Voltage Collapse Prediction Based on Artificial Neural Network
Volume 12 , Issue 1 , December 2025 , Pages 104-126
Authors
Warda Ali 1 ; Bakhtyar A. Shareef Shareef 2 ; Dana O. Qadir Qadir 3
1 Sulaimani Polytechnic University
2 Department of Power and Control Engineering / Engineering College/Sulaimani polytechnic University / Sulaimani, Kurdistan Region, Iraq
3 Department of Power and Control Engineering / Engineering College/Sulaimani polytechnic University / Sulaimani, Kurdistan Region, Iraq
10.17656/sjes.102004
Keywords
Abstract
Abstract
Stability analysis electric power system is a significant issue for a robust and reliable electric power system and it is a subject of concern throughout the world. Voltage stability can be defined as the ability of electric power system to sustain steady allowable voltages of all buses in the power system operating at usual conditions and when exposed to a different disturbance. Continuously increasing in load demand day by day that leads to low voltage profile and forcing generators to operate near to their maximum limits will lead to voltage collapse and to a blackout conditions. In this paper a line stability Index (Lmn) evaluation can indicate accurately the closeness of the power system to voltage collapse or voltage instability. Lmn index value was also considered as an effective indicator to find stressed line in the power system, where its value ranges from 0 to 1, where zero represents no loads and one represents voltage collapse. Artificial Neural Network (ANN) applied for the evaluation of Lmn Index for all transmission lines in the power system because of its ability in solving non-linear equations, the input-output data set of ANN are yield from the well-known power flow analysis method Newton-Raphson in the MATLAB /Simulink environment. The proposed approach is carried out on the real 132KV, 48-Bus Kurdistan rejoin power system and the results are analyzed and reported.