Manish Kumar Saini
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Interests
Manish Kumar Saini has a total of 1 co-authors. Here are the top co-authors by number of shared publications:
Rajiv Kapoor
Department of Electronics and Communication Engineering, Delhi College of Engineering, New Delhi-110045, India
1 shared publication
Latest Publications
- 18 May 2011
- Published by Wiley in European Transactions on Electrical Power
- Vol. 22 (4) , 518-532
Multiwavelet transform based classification of PQ events
SUMMARY: Wavelets have been used extensively in the recent past. The work presented uses multiwavelet because of its inherent property to resolve the signal better than all single wavelets. Multiwavelets are based on more than one scaling function. The resolution is better compared to Daubechies (D4) and hence the event can be detected from lesser number of samples as compared to Daubechies (D4). The proposed methodology utilizes an enhanced resolving capability of multiwavelet to recognize power system disturbances. Dempster–Shafer (DS), an intelligent classifier, has been implemented and tested for various PQ events. Two sub‐classifiers, heuristic classifier and statistical classifier (χ2distribution), have been used to support and strengthen the structural identification and temporal distribution factor of DS classifier. Results on various PQ events, such as voltage sag, voltage swell, outage, interruption, impulsive‐transient (IT), oscillatory‐transient (OT), noise, and notching, show that multiwavelets can detect and classify different PQ events efficiently and consistently. Copyright © 2011 John Wiley & Sons, Ltd.
- 18 May 2011
- Published by Wiley in European Transactions on Electrical Power
- Vol. 22 (4) , 518-532
Multiwavelet transform based classification of PQ events
SUMMARY: Wavelets have been used extensively in the recent past. The work presented uses multiwavelet because of its inherent property to resolve the signal better than all single wavelets. Multiwavelets are based on more than one scaling function. The resolution is better compared to Daubechies (D4) and hence the event can be detected from lesser number of samples as compared to Daubechies (D4). The proposed methodology utilizes an enhanced resolving capability of multiwavelet to recognize power system disturbances. Dempster–Shafer (DS), an intelligent classifier, has been implemented and tested for various PQ events. Two sub‐classifiers, heuristic classifier and statistical classifier (χ2distribution), have been used to support and strengthen the structural identification and temporal distribution factor of DS classifier. Results on various PQ events, such as voltage sag, voltage swell, outage, interruption, impulsive‐transient (IT), oscillatory‐transient (OT), noise, and notching, show that multiwavelets can detect and classify different PQ events efficiently and consistently. Copyright © 2011 John Wiley & Sons, Ltd.