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作 者:沈颖 孙继欣 陈炜琦 SHEN Ying;SUN Jixin;CHEN Weiqi(Shanghai Computer Software Technology Development Center,Shanghai Key Laboratory of Computer Software Testing&Evaluation,Shanghai,China;Hebei Industrial and Information Technology Development Research Institute,Hebei,Shijiazhuang,China;Hebei University of Science and Technology,Hebei,Shijiazhuang,China)
机构地区:[1]上海计算机软件技术开发中心,上海市计算机软件评测重点实验室,上海201112 [2]河北省工业和信息化发展研究院,河北石家庄050071 [3]河北科技大学,河北石家庄050000
出 处:《东华大学学报(自然科学版)》2024年第2期144-152,共9页Journal of Donghua University(Natural Science)
基 金:上海市科委项目“上海市计算机软件评测重点实验室”(22DZ2229010);上海市2022年度“科技创新行动计划”技术标准项目(22DZ2205700)。
摘 要:随着大规模可再生能源的接入以及数字孪生技术在电力领域的深入应用,数字孪生电网数据质量问题日益突出,但是数字孪生数据质量仍缺乏系统的评价理论和指导方法,严重阻碍了进一步的推广和应用。针对以上问题,根据有源配电网数字孪生技术的特点,通过分析有源配电网数字孪生数据类型,构建了面向有源配电网的业务驱动的数字孪生数据质量模型。采用层次分析法(AHP)来确定质量特性的权重。为了验证模型的有效性,选取电池储能数字孪生数据进行评估。结果表明,提出的模型能有效评估有源配电网数字孪生数据质量,为系统评价数字孪生电网数据质量提供了理论支持和实践指导。With the access of large-scale renewable energy and the deepening of the application of digital twin technology in the field of power,the problem of digital twin power grid is becoming increasingly prominent,but the digital twin data quality still lacks systematic evaluation theory and guidance methods,which seriously hinders the further promotion and application.To address these issues,this paper constructs a business-driven digital twin data quality model for active distribution networks,based on an analysis of the characteristics of digital twin technology by analyzing the types of digital twin data in active distribution networks and the types of digital twin data in these networks based on the characteristics of digital twin technology in active distribution networks.The Analytic Hierarchy Process(AHP)was employed to determine the weight of quality characteristics.To verify the effectiveness of the model,battery storage digital twin data was assessed.The results demonstrate that the model proposed in this paper can effectively evaluate the data quality of digital twins in active distribution networks,providing theoretical support and practical guidance for systematically evaluating the data quality of digital twin power grids.
分 类 号:TP3[自动化与计算机技术—计算机科学与技术]
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