Optimal capacitor planning for power factor improvement using hybrid particle swarm and harmony search optimization

Abstract

Industrial loads reduce the Power Factor (PF) of supply systems, causing increases in power losses, damaging equipment and higher utility bills. Optimization techniques are used in planning reactive sources to improve PF of power systems. However, conventional techniques suffer difficulties in passing over local optimal, divergence risk, constraints handling or computing higher order derivatives. Herein, the hybridization of Particle Swarm and Harmony Search Algorithm (PS – HSA) is developed for optimal capacitor planning to improve PF, and comparison is made with the Enhanced Particle Swarm Optimization (EPSO) and Improved Adaptive Harmony Search Algorithm (IAHSA). The test systems are the Modified IEEE 6 and 16 buses and nodes respectively. To create semblance of industrial load dominated power systems, the test networks were modified by increasing the reactive load demand at all buses of the IEEE 6 and 16 by 50% and 70% respectively. The capacitor is modelled as static shunt-controlled element deployed to inject reactive power at buses/nodes. Results show that for IEEE 6 buses, PF improved from 0.68 to 0.8983, 0.8986 and 0.8992 with EPSO, IAHSA and hybrid PS – HSA respectively. Similarly, in IEEE 16 nodes, PF improved from 0.76 to 0.9439, 0.943, and 0.944 with EPSO, IAHSA and hybrid PS – HSA respectively. Furthermore, real power losses reduced from 16.94 MW to 14.03 MW in IEEE 6 buses, translating to 17.2% reduction with the hybrid PS - HSA. While in IEEE 16 nodes, reduction is from 0.719 MW to 0.69 MW accounting for 4% reduction, also with the hybrid PS - HSA.