GCA-CNN based transformer digital twin model construction and fault diagnosis and condition evaluation analysis

Abstract

Oil-immersed power transformer is the most important piece of equipment in the transmission system, and the stable operation of this equipment is of great significance to the normal work of the power system. At present, deep learning has been widely used in transformer condition evaluation and fault detection. About the shortage of deep learning algorithm models, this paper proposes a transformer digital twin model construction and fault diagnosis and condition evaluation analysis based on gray clustering algorithm (GCA) and conventional neural network (CNN). The data are first collected and filtered by combining the operation data, condition information amount and faulty features of power transformers, and then the condition features and fault features are combined to evaluate and detect the condition and faults of power transformers using GCA-CNN respectively. The whitening weight function is determined by expert scoring, the state evaluation matrix is established, the evaluation coefficients are obtained to calculate the evaluation weights, and the transformer state is obtained according to the clustering coefficients; 2000 pieces of raw data are input into the model to obtain the output fault types. Finally, the results are derived and compared with the real results. This paper uses real data from a power plant in Yunnan, and according to the results, the model established in this paper has higher accuracy and better evaluation and detection effects.