Detection and Classification of Power Signal Disturbances Using Wavelet Transform and Probablistic Neural Network (Record no. 58716)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02014nam a22001457a 4500 |
| 100 ## - MAIN ENTRY--AUTHOR NAME | |
| Personal name | Aamir Ali Bhatti |
| -- | 13MPE07 |
| -- | Supervisor Dr. Aslam Parvez Memon |
| 100 ## - MAIN ENTRY--AUTHOR NAME | |
| Personal name | 13-MPE-07 |
| 245 ## - TITLE STATEMENT | |
| Title | Detection and Classification of Power Signal Disturbances Using Wavelet Transform and Probablistic Neural Network |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication | Nawabshah: |
| Name of publisher | QUEST, |
| Year of publication | 2015. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | 69p. |
| 500 ## - GENERAL NOTE | |
| General note | ABSTRACT<br/><br/>The electrical power quality (EPQ) analysis has become an exponentially increasing field of interest, gaining the attention of researchers, electrical utilities and customers particularly in the past few decades. It is well known that the variation in electric power quality disturbances usually in a wide range of joint time and frequency, therefore automatic detection of PQ problems is highly desirable, and it is very difficult and elusive to be diagnosing these disturbances with conventional approaches of signal processing techniques.<br/>The electrical power quality EPQDs) are detected and classified by<br/>using joint time-frequency analysis technique of discrete wavelet transform (DWT) and artificial neural network (ANN) is proposed in this work. The distorted waveforms of PQD signals are generated using Matlab, based on the parametric equations and controlling parameters described by IEEE 1159-2009 standards. The distorted signals of electrical PQ are decomposed by using discrete wavelet transform (DWT) and to select its useful information as feature extraction (FE). For the classification of PQDs appropriate feature vectors are selected and applied to train the probabilistic neural network (PNN) as classifier. Comparison of test results of proposed technique with those generated by other existing methods, show enhanced performance and accuracy.<br/><br/><br/>Keywords: Power Quality Disturbances (PQDs), Discrete Wavelet Transform (DWT), Energy distribution (ED), and Probabilistic Neural Network (PNN).<br/> |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Department of Electrical Engineering |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | http://tiny.cc/wbubvz |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | Thesis and Dissertation |
| Withdrawn status | Lost status | Home library | Current library | Date acquired | Accession Number | Koha item type |
|---|---|---|---|---|---|---|
| Research Section | Research Section | 25/09/2018 | MP/25-274 | Thesis and Dissertation | ||
| Research Section | Research Section | 24/11/2016 | MP/09-86 | Thesis and Dissertation |