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Biswal B.,GMR Institute of Technology | Mishra S.,Indian Institute of Technology Delhi
IET Generation, Transmission and Distribution | Year: 2014

This study presents a novel approach to localise, detect and classify non-stationary power signal disturbances using a modified frequency slice wavelet transform (MFSWT). MFSWT is an extension of frequency slice wavelet transform (FSWT), which provides frequency-dependant resolution with additional window parameters for better localisation of the spectral characteristics. An advantage of the MFSWT is attributed to the fact that the modulating sinusoids are fixed with respect to the time axis, whereas a localising scalable modified Gaussian window dilates and translates. Several practical power signals are considered for visual analysis using MFSWT, and the disturbance patterns are appropriately localised with unique signature corresponding to each type. This work also evaluates the detection capability of the proposed methodology and a comparison with earlier FSWT and Hilbert transform to show the superiority of proposed technique in detecting the power quality disturbances. A probabilistic neural network (PNN) based classifier is used for identifying the various disturbance classes. The spread parameter of the Gaussian activation function in PNN is tuned and its effect on the classification at different strengths of noise is studied. © The Institution of Engineering and Technology 2014. Source


Swain G.,GMR Institute of Technology
International Journal of Security and its Applications | Year: 2013

In this paper a pixel value differencing steganography based on the maximum difference of the neighboring pixel values have been proposed. There are four variants such as five, six, seven and eight neighbor differencing. In five neighbor differencing method the maximum difference amongst the five neighboring pixels such as upper, left, right, bottom and upper-right corner are used to take embedding decision. In six neighbor differencing method the maximum difference amongst the six neighboring pixels such as upper, left, right, bottom, upper-right corner, and upper-left corner are used to take embedding decision. In seven neighbor differencing method the maximum difference amongst the seven neighboring pixels such as upper, left, right, bottom, upper-right corner, upper-left corner and bottom-left corner are used to take embedding decision. In eight neighbor differencing method the maximum difference amongst all the eight neighboring pixels are used to take embedding decision. The message extraction process is very simple and does not require any knowledge of the original image. The experimental results show that the distortion is the minimum in five neighbor differencing. But the theoretical studies reveal that the hiding capacity is the highest in eight neighbor differencing. © 2013 SERSC. Source


Injeti S.K.,GMR Institute of Technology
World Journal of Modelling and Simulation | Year: 2016

This work presents a methodology for optimal parameter tuning of PID controller for frequency regulation in PV-Diesel two area power system. The gains of PV generator and diesel generator are optimized in a coordinated manner by employing biologically inspired optimized algorithms like Bat Algorithm (BA) and Firefly Algorithm (FA). Transfer function model of the diesel generator is interfaced with the photovoltaic system to create a two area hybrid power system. Load frequency control (LFC) of this two area hybrid power system is achieved by minimization of the objective function at different step load perturbation using optimization algorithms. Finally an analysis is carryout based on the obtained results. The simulation/coding of the entire system is performed in the MATLAB R2010a environment. Source


Biswal B.,GMR Institute of Technology | Biswal M.,Silicon Institute of Technology | Mishra S.,Indian Institute of Technology Delhi
IEEE Transactions on Industrial Electronics | Year: 2014

This paper proposes an empirical-mode decomposition (EMD) and Hilbert transform (HT)-based method for the classification of power quality (PQ) events. Nonstationary power signal disturbance waveforms are considered as the superimposition of various undulating modes, and EMD is used to separate out these intrinsic modes known as intrinsic mode functions (IMFs). The HT is applied on all the IMFs to extract instantaneous amplitude and frequency components. This time-frequency analysis results in the clear visual detection, localization, and classification of the different power signal disturbances. The required feature vectors are extracted from the time-frequency distribution to perform the classification. A balanced neural tree is constructed to classify the power signal patterns. Finally, the proposed method is compared with an S-transform-based classifier to show the efficacy of the proposed technique in classifying the PQ disturbances. © 1982-2012 IEEE. Source


Behera H.S.,Orissa Engineering College, Bhubaneswar | Dash P.K.,iversity | Biswal B.,GMR Institute of Technology
Applied Soft Computing Journal | Year: 2010

This paper presents a new approach for power quality time series data mining using S-transform based fuzzy expert system (FES). Initially the power signal time series disturbance data are pre-processed through an advanced signal processing tool such as S-transform and various statistical features are extracted, which are used as inputs to the fuzzy expert system for power quality event detection. The proposed expert system uses a data mining approach for assigning a certainty factor for each classification rule, thereby providing robustness to the rule in the presence of noise. Further to provide a very high degree of accuracy in pattern classification, both the Gaussian and trapezoidal membership functions of the concerned fuzzy sets are optimized using a fuzzy logic based adaptive particle swarm optimization (PSO) technique. The proposed hybrid PSO-fuzzy expert system (PSOFES) provides accurate classification rates even under noisy conditions compared to the existing techniques, which show the efficacy and robustness of the proposed algorithm for power quality time series data mining. © 2009 Elsevier B.V. All rights reserved. Source

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