Dr. Vidya H. A.

Power transformer forms a very important link in the power system. A fault in transformer can cause a huge loss to the utility and consumer depending on the duration of the outage. Dissolved Gas Analysis (DGA) acts as a key tool to diagnose transformer fault based on gas ratios. In this paper an effort to predict Power transformer fault more precisely using kNN algorithm has been made. DGA data of various transformer oil samples were collected and analyzed to select the best kNN algorithm to be used and to observe the prediction accuracy.

In the recent decades Multilevel inverters have been attracted the wide interest of researcher as well as industry for high-power and medium-voltage control. Additionally, they can combine switched waveforms with lower levels of harmonic distortion compared with the conventional two-level converter. The multilevel model is used to decrease the harmonic distortion in the output waveform without decreasing the inverter output power. This paper presents the most important popular topologies like diode-clamped or neutral point clamped inverter, flying capacitor or capacitor-clamped inverter, and cascaded H bridge multilevel inverter. Most commonly used modulation technique such as Sinusoidal, Modified and Random Pulse Width Modulation, Third harmonic injection, Space Vector Pulse Width Modulation Technique (SVPWM), Delta Modulation, Selective Harmonic Elimination (SHE) and Wavelet Modulation Technique (WM) are discussed and a review on CCHMLI with most frequently used control techniques such as SPWM and SHE methods are discussed enabling improvement in technology in both industry and research areas

The Industrial furnaces are used globally for wide range of applications from lab scale to complete production lines. They provide continuous high temperatures of 300°C to 3000°C with precise control of temperature and material flow to ensure consistent and uniform output. The furnace operators that deliver at higher efficiencies are prone to power supply system challenges with greater potential for power quality implications on supply system. Hence such operators of high intensity power furnaces should consider design and development of appropriate mitigation techniques to upgrade the power supply quality. In this paper the mitigation of harmonics in Electric Arc Furnace is dealt with suitable passive filter and DSTATCOM techniques. Also, the performance analysis of Electric Arc Furnace has been carried out with MATLAB/SIMULINK based model using Sinusoidal pulse width modulation, hysteresis and the sliding mode controllers.

A closed loop bidirectional ac to ac buck boost converter circuit using power MOSFET is analyzed for reducing the output voltage sag and improving efficiency of the converter. The regenerative dc snubbers are used directly to power semiconductor switches to absorb energy stored in stray line inductances. These dc snubbers enhance the conversion efficiency as it has very simple structure consisting only of a capacitor. A fast response peak voltage detector for fast output voltage control is also proposed. It is observed from the simulation results obtained using MATLAB simulink, that the proposed closed loop scheme gives good dynamic and steady state performances with a high-quality output voltage, improved power factor, low harmonics, improved efficiency and significant reduction of the filter size.

It is essential to detect partial discharge (PD) as a symptom of insulation breakdown in high voltage (HV) applications. However accuracy of such measurement is often degraded due to the existence of noise in the signal. Wavelet Transform (WT) seems to be more suitable than traditional Fourier Transform in analyzing signals with interesting transient information such as partial discharge (PD) signals. In this paper a WT method with soft thresholding is used for signal denoising. PD signals and corona obtained from actual measurements with different voltage magnitudes are processed. Processed signals show the better result.

Power transformer forms a very important link in the power system. A fault in transformer can cause a huge loss to the utility and consumer depending on the duration of the outage. Dissolved Gas Analysis (DGA) acts as a key tool to diagnose faults in transformer based on gas ratios. In this paper an effort to predict Power transformer fault more precisely using modified fuzzy logic technique has been attempted. DGA data of various transformer oil samples were collected and analyzed to prove that the modified fuzzy logic technique provides a better accurate prediction.

The pre-processing of the Partial Discharge (PD) signal is very important and crucial stage in analyzing condition of the electrical insulation. The PD information is lost in the presence of various noises. The wavelet based adaptive Thresholding de-noising techniques are well suited for reducing the noise. This paper considers the various adaptive thresholding techniques such as Heursure, Minimaxi, VisuShrink, SureShrink, thresholding methods for denoising of the PD signals obtained in various insulation samples using the experimental setup. The PD pulses produced by the insulation samples were detected in the form of discharges through the impedance and is fed through a cable to the PD detector along with a amplitude modulating signal. The developed Wavelet Transform based adaptive thresholding algorithm suitably removes the interferences from the measured PD signals. The performance indices are evaluated which justifies the strength of the algorithm in denoising of experimental PD data.

A 1-phase AC chopper circuit is rigged up and analyzed with single phase induction motor for input power factor, harmonic profile and efficiency of the converter using FPGA real time controller. The various PWM techniques such as symmetrical ramp-DC PWM (SRDPWM), asymmetrical ramp-triangular PWM (ARTPWM), asymmetrical sinusoidal PWM type-1 [ASPWM1] and asymmetrical sinusoidal PWM type-2 [ASPWM2] techniques are generated using FPGA real time controller in order to analyze these electrical parameters. It is observed from experimental results that the SRDPWM technique gives lesser total harmonic distortion [THD] of output voltage and input current than other techniques. It is also observed that the ASPWM1 switching strategy gives high input power factor, more output voltage and high efficiency and moderate improvement of harmonic profile for the entire range of modulation index MI compared to other PWM techniques. Hence ASPWM1 technique is preferred for AC chopper for better input power factor, output voltage, harmonic profile and efficiency of the converter than other techniques that renders easy filteration at lower cost.

Partial Discharge (PD) signal denoising is very significant in analyzing its characteristics and its effect on high voltage insulation equipments. Mainly, the PD information is lost in the presence of various noises. The wavelet transform provides a better platform for PD signal pre and post processing. The wavelet adaptive thresholding denoising techniques provides a better method to reduce noise. This paper adopts the various adaptive thresholding techniques such as VisuShrink, SureShrink, combination of the two called Heursure and the minimax thresholding which are broadly classified as Global and Local thresholding methods. The algorithm presents the comparative analysis for the selection of optimal wavelet. Once the optimal mother wavelet is chosen, selection of best thresholding rule is identified by comparing the values of signal to noise ratio (SNR), mean square error (MSE) and root mean square error (RMSE) of all the techniques. The algorithm also presents the comparison between Hard and Soft thresholding. It is shown that the soft thresholding is best suited to remove the noise, but weak in preserving the edges and the hard thresholding is best suited for preserving the edges, but weak in denoising the signal. The simulated damped exponential pulse (DEP) and damped oscillatory pulse (DOP) has been used to check the performance of the algorithm.

Wavelet Transform methods are effective for de-noising of partial discharge (PD) signals. Base wavelets are related to distortion of PD signals de-noised by wavelet methods. This paper presents a level dependent wavelet selection scheme for de-noising of PD and called the energy based wavelet selection (EBWS) scheme, because an energy criterion is proposed for the scheme. In the proposed energy criterion, a base wavelet is selected as an optimal base wavelet if it can generate an approximation with the largest energy among all base wavelets for selection at each level. The simulated damped exponential pulse (DEP) and damped oscillatory pulse (DOP) has been used to check the performance of the proposed method. In comparison with the scale independent scheme, the wavelet method, based on the EBWS, generates significantly smaller waveform distortion and magnitude errors in the de-noised PD signals. The results of the tests carried out show clearly that this technique can produce excellent results when applied to simulated PD data

The objective of the electric utility is to deliver sinusoidal voltage at fairly constant magnitude throughout their system. As the load is more nonlinear, complexities also increase since these nonlinear loads produce harmonic currents. These harmonic currents result in voltage and current distortions that can adversely impact the system performance in different ways. With the increase in harmonic producing loads at the installation, severity to address the issue also increases. As an impact of these phenomena, two important concepts are to be borne in mind with regard to power system harmonics. They are harmonic current generated due to nonlinear loads such as computers and printers, and the second is the effect of harmonic currents [1, 2]. In this work, the harmonics generated due to computer and printer is analyzed using power quality analyzer and harmonics are eliminated using wavelet transform.