Fault classification in SEIG system using Hilbert-Huang transform and least square support vector machine

• The transient performance analysis of self-excited induction generator (SEIG) during both balanced and unbalanced faults.
• Significance of fault detection and fault classification of SEIG is also investigated in this study.
• To classify different faults of SEIG system, least square support vector machine (LSSVM) is used using features extracted from HHT.

This paper presents the transient performance analysis of self excited induction generator (SEIG) during both balanced and unbalanced faults using stationary frame d–q axis. Significance of fault detection and fault classification is also investigated in this study. Current signal of SEIG is extracted. Non stationary distorted current waveforms of SEIG during fault condition are considered as superimposition of various oscillating modes. To separate out these oscillating components known as intrinsic mode functions (IMFs), empirical-mode decomposition (EMD) is used. Hilbert transform (HT) is applied on the first four IMFs to extract instantaneous amplitude and frequency. Combination of EMD and HT is known as Hilbert-Huang transform. To classify different faults of SEIG system, least square support vector machine (LSSVM) is used. Finally the superiority of the proposed SVM is established through comparison with support vector machine and probabilistic neural network.