In security systems, different verification and identification techniques like facial features, voice patterns, hand geometry, retinal patterns, etc., are used as biometric identifiers. Human iris technology is gaining attention as no two iris patterns are alike. A review of iris recognition techniques is given in the paper, “A Survey on Iris Recognition”, by Lenina Birgale and Manesh Kokare. A combination of standard Discrete Wavelet Transform (DWT) and histograms are proposed by the authors for secure recognition.

In the paper, “H-Shaped Slotted Microstrip Fractal Antenna for Wideband Applications”, Jagadeesha S, Vani R M and P V Hunagund propose a H-shaped fractal antenna that consists of H-shaped slot within a H-shaped microstrip patch which combines the concepts of self-similar property of fractal and H-shaped structure to achieve a wideband antenna. The designed antenna, having size of 20 mm ´ 40 mm ´ 1.6 mm, is based on the fractal geometry with the unique property of self-similarity and is used to achieve wider bandwidth of 44.72% with a reduction in the frequency. The authors use the parameters, measured return loss, bandwidth and radiation patterns to compare the antenna with other designs of compact and multiband microstrip antennas and conclude that the proposed antenna gives the wide bandwidth because of the self-similarity property of fractal geometry.

A novel speed control scheme for Induction Motor (IM) using Adaptive Neuro-Fuzzy (ANF) controller is proposed in the paper, “Robust Indirect Vector Control of Induction Motor Using Neuro-Fuzzy Controller”, by B Mahesh Kumar, G Ravi and R Chakrabarti. In this paper, Adaptive Neuro-Fuzzy Inference System (ANFIS) that tunes the fuzzy inference system with a backpropagation algorithm based on collection of input-output data is implemented. The simulation of the complete vector control scheme incorporating the neuro-fuzzy controller is carried out using MATLAB for 5 hp three-phase squirrel cage induction motor. The simulation results show that the neuro-fuzzy controller is more robust compared to the conventional Proportional-Integral (PI) controller for high-performance industrial drive applications.

Transient stability studies are carried out to ensure suitable operating conditions for the electric power system. The severity of the fault, its location, speed of corrective action and restoration, control modulation, etc., affect the stability of the system and its recovery to a final stable operating condition. The critical clearing time (TCC) is one important factor to be studied to maintain the stability of the power system under fault clearing conditions. To determine the critical clearing time, Hari Om Bansal proposes a Support Vector Machine (SVM) method in his paper, “Determination of Critical Clearing Time in a Power System Using Support Vector Machine”. SVM method is a new supervised machine learning system based on the statistical learning theory. In this paper, the author proposes a method to determine the critical clearing time using SVM for a system exposed to a fault and these results are compared with those of the classical method called ‘Step-by-Step’ method. The SVM approach is fast and can be used for online applications in transient stability assessment. The detection of motor faults at their incipient stage is important as it leads to increased reliability and reduced machine downtime. In the paper, “Incipient Detection of Faults in Three-Phase Induction Motors Using Stator Current Spatial Angular Vector Analysis”,

R A Gupta, A K Wadhwani and S R Kapoor give a simple technique for induction motor fault detection and identification. Deviation in the shape of Spatial Angular Vector is used as an indicator of the inset of the irregularities, mechanical or electrical, in the induction motor. Three major types of induction motor faults—bearing fault, broken rotor bar fault, and unbalanced supply faults—have been experimentally investigated. The experimentation has been performed on a 3f, 1.5 kW, 4 poles, 1440 RPM squirrel cage motor.

In the analysis of the power systems, Newton-Raphson method is used for solving the nonlinear power equations. Sometimes, difficulties arise and divergence occurs in the load flow solution. Some of these difficulties are related to the selection of reference slack bus, negative line reactance, and high resistance to reactance (R/X) ratio. When such difficulties are present, a small change in input parameter produces a large change in the solution of the network systems called ‘ill-conditioned systems’. The matrix describing the power system may have some eigenvalues that are very sensitive and some are comparatively insensitive. A ‘condition number’ is used to define the condition of a matrix. Authors S Suresh Reddy, Sarat Kumar Sahu and S V Jayaram Kumar study the impact of Flexible Alternate Current Transmission System (FACTS) devices on the ill-conditioned power systems in their paper, “Effect of TCSC on Ill-Conditioned Power Systems”. They consider an IEEE 14-bus test system (well-conditioned) and a 13-bus ill-conditioned power systems. It is observed that the condition number (K) of a system decreases with the incorporation of the FACTS devices such as Thyristor-Controlled Series Compensator (TCSC). As the degree of compensation is increased, the authors observe that the condition number of the system decreases, thereby tending the system to well-conditioned state.

The paper, “Statistical Analysis of Vibration Signals: A Predictive Maintenance Strategy for Locomotive Auxiliary Drives”, by Jaideep Gupta, A K Wadhwani and S Wadhwani deals with the reliability and availability of different electrical drives in locomotives in which a complex combination of a number of electrical drives is used to carry out a several auxiliary functions like providing compressed air to the air breaking system, providing cooling by blowing air to different units, working as drive motors for different pump applications, etc. A defect or a malfunction in any of the drives in the entire process affects the working of the locomotive. This paper deals with the maintenance practice based on the online condition monitoring of different components. The authors use statistical analysis of condition-based maintenance data to establish a relation between vibration patterns of the locomotive at different speeds, the vibration pattern of healthy motors mounted on the locomotive base and the vibration pattern of the same motors under certain known defects. The vibration patterns of the motors are compared with those available in the database to predict the condition of the motor, and a strategy for predictive maintenance is worked out based on the deviations.

-- M S R Murty
Consulting Editor