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Abstract
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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.
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