• P. Anuhya

Voltage instability is a phenomenon that limits the operation and the transmission capacity of a power system. Sufficiently fast detection and appropriate remedial actions can prevent the system from undergoing a voltage collapse. These considerations motivate the development of methods to identify operating conditions that are near or within the region for which the system is voltage unstable, and to suggest remedial actions to bring back the system to a condition where it has sufficient margin to voltage collapse. This work presents a method that performs voltage stability assessment and voltage instability by using machine learning algorithms. However, assessment of voltage security margins is computationally challenging and can in most cases not be estimated in the time frame required by system operators in critical situations. To overcome this challenge, a machine learning-based method for fast and robust computing of the voltage security margin is proposed and tested. The main contribution of the proposed work is calculation of voltage stability indices using different machine learning algorithms. Finally, a method for voltage instability prediction is developed. This method is proposed to be used as an online tool for system operators to predict the system’s near-future stability condition given the current operating state. The method uses KNN, Decision tree and SVM machine algorithms. The results from case studies using the IEEE 14-bus system and IEEE 30 bus systems show good performance and the network can accurately, within only a few seconds, predict voltage instability events in almost all test cases.

"Voltage Stability Analysis using Machine Learning Algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 7, page no.d165-d176, July-2023, Available :http://www.jetir.org/papers/JETIR2307322.pdf

"Voltage Stability Analysis using Machine Learning Algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 7, page no. ppd165-d176, July-2023, Available at : http://www.jetir.org/papers/JETIR2307322.pdf