Using disturbance records to automate the diagnosis of faults and operational procedures in power generators


Today, it is a typical apply in power generation utilities to observe the generation units using digital fault recorders. As the disturbance records are usually analysed and stored at a central office or management centre, it's become tough for engineers to analyse all this data. Some of the main steps in developing automated diagnosis tools to help in this task are the segmentation and have extraction of the recorded signals and decision making. This study presents a strategy to extract meaningful data from every phase of a disturbance signal. Within the approach described in this study, the segmentation is performed by an extended complicated Kalman filter. The main features extracted from each phase are symmetrical elements at elementary frequency of voltage and current signals. Feature extraction uses root-mean-square values to obtain the symmetrical elements of the 3 phase quantities. This methodology focuses on offline analysis of fault recorder knowledge of power generators and it's developed not only to fault analysis, however additionally to verify traditional operational procedures, from which result most of the disturbance records. This study additionally describes an knowledgeable system which will be used to automatically classify every record into known classes, focusing the engineer's attention to the foremost relevant occurrences.

Did you like this research project?

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

PROJECT TITLE : A Multitask Learning Model for Traffic Flow and Speed Forecasting ABSTRACT: Accurate short-term traffic state forecasting is beneficial to Intelligent Transportation Systems (ITS) research and applications. This
PROJECT TITLE : A Novel Electricity Price Forecasting Approach Based on Dimension Reduction Strategy and Rough Artificial Neural Networks ABSTRACT: In deregulated energy markets, accurate electricity price forecasting (EPF)
PROJECT TITLE : A Supervised Machine Learning Algorithm for Heart Rate Detection Using Doppler Motion-Sensing Radar ABSTRACT: The development of vital sign radar technology has shown to be an effective tool for measuring various
PROJECT TITLE : Comparing Different Resampling Methods in Predicting Students Performance Using Machine Learning Techniques ABSTRACT: Predicting students' performance is one of the most valuable and important research areas in
PROJECT TITLE : Convolutional Recurrent Neural Networks for Glucose Prediction ABSTRACT: Blood glucose control is critical for diabetes management. Machine learning techniques are used in current digital therapy approaches for

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

Project Enquiry