Rongrong Shan
Papers
2
Total Citations
12
H-Index
2
About
Rongrong Shan is a researcher specializing in the intersection of robotics, deep learning, and smart grid technologies, with a primary focus on intelligent fault diagnosis and self-healing in distribution networks. Her work addresses critical challenges in modern power systems, particularly the need for automated, efficient, and accurate methods to reduce reliance on manual inspection and improve grid resilience. In her 2022 study, "Fault Diagnosis Method of Distribution Equipment Based on Hybrid Model of Robot and Deep Learning" (8 citations), Shan proposed a novel hybrid approach that combines robotic platforms with deep learning algorithms to enhance the intelligent recognition of equipment images, significantly improving fault diagnosis accuracy. Building on this, her 2023 paper, "Method of Fault Self-Healing in Distribution Network and Deep Learning Under Cloud Edge Architecture" (4 citations), tackles persistent issues of low accuracy and insufficient feature extraction in existing self-healing methods by integrating robotic systems with cloud-edge computing architectures. These contributions demonstrate Shan’s commitment to advancing smart grid automation, with her work laying the groundwork for more reliable, autonomous power distribution systems. Her research is particularly notable for its practical application of deep learning in real-world energy infrastructure, offering scalable solutions for modern electrical networks.
Research Focus
Key Achievements
Top Papers
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