Brushless Direct Current (BLDC) motor is a 3-phase brushless permanent sine wave motor fed from an adjustable oscillated inverter managed through a halleffect sensor. A BLDC motor is a DC motor with an armature on the stator side and a field winding on the rotor side. BLDC is an improved 3-phase brushless permanent magnet sine wave motor where Sinusoidal Counter-Electromotive Force (SCE) is replaced by Trapezoidal Counter-Electromotive Force (TCE) in BLDC motor. The turn-off region in the counter-Electromotive Force (emf) of a BLDC motor must be smaller to commutate the phase current of the motor [1]. In 120 ° mode, trapezoidal back-emf will be constant for producing smooth torque. To turn off armature current, electronic commutation is replaced in BLDC motor, which abolishes the problems associated with firing and enervating, thereby making a BLDC a robust machine [1].*Author for correspondence BLDC drive has been replaced from Induction Motor (IM) drive in servo applications [2-4] with improved power factor. To improve the speed and to provide smooth torque during transient conditions, torque and flux are controlled using conventional two-level inverter systems. The phase current of the inverter gets distorted, which produces torque distortion in a conventional method. Several converter topologies with DC-DC converters are suggested to improve the torque distortion. Conventional three-level inverters create large back emf and fast switching action during high-speed operation. The three-level inverter produces a large current ripple which leads to torque ripple and vibration. The problem associated can be reduced by replacing conventional three-level inverter with Single-Ended Primary Inductor (SEPI) converter fed BLDC motor [5,6]. The main goal of our suggested method is to minimize the torque distortion and improve the performance of the BLDC motor.

This paper presents the study of power system transients using wavelet transform. Transients due to energization of capacitor banks and due to fault has been taken for consideration. The wavelet transform generates wavelet coefficients for the generated transients. Using Parsevals's theorem, energy and standard deviation are retrieved for transients. The wavelet transform is used to produce instantaneous frequency vectors of the signals, and then the energies of these vectors, obtained using Parseval's theorem, are utilized for the classification of different transients. The advantage of the proposed algorithm is its ability to distinguish different transients based on frequency change. The performance of this algorithm is bought bespeak by simulation of different events using MATLAB & SIMULINK software. The test results show that the new algorithm is very fast and accurate in identifying events. Keyword: Transients due to fault, Oscillatory transients, Wavelet transform and Parseval's theorem. I. INTRODUCTION Transients in power systems are temporary over voltages or over currents of short duration that lasts from few nano seconds to few milliseconds. The duration for which transients lasts is very insignificant when compared to the total operating time of the power system. But they cause immediate and most severe danger to sensitive electrical and electronic equipments, fire in some buildings, blackout in a city and shutdown of a plant, etc. Almost 80% of transients are internally generated like normal switching on or off of equipments, heating and ventilation systems, etc. Every industrial machine on power system practically causes transients or is adversely affected by transients. For better understanding the nature of the transient, they have to be sampled at a higher sampling rate because they are very fast and short duration waves [1]. In literature, there are several signal processing techniques like Fourier Transform (FT), Short-Time Fourier Transform (STFT), S-Transform, wavelet transform (WT) and wavelet packet transform (WPT) are used for examining the transients. Fourier Transform and Short-Time Fourier Transform can be used only for a fixed window width which is inadequate for the analysis of the transient non-stationary signals [2]. In modern spectrum and harmonic analysis, Discrete Fourier Transform (DFT) is used to monitor and assess the recorded data. Transient signal tracking using DFT is not successful, because of its fixed length window. The DFT method gives magnitude and phase angle of different frequency components of a periodic and stationary voltage or current waveform. Rectangular sampling windows of 10 cycles width in 50Hz power system is used and grouping of output bins of DFT analysis is done to compute the voltage and current waveform harmonic distortion. However DFT analysis only provides information in the frequency domain with a resolution that depends on the width of the time window. It doesn't give any time information about the signal provided [3]. The wavelet transform approac...

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