A case study on fault detection in power transformers using dissolved gas analysis and electrical test methods

This paper presents methodologies for power transformer fault diagnosis using dissolved gas analysis and electrical test methods. These methods are widely used in determination of inception faults of power transformers. Dissolved gas analysis test provides fault diagnosis of power transformers. On the other hand the electrical test methods are used for detection of root causes and fault locations and they provide more specific information about the faults. The aim of this work is to study the faults that are measured and recorded in Turkish Electricity Transmission Company (TEIAS) power systems. For this purpose, four specific cases are considered and analyzed with dissolved gas analysis and electrical testing methods. Three of these cases are defective situations and one case is a non-defective situation. These real cases of measurements have been analyzed with both methods in detail. Assessment results showed that a single method cannot yield accurate enough results in some specific fault conditions. Therefore it was concluded that cooperation of both methods in the assessment of fault condition gives more trustworthy results. © JES 2016 on-line : journal/esrgroups.org/jes

Diagnosis of power transformer faults based on multi-layer support vector machine hybridized with optimization methods

This article presents an intelligent diagnosis and classification method for power transformer fault classification based on dissolved gas analysis: the support vector machine. It is a powerful algorithm for classification of faults that needs a limited set of small sampling data, a case of applications with non-linear behavior, and a high number of parameters; however, appropriate model parameters must be determined carefully. The selection of parameters has a direct effect on the machine's classification accuracy. In this study, a multi-layer support vector machine classifier is optimized by a grid search method and three heuristic approaches: (1) genetic, (2) differential evolution, and (3) particle swarm optimization algorithms. The performance analysis of the support vector machine hybridized with these optimization methods is demonstrated using the same classification set. The employed structure has five support vector machine layers, each of which uses a Gaussian kernel function due to its advantages of needing one parameter for optimization and providing excellent classification ability for non-linear data. The proposed approach gives highly accurate performance for diagnosis of power transformers. The support vector machine optimized with the particle swarm optimization algorithm has the best accuracy and requires less computational time compared to the other methods. © 2016, Copyright © Taylor & Francis Group, LLC

DESING OF AN INDUCTION HEATING SYSTEM WITH A HALF BRIDGE SERIES RESONANT INVERTER FOR DOMESTIC COOKING APPLICATIONS

The technique of heating by electromagnetic induction is a well established and an invaluable tool for industries engaged in the heat treatment of hot working of metals. In this study, it is shown that induction heating can succesfully be applied for domestic cooking applications. For this purpose, a complete induction heating system has been designed and a prototype of the proposed system has been built and tested. By the use of resonant technique, switching losses have been reduced significantly. Coil design procedure of the proposed system was also given. It is shown that induction heating system has many advantages among the conventional heaters

ANALYSIS OF THE HARMONIC LOSSES WITH ARTIFICIAL NEURAL NETWORKS IN UNBALANCED SYSTEM LOSSES USING BALANCED ELECTRIC POWER SYSTEM DATA

The losses in the power systems should be low as possible as. Saving energy instead of loses (kWh) in power utilities can supply much more energy to the consumers. The lower losses the more energy is saved and thus the power system becomes more economical. In recent years, the increasing number of applications and power ratings of the devices which have nonlinear voltage-current characteristics cause voltage waveform distortion and additional losses. While evaluating losses considering harmonics will provide more contribution to obtain more accurate results. In this study, Artificial Neural Networks (ANN) method has been presented to predict the harmonic losses in unbalanced power systems by using the data from balanced power system with nonlinear loads

Artificial Neural Networks and Adaptive Neuro-Fuzzy Inference Systems Approaches to Forecast the Electricity Data for Load Demand, an Analysis of Dinar District Case

Short-term load forecasting is an important issue for the electric power system in efficiently managing the network and reducing operating costs. In addition, with the recent improvements in distributed generation and storage systems, this has become even more important. Access to the high-resolution dataset derived from smart counters allows new forecasting strategies to evolve to match distributed load on the demand side. In this study, short-term load forecasting (STF) of a small region was performed using Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) methods. For load forecasting, the electricity consumption and temperature data for the year 2017 were used as input to the network and next hour demand was predicted. The smallest forecasting error is investigated with the Mean Absolute Percent Error (MAPE) and Root Mean Square Error (RMSE) performance criteria. It showed that RMSE is better than ANFIS with 616.2753 and MAPE 8.8688 prediction error. © 2018 IEEE

Fault diagnosis of oil-immersed power transformers using common vector approach

This paper considers the problem of classifying power transformer faults in the incipient stage by using dissolved gas analysis (DGA) data. To solve this problem with high accuracy, we propose to use the common vector approach (CVA) that is a successful classifier when the number of data is insufficient. The feature vector required for the training and testing phases of the CVA is established by using both raw dissolved gas analysis data and some characteristics extracted from this data. The performance of the proposed method is evaluated over DGA data sets supplied from the Turkish Electricity Transmission Company and is compared with some conventional and intelligent methods in terms of classification accuracy and training/testing duration. The achieved results show that the proposed method exhibits superior performance than that of the other methods compared in the meaning of both diagnosis accuracy and computational time. Analysis performed on the physical faults, where the transformers fault types are verified with the electrical test methods, confirms the validity and reliability of the proposed method, as well. Being free from parameter settings is another advantage of this method for using it in online oil-gas analysis applications. © 2020 Elsevier B.V