Neural Network-Based Classification of Single-Phase Distribution Transformer Fault Data
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The ultimate goal of this research is to develop an online, non-destructive, incipient fault detection system that is able to detect incipient faults in transformers and other electric equipment before the faults become catastrophic. With the condition assessment capability of the detection system, operators are equipped with better information during their decision-making process. Corrective actions are taken prior to transformer and equipment failures to prevent down-time and reduce operating and maintenance costs. Diagnosis of data associated with incipient failures is essential to develop an efficient, non-destructive, and online system. Field testing data were collected from controlled experiment and simulation data from mathematical models are studied. This thesis presents a data-mining approach to analyze field recorded and simulation data to characterize incipient fault data and study its properties. A supervised classifier using neural network (NN) toolbox in Matlab provides an efficient and accurate classification method to separate monitoring signal data into clusters base on their properties. However, raw data collected from the field and simulations will create too many dimensions and inputs to the neural network and make it a complex and over-generalized classification. Therefore, features are extracted from the data set, and these features are formed into feature clusters in order to identify patterns in signals as they are related to various physical behaviors of the system. The similarity between recognized patterns and patterns shown in future monitoring signals will trigger the warning of initializing or developing faults in transformers or equipment. This thesis demonstrates how different features were extracted from the raw data using various analysis techniques in both time domain and time-frequency domain, and the design and implementation of a neural network-based classification method. The classifier outputs are classes of data being separated into groups based on their characteristics and behaviors. Meaning of different classes is also explained in this thesis.
Zhang, Xujia (2006). Neural Network-Based Classification of Single-Phase Distribution Transformer Fault Data. Available electronically from