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您的检索式：作者名="Robert C.Qiu"
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| 1 | Deep Reinforcement Learning for Power System Applications: An Overview显示文摘Due to increasing complexity, uncertainty and data dimensions in power systems, conventional methods often meet bottlenecks when attempting to solve decision and control prob- lems. Therefore, data-driven methods toward solving such prob- lems are being extensively studied. Deep reinforcement learning (DRL) is one of these data-driven methods and is regarded as real artificial intelligence (AI). DRL is a combination of deep learning (DL) and reinforcement learning (RL). This field of research has been applied to solve a wide range of complex sequential decision-making problems, including those in power systems. This paper firstly reviews the basic ideas, models, algorithms and techniques of DRL. Applications in power systems such as energy management, demand response, electricity market, operational control, and others are then considered. In addition, recent advances in DRL including the combination of RL with other classical methods, and the prospect and challenges of applications in power systems are also discussed. | Zidong Zhang Dongxia Zhang Robert C.Qiu | 2020 | CSEE Journal of Power and Energy Systems2020，6，1： | 32 |
| 2 | An Accurate and Real-time Method of Self-blast Glass Insulator Location Based on Faster R-CNN and U-net with Aerial Images显示文摘This paper proposes a new deep learning framework for the location of broken insulators(in particular the self-blast glass insulator)in aerial images.We address the broken insulators location problem in a low signal-noise-ratio(SNR)setting.We deal with two modules:1)object detection based on Faster R-CNN,and 2)classification of pixels based on U-net.For the first time,our paper combines the above two modules.This combination is motivated as follows:Faster R-CNN is used to improve SNR,while the U-net is used for classification of pixels.A diverse aerial image set measured by a power grid in China is tested to validate the proposed approach.Furthermore,a comparison is made among different methods and the result shows that our approach is accurate in real time. | Zenan Ling Dongxia Zhang Robert C.Qiu Zhijian Jin Yuhang Zhang Xing He Haichun Liu | 2019 | CSEE Journal of Power and Energy Systems2019，5，4： | 22 |
| 3 | Detection and Classification of Transmission Line Transient Faults Based on Graph Convolutional Neural Network显示文摘We present a novel transient fault detection and classification approach in power transmission lines based on graph convolutional neural network.Compared with the existing techniques,the proposed approach considers explicit spatial information in sampling sequences as prior knowledge and it has stronger feature extraction ability.On this basis,a framework for transient fault detection and classification is created.Graph structure is generated to provide topology information to the task.Our approach takes the adjacency matrix of topology graph and the bus voltage signals during a sampling period after transient faults as inputs,and outputs the predicted classification results rapidly.Furthermore,the proposed approach is tested in various situations and its generalization ability is verified by experimental results.The results show that the proposed approach can detect and classify transient faults more effectively than the existing techniques,and it is practical for online transmission line protection for its rapidness,high robustness and generalization ability. | Houjie Tong Robert C.Qiu Dongxia Zhang Haosen Yang Qi Ding Xin Shi | 2021 | CSEE Journal of Power and Energy Systems2021，7，3： | 0 |
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