Date of Award
2015
Publisher
North Dakota State University
Document Type
Thesis
Degree Awarded
Master of Science (MS)
Program
Electrical and Computer Engineering
Department
Electrical and Computer Engineering
College
Engineering
Faculty Advisor
Kavasseri, Rajesh G.
Description
Connection of the distributed generators (DGs), poses new challenges for operation and management of the distribution system. An important issue is that of islanding, where a part of the system gets disconnected from the DG. This thesis explores the use of several data-mining, and machine learning techniques to detect islanding. Several cases of islanding and non- islanding are simulated with a standard test-case: the IEEE 13 bus test distribution system. Different types of DGs are connected to the system and disturbances are introduced. Several classifiers are tested for their effectiveness in identifying islanded conditions under different scenarios. The simulation results show that the random forest classifier consistently outperforms the other methods for a diverse set of operating conditions, within an acceptable time after the onset of islanding. These results strengthen the case for machine-driven based tools for quick and accurate detection of islanding in microgrids.
Recommended Citation
Al-Bataineh, Hussien Suleiman, "Islanding Detection Using Data Mining Techniques" (2015). Electrical and Computer Engineering. 67.
https://digitalcommons.ndsu.edu/elec-comp-eng/67
Rights
NDSU Policy 190.6.2
Rights Link
https://www.ndsu.edu/fileadmin/policy/190.pdf
Handle Identifier
https://hdl.handle.net/10365/27634