| 数据驱动电能质量分析现状及其支撑技术与展望 |
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| 引用本文： | 张宇帆,艾芊,肖斐,张昭丞,谢善益.数据驱动电能质量分析现状及其支撑技术与展望[J].电力自动化设备,2018,38(11). |
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| 作者姓名： | 张宇帆 艾芊 肖斐 张昭丞 谢善益 |
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| 作者单位： | 上海交通大学 电子信息与电气工程学院，上海200240,上海交通大学 电子信息与电气工程学院，上海200240,上海交通大学 电子信息与电气工程学院，上海200240,上海交通大学 电子信息与电气工程学院，上海200240,广东电网有限责任公司电力科学研究院，广东广州510080 |
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| 基金项目： | 国家自然科学基金资助项目(51577115) |
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| 摘 要： | 随着多能源互补协调、电力市场建设、数据资源获取的便捷与廉价，大能源系统给传统电能质量领域带来了挑战。针对该问题，围绕利用电能质量数据辅助电能质量以外领域进行决策支持的研究现状进行了概述；针对多样化数据处理、海量电能质量扰动数据评估、计及电能质量的电力交易等要求，指出数据融合技术、大规模分布式计算技术、信息物理融合系统、区块链技术等支撑技术，并从中央处理器-图像处理器联合计算平台、融合“信息-物理-社会”系统多源数据电动汽车充电负荷建模、人工智能技术以及基于客户画像的电能质量监测信息平台4个方面对数据驱动电能质量分析进行了展望。
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| 关 键 词： | 电能质量 数据驱动 大能源系统 “信息-物理-社会”系统 大数据 人工智能 信息物理融合系统 区块链技术 |
Present situation, supporting technologies and prospect of data driven power quality analysis |
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| ZHANG Yufan,AI Qian,XIAO Fei,ZHANG Zhaocheng and XIE Shanyi.Present situation, supporting technologies and prospect of data driven power quality analysis[J].Electric Power Automation Equipment,2018,38(11). |
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| Authors: | ZHANG Yufan AI Qian XIAO Fei ZHANG Zhaocheng and XIE Shanyi |
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| Affiliation: | School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China,School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China,School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China,School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China and Power Research Institute of Guangdong Power Grid Co.,Ltd.,Guangzhou 510080, China |
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| Abstract: | With the complementarity and coordination among multiple energy sources, the construction of electricity market and the convenient and cheap access to data resources, the comprehensive energy system has brought challenges to the traditional power quality field. Aiming at the above problem, the current situation of research on the application of power quality data to aid decision making process in other fields is summarized. Supporting technologies such as data fusion technology, large-scale distributed computing technology, cyber-physical system, block chain technology and so on are introduced according to the requirements of diversity data processing, estimation of mass power quality disturbance data, power transactions considering power quality, etc. The data driven power quality analysis is prospected from four aspects of the CPU GUP(Central Processing Unit and Graphics Processing Unit) joint computing platform, the electric vehicle charging load modeling based on data fusion of cyber-physical-social system, the artificial intelligence and the power quality information platform based on customer portrait technology. |
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| Keywords: | power quality data driven comprehensive energy system cyber-physical-social system big data artificial intelligence cyber-physical system block chain technology |
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