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Online Model-Based Condition Monitoring for Brushless Wound-Field Synchronous Generator to Detect and Diagnose Stator Windings Turn-to-Turn Shorts Using Extended Kalman Filter

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PROJECT TITLE :

Online Model-Based Condition Monitoring for Brushless Wound-Field Synchronous Generator to Detect and Diagnose Stator Windings Turn-to-Turn Shorts Using Extended Kalman Filter

ABSTRACT:

In this paper, a model-primarily based approach is proposed to detect and diagnose stator winding fault in the Brushless wound-field synchronous generator (BWFSG). The extended Kalman filter is employed as a state and parameter estimation technique for the proposed model-primarily based approach. The mathematical model of the BWFSG with stator winding fault is developed and simplified for on-line implementation. An experimental check-rig is used to acquire the required inputs for the developed state estimation technique. The estimated rotor currents and fault parameter are analyzed to spot key signatures for condition monitoring (CM). The harmonic elements such as the second harmonic elements of the estimated field and damper currents, and the rms price of the estimated fault parameters are identified as appropriate signatures for winding fault and diagnose. Based mostly on the identified signatures, a model-based CM algorithm is proposed and validated in real time. The validation results confirmed that the proposed algorithm is in a position to detect and diagnose winding inter-flip short-circuit faults in real-time reliably.


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Online Model-Based Condition Monitoring for Brushless Wound-Field Synchronous Generator to Detect and Diagnose Stator Windings Turn-to-Turn Shorts Using Extended Kalman Filter - 4.8 out of 5 based on 46 votes

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