Comparison between Learning Mechanism and Pattern Presentation Techniques in Voltage Stability Assessment
Saeed rahimi gholami1 Ali Akbar Motie Birjandi2
1Saeed Rahimi, Shaheed Rajaee Teacher Training/ Electrotechniques/ Tehran, Iran.
2Ali Akbar Motie Birjandi, Shaheed Rajaee Teacher Training, University/ Electrotechniques, Tehran, Iran.
Manuscript received on january 17, 2012. | Revised Manuscript received on February 05, 2012. | Manuscript published on February 29, 2012. | PP: 7-11 | Volume-1 Issue-3, February 2012. | Retrieval Number: C0165121311/2011©BEIESP
Open Access | Ethics and Policies | Cite
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In this paper we compare learning mechanism and pattern presentation techniques in voltage stability assessment. In this way we use multilayer perceptron and classifiers models for assessing power system voltage stability margin in unstable point. In this paper we consider voltage magnitudes and phase angles as input and voltage stability margin as target of ANNs. Simulation was carrying out on IEEE-14 bus test system and numerical results show that minimum rule in combination gives better results rather than other models. Also be specified that use learning mechanism lead to better results than apply pattern presentation techniques.
Keywords: Artificial Neural Network , Combination of Classifiers ,Voltage Stability ,Voltage Stability Margin.