Power System Transient Stability: An Algorithm for Assessment and Enhancement Based on Catastrophe Theory and FACTS Devices

It is of crucial importance to obviate power system damage and cascading failures that may cause a full or partial blackout when the system is exposed to severe contingencies. Flexible alternating current transmission system (FACTS) devices have been harnessed for solving several power system problems including transient stability. Ever since, to emphasize the effectiveness of the FACTS technology, the number and allocation of these devices must be selected properly. So, a novel algorithm is proposed in this paper to determine the best least number (BLN) and allocation of the thyristor-controlled series capacitor (TCSC) with a goal of improving the transient stability in an optimal manner. A combination of the catastrophe theory (CT) and the multi-objective particle swarm optimization (MOPSO) method in addition to a clustering technique is used to structure the proposed algorithm. The CT is used to assess the transient stability and calculate the critical clearing time (CCT). MOPSO is applied to compromise between maximizing the CCT and minimizing the cost of TCSCs as two contradictory objective functions. The clustering technique is designed to provide the BLN of TCSC devices. Accordingly, at least investment, the proposed algorithm satisfies an increase of the stability margin by increasing the value of CCT for each generator and improves the location of operating points in the CT's stability region. Simulation of the proposed algorithm application to New England 39-bus power system is presented to verify the algorithm effectiveness. The results confirm the feasibility of this algorithm and are validated in comparison with those obtained through time-domain simulation.