P. Supriya

Harmonic current estimation using blind signal processing techniques

Blind Source Separation methods are used for feature extraction in biomedical and image processing applications. An increased use of non-linear loads results in generation of harmonics, which cannot be easily identified in electric power systems. Using blind source separation methods like fastICA and efficient fastICA the harmonic currents are estimated in an interconnected system. In this work, a performance evaluation between these two methods for harmonic state estimation is done in the form of recording and analyzing the miniscule error that exists between the actual and estimated harmonic currents. The graphical results of a simple five bus system for the two methods are also discussed.

Implementation of robotic path planning using Ant Colony Optimization Algorithm

Mobile robot path planning is critical in the present day of automation. Several situations may occur for humans, like the environment may be dirty, hazardous, might cause death, or injury as in case of mining, detecting leakage in pipe, cleaning of pipe etc. where robots can be successfully employed. The idea of this paper is to develop a mobile robot that finds the shortest route from source to destination by using Ant Colony Optimization Algorithm with a single robot. The hardware used is iRobot Create interfaced to NXP LPC1768 Cortex M3 controller. The same is simulated using MATLAB. The output of the hardware is also made visible in Teraterm. It is observed that the mobile robot is enhanced with considerable skills to trace a path of optimum distance from source to destination without any collision in most of the situations, barring a few.

Integrated Kalman-Independent Component Analysis Method for Harmonic Current Estimation on an Interconnected Four Bus Simulated and Laboratory Model

Abstract Wide use of non-linear loads results in harmonic propagation throughout the entire power system. The harmonics generated in the power system by the harmonic injection buses need to be properly measured and quantified using minimal information about the power system network. Independent Component Analysis (ICA) provides several algorithms for harmonic state estimation, some of which are more accurate at specific harmonic frequencies. In this paper, the best ICA algorithm for steady state performance (i.e. the algorithm with the least error) is chosen and the resulting mixing matrix is processed by a Kalman Filter which functions as an optimal estimator. The harmonic state estimation is implemented on a simulated four bus system and a laboratory four bus model is also wired and the results of the work are presented.

Independent Component Approach for the Analysis of ECG Signals

Automated analysis of electrocardiogram (ECG) has got great attention for cardiac diagnosis in the recent years. This paper describes two different ECG analysis algorithms using Independent Component Analysis (ICA) algorithm. ICA refers to set of algorithms for blind source separation (BSS). The underlying principle is to separate N signals from a mix of different source contributions, into signals of independent components. The simulation is proposed to be done in MATLAB.

Fault distance identification in transmission line using STFT algorithm

This paper primarily aims in analyzing the performance of STFT algorithm in locating faults and tries to analyse its accuracy for mainly three conventional faults namely LL, LG, LLG faults and study variation in error for different fault times and fault location distances using MATLAB software. Various algorithms have been devised for fault classification of transmission line in the research field. However, completely optimized and efficient solution for fault identification and location is yet to be materialized. The computation time taken to perform the STFT algorithm was also estimated. The novelty of the work lies in using STFT for fault location as STFT has already been used for fault classification in literature. The least error achieved was marginally low. The test was conducted for 300km transmission line at 120km of fault distance.

Harmonic state estimation in a distribution generation system environment

Harmonics degrade the power quality of Power Systems. It becomes vital to compute the harmonic generated by non linear loads present in the Power System. With renewable energy becoming the future of the power technology, a Harmonic estimation in a distributed generation environment becomes indispensable. Harmonic State Estimation (HSE) in such an environment is attempted using time structured Independent Component Analysis (ICA) employing least information about the power system topology. Various ICA algorithms are used for HSE, of which COMBI (Combination of WASOBI and EFICA) is applied to a four bus model and is found to be more accurate than JADE in terms of certain performance measures. It is proposed to implement the HSE problem with suitable selection algorithms to intensify the performance.

Amplitude computation of harmonic voltages based on Adaptive Comb filter in a smart grid system

Harmonics are a major source of pollution in power system networks. These harmonic current and voltage are to be estimated with great accuracy. Several methods exist for the estimation of harmonic voltage magnitude like FFT, FIR filters etc. But these methods have a common drawback of requiring high sampling frequency and difficulty in making them adaptive. Adaptive Comb filter is a potential solution to these problems. It is a cascade filter requiring a comparatively low sampling frequency and gives a comparatively more accurate result. In this paper, simulation results for various sampling frequencies of Adaptive Comb filter are presented. The results are compared with other methods like FFT and FIR filter using figure of merit like maximum absolute error (MAE) and mean square error (MSE). The amplitude computation scheme is proposed to be implemented on a suitable embedded platform.

Noise Based Independent Component Analysis Model for Harmonic Current Estimation

In a deregulating environment, Independent Component Analysis (ICA) is used to estimate the harmonic currents of non linear loads as it does not require information about the topology of the network. However, analysis is done by ignoring the effect of various noises that creep into the measurement system. In the present work, the effect of environmental noise on a simple interconnected power system with five buses is taken up. The two algorithms namely Fast ICA (FICA) and Efficient Variant Fast ICA(EFICA) are used for the analysis. A fixed noise is added and it is eliminated using whitening technique The simulation results of both algorithms show that noise elimination by whitening technique is highly successful. However, EFICA gives better results than FICA when random fluctuations of load exist rather than when fixed variations exist.