Browsing by Author "Zheng, Xinhu"

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    Leveraging Machine Learning Techniques in Power and Transportation Systems
    (2022-06) Zheng, Xinhu
    With the data explosion, and the emerging demand for system modeling and simulation, the challenges are growing exponentially in terms of model accuracy and authenticity. Fortunately, the surge of modern machine learning techniques has enabled us to grapple with seemingly impossible to solve problems, to overcome the computational complexity, and mine the knowledge to guide the operational tasks, especially in complex cyber physical systems such as power and transportation systems. To handle the increasing scale and complexity in power systems, we propose a novel and efficient method to solve the Optimal Power Flow (OPF) problems, by decomposing the entire system into multiple sub-systems based on automatic regionalization. Meanwhile, by utilizing the demonstration and the deep reinforcement learning (DRL), a novel hybrid emergency voltage control method is proposed. Specifically, the experts' knowledge is extracted through a behavioral cloning model and novel insights are gained via DRL. The major advances witnessed by leveraging big data in transportation networks bring opportunities to study the driving style and car-following model in a data-driven manner. We propose an algorithm that classifies drivers into different driving styles and only requires data from a short observation window. Meanwhile, by exploiting the modeling expressiveness of deep neural networks (DNNs), we propose a DNN based car-following model that can achieve higher simulation accuracy. Accurate understanding of the environment is a prerequisite to ensure safety in autonomous driving. However, the capabilities of a single vehicle can hardly meet the requirements of a complex driving environment. To cope with these issues, a multi-vehicle and multi-sensor (MVMS) cooperative perception method is introduced to construct a global view of the environment. In addition, to justify the robustness of the perception results, we evaluate the confidence of the perception output and propose a semantic information fusion scheme based on confidence levels.