Aurobinda Routray

A novel Kalman filter for frequency estimation of distorted signals in power systems

A simple and novel approach in the design of an extended Kalman filter (EKF) for the measurement of power system frequency has been presented in this paper. The design principles and the validity of the model have been outlined. The performance of this filter has been compared with some of the existing methods for estimating the frequency of a signal under noisy conditions. The feasibility of the proposed filter has been tested in the laboratory under worst-case measurement and network conditions, which might occur in a typical power system. Also, the proof of the stability for the proposed filter has been discussed for a single sinusoid. It has been found that the proposed algorithm is suitable for real-time applications especially when the frequency changes are abrupt and the signal is corrupted with noise and other disturbances due to harmonics.

Power system frequency estimation using least mean square technique

Frequency is an important parameter in power system monitoring, control, and protection. A least mean square (LMS) algorithm in complex form is presented in this paper to estimate power system frequency where the formulated structure is very simple. The three-phase voltages are converted to a complex form for processing by the proposed algorithm. To enhance the convergence characteristic of the complex form of the LMS algorithm, a variable adaptation step-size is incorporated. The performance of the new algorithm is studied through simulations at different situations of the power system.

Automatic Facial Expression Recognition Using Features of Salient Facial Patches

Extraction of discriminative features from salient facial patches plays a vital role in effective facial expression recognition. The accurate detection of facial landmarks improves the localization of the salient patches on face images. This paper proposes a novel framework for expression recognition by using appearance features of selected facial patches. A few prominent facial patches, depending on the position of facial landmarks, are extracted which are active during emotion elicitation. These active patches are further processed to obtain the salient patches which contain discriminative features for classification of each pair of expressions, thereby selecting different facial patches as salient for different pair of expression classes. One-against-one classification method is adopted using these features. In addition, an automated learning-free facial landmark detection technique has been proposed, which achieves similar performances as that of other state-of-art landmark detection methods, yet requires significantly less execution time. The proposed method is found to perform well consistently in different resolutions, hence, providing a solution for expression recognition in low resolution images. Experiments on CK+ and JAFFE facial expression databases show the effectiveness of the proposed system.

Wavelet fuzzy combined approach for fault classification of a series-compensated transmission line

Series capacitor protected by metal-oxide varistor and air-gap arrangement imposes problems to line protection and other online decisions. Discrete wavelet transform integrated with a fuzzy logic system is designed for fault classification of a transmission line possessing a series capacitor at the midpoint. The approach uses information obtained from the wavelet decomposition of current signals for faulty phase selection and section identification. Two different FLSs are designed for the two classification objectives in this paper.

Fault Direction Estimation in Radial Distribution System Using Phase Change in Sequence Current

Summary form only given. This paper presents a method for estimating the direction of fault in such radial distribution systems using phase-change in current. The difference in phase angle between the positive sequence component of current during fault and prefault conditions has been found to be a good indicator of the fault direction in a three phase system. A rule base formed for the purpose decides the location of fault with respect to the relay in a distribution system. Such a strategy reduces the cost of voltage sensor and/or connection for a protection scheme which is of relevance in emerging distributed generation (DG) systems. The algorithm has been tested through simulation for different radial distribution systems.

Applying distance relay for voltage sag source detection

This paper proposes the application of distance relay information for a voltage sag source detection problem. It is found that the magnitude and angle of impedance before and after the sag event clearly indicate on which side of the power-quality (PQ) monitor, the sag source lies. The required information on impedance can be obtained by the PQ monitor using the estimates of distance relay at that point or applying the algorithm of such relay.

A Cumulative Sum-Based Fault Detector for Power System Relaying Application

In this paper, a cumulative-sum-based fault detection algorithm is proposed for the power system relaying application. Literature suggests the successful application of this method to process control systems where the deviation of parameters is tracked to indicate any abnormal conditions. The effectiveness of the algorithm is found to be better than the traditional methods in the presence of noise, system frequency deviation, and other uncertainties. It is also not affected by load change in a system. Above all, it provides relatively higher index values without compromising detection speed.

Filtered-s LMS algorithm for multichannel active control of nonlinear noise processes

This correspondence proposes a novel nonlinear adaptive algorithm named as filtered-s least mean square (FSLMS) algorithm for multichannel active control of nonlinear noise processes. A reduced complexity FSLMS algorithm using filter bank approach is also suggested. The performance of the proposed algorithm is validated through computer simulations for nonlinear noise processes. It is demonstrated that the proposed method outperforms the conventional filtered-x least mean square algorithm and second-order Volterra filtered-x LMS (VFXLMS) algorithm for control of nonlinear noise processes. Computational complexity analysis shows the proposed method involves lesser number of computations as compared to second-order VFXLMS algorithm

A Vision-Based System for Monitoring the Loss of Attention in Automotive Drivers

Onboard monitoring of the alertness level of an automotive driver has been challenging to research in transportation safety and management. In this paper, we propose a robust real-time embedded platform to monitor the loss of attention of the driver during day and night driving conditions. The percentage of eye closure has been used to indicate the alertness level. In this approach, the face is detected using Haar-like features and is tracked using a Kalman filter. The eyes are detected using principal component analysis during daytime and using the block local-binary-pattern features during nighttime. Finally, the eye state is classified as open or closed using support vector machines. In-plane and off-plane rotations of the drivers face have been compensated using affine transformation and perspective transformation, respectively. Compensation in illumination variation is carried out using bihistogram equalization. The algorithm has been cross-validated using brain signals and, finally, has been implemented on a single-board computer that has an Intel Atom processor with a 1.66-GHz clock, a random access memory of 1 GB, ×86 architecture, and a Windows-embedded XP operating system. The system is found to be robust under actual driving conditions.

Emotion recognition from Assamese speeches using MFCC features and GMM classifier

This paper presents a method based on Gaussian mixture model (GMM) classifier and Mel-frequency cepstral coefficients (MFCC) as features for emotion recognition from Assamese speeches. For training and testing of the method, data collection is carried out in Jorhat (Assam, India), which consisted of acted speeches of one short emotionally biased sentence repeated 5 times with different styles by 27 speakers (14 Male and 13 female) for training and one long emotional speech by each speaker for testing. The experiments are performed for the cases of (i) text-independent but speaker-dependent and (ii) text-independent and speaker-independent.