Voltage SAG mitigation by optimizing the location of distributed generation using genetic algorithm for three distributed generation types

• Main Author: Abdrabou Ahmed, Ahmed Mohamed
• Format: Thesis
• Language: English
• Published: 2014
• Subjects: TK7885-7895 Computer engineer. Computer hardware
• Online Access: http://eprints.utm.my/id/eprint/48912/1/AhmedMohamedAbdrabouMFKE2014.pdf; http://eprints.utm.my/id/eprint/48912/; http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:86769

Voltage sag is considered to be one of the most serious hazards of power quality and can produce a harmful effect on electrical power system stability and most electronic devices such as personal computers, programmable logic controllers and variable speed drives. At the same time, Distributed Generation (DG) is playing an important role in power system and is widely used nowadays to improve grid performance and system flexibility and stability, and is predicted to play an increasing role in the future. Many researchers used one DG type for different purposes but few of them used two DG types to mitigate sag while none used more than two. The locations of DGs have to be optimized to improve the grid performance and to avoid degradation of power system networks using an optimization algorithm such as Genetic Algorithm (GA). The type of DG directly influences the penetration level and the placement of DG. GA is used to determine the optimum locations for three DG types namely synchronous, Wind Turbine (WT) and Photovoltaic (PV). The performance of power system for the three DG types is compared in terms of the optimum location. Effect of three DGs on voltage sag is studied in this thesis when connected to power system grid. This approach is applied on IEEE 13 bus system. Optimizing each type individually will become increasingly important because each type has different features and response. The locations of DG installation in this study are optimized using GA. GA is a capable optimization technique which is used to find the optimum solution of multi-objective functions; the objective function combines the overall number of buses experience voltage sag, the overall number of buses experience voltage drop, the overall number of buses experience voltage less than 10% and the overall number of buses experience voltage swell. Finally, it is found that the best location of each DG varies according to the type of DG and synchronous generator mitigates voltage sag better than WT and PV. Particle Swarm Optimization is used for comparative studies.