Title :
Fast voltage collapse evaluation via fuzzy decision tree method
Author :
Abidin, Haji Izham Haji Zainal ; Lo, K.L. ; Hussein, Zahrul Faizi
Author_Institution :
Univ. Tenaga Nasional, Selangor, Malaysia
Abstract :
Voltage stability is considered to be a complex field of study since it has a number of contributing factors. Due to this, numerous studies or research has been made to look into various methods of analysis, detection and mitigation. In general, these methods would involve either complex computation for accurate results but suffers from high computation time. Some methods may also be simple and fast but then has the disadvantage of inaccuracy. This paper presents an alternative method of analysing the voltage stability problem by incorporating machine learning techniques, i.e. fuzzy decision tree method. The author proposed a general overview on how the algorithm is created. The algorithm is then tested using an IEEE 300 bus test system to test the algorithm´s capability. Results presented show that the proposed FDT has a lot of future potential as an online tool for voltage stability analysis.
Keywords :
IEEE standards; decision trees; fuzzy set theory; learning (artificial intelligence); load flow; power system dynamic stability; IEEE 300 bus test system; fuzzy decision tree method; machine learning techniques; power system stability; static load flow; voltage collapse; voltage stability; Artificial intelligence; Decision trees; Fuzzy sets; Machine learning; Machine learning algorithms; Power system analysis computing; Power system stability; Stability analysis; System testing; Voltage;
Conference_Titel :
Power Engineering Conference, 2003. PECon 2003. Proceedings. National
Print_ISBN :
0-7803-8208-0
DOI :
10.1109/PECON.2003.1437406