Intelligent algorithm based robust and fault tolerant control of induction machines

Abstract

Induction machines (IMs) are the driving force in industries such as manufacturing, transportation and wind power generation. Hence, it is essential to reliably detect faults in IMs so as to enhance production quality in manufacturing and avoid operational degradation. However, it is still challenging to reliably detect faults in IMs as fault feature properties could change under variable IM operating conditions. The first objective of this thesis is to develop an enhanced empirical mode decomposition (EEMD) technique to detect an IM broken rotor bar (BRB) fault based on motor current signature analysis. In the developed EEMD technique, a phase insensitive similarity function is initially suggested to determine the representative intrinsic mode functions (IMFs). Moreover, an optimized adaptive multi-band filter is suggested to process the current spectrum and to recognize the fault characteristic features. Likewise, a modified whale optimization algorithm (MWOA) is proposed, which is utilized to optimize the parameters in adaptive multi-band filter. Finally, a reference function is recommended to enhance feature properties and IM fault detection. The effectiveness of the proposed EEMD technique is verified through experimental analysis under different IM operating conditions. [...]