PROJECT TITLE :

Adaptive neuro-fuzzy inference system approach for simultaneous diagnosis of the type and location of faults in power transformers

ABSTRACT :

Electrical, mechanical, and thermal stresses will degrade the quality of the insulation in power transformers, inflicting faults [one]. Many methods are used for fault diagnosis in transformers, e.g., dissolved gas analysis (DGA), measurement of breakdown voltage, and tan ??????, pollution, sludge, and interfacial tension tests [a pair of]. Of those, DGA is the foremost frequently used. Thermal and electrical stresses result in fracture of the insulating materials and the release of several gases. Analysis of these gases may offer information on the sort of fault. Numerous standards are instructed for the identification of transformer faults primarily based on the ratio of dissolved gases within the transformer oil, e.g., International Electrotechnical Commission (IEC) standards [three]?????????[7], and these standards has been quoted in many papers, e.g., [eight]?????????[15]. However, they are incomplete in the way that, in some cases, the fault can not be diagnosed or located accurately. Intelligent algorithms, e.g., wavelet networks [sixteen], neuro-fuzzy networks [17], [18], fuzzy logic [8], [twelve], and artificial neural networks (ANN) [two], [nine], [10], [19], [twenty] have been used to enhance the reliability of the diagnosis. In these algorithms, the sort of fault is diagnosed 1st, and therefore the fault is then located using the ratio of the concentrations of CO2 and CO dissolved in the transformer oil [twenty one], [22]. The algorithms don't seem to be entirely satisfactory. The wavelet network has high efficiency however low convergence, the fuzzy logic technique has a limited variety of inputs and, in some cases, it is terribly difficult to derive the logic rules, and therefore the ANN want reliable coaching patterns to enhance their fault diagnosis performance. During this paper, we present a new method for simultaneous diagnosis of fault sort and fault location. It uses an adaptive neuro- fuzzy inference system (ANFIS) [23]?????????[27], based mostly on DGA. The ANFIS is initial ?????????trained????????? in accordance with IEC 599 [three], so that it acquir- s some fault determination ability. The CO2/CO ratios are then thought-about extra input information, enabling simultaneous diagnosis of the sort and location of the fault. The results obtained by applying it to six transformers are presented and compared with the corresponding results obtained using ANN and some other standards and ways.


Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here


PROJECT TITLE : Adaptive Pulse Wave Imaging Automated Spatial Vessel Wall Inhomogeneity Detection in Phantoms and in-Vivo ABSTRACT: Imaging the mechanical characteristics of the artery wall may aid in the diagnosis of vascular
PROJECT TITLE : An Adaptive and Robust Edge Detection Method Based on Edge Proportion Statistics ABSTRACT: One of the most important preprocessing steps for high-level tasks in the field of image analysis and computer vision is
PROJECT TITLE : Learned Image Downscaling for Upscaling Using Content Adaptive Resampler ABSTRACT: SR models based on deep convolutional neural networks have shown greater performance in recovering the underlying high-resolution
PROJECT TITLE : Multipatch Unbiased Distance Non-Local Adaptive Means With Wavelet Shrinkage ABSTRACT: Many existing non-local means (NLM) approaches either utilise Euclidean distance to quantify the similarity between patches,
PROJECT TITLE : Depth Restoration From RGB-D Data via Joint Adaptive Regularization and Thresholding on Manifolds ABSTRACT: By integrating the properties of local and non-local manifolds that offer low-dimensional parameterizations

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry