Application of swarm based intelligent computing algorithms for dynamic evaluation of maximum loadability of transmission network

This work proposes the application of soft computing techniques to evaluate the optimal location and control settings of Flexible AC Transmission System (FACTS) devices inorder to improve the Maximum Loadability (ML) of pool, hybrid model of deregulated power system during scheduled outages like single (N-1) and double (N-2) contingencies. Particle Swarm Optimization (PSO), Hybrid Particle Swarm Optimization (HPSO) algorithms are applied for optimizing the solution. In this case study, three different FACTS devices are considered namely, series type Thyristor Controlled Series Compensator (TCSC), shunt type Static Var Compensator (SVC) and series - shunt type Unified Power Flow Controller (UPFC). To reduce the Installation Cost (IC) of these devices, they are optimally located in the system, based on priority indices like Contingency Severity Index (CSI) and Fast Voltage Stability Index (FVSI). This proposed approach uses Newton Raphson Power flow equations with voltage, real and VAr power, settings of FACTS devices as constraints. Hybrid structure is modeled using bilateral transactions. The problem is simulated using IEEE 57 bus system. The ML of transmission system obtained for various numbers, settings and locations of FACTS devices are compared both numerically and graphically. It is concluded that HPSO provides better result with minimum Time of Computation (ToC) and quicker convergence.