Improved gravitational search algorithm for optimal placement and sizing of distributed generation for power quality enhancement

• Main Author: Daud, Sa'adah
• Format: Thesis
• Language: English; English
• Published: 2017
• Subjects: T Technology (General); TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://eprints.utem.edu.my/id/eprint/20559/; https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=106348

Distributed generation (DG) is one of the foremost elements in distribution planning. DG units play a significant role in distribution system stability and to enhance its power quality. Numerous benefits can be attained by the integration of DG unit in distribution networks, such as power loss reduction and improvement in voltage profiles and power quality. Such advantages can be accomplished and elevated if the DG units in the systems are optimally located and sized. Inappropriate placement and sizing of DG units would lead to negative impacts such as further loss in the system original power and system realibility. Meanwhile, power quality issues such as harmonic distortion and voltage dip have gained interest especially in power system researches. Therefore, this study deals with inverter-based DG with renewable source, which is the photovoltaicbased distributed generation (PVDG). In this research, a method for determining the optimal sizing and location of single and multiple PVDG in distribution systems is presented. A multi-objective function is formed to minimize total real losses and average voltage deviation, voltage total harmonic distortion and voltage dip magnitude. In this study, three-phase fault has been generated and injected to all the bus in the distribution systems for the voltage dips assessment. The optimization problem is generated using a weighted sum method. In order to obtain the best compromise solution, a novel heuristic algorithm based on improved gravitational search algorithm (IGSA) is proposed as an optimization technique. IGSA has the ability to search for the best solutions and it executes faster. The IGSA performances has then been compared with other heuristic algorithm such as particle swarm optimization (PSO) and GSA. The load flow algorithms from MATPOWER, harmonic load flow and method of fault position has been integrated in MATLAB environment to solve the multi-objective function. Single and multiple PVDG installation cases have been examined and compared to cases without PVDG. A comparison of the performances has also been made using optimization techniques when PVDG units are fixed at critical buses. The optimization techniques have been tested on two radial distribution systems, which are IEEE 33-bus and IEEE-69 bus with several scenarios and case studies. The overall results show that IGSA outperforms PSO and GSA in obtaining the best fitness value and has the fastest average computational time.