Optimization of power quality monitors in transmission system network

• Main Authors: Abubakar, K. A., Dalila, M. S., Ahmad, N., Abdirahman, M. A., Abba, L. B.
• Format: Article
• Language: English
• Published: Penerbit UTM Press 2017
• Subjects: TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://eprints.utm.my/id/eprint/80306/1/NAhmad2017_OptimizationofPowerQualityMonitors.pdf; http://eprints.utm.my/id/eprint/80306/; https://elektrika.utm.my/index.php/ELEKTRIKA_Journal/article/view/20
• Institution: Universiti Teknologi Malaysia

Due to an increase in population growth, more sensitive equipment and the significant usage of electricity. Power quality issues are of paramount importance, in order to get reliable and of high efficient power supply. Among all power disturbances, voltage sags are considered as the most frequent and severe type of disturbances that lead to loss of operation of equipment’s. The power quality monitoring system is the first to consider in power quality assessment and mitigation so as to get a reliable and efficient power supply. Installing power quality monitors in every component of the power system network is not feasible due to economic reasons and its need to be minimized. And then how to get the optimal number and locations of power quality monitors while maintaining system observability becomes an important problem. The aim of this paper is to find the optimal number and location of power quality monitors in the transmission system network. The IEEE 14 bus test system was modelled and simulated using POWERWORLD software, to obtain fault voltage and monitor reach area considering both balanced and unbalanced faults in the system. The optimization formulation problem is also formed and solved using an Integer Programming (IP) algorithm MATLAB toolbox to find the minimum number of Power Quality Monitors (PQMs) in the transmission system that lead to voltage sags, the Sag Occurrence Value (SOV) is used to find the best position of the PQMs. Finally, to prove the proposed Integer Programming (IP) algorithm, this paper end with optimizing the PQMs using Genetic Algorithm (GA) techniques.