An intelligent multi-hierarchically weighted neural network for condition assessment of power transformer insulation

Abstract

Condition assessment of a transformer provides information about the overall health status of the insulation. Accurate health index (HI) prediction at regular intervals prevents catastrophic failures. In this study, a novel multi-hierarchically weighted neural network is proposed to estimate the HI of power transformers. It is designed with 12 distinct features of an oil- and paper-insulation system. Initially, collected attributes are normalized using multi-criterion analysis (MCA). The correlation and appropriate weight prioritization of each attribute are determined using the analytical hierarchy process (AHP). The combined MCA-AHP facilitates the calculation of weighted scores and converts the 12 attributes into 3 quality grades. These grades are used as inputs to the hybrid artificial neural network (ANN), and the output is the overall HI. The model is trained and tested using 350 data samples collected from the Himachal Pradesh State Electricity Board, India. The performance of the proposed hybrid model is validated by root mean square error, mean absolute error, mean relative error, and correlation coefficient. Furthermore, a comprehensive comparison is conducted using 300 data samples with pre-known health conditions (HCs), and other expert models in the literature achieved 97% of accuracy. The proposed hybrid model effectively addresses the general issues raised by intelligent models, such as reliance on expert rule-based approaches in Fuzzy models, greater computational demand in ANN and ANFIS models, and increased complexity due to diagnostic attributes. Based on the predicted HCs, preventive maintenance actions are proposed to ensure effective maintenance of the asset.