| dc.contributor.author |
Ibne Yousuf, Md. Adib |
|
| dc.contributor.author |
Shamim, S.M. |
|
| dc.contributor.author |
Rahman Akanda, Md. Touhidur |
|
| dc.date.accessioned |
2015-06-30T05:22:08Z |
|
| dc.date.available |
2015-06-30T05:22:08Z |
|
| dc.date.issued |
2013-12 |
|
| dc.identifier.uri |
http://hdl.handle.net/123456789/127 |
|
| dc.description |
The authors would specially like to express their most sincere gratitude to their respected Supervisor, Hafiz Imtiaz, Assistant Professor, Department of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology for his continuous guidance, encouragement, valuable suggestion and inspiration. His sincere and wise advice helped the authors greatly to make the work successful. Without his initiatives this work would not have been possible.
The authors would like to express their thanks to their department for all types of help they offers.They are also grateful to their library & laboratories for lots of help.The authors acknowledge the help of the individuals who contributed to the successful completion of the whole work. |
en_US |
| dc.description.abstract |
Discrete Cosine Transform feature has become an effective feature extraction method in modern power system. In this paper, we used algorithm of a unique feature for power quality (PQ) disturbance signal classification. Here, we proposed the extraction of spectral features from Discrete Cosine Transform (DCT) domain. This feature extraction offers the ability to detect and localize harmonic events and it also classifies different power quality disturbance signals. A useful technique of selecting significant DCT coefficients is proposed for optimal feature selection. This process offers dimensional feature reduction. In this paper we consider seven types of power quality disturbance signals and simulate for each of the given categories. Using this extracted feature we can get not only very high classification accuracy but also a low computational burden. This feature extraction using Discrete Cosine Transform is one of the best feature extraction formula, we have ever seen. |
en_US |
| dc.description.sponsorship |
DEPARTMENT OF ELECTRICAL, ELECTRONIC AND COMMUNICATION ENGINEERING (EECE),
MILITARY INSTITUTE OF SCIENCE AND TECHNOLOGY, DHAKA, BANGLADESH |
en_US |
| dc.language.iso |
en |
en_US |
| dc.publisher |
DEPARTMENT OF ELECTRICAL, ELECTRONIC AND COMMUNICATION ENGINEERING (EECE), MILITARY INSTITUTE OF SCIENCE AND TECHNOLOGY, DHAKA, BANGLADESH |
en_US |
| dc.relation.ispartofseries |
Bachelor of Science In Electrical Electronic and Communication Engineering; |
|
| dc.subject |
Power Quality, Disturbance, Classification, Frequency, Domain, Features |
en_US |
| dc.title |
Power Quality Disturbance Classification Using Frequency Domain Features |
en_US |
| dc.type |
Thesis |
en_US |
