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作 者:袁捷 卫薇 许逵 舒彧 刘龙 YUAN Jie;WEI Wei;XU Kui;SHU Yu;LIU Long(Northeast Electric Power University,Jilin 132011,China;Guizhou Power Grid Co.,Ltd.,Guiyang 550000,China)
机构地区:[1]东北电力大学,吉林132011 [2]贵州电网有限责任公司,贵阳550000
出 处:《自动化与仪器仪表》2024年第10期5-9,共5页Automation & Instrumentation
基 金:南方电网有限责任公司科技项目(GZKJXM20200770)。
摘 要:随着智能电网的发展,输变电设备的稳定运行对于保障电力系统的可靠性至关重要。然而,设备在长期运行过程中可能会产生数据缺失的问题,这不仅影响了设备状态的准确评估,也给电网的安全管理带来了挑战。针对输变电设备缺失数据补齐问题,提出了一种基于机理模型驱动的方法。首先,通过分析输变电设备的工作机理和历史数据,构建了设备状态变化的机理模型。然后,结合设备的实际运行数据,利用数据驱动技术对机理模型进行校准和优化。在此基础上,提出了一种融合机理模型和数据驱动优势的缺失数据补齐算法,能够有效地预测和填补设备在运行过程中产生的数据空缺。通过实际案例验证,所提方法能够提高数据补齐的准确性和可靠性,为输变电设备的故障诊断和健康评估提供了有力支持。With the development of smart grids,the stable operation of power transmission and transformation equipment is crucial for ensuring the reliability of the power system.However,devices may generate issues of data loss during long-term operation,which not only affects the accurate assessment of equipment status but also poses challenges to the safe management of the power grid.This paper addresses the issue of missing data completion for power transmission and transformation equipment by proposing a mechanism model-driven approach.Firstly,a mechanism model of equipment status changes is constructed by analyzing the working principles and historical data of transmission and transformation equipment.Then,the mechanism model is calibrated and optimized using actual operational data of the equipment through data-driven techniques.On this basis,an algorithm that integrates the advantages of mechanism models and data-driven methods for missing data completion is proposed,which can effectively predict and fill in the data gaps generated during the equipment operation.The practical case verification shows that the proposed method can improve the accuracy and reliability of data completion,providing strong support for the fault diagnosis and health assessment of power transmission and transformation equipment.
分 类 号:TM73[电气工程—电力系统及自动化] TP29[自动化与计算机技术—检测技术与自动化装置]
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