The application of wavelets to condition monitoring of energy conversion equipment

The aim of this research is to provide new condition monitoring techniques for energy conversion equipment such as electrical machines, shipboard power supplies and fuel cells.; We have presented a new method for the detection of broken rotor bars in induction machines operating under direct transient conditions. The method of detection employs an adaptive filter that removes the non-stationary fundamental from the starting current. A wavelet analysis of the residual current is presented and shows that broken rotor bars can be detected. This method has advantages over current steady-state techniques that are not accurate when applied to machines that operate predominantly in the transient such as motor operated valves and wind generators.; Stator faults contribute to 38% of all electrical machine failures. The detection of inter-turn stator faults has therefore been the focus of research for many years. The contribution of this research is to formulate a method of turn fault detection that is applicable when these machines operate during transient conditions. The machines used in this research are doubly-fed induction generators. A new combination of the Extended Park's vector, wavelet analysis and a statistical approach is used to detect turn faults in doubly fed induction generators.; The shipboard integrated power system IPS is an ungrounded system and is designed to operate even under faulted conditions. There is no physical connection between the power system and the ship hull, however the natural capacitance of the system causes currents to flow through the virtual ground (ship's hull). The magnitude of the fault currents are very low and could easily go undetected. The contribution of this research is to formulate fault detection techniques that can determine if certain grounding faults have occurred as well as their location. These faults include single line to ground faults, phase to phase faults and three phase faults.; Fuel cells are becoming increasingly popular in the automotive and distributed generation field. In an attempt to develop the foundation for a fuel cell condition monitoring system, we have examined the latest models so that we are able to relate the measured external parameters, such as voltage and current, to the physical defects found inside the fuel cell. The types of internal defects that we are attempting to detect are the degradation in the electrodes and catalyst impedance, as well as changes in the double layer capacitance which affects the time constant of the fuel cell. A wavelet analysis of the voltage and current waveforms show that these defects can be detected as they occur over time.