A short-term voltage stability online assessment based on multi-layer perceptron learning

Abstract

In this paper, a deep learning-based approach is used to analyze short-term voltage stability of power systems in order to completely learn the hidden timely dependencies from post fault behavior of the system. The direct measurements from the PMU, which did not require any significant amount of computation, have been used, and the results will be accurate. The proposed approach makes considerable use of a Multi-Layer Perceptron-based deep learning model in order to make the most accurate prediction feasible regarding the short-term voltage stability status. Rapid prediction of voltage stability allowed system operators sufficient time to take effective corrective action against large disturbances. In-depth offline simulations will be executed against various fault types, with the results being fed into the classifier as inputs. After the classifier has been trained using the results of the offline simulations, the online application will be applied to the test samples. The effectiveness of the suggested method will be evaluated based on the results of tests conducted on both small and large-scale test systems.