Feedforward Artificial Neural Network for Predicting Voltage Stability Indices in Power Systems
Sim Sy Yi1, Goh Hui Hwang2, Chua Qing Shi3, Ling Chin Wan4, Goh Kai Chen5, Siong Kai Chien6, Cham Chin Leei7
1Sim Sy Yi, Faculty of Engineering Technology, Universiti Tun Hussein Onn Malaysia, Parit Raja, Batu Pahat, Johor, Malaysia.
2Goh Hui Hwang, Faculty of Electrical & Electronic Engineering, Universiti Tun Hussein Onn Malaysia, Parit Raja, Batu Pahat, Johor, Malaysia.
3Chua Qing Shi, Faculty of Electrical & Electronic Engineering, Universiti Tun Hussein Onn Malaysia, Parit Raja, Batu Pahat, Johor, Malaysia.
4Ling Chin Wan, Faculty of Electrical & Electronic Engineering, Universiti Tun Hussein Onn Malaysia, Parit Raja, Batu Pahat, Johor, Malaysia.
5Goh Kai Chen, Faculty of Technology Management & Business, Universiti Tun Hussein Onn Malaysia, Parit Raja, Batu Pahat, Johor, Malaysia.
6Siong Kai Chien, Faculty of Engineering Technology, Universiti Tun Hussein Onn Malaysia, Parit Raja, Batu Pahat, Johor, Malaysia.
7Cham Chin Leei, Multimedia University MMU, Faculty of Engineering, Persiaran Multimedia, Cyberjaya, Malaysia.
Manuscript received on 25 September 2019 | Revised Manuscript received on 04 October 2019 | Manuscript Published on 22 October 2019 | PP: 47-54 | Volume-8 Issue-3S October 2019 | Retrieval Number: C10101083S19/2019©BEIESP | DOI: 10.35940/ijrte.C1010.1083S19
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© 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: Several electricity failures associated with the voltage stability incident have appeared in a few countries. Nowadays, main concern towards voltage stability control and prediction is no longer crucial, however significant awareness is arising to sustain power system’s stability to conceal recurrence of major blackouts. Numerous types of line voltage stability indices (LVSI) being appointed to validate the weakest lines in IEEE 30-Bus test system. Besides that, LVSI is being forecasted by Feedforward Back Propagation Artificial Neural Network (FFBPNN) in order to recognize the voltage stability in IEEE 30-Bus test system. The calculated indices by using LVSI and forecasted indices by using FFBPNN are realistically applicable to discover the voltage collapse event in the system. The actual output for the VCPI(Power) in line 2-5 is 1.0459, while the predicted VCPI(Power) by using FFBPNN is 1.0459 with 3 seconds training time with 0% error percentage. Generally, the voltage collapse event has been successfully proven based on the capability of VCPI(Power). Therefore, necessary measures are capable to be performed by the power system operators to evade voltage collapse events occurred.
Keywords: Feedforward Back Propagation Neural Network (FFBPNN), Line Voltage Stability Indices (LVSI), Voltage Collapse, Voltage Instability, Voltage Stability Analysis (VSA).
Scope of the Article: Artificial Intelligence and Machine Learning