Bacterial foraging optimization algorithm for optimal load shedding in power systems / Wan Nur Eliana Afif Wan Afandie

Today’s power grids transfer more electricity over a wider area. Present-day economic and environmental constraints push power system to be operated closer to their limits, which make them more vulnerable to disturbance. Thus, the wide-area outage has become a real threat to modem power systems. A common limiting factor for power transmission is the risk of voltage instability in recent years. As the ultimate countermeasure to voltage collapse, load shedding is normally considered the last resort, where there are no other alternatives to stop as approaching voltage collapse. It is an emergency control action in power system that can save systems from blackout. This thesis mainly focus on the widely used under voltage load shedding schemes (UVLS). There are two important things to be considered while performing load shedding. They are locations of load to be shed and the amount of load to be shed. For this research, there are two types of load shedding being tested, Random Generated Locations Load Shedding and Fixed Locations Load Shedding. Voltage stability index, Lindex, is used in determining the load shedding locations for Fixed Locations Load Shedding. By using Bacterial Foraging Optimization Algorithm (BFOA) technique, for each types of load shedding, the simulations are tested on IEEE 30-bus system for 3 and 5 load shedding locations. 3 different load conditions and one line outage cases are tested for 3 objective function and 3 multi-objective functions. The best objective functions for each case then determined by comparing the simulation results. These simulation results are also compared to the simulation results obtained using Evolutionary Programming (EP) technique. It is proven that BFOA gives better performance when compared to EP.