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.