Hybrid fuzzy and gated recurrent network based artificial intelligence approach for fault diagnosis and prognosis of transformers using dissolved gas analysis
Affiliations: Department of Electrical and Electronics Engineering, Mepco Schlenk Engineering College, Sivaskasi, Tamilnadu, India
Correspondence:
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Corresponding author. K.S. Krishna Veni, Department of Electrical and Electronics Engineering, Mepco Schlenk Engineering College, Sivaskasi, Tamilnadu, India. E-mail: krishnaveni.ks@mepcoeng.ac.in.
Abstract: In the electrical energy transmission and distribution sector, power transformers play an important role. Early fault diagnosis and prognosis are essential to ensure continuous operation and also to prepare a proper maintenance schedule based on the requirements. The occurrence of a fault in the transformer will lead to the formation of various gases inside the transformer tank. For fault diagnosis in the transformer, Dissolved Gas Analysis (DGA) is an excellent method. An Artificial Intelligence (AI) based fault diagnosis and prognosis system using dissolved gases in transformer oil is helpful to predict the health state of the transformer well in advance. Hence, based on the fault severity level, the remaining useful life of the transformer, fault type and current state of the transformer can be estimated effectively by imparting AI to the existing system. A Two-Tier Fuzzy Logic Controller (TTFLC) is proposed in this article to find the type of fault and health index (HI) of the transformer. For further fault prognosis, an effective Gated Recurrent Network (GRN) based deep learning enabled future learning estimator is used for predicting the Criticality Index (CI) of the Transformer. The performance of the proposed method is evaluated for both data from the IEEE data set and expert data collected from the southern Tamil Nadu region. The proposed system shows better results even in multivariate, complex process systems. The diagnosis accuracy of the proposed system is obtained as 95.28% and it compared with conventional methods such as Rogers Ratio Method (RRM), Duval Triangle Method (DTM) and Duval Pentagon Method (DPM) and other AI based methods such as Radial Basis Neural Network (RBNN), k-nearest neighbors (KNN). The diagnosis accuracy of other conventional and AI based methods are less than 90% for the collected dataset.
Keywords: Transformer, dissolved gas analysis, two tier fuzzy logic controller, fault diagnosis, fault prognosis, gated recurrent network, health index, criticality index
