Power Loss Mitigation and Voltage Profile Improvement with Distributed Generation Using Grid-based Multi-Objective Harmony Search Algorithm

Power distribution systems are challenged with rapid growth in load demand. Most times, it is observed that under certain critical loading conditions, distribution systems have high power loss and poor voltage profile which result in the collapse of certain areas in the network. To overcome these ch...

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Veröffentlicht in:Journal of electrical and electronics engineering 2020-10, Vol.13 (2), p.5-10
Hauptverfasser: Ferede, Agajie Takele, Olalekan, Salau Ayodeji, Abel, Hailu Engidaw, Ayalew, Awoke Yayehyirad
Format: Artikel
Sprache:eng
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Zusammenfassung:Power distribution systems are challenged with rapid growth in load demand. Most times, it is observed that under certain critical loading conditions, distribution systems have high power loss and poor voltage profile which result in the collapse of certain areas in the network. To overcome these challenges, a number of power distribution systems incorporate distributed generation (DG) on the grid near to the load center. However, for DG to serve its purpose, its location and size has to be determined optimally. In this paper, Grid-Based Multi-Objective Harmony Search Algorithm (GrMHSA) has been utilized to determine the size and location of DG in the Debre Markos town distribution system. An algorithm was developed to optimally place the distributed generators (DGs). This was with the aim of reducing power losses in the distribution network and enhancing the voltage profile of the system under consideration. A matlab program was developed to mitigate power losses and improve the voltage profile by the optimal sizing and placing of DGs in the distribution network. After sizing and placing the DG in the network, the total voltage deviation, active and reactive power losses were reduced by 85.20%, 84.94%, and 85.73%, respectively. In addition, the performance of the proposed algorithm (GrMHSA) was compared with Multi-objective Particle Swarm Optimization (MOPSO) and was found to outperform the MOPSO.
ISSN:1844-6035
2067-2128