摘要
变压器健康状态评估是提高供电可靠性的重要前提,也是实现设备精益化运维管控基础任务之一。现有方法实现变压器健康状态精准评估的前提是获得完备、有效的特征参量集。然而,由于实际中特征参量采样间隔不固定、部分参量采集错误或传输丢失等问题,实际数据的不规则性显著降低了现有方法健康评估的精准度。为此,首次在本领域探索应用高性能处理非完备、不规则数据集的Raindrop学习算法,并在模型训练过程中引入形式化表达的领域知识规则,从而增强样本空间有效信息、引导学习器趋优训练。最后,基于实际获取的110kV变压器数据开展多场景下的健康评估,结果表明所提方法在各场景下均优于基线学习方法,能够更好地适应实际电力设备的健康评估需求。
Transformer health status evaluation is an important prerequisite to improving power supply reliability and is also one of the basic tasks in achieving lean operation and maintenance control of equipment.The prerequisite of the existing method to achieve accurate transformer health evaluation is to obtain a complete and effective set of characteristic parameters.However,the irregularity of the actual data significantly reduces the accuracy of the existing health evaluation methods due to problems such as the unfixed sampling interval of the characteristic parameters,the error in acquiring some parameters,or the loss of transmission.For this reason,we explore the application of the Raindrop learning algorithm with high performance to deal with incomplete and irregular data sets for the first time in this field and introduce formal domain knowledge rules in the process of model training to enhance the effective information of sample space and guide the learner to optimize training.Finally,health evaluation under multiple scenarios is carried out based on the actual obtained 110kV transformer data.The results show that the proposed method is superior to the baseline learning method in all scenarios and can better adapt to the health evaluation needs of actual power equipment.
作者
李丰胜
王金丽
尚宇炜
周莉梅
LI Fengsheng;WANG Jinli;SHANG Yuwei;ZHOU Limei(China Electric Power Research Institute,Haidian District,Beijing 100192,China)
出处
《电网技术》
北大核心
2025年第1期399-407,I0119,共10页
Power System Technology
基金
中国科协青年人才托举工程项目(2022QNRC001)
北京市科技新星计划项目(20220484087)。
