Browsing by Author "St-Onge, Xavier F."

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    A battery storage system to support the frequency stability of grid-connected PMG-based wind energy conversion systems
    (University of New Brunswick, 2019) St-Onge, Xavier F.; Saleh, Saleh
    Over the past few decades, the generation of electric power has become concerning for the environment. As a result, several changes to power systems have been introduced. Among these changes, is the significant integration of distributed generation units (DGUs). For, DGUs strive to offset conventional generation by utilizing renewable energy. Largely, this approach to generation has trended toward wind systems. Modern wind-energy-conversion-systems (WECSs) have favored variable speed permanent magnet generator (PMG)-based topologies. The relative novelty of PMG-based WECSs has emphasized challenges that have limited their applicability. Of these challenges, PMG electromechanical torque pulsations and point-of-common coupling (PCC) frequency instability are regularly regarded as the most troublesome. On one hand, PCC frequency variations are dependent on the stable delivery of power by the interconnected WECS. On the other hand, generator torque pulsations are a consequence of the extensive use of power electronic converters (PECs) in PMG-based WECSs. As of late, energy storage systems (ESSs) and novel PEC technologies are being recommended to overcome these challenges. This thesis aims to develop and evaluate a split-bus PMG-based WECS. The developed WECS includes a generator-charged and PCC-discharged battery storage system (BSS) to support PCC frequency stability, as well as a modified cascaded H-bridge (MCHB) generator-side PEC to reduces PMG torque pulsations. The developed system is modeled in simulation and constructed in laboratory. Several operating conditions of wind speed, power command, and BSS charging are investigated. Experimental performance highlights the developed WECS’s ability in suppressing PMG torque pulsations while minimizing PCC frequency variation.
  • Thumbnail Image
    Item
    A symmetrical component feature extraction method for fault detection in induction machines
    (IEEE, 2019-09) St-Onge, Xavier F.; Cameron, James; Saleh, Saleh; Scheme, Erik J.
    Induction motors (IMs) are among the fully developed electromechanical technologies that are still in use today. Over the course of the last century, their structure, control, and operation have been undergone through several stages of development. Among stages of development, the automated control and continuous monitoring of IMs has benefited from the emergence of modern artificial intelligence (AI) methods. IM automation schemes have demonstrated the ability to provide machine fault detection and diagnosis (FDD) function. AI-based FDD methods in IMs have employed frequency-domain, time-frequency, and time-domain analyses as the basis of their feature extraction schemes. A promising feature extraction scheme is one that uses symmetrical components (SCs) in time-domain FDD systems. Current SC-based approaches, however, are limited in their generalizability to different fault classes, may require detailed machine models, and can suffer from computational limitations. In this paper, an improved feature extraction method that uses SCs for a pattern recognition based FDD scheme for three-phase (3φ) IMs will be presented. This novel feature extraction method will be implemented and verified experimentally to demonstrate high classification performance, increased generalizability, and low computational cost.