Control Method of Mass Block Grasping Device of Slope Gravity Energy Storage System Based on Deep Neural Network
This paper studies the workflow of the mass block stacking process of the slope-type gravity energy storage system, combines deep learning with the stacking method, and proposes a gripping device control method suitable for slope-type gravity energy storage.
In the gravity energy storage system, the stacking link is an important component. Due to the timeliness requirements of the stacking link, the traditional method of identifying the position of heavy objects by traveling cranes is no longer applicable. This paper uses deep learning to build a prediction model for the running distance of mass blocks, allowing the grabbing device to move to the designated position in advance and shorten the overall time of the stacking process. At the same time, thanks to the introduction of deep learning, the distance prediction model can be established directly based on actual operating data, which does not require complex mathematical derivation processes and is convenient for practical engineering applications.
The main content of this article is divided into three parts: firstly, it introduces the overall workflow of the slope gravity energy storage system and the requirements for the mass block grasping device. Secondly, a method is proposed to calculate the running distance of the mass block by measuring its instantaneous acceleration. The errors of this method are analyzed based on factors affecting the running distance of the mass block on the buffering platform. Finally, a control method for the mass block grasping device of the inclined gravity energy storage system based on deep neural networks is proposed. This method meets the timeliness requirements of the system in the stacking process and improves the efficiency of the stacking process.
This work conducts a detailed analysis of the stacking process of the gravity energy storage system, and the method proposed has great promotion and application value.
支持基金：
南方电网有限责任公司重点科技项目（GZKJXM20220033）
论文链接：
http://www.motor-abc.cn/djykzyy/article/abstract/20231105
推荐引用格式：
陈巨龙，李震，朱永清，刘大猛，张裕，王祖凡，高天，郝梓琳，董琳琳，赵海森. 基于深度神经网络的斜坡式重力储能系统质量块抓取装置控制方法[J]. 电机与控制应用, 2023, 50(11): 37-45.
CHEN Julong, LI Zhen, ZHU Yongqing, LIU Dameng, ZHANG Yu, WANG Zufan, GAO Tian, HAO Zilin, DONG Linlin, ZHAO Haisen. Control Method of Mass Block Grasping Device of Slope Gravity Energy Storage System Based on Deep Neural Network[J]. Electric Machines & Control Application, 2023, 50(11): 37-45.
陈巨龙，硕士，高级工程师，贵州电网有限责任公司电网规划研究中心能源经济研究室副总经理，研究方向新能源并网与新型储能技术。
Julong Chen, master's degree, senior engineer, is the deputy general manager of the Energy Economic Research Office of the Grid Planning Research Center of Guizhou Power Grid Co., Ltd., with research interests in new energy grid integration and new energy storage technology.
王祖凡，华北电力大学电气与电子工程学院博士研究生，研究方向为电能转换与高效利用，重力储能技术等。
Zufan Wang is a doctoral candidate at the School of Electrical and Electronic Engineering at North China Electric Power University. His research interests include electric energy conversion and efficient utilization, and gravity energy storage technology.
赵海森，男，博士，教授，博士生导师，全国旋转电机标准化技术委员会委员，IET Fellow，IEEE Senior Member，中国电机工程学会高级会员，中国电工技术学会高级会员。2011年6月毕业于华北电力大学，获得工学博士学位；2011年8月至今，就职于华北电力大学电气与电子工程学院，历任讲师、副教授、教授。主要研究方向为电能转换与高效利用、先进电工材料及其电磁特性、新能源电力系统分析与控制。
承担国家自然科学基金2项、国家重点研发计划课题1项，国家电网公司、南方电网公司、华电集团、中广核、中石油及中船重工等企业委托科研项目20余项。发表论文140余篇，其中SCI收录50余篇、EI收录60余篇，获得省部级科技奖励2项，授权国家发明专利24项。作为主研人员，曾参与我国高效、超高效系列电机研制工作。自主研发了“EMCAD电机设计软件”及“基于时步有限元的电机损耗及振动分析软件”，应用于我国“核动力装置冷却机泵主泵电机”及“舰船推进屏蔽电机”研发；设计了数十个规格舰艇用高效低振动噪声电机，已装备多型舰艇；研发的变频电机系统能效评估、节能降耗以及避免大功率电机转子断条等专利技术在油田、电厂等企业获得推广应用。
Haisen Zhao, male, Ph.D., professor, doctoral supervisor, member of the National Technical Committee for Standardization of Rotating Electrical Machines, IET Fellow, IEEE Senior Member, senior member of the China Electrical Engineering Society, and senior member of the China Electrotechnical Society. Graduated from North China Electric Power University in June 2011 with a doctorate in engineering. From August 2011 to present, he has worked in the School of Electrical and Electronic Engineering at North China Electric Power University, serving as a lecturer, associate professor, and professor. The main research directions include electric energy conversion and efficient utilization, advanced electrical materials and their electromagnetic properties, and new energy power system analysis and control.
It has undertaken 2 National Natural Science Foundation projects, 1 National Key Research and Development Plan project, and more than 20 scientific research projects commissioned by enterprises such as State Grid Corporation of China, China Southern Power Grid Corporation, Huadian Group, China General Nuclear Power Corporation, PetroChina and China Shipbuilding Industry Corporation. He has published more than 140 papers, of which more than 50 are included in SCI and more than 60 are included in EI. He has won 2 provincial and ministerial science and technology awards and authorized 24 national invention patents. As a principal researcher, he has participated in the development of high-efficiency and ultra-high-efficiency series motors in my country. Independently developed "EMCAD motor design software" and "motor loss and vibration analysis software based on time-step finite element", which are used in the research and development of my country's "nuclear power plant cooling pump main pump motor" and "ship propulsion shielded motor"; design We have developed dozens of high-efficiency, low-vibration noise motors for ships of various specifications, and have equipped them with many types of ships; our patented technologies such as variable frequency motor system energy efficiency assessment, energy saving and consumption reduction, and prevention of high-power motor rotor breakage have been promoted and applied in oil fields, power plants and other enterprises.
邮箱：zhaohisen@163.com
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