Condition monitoring for identification and detection of fault in electrical machines

Abstract

Condition monitoring of electrical machines such as induction newlinemachines and transformers deployed in small and large scale industries is of newlinegreat interest to operating engineers in power system utilities. Condition newlinemonitoring of electrical machine is essential for monitoring the health of the newlinesystem under normal and abnormal operating conditions to avoid sudden newlinebreakdown of work flow, to minimize downtime and to enhance the life time newlineof these systems.This thesis focuses on application of Digital Signal Analyzing newlinetechniques to the signals acquired from commonly used electrical machines newlinelike the Squirrel Cage Induction Machines (motor and generator) and newlinetransformers. Their performance is analyzed through Conditional monitoring newlinefor detecting the probable faults occurring in them. Objective of this research newlinework is in the domain of electrical equipment fault detection based on Digital newlineSignal Processing techniques.Rotating machines like the induction motor and induction generator newlineare more prone to mechanical and electrical faults. Mechanical fault occurring newlinein three phase induction machines include broken rotor bar and machine newlineimbalance. Analyses of these faults have been done on electrical and magnetic newlineflux samples of data acquired from machines. The Digital techniques like newlinespectral analysis, histogram analysis are applied to detect the type, nature and newlinelocation of various faults newline newline