their operational capabilities and resilience [160].