Implementation of Artificial Neural Networks for Determining Power Transfomer Condition

Power transformer is one of the most critical and expensive components in power grid which occupies almost 60% of total investment. Due to the expensiveness of power transformer investments, monitoring and maintenance of transformer condition are the important tasks in the field. There exist various diagnostic methods to monitor transformer health condition in the literatures. However, these methods fail to interpret the condition when multiple faults are occurred. Moreover, the appearance of artificial intelligence attracts many interests of researchers. In this paper, the 2-tier multilayer neural network as a family of AI is proposed to be used for diagnosing transformer health condition. By using this method, the accuracy of interpretation on tier-1 and tier-2 analysis achieves 92.4% and 99.5%, respectively. The proposed method is also validated using k-fold cross-validation.