A smart voltage stability maneuver algorithm for voltage collapses mitigation

In the power system, the instantaneous and permanent stability is a major requirement cannot be overlooked. Because of the power grid large-scale systems, any disturbance anywhere on the power grid could pose a reason of overall dynamic imbalances. Major consequences could be occurred to the electricity feeding across wide areas of country which is called partial blackout, even entire country which is called overall blackout. It is perhaps for this reason that the existence voltage stability indices which indicate the power grid system stability level is very essential. With knowing the voltage stability level of the transmission lines that involves the power grid in real time (online operation), the voltage stability of the entire power grid could be obtained easily. There are several mathematical base voltage indices. But in this proposal, another voltage stability index will be build based on the machine learning techniques to mitigate the voltage collapse phenomenon. This novel predictor is proposed in transient stability analysis based on machine learning techniques such as (Linear regression, neural network, and Decision tree). This predictor is built after a comparison was made between the impacts of various machine learning algorithms using different datasets. Three different mathematical voltage stability indices (FVSI, Lmn, and NLSI) had been used to prepare datasets for the training purpose. An early warning system had been built based on the proposed predictor. This early warning system could be used to inform the system operator with the hazards of voltage instability issues face the electric power grid and visualize these hazards. The E.W.S had then been used as a kernel to build V.S.A.M.A (Voltage Stability Automatic Maneuver Algorithm) that can handle the voltage instability issue.