Fast assessment of the voltage stability using reconfigured power system network and artificial neural network approaches

Abstract

Planning and running power systems must take voltage stability into account. Instability is mostly caused by the power system’s failure to supply the demand for reactive power. The voltage stability margin must be understood by utilities if they are to operate the power system with the highest level of security and dependability. This paper uses reconfigured 12 bus, 10 bus, and 8-bus reconfigured networks of the interconnected IEEE 14 bus system to demonstrate the proposed quick method for assessing the voltage stability. The original (IEEE 14 bus) and the reconfigured (12 bus, 10 bus, and 8-bus) systems’ voltage stability has been evaluated using the line stability index indicators: fast voltage stability index (FVSI), line voltage stability index (LVSI), and line stability index (Lmn). Based on the maximum loadability factor, the contingencies for the original and reconfigured systems are ranked. The system loadability factor is used as the input parameter, and the LVSI, Lmn, and FVSI indices for the critical line under critical contingency are used as an output to train the ANN network. It has been found that there is no discernible difference between the actual (NR method) and predicted (ANN approach) output. For accessing the voltage stability of the IEEE 14 bus system by its reconfigured networks using the proposed approach, the computational time and error are very low, showing the effectiveness, rapidity, and accuracy of the suggested approach.