Showing papers by "Palash Kumar Kundu published in 2020"
TL;DR: Multivariate statistical methods like principal component analysis (PCA) alone, and in combination with probabilistic neural network (PNN), have been applied here to classify fault.
Abstract: The proposed work illustrates a simple research approach to identify the type of fault in a three-phase overhead single-end-fed long transmission line. Multivariate statistical methods like principal component analysis (PCA) alone, and in combination with probabilistic neural network (PNN), have been applied here to classify fault. An attempt has been made to use the PCA features obtained from the analysis of electrical parameters for each of the faults, in two ways. The first approach of fault classification is based on analyzing the PCA features by a modified ratio-based analysis. In the second method, an attempt has been made to use the PCA features directly to a structured PNN model. Electromagnetic Transient Program simulation software has been used to simulate a transmission line model. Sending-end three-phase line currents corresponding to various faults carried out at different geometric distances along the transmission line have been analyzed in MATLAB environment. The proposed algorithms are tested with unknown and intermediate distant faults with variable fault resistance to validate the same. Finally, a comparative analysis of the proposed two methods is illustrated, which shows 100% classifier accuracy of both the models.
14 citations
6 citations
10 Dec 2020
TL;DR: In this article, the authors investigated the performance of lambda λ-iteration method for solving the ELD problem of two generating station system in the cloud based generation scheduling data management system.
Abstract: ELD problem of the two generating station system was solved by IW-PSO in the cloud based generation scheduling data management system Due to higher execution time of IW-PSO, it becomes important to investigate the performance of other methods for solving the ELD problem for this system With decrease in the number of swarm particles, execution time reduces but there is also a subsequent degradation in the performance To overcome these problems the performance of simpler, lambda λ-method is investigated for solving this system resembling IEEE 5-bus system From the obtained result, it is clear that the execution time of λ-iteration method is much lower in comparison to that obtained with IW-PSO and thus can be good choice for the proposed generation scheduling data management system But for larger systems, it is obvious that computations increases significantly with λ-iteration method and therefore optimization techniques can be a better choice
2 citations
07 Oct 2020
TL;DR: Segment specific modelling approach for different wave segments of ECG signal provides better reconstruction performance in comparison with the few published works using Gaussian and Fourier model.
Abstract: Electrocardiogram (ECG) modeling is useful for abnormality detection and data compression. The common research problem in modeling is retaining pathological information using minimum number of model coefficients. In this paper, a new modeling technique for different wave segments of ECG signal, viz., baseline to P-onset, P wave, P-offset to Q, QRS complex, S to T-onset, T wave and T-offset to next baseline is presented. The processing steps included preprocessing, R-peak detection, beat segmentation and waveform partitioning, followed by modeling of individual partitions. For P, QRS and T wave, Gaussian model was adopted and for other segments, Fourier model was adopted to minimize reconstruction error. For testing of the proposed model, normal sinus rhythm (NSR) and myocardial infarction (MI) data records of PTB Diagnostic ECG database (ptbdb) and atrial premature (APC), premature ventricular contraction (PVC), left bundle branch block (LBBB) and right bundle branch block (RBBB) data records of MIT-BIH arrhythmia database (mitdb) under PhysioNet were used. The average SNR, and MSE using proposed method for ptbdb NSR was 86.33, and 4.41×10-6, respectively; for AMI 96.18, and 3.70×10-6 respectively; for IMI 80.86, and 1.36×10-6 respectively; for mitdb NSR 90.94 and 3.50×10-6 respectively; for APC 89.42, and 2.34×10-6 respectively; for PVC 93.28 and 3.06×10-6, respectively; for LBBB 93.77 and 2.74×10-6, respectively; for RBBB 92.83 and 3.52×10-6 respectively. Segment specific modelling approach provides better reconstruction performance in comparison with the few published works using Gaussian and Fourier model.