Return to search

Voltage-Current Based Features for Power Quality Detection by Using Artificial Intelligence

Power quality is a main subject to considerable attentions from utilities and customers owing to the popular uses of many non-linear electronic equipment in recent years. Harmonics, voltage swell, voltage sag, and, power interruption could downgrade the service quality. To ensure the power quality, detecting harmonic and voltage disturbances becomes an important issue. In other words, a detection method with classification capability will be helpful for detecting disturbances.
The thesis proposed two models of power quality detection for power system disturbances using voltage-current(V-I) characteristics in the time domain with hybrid wavelets grey relational analysis (WGRA), and self-organizing feature map network (WSOM). Morlet wavelets are responsible for extracting features from voltages and currents. GRA and SOM were employed to identify the types of various disturbance patterns. Computer simulations have demonstrated the computational efficiency and accurate recognition capability for power quality detection and discrimination with an IEEE 14-Bus power system.

Identiferoai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0710106-145629
Date10 July 2006
CreatorsWang, Long-wei
ContributorsTa-Peng Tsao, Whei-Min Lin, Fu-Sheng Cheng, Hong-Chan Chin
PublisherNSYSU
Source SetsNSYSU Electronic Thesis and Dissertation Archive
LanguageCholon
Detected LanguageEnglish
Typetext
Formatapplication/pdf
Sourcehttp://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0710106-145629
Rightscampus_withheld, Copyright information available at source archive

Page generated in 0.0019 seconds