On-line Condition Monitoring, Fault Detection and Diagnosis in Electrical Machines and Power Electronic Converters

Authors

Alberto Berzoy, Florida International UniversityFollow

Document Type

Dissertation

Degree

Doctor of Philosophy (PhD)

Major/Program

Electrical Engineering

First Advisor's Name

Osama Mohammed

First Advisor's Committee Title

Committee Chair

Second Advisor's Name

Bilal El-Zahab

Second Advisor's Committee Title

committee member

Third Advisor's Name

Mark Roberts

Third Advisor's Committee Title

committee member

Fourth Advisor's Name

Sakhrat Khizroev

Fourth Advisor's Committee Title

committee member

Fifth Advisor's Name

Jean Andrian

Fifth Advisor's Committee Title

committee member

Keywords

electrical engineering; electrical and electronics; power and energy

Date of Defense

4-22-2018

Abstract

The objective of this PhD research is to develop robust, and non-intrusive condition monitoring methods for induction motors fed by closed-loop inverters. The flexible energy forms synthesized by these connected power electronic converters greatly enhance the performance and expand the operating region of induction motors. They also significantly alter the fault behavior of these electric machines and complicate the fault detection and protection. The current state of the art in condition monitoring of power-converter-fed electric machines is underdeveloped as compared to the maturing condition monitoring techniques for grid-connected electric machines.

This dissertation first investigates the stator turn-to-turn fault modelling for induction motors (IM) fed by a grid directly. A novel and more meaningful model of the motor itself was developed and a comprehensive study of the closed-loop inverter drives was conducted. A direct torque control (DTC) method was selected for controlling IM’s electromagnetic torque and stator flux-linkage amplitude in industrial applications. Additionally, a new driver based on DTC rules, predictive control theory and fuzzy logic inference system for the IM was developed. This novel controller improves the performance of the torque control on the IM as it reduces most of the disadvantages of the classical and predictive DTC drivers. An analytical investigation of the impacts of the stator inter-turn short-circuit of the machine in the controller and its reaction was performed. This research sets a based knowledge and clear foundations of the events happening inside the IM and internally in the DTC when the machine is damaged by a turn fault in the stator. This dissertation also develops a technique for the health monitoring of the induction machine under stator turn failure. The developed technique was based on the monitoring of the off-diagonal term of the sequence component impedance matrix. Its advantages are that it is independent of the IM parameters, it is immune to the sensors’ errors, it requires a small learning stage, compared with NN, and it is not intrusive, robust and online. The research developed in this dissertation represents a significant advance that can be utilized in fault detection and condition monitoring in industrial applications, transportation electrification as well as the utilization of renewable energy microgrids.

To conclude, this PhD research focuses on the development of condition monitoring techniques, modelling, and insightful analyses of a specific type of electric machine system. The fundamental ideas behind the proposed condition monitoring technique, model and analysis are quite universal and appeals to a much wider variety of electric machines connected to power electronic converters or drivers. To sum up, this PhD research has a broad beneficial impact on a wide spectrum of power-converter-fed electric machines and is thus of practical importance.

Identifier

FIDC004096

ORCID

https://orcid.org/0000-0003-3195-1274

Recommended Citation

Berzoy, Alberto, "On-line Condition Monitoring, Fault Detection and Diagnosis in Electrical Machines and Power Electronic Converters" (2018). FIU Electronic Theses and Dissertations. 3643.
https://digitalcommons.fiu.edu/etd/3643