DefinePK

Abstract

Power systems operating under stress may approach a collapse point resulting in blackouts. To avoid this problem corrective measures such as load shedding are required. Conventional techniques are fail to provide optimal load shed. This paper focuses on optimal load shed as well as enhancing the system voltage profile using a hybrid optimization algorithm based on the well-known Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). GA has traditionally been known for its accuracy while the PSO algorithm is popular for its fast convergence time. GA algorithms require longer convergence times due to the complex nature of their cost functions; therefore, in this work PSO is applied to the GA construction to solve this problem. This result in a fast and accurate algorithm named GAPSO. This paper focuses on optimal load shed by using hybrid optimization termed as GAPSO. The proposed algorithm is utilized to minimize the total amount of load shed on the weak buses. Weak buses are identified using the Fast Voltage Stability Index. The performance of the proposed technique was assessed by simulations in MATLAB/SIMULINK under the IEEE-30 and IEEE-57 bus meshed networks. The proposed technique was also compared to the GA and PSO algorithms individually and it outperform both in terms of optimal load shed which is comparable to GA while a convergence time is comparable to PSO. Proposed technique is not only robust against system failures but is also efficient enough for real time applications.