Optimal voltage stability assessment based on voltage stability indices and artificial neural network - Chua, Qing Shi (2015)
The evaluation of voltage stability assessment experiences sizeable anxiety in the safe operation of power systems, due to the complications of a strain power system. With the snowballing of power demand by the consumers and also the restricted amount of power sources, therefore, the system has to perform at its maximum proficiency. The noteworthy to discover the maximum ability boundary prior to voltage collapse should be undertaken. A preliminary warning can be perceived to evade the interruption of power system’s capacity. This research considered the implementation of static and time-step system monitoring methods that able to provide a timely warning in the power system. Numerous types of line voltage stability indices (LVSI) are differentiated in this research to resolve their effectuality to determine the weakest lines for the power systems. The main motivation of these indices is used to predict and forecast the proximity towards voltage instability in the power system control and security applications. The indices are also able to decide the weakest load buses which are close to voltage collapse in the power system. Therefore, the static and time-step simulation (TSS) results are used to calculate the line stability indices and to ratify with voltage stability indices theory. The line voltage stability indices are assessed using the IEEE 9-Bus system, IEEE 14-Bus System and IEEE 30-Bus system to validate their practicability. The results are used to calculate the line stability indices by using Matlab software. This research also introduced the implementation of voltage stability monitoring by using Artificial Neural Network (ANN). Results demonstrated that the calculated indices and the estimated indices by using ANN are practically relevant in predicting the manifestation of voltage collapse in the system. Overall, VCPI(Power) index is able to detect the voltage collapse point precisely due to its accuracy in forecasting. This index successfully showed the capability to forecast the voltage collapse point either in small or a larger power system network. Therefore, essential actions can be taken by the operators in order to dodge voltage collapse incident from arising.