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In this paper, a softcomputing technique namely, Adaptive Neuro-Fuzzy Inference System (ANFIS) has been used to identify and locate the incipient faults developing in an oil immersed power transformer. Dissolved gas analysis (DGA) of the transformer insulating oil helps in effective condition motoring of a transformer. A number of interpretation st...
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Accurate diagnoses of faults in power transformers for life-long maintenance is ever-demanding. Transformers insulation excellence is known to deteriorate over time because of temperature fluctuationsand moisture contents, which significantly affects its duurability and functionality.developing an intelligent program for precise and efficient deter...
Dissolved gas analysis (DGA) is one of the regular routine tests accepted by worldwide utilities to detect power transformer incipient faults. While the DGA measurement has fully matured since the development of offline and online sensors, interpretation of the DGA results still calls for advanced approaches to automate and standardize the process....
Abstract— Accurate diagnoses of faults in power transformers for life-long maintenance is ever-demanding. Transformers insulation excellence is known to deteriorate over time because of temperature fluctuations and moisture contents, which significantly affects its durability and functionality. Developing an intelligent program for precise and effi...
In this study technical diagnostic tests and economical lifetime assessment of transformers are investigated to evaluate the overall health condition of working transformers. Two artificial intelligence models including artificial neural network and adaptive neuro-fuzzy inference system models are presented to determine the health index for transfo...
This article presents an algorithm for modelling an Adaptive Neuro Fuzzy Inference System (ANFIS) for power transformer paper conditions in order to estimate the transformer’s expected life. The dielectric characteristics, dissolved gasses, and furfural of 108 running transformers were collected, which were divided into 76 training datasets and ano...
Citations
E-Nose finds its use in a wide range of applications such as quality assessment in food processing to toxic gas identification in chemical industry either in the offline or online mode. Their usage can be extended to transformer condition monitoring in the online mode. Considering the importance of transformers in power system and the impact it could create if faults in them are unidentified or left unattended, their functioning should be monitored on a real time basis. This work describes the realization of a prospective E-Nose for online transformer incipient fault identification. The resistive gas sensor array has been simulated in real time using variable resistances forming one arm of a Wheatstone bridges. Separate variable resistances have been calibrated using characteristics of different fault gas sensors. The sensor array of the E-Nose helps to identify the transformer fault gases resulting from an incipient fault condition at the nascent stage itself and prompts for the necessary corrective action well before a catastrophic situation arises. Furthermore, ANFIS model of the Duval's Triangle (DT) method have been developed to facilitate the online classification of incipient faults. The ANFIS models of other popularly used incipient fault interpretation methods, reported in earlier works, have also been used for a comparative analysis on their diagnostic capabilities. The developed model has been tested using the fault cases of IEC-TC10 fault database and the results thus obtained have been found to be very promising.
In this work, a fine correlation among destructive and diagnostic parameters of transformer’s paper insulation is presented. The Degree of Polymerization (DP), directly assess the condition of the paper insulation strength and is measured through destructive procedure. Generally, 2-FAL, CO2 and CO are the aging products of paper decomposition and referred as diagnostics parameters. An Adaptive Neuro Fuzzy Inference System (ANFIS) is proposed and developed to estimate the value of DP as the function of amount of diagnostic parameters. The proposed system has an advantage of diagnosing the health of solid insulation without performing destructive tests. The diagnostic parameters are taken as inputs to the system to determine the value of DP. The system uses 630 data points for training the ANFIS model and follows a ten-fold cross validation approach. The average validation error has been determined to be. 0.0029. Further, the model’s performance has been assessed using experimental data. The optimal ANFIS model has been achieved by suitably selecting the number and type of membership function. The estimated value of DP has been found to conform to the experimental measurements for every case under test. The performance of this model has also been compared with a fuzzy inference system (FIS) model in a reported literature. The comparison shows that the ANFIS model determines the DP values with a greater degree of accuracy than the FIS model.
This research aims to determine the feasibility of a location to build Wind Power Plant (WPP). One of the challenges in wind energy development is to find area or location with high suitability for optimizing wind farm to allow the installation of as many wind turbines as possible within a limited area. To construct as many wind turbines as possible, the Adaptive Neuro Fuzzy Inference System (ANFIS) method is adopted using 7 parameters i.e wind potential, elevation, slope, type of land use, soil condition, access to roads, and populated areas and 2178 rules of fuzzy logic. The research was conducted in Kelara District, Jeneponto Regency, South Sulawesi, Indonesia. The most optimal result is obtained in the village of Tolo with a percentage of feasibility of 91% and error 0.48287. The output of this system can determine the feasibility of a location of wind turbine installation and can be used as a reference for determining wind farms in Indonesia and other countries.
Incipient faults are the slowly growing faults found within oil-immersed power transformers. These anomalies develop due to the deteriorating transformer insulating system. Various types of electrical, mechanical and environmental stress; manufacturing defects, in addition to, normal ageing phenomenon contribute to the degrading health of transformer insulation. The degradation results in the release of gases that remain dissolved in oil. The concentration of these dissolved gases has direct relationship with incipient transformer faults. Persistence of these faults within the transformers can lead to irreversible insulation damage. Hence transformers need to be put out of service, and consequently customers have to face interruptions in the power supply. The recognition of these faults in incipient stages can expand the service life of transformers and decrease these unplanned interruptions. DGA-based transformer condition monitoring is the widely accepted practice for reliable and economic functioning of power system. Several DGA interpretation methods are provided by experts to classify incipient faults. However, these interpretation methods have significant limitations such as wrong and uncertain diagnoses. The inaccuracy and uncertainty are mainly due to unidentified and missing codes, boundary problems and presence of concurrent incipient faults or multiple faults. There is lot of scope to address above-mentioned limitations of the DGA-based methods using different soft-computing techniques. These techniques can improve the performance of DGA methods when used alone or as a hybrid (more than one). Further, it is possible to enhance the performance of DGA-based methods by combining more than one soft-computing aided DGA methods, and by energy weighting of concentration of fault gases. A comprehensive study of application of soft-computing techniques in DGA-based transformer condition monitoring is presented in the article.
Decision making on transformer insulation condition based on the evaluated incipient faults and aging stresses has been the norm for many asset managers. Despite being the extensively applied methodology in power transformer incipient fault detection, solely dissolved gas analysis (DGA) techniques cannot quantify the detected fault severity. Fault severity is the core property in transformer maintenance rankings. This paper presents a fuzzy logic methodology in determining transformer faults and severity through use of energy of fault formation of the evolved gasses during transformer faulting event. Additionally, the energy of fault formation is a temperature-dependent factor for all the associated evolved gases. Instead of using the energy-weighted DGA, the calculated total energy of related incipient fault is used for severity determination. Severity of faults detected by fuzzy logic-based key gas method is evaluated through the use of collected data from several in-service and faulty transformers. DGA results of oil samples drawn from transformers of different specifications and age are used to validate the model. Model results show that correctly detecting fault type and its severity determination based on total energy released during faults can enhance decision-making in prioritizing maintenance of faulty transformers.
