Artificial Neural Network for Stability-Constrained Optimal Power Flow

The usage of the development of machine learning is extremely important these days, especially in power systems. Artificial neural networks (ANN) have been developed and implemented successfully to improve power system performance and solve complex problems. This paper introduces a new machine learning-based approach to the power flow operation using different types of ANNs, including simple feedforward neural networks (SFNN), learning neural networks (TLNN), simple deep neural networks (SDNN), deep feedforward neural networks (DFFNN) and long short long memory neural network (LSTM). The modeling aims to predict the optimal power flow parameters to ensure efficient and economical power system operation considering the power system stability constraints such as voltage control limits and power angle limits. ANN performance is evaluated using each parameter's mean square error (MSE). The study results show high system stability prediction data accuracy, essential for power system operation. The results also confirm that the proposed approach can offer a significant advancement in utilizing machine learning to improve the monitoring of optimal power flow in real-time operation, considering stability constraints.