Construction Method for Transformer Operating State Portrait Based on Multi-dimensional Capability and Knowledge Graph-multilayer Perceptron

Accurate evaluation for operating state of power transformer using big data and portrait technology is beneficial to ensure the safe and stable operation of power system. Aiming to the shortcomings of traditional condition evaluation methods, such as too single evaluation dimension and strong subjectivity, a construction method for transformer operating state portrait based on multi-dimensional capability and knowledge graph-multilayer perceptron (KG-MLP) is proposed. Firstly, the portrait system of transformer operation state is constructed, which is composed of insulation level, load capacity, anti-short circuit ability, energy efficiency level and voltage regulation capacity. Then, by combining the KG and MLP, a portrait analysis model of transformer operating state is built. Finally, according to the actual operation data of 1368 110kV transformers in a certain area, an example analysis of transformer operation state portrait is carried out. Also, the results are compared with those of Random Forest (RF) and Support Vector Machine (SVM) methods. The results show that the proposed method can accurately construct the transformer running state portrait with an accuracy of 96.35%, which is superior to RF algorithm (accuracy of 89%) and SVM algorithm (accuracy of 77%), providing a new way of evaluation for power transformer operating state. © 2024 Power System Technology Press. All rights reserved.