Recent Development of Smart Grids and Microgrids in China
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Editor
Prof. Dr. Qi Huang
Prof. Dr. Qi Huang
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1. School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
2. School of Energy, Chengdu University of Technology, Chengdu 610059, China
Interests: power system wide-area measurement and control; informatics for smart electric energy system; smart grid and energy internet
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Topical Collection Information
Dear Colleagues,
Today, with the growing concerns about the environmental effects of electricity generation, transmission, and distribution, the concepts of smart grids and microgrids have received considerable attention in both the industrial and academic communities. Chinese scientists have made great contributions to the basic science and engineering of smart grids and microgrids, with China currently holding the largest number of publications in this field. Therefore, this issue on “Recent Development of Smart Grids and Microgrids in China” aims to provide a platform to demonstrate the innovation of Chinese scientific and technological works in theoretical research and engineering research on smart grids and microgrids technologies, reporting the latest research progresses in China.
The main topics of the section include but are not limited to:
- Multienergy systems and microgrids;
- Hybrid microgrid power systems;
- Energy storage management;
- Power quality, grid monitoring, and smart metering;
- Innovative measurement and metrology for smart grids and microgrids;
- Synchronization technology for smart grids and microgrids;
- Stability analysis of microgrids;
- Operation and advanced control systems for smart grids and microgrids;
- Impedance modeling of microgrids;
- IoT integration for smart grids and microgrids;
- Cybersecurity, reliability, and resiliency in smart grids and microgrids;
- Microgrid protection;
- Regulatory framework impact on microgrids;
- Demand response and demand-side management;
- Electric vehicle integration;
- Energy internet;
- Computing and communication technologies applications for smart grids and microgrids.
Prof. Dr. Qi Huang
Collection Editor
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Keywords
- multienergy systems and microgrids
- hybrid microgrid power systems
- energy storage management
- power quality, grid monitoring, and smart metering
- innovative measurement and metrology for smart grids and microgrids
- synchronization technology for smart grids and microgrids
- stability analysis of microgrids
- operation and advanced control systems for smart grids and microgrids
- impedance modeling of microgrids
- IoT integration for smart grids and microgrids
- cybersecurity, reliability, and resiliency in smart grids and microgrids
- microgrid protection
- regulatory framework impact on microgrids
- demand response and demand-side management
- electric vehicle integration
- energy internet
- computing and communication technologies applications for smart grids and microgrids
Published Papers (2 papers)
2023
Open AccessArticle
Research on Object Detection of Overhead Transmission Lines Based on Optimized YOLOv5s
by
Juping Gu, Junjie Hu, Ling Jiang, Zixu Wang, Xinsong Zhang, Yiming Xu, Jianhong Zhu and Lurui Fang
Cited by 4 | Viewed by 1536
Abstract
Object detection of overhead transmission lines is a solution for promoting inspection efficiency for power companies. However, aerial images contain many complex backgrounds and small objects, and traditional algorithms are incompetent in the identification of details of power transmission lines accurately. To address
[...] Read more.
Object detection of overhead transmission lines is a solution for promoting inspection efficiency for power companies. However, aerial images contain many complex backgrounds and small objects, and traditional algorithms are incompetent in the identification of details of power transmission lines accurately. To address this problem, this paper develops an object detection method based on optimized You Only Look Once v5-small (YOLOv5s). This method is designed to be engineering-friendly, with the objective of maximal detection accuracy and computation simplicity. Firstly, to improve the detecting accuracy of small objects, a larger scale detection layer and jump connections are added to the network. Secondly, a self-attention mechanism is adopted to merge the feature relationships between spatial and channel dimensions, which could suppress the interference of complex backgrounds and boost the salience of objects. In addition, a small object enhanced Complete Intersection over Union (CIoU) is put forward as the loss function of the bounding box regression. This loss function could increase the derived loss for small objects automatically, thereby improving the detection of small objects. Furthermore, based on the scaling factors of batch-normalization layers, a pruning method is adopted to reduce the parameters and achieve a lightweight method. Finally, case studies are fulfilled by comparing the proposed method with classic YOLOv5s, which demonstrate that the detection accuracy is increased by 4%, the model size is reduced by 58%, and the detection speed is raised by 3.3%.
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Open AccessReview
A Brief Survey on the Development of Intelligent Dispatcher Training Simulators
by
Ao Dong, Xinyi Lai, Chunlong Lin, Changnian Lin, Wei Jin and Fushuan Wen
Cited by 1 | Viewed by 1809
Abstract
The well-known dispatcher training simulator (DTS), as a good tool to train power system dispatchers, has been widely used for over 40 years. However, with the high-speed development of the smart grid, the traditional DTSs have struggled to meet the power industry’s expectations.
[...] Read more.
The well-known dispatcher training simulator (DTS), as a good tool to train power system dispatchers, has been widely used for over 40 years. However, with the high-speed development of the smart grid, the traditional DTSs have struggled to meet the power industry’s expectations. To enhance the effectiveness of dispatcher training, technical innovations in DTSs are becoming more and more demanding. Meanwhile, the ever-advancing artificial intelligence (AI) technology provides the basis for the design of intelligent DTSs. This paper systematically reviews the traditional DTS in terms of its origin, structure, and functions, as well as limitations in the context of the smart grid. Then, this paper summarizes the AI techniques commonly used in the field of power systems, such as expert systems, artificial neural networks, and the fuzzy set theory, and employs them to develop intelligent DTSs. Regarding a less studied aspect of DTSs, i.e., intelligent training control, we introduce the Adaptive Learning System (ALS) to develop a personalized training program, which will also be an important aspect of future research.
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