An Accurate Bitcoin Price Prediction using logistic regression with LSTM Machine Learning model
Volume-3 | Issue-3
Design of Distribution Transformer Health Management System using IoT Sensors
Volume-3 | Issue-3
Energy Management System in the Vehicles using Three Level Neuro Fuzzy Logic
Volume-3 | Issue-3
Cloud Load Estimation with Deep Logarithmic Network for Workload and Time Series Optimization
Volume-3 | Issue-3
Design of a Food Recommendation System using ADNet algorithm on a Hybrid Data Mining Process
Volume-3 | Issue-4
Review on Data Securing Techniques for Internet of Medical Things
Volume-3 | Issue-3
Automatic Diagnosis of Alzheimer’s disease using Hybrid Model and CNN
Volume-3 | Issue-4
Population Based Meta Heuristics Algorithm for Performance Improvement of Feed Forward Neural Network
Volume-2 | Issue-1
Comparative Analysis of an Efficient Image Denoising Method for Wireless Multimedia Sensor Network Images in Transform Domain
Volume-3 | Issue-3
A Comprehensive Review on Power Efficient Fault Tolerance Models in High Performance Computation Systems
Volume-3 | Issue-3
An Integrated Approach for Crop Production Analysis from Geographic Information System Data using SqueezeNet
Volume-3 | Issue-4
An Accurate Bitcoin Price Prediction using logistic regression with LSTM Machine Learning model
Volume-3 | Issue-3
Design of Distribution Transformer Health Management System using IoT Sensors
Volume-3 | Issue-3
Design of a Food Recommendation System using ADNet algorithm on a Hybrid Data Mining Process
Volume-3 | Issue-4
Automatic Diagnosis of Alzheimer’s disease using Hybrid Model and CNN
Volume-3 | Issue-4
Effective Prediction of Online Reviews for Improvement of Customer Recommendation Services by Hybrid Classification Approach
Volume-3 | Issue-4
Acoustic Features Based Emotional Speech Signal Categorization by Advanced Linear Discriminator Analysis
Volume-3 | Issue-4
Analysis of Statistical Trends of Future Air Pollutants for Accurate Prediction
Volume-3 | Issue-4
Identification of Electricity Threat and Performance Analysis using LSTM and RUSBoost Methodology
Volume-3 | Issue-4
Review on Data Securing Techniques for Internet of Medical Things
Volume-3 | Issue-3
Volume - 4 | Issue - 1 | march 2022
Published
30 April, 2022
In an electric power system, power transformers are essential. Transformer failures can degrade the quality of the power and create power outages. Partial Discharges (PD) are a condition that, if not adequately monitored, can cause power transformer failures. This project addresses the diagnosis of PD in power transformer using the Phase Amplitude (PA) response of PRPD (Phase-Resolved Partial Discharge) patterns recorded using PD Detectors. It is a widely used pattern for analysing Partial Discharge. A Convolutional Neural Network (CNN) is used to classify the type of PD defects. The PRPD patterns of 240 PA sample images have been taken from power transformer of rating 132/11 KV and 132/25 KV for training and testing the network. The feature extraction has also been done using CNN. In this work, the classification of PD faults is done using a supervised machine learning technique. The three different classes of PD faults such as Floating PD, Surface PD and Void PD are considered and predicted using Support Vector Machine (SVM) classifier. Simulation study is carried out using MATLAB. Based on the results obtained, it is found that CNN model has achieved a greater classification accuracy and thereby the life span of power transformer is enhanced.
KeywordsPower Transformer Partial Discharge Support Vector Machine Convolutional Neural Network
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