基于多源信息融合的变压器检测与评估技术

Transformer State Detection and Evaluation Technology Based on Multi-source Information Fusion

传统电力变压器状态检测大多针对单类传感器获得的数据进行处理,忽视了不同传感器之间的潜在联系,导致误判时常发生。为了避免信息孤岛,高效利用多源信息,根据多源信息的差异和互补性,将不同来源、不同模式的多类传感器信息进行多级别、多方面、多层次的信息检测,以获得电力变压器故障状态和特征估计,进而实现被测电力变压器故障态势的精确描述。首先,设计了多维状态参量的带通滤波器、能级放大及脉络检波等信息处理电路,有效提高了传感信息精确度。其次,建立基于信息融合的变压器健康状态评估模型,提高了系统整体的在线检测水平和故障诊断能力。最后,利用多源信息融合模型,验证了该方案的可行性和优越性。

Traditional transformer state detection mostly deals with data obtained from single sensor,ignoring the potential connection between different sensors,resulting in simultaneous interpreting.In order to avoid information island and efficiently use multi-source information,this paper uses the difference and complementarity of multi-source information to make up for the defect of detection based on single information,and detects multi-level,multi-faceted and multi-level information from different sources and different modes of multi-sensor information to obtain the state and feature estimation of power transformer fault,and then realizes the fault detection of power transformer.In order to improve the accuracy of the sensing information,a multi-dimensional state parameter information processing circuit,such as band-pass filter,energy level amplification and choroid detection,is designed.Then,the set pair analysis theory is introduced into the uncertainty of transformer health state evaluation,and the transformer health state evaluation model based on information fusion is established by using D-S evidence theory fusion,which improves the system overall on-line detection level and fault diagnosis ability.Finally,the multi-source information fusion model is used to verify the feasibility and superiority of the scheme.