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The dissolved gas-in-oil analysis is a prevailing methodology being extensively utilized to diagnose incipient faults in oil-immersed power transformers. However distinct approaches have been implemented to find out dissolved gas analysis (DGA) results, they may sometimes fail to diagnose precisely. The incipient fault identification accuracy of various artificial intelligence (AI)-based methodology is assorted with change of input parameters. Thus, selection of input variable to an AI model is major research area. In this paper, principle component analysis algorithm using RapidMiner is applied to 360 experimental datasets, imitated in lab to identify most pertinent input variables for incipient fault classification. Thereafter, multi-class Extreme Learning Machine (ELM) technique is implemented to classify the incipient faults of power transformer and its performance is compared with artificial neural network, gene expression programming, fuzzy-logic, and support vector machine. The compared result shows that ELM provides better diagnosis results up to 100% accuracy at proposed input variable in short of time period which is helpful in on-line condition monitoring. [ABSTRACT FROM AUTHOR]

The diagnosis of incipient fault is important for power transformer condition monitoring. Incipient faults are monitored by conventional and artificial intelligence based models. Key gases, percentage value of gases, and ratio of the Doernenburg, Roger, IEC methods are input variables to artificial intelligence models, which affects the accuracy of incipient fault diagnosis, so selection of the most influencing relevant input variable is an important research area. With this main objective, Waikato Environment for Knowledge Analysis software is applied to 360 simulated samples having different operating lives to find the most influencing input parameters for incipient fault diagnosis in the gene expression programming model. The Waikato Environment for Knowledge Analysis identifies%C2H2,%C2H4,%CH4, C2H6/C2H2, C2H2/C2H4, CH4/H2, C2H4/C2H6, and C2H2/CH4as the most relevant input variables in incipient fault diagnosis, and it is used for fault diagnosis using different artificial intelligence methods,i.e., artificial neural networks, fuzzy logic, support vector machines, and gene expression programming. The compared results shows that gene expression programming gives better results than the artificial neural network, fuzzy logic, and support vector machine with accuracy variation from 98.15 to 100%, proving the gene expression programming method can be used in transformer fault diagnosis research. [ABSTRACT FROM AUTHOR]

This article presents an adaptive control algorithm for power quality enhancement of a synchronous reluctance generator based stand-alone generator system with a battery energy storage system by employing the compensation capabilities of a voltage source converter. The proposed synchronous reluctance generator system is implemented using a 3.75-kW synchronous reluctance generator, a voltage source converter with a battery support, and a digital signal processor. The control algorithm is used for estimation of reference currents keeping in view the limited rating of the voltage source converter. A modified leaky least mean square algorithm using a variable-step–variable-weight factor is proposed for the control. The variable-step–variable-weight least mean square algorithm decomposes the load currents, extracts its real positive-sequence component to track the unit vectors by varying the rate of convergence of the algorithm, and accordingly tunes the weights, thus enabling selective compensation. The proposed control algorithm offers satisfactory harmonics mitigation, compensation of reactive power demand, and load balancing of a stand-alone synchronous reluctance generator system under linear/non-linear and dynamic loads. [ABSTRACT FROM AUTHOR]