Artificial Neural Network-based Voltage Stability Analysis of Power Transmission Networks with Distributed Generation using Phasor Measurement Unit Synthetic Data

Voltage stability refers to the ability of an electrical network to sustain the steady state voltage at each bus upon the subjection of the network to a sudden change or disturbance. Fast Voltage Stability Index (FVSI) and Line Stability Index (L
MN
) are indices used to determine the voltage stability of a line or component within the electrical network. This research focuses on the voltage stability analysis of Power Transmission networks with Distributed Generation. The FVSI and L
MN
will be derived from Artificial Neural Network (ANN) using synthesized Phasor Measurement Unit (PMU) data placed optimally across the network. The algorithm used for determining the optimal PMU placement was Mixed Integer Linear Programming and its data was synthesized from Newton Raphson Load Flow. Mixed Integer Linear Programming for Optimal PMU placement and Newton Raphson Load Flow were conducted using MATLAB programming. The ANN training and simulation is conducted using the nntool MATLAB toolbox. The test cases for this study are the IEEE 14, 30, and 57 bus systems. The developed algorithm yielded accurate results upon validation using Mean Squared Error (MSE) and Regression Analysis.