A two stage model for rotor angle transient stability constrained optimal power flow

•Proposing a new two stage scheme for transient stability constrained optimal power flow (TSCOPF).•Application of Artificial Neural Network (ANN) for transient stability estimation.•Application of the Imperialist Competitive Algorithm (ICA) as an evolutionary optimization algorithm.

Transient stability constrained optimal power flow (TSCOPF) is a nonlinear optimization problem with both algebraic and differential equations. This paper utilizes the Imperialist Competitive Algorithm (ICA) as an evolutionary optimization algorithm and Artificial Neural Network (ANN) to develop a robust and efficient two stages scheme to solve TSCOPF problem. In the first stage an Artificial Neural Network is constructed to predict the rotor-angle transient stability margin, and is then incorporated in the TSC-OPF as the transient stability estimator. To solve the proposed TSC-OPF problem the ICA is used as the optimizer. The performance of the proposed method is verified over the WSCC three-machine, nine-bus system under different loading conditions and fault scenarios.