Citation

Sim, Sy Yi and Goh, Hui Hwang and Chua, Qing Shi and Ling, Chin Wan and Goh, Kai Chen and Siong, Kai Chien and Cham, Chin Leei (2019) Feedforward Artificial Neural Network for Predicting Voltage Stability Indices in Power Systems. International Journal of Recent Technology and Engineering, 8 (3S). pp. 47-54. ISSN 2277-3878

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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.

Item Type:	Article
Uncontrolled Keywords:	Feedforward control systems, Feedforward back propagation neural network (FFBPNN), Line voltage stability indices (LVSI), Voltage collapse, Voltage instability, Voltage stability analysis (VSA).
Subjects:	T Technology > TJ Mechanical Engineering and Machinery > TJ212-225 Control engineering systems. Automatic machinery (General)
Divisions:	Faculty of Engineering (FOE)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	07 Sep 2021 14:41
Last Modified:	07 Sep 2021 14:41
URII:	http://shdl.mmu.edu.my/id/eprint/8760

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