A New Data Mining Application in Smart Monitoring Systems Using Self Organizing Map Neural Network to Distinguish Disk Space Variations in Distribution Transformers

Online monitoring of electric power components in smart grids is of great importance to enhance reliability. Fault detection at primary levels in distribution transformers, the chief components to maintaining the integrity of modern power networks, prevents following significant destructive damages and high costs of failures in smart grids. Data-driven structure in smart grids provides accessibility to data related to the condition of transformers in data centers. Frequency response analysis (FRA), an efficient and sensitive technique to identify transformer defects, can be utilized in online monitoring. However, a trustworthy and consistent code for interpreting frequency responses has not yet been proposed by standards. This study proposes a self-organizing map (SOM) neural network as an intelligent interpreter using appropriate feature groups obtained from suitable statistical indices (SIns). In order to distinguish the severities and locations of disk space variation (DSV) defects as common faults in transformers, an experimental setup including 20 kV windings of a 1.6 MVA distribution transformer and an impedance analyzer are provided. The promising performance of SOM in detecting DSV faults with 100% accuracy shows that the proposed method is capable of identifying faults using high dimensional and nonlinear FRA data sets.