On the Application of Artificial Neural Network for Classification of Incipient Faults in Dissolved Gas Analysis of Power Transformers

Abstract

Oil-submerged transformer is one of the inherent instruments in the South African power system. Transformer malfunction or impairment may interpose the operation of the electric power distribution and transmission system, coupled with liability for high overhaul costs. Hence, recognition of inchoate faults in an oil-submerged transformer is indispensable and it has turned into an intriguing subject of interest by utility owners and transformer manufacturers. This work proposes a hybrid implementation of a multi-layer artificial neural network (MLANN) and IEC 60599:2022 gas ratio method in identifying inchoate faults in mineral oil-based submerged transformers by employing the dissolved gas analysis (DGA) method. DGA is a staunch practice to discover inchoate faults as it furnishes comprehensive information in examining the transformer state. In current work, MLANN was established to pigeonhole seven fault types of transformer states predicated on the three International Electrotechnical Commission (IEC) combustible gas ratios. The designs enmesh the development of numerous MLANN algorithms and picking networks with the optimum performance. The gas ratios are in accordance with the IEC 60599:2022 standard whilst an empirical databank comprised of 100 datasets was used in the training and testing activities. The designated MLANN design produces an overall correlation coefficient of 0.998 in the categorization of transformer state with reference to the combustible gas produced.

Fault Detection and Normal Operating Condition in Power Transformers via Pattern Recognition Artificial Neural Network

“…The dataset is composed of 815 samples used for training and validation phases obtained from reference [19][20][21][22][23][24][25]. The data is distributed as 691 normal operation samples, 52 thermal fault samples, and 72 electrical fault samples for five types of combustible gases (H 2 , CH 4 , C 2 H 2 , C 2 H 4 , and C 2 H 6 ).…”

“…Topics related to the application of artificial neural networks (ANNs) in the analysis of the operating conditions of power transformers and electrical energy systems have currently received attention [13][14][15]. In recent years, a series of studies and research have been published in this field, demonstrating the growing interest and relevance of these approaches [16][17][18][19].…”

A Review of Methods for Diagnosing Incipient Faults in Power Transformers using Dissolved Gas Analysis

“…During training, the main parameters of the network model are progressively adjusted until a mapping is established between the information on the dissolved gas ratios and the actual fault present [40]. In [41,42], an ANN trained using the Levenberg-Marquardt algorithm was developed to classify seven transformer incipient fault types. Three combustible gas ratios were calculated and the IEC ratio method was deployed in interpreting the fault present.…”

Fault Diagnosis of Centrifugal Chiller Based on Extreme Gradient Boosting

“…In addition, the operating conditions of chiller units are varied, and certain operating parameters are highly similar in the micro-fault state and the no-fault state in incipient faults [5]. Due to the complexity and uncertainty of the system, machine learning algorithms like Artificial Neural Networks (ANNs) [6] and support vector machines (SVMs) [7] were applied to the diagnosis of minor faults, and some results have been achieved. In the study of chiller fault diagnosis, study [8] used an ANN to diagnose various faults in chillers and found that the diagnosis of system faults is more difficult than local faults.…”