An Algorithm for Optimal Placement of Voltage Sag Monitors

Abstract

Voltage sags are disturbances that deserve special attention in power quality (PQ) area, given its frequent occurrences. Their constant monitoring is, therefore, essential to diagnose its causes and mitigate economic losses of electric utility customers. However, the cost of a monitoring system may be excessive if not evaluated strategically. In this context, this work presents an algorithm for the installation of PQ monitors at strategic points of electric power distribution systems in order to diagnose voltage sags. Observability area concept and binary particle swarm optimization method were used to evaluate the problem. A sensitivity analysis was also performed, in which the influence of several parameters, such as fault resistance, system loading, detection threshold, fault type, and system expansion, was evaluated. The algorithm was validated in a Brazilian distribution system and in IEEE 34-bus system. The results indicated that the algorithm was able to detect voltage sags throughout the system using monitors at few buses, reducing the cost of the monitoring system.

Optimal harmonic resonance monitoring in electrical network considering area of harmonic pollution and system uncertainty

“…Various approaches have been suggested in the literature for the optimal positioning of power quality monitors with different monitoring targets. Generally, these approaches can be divided into four categories: methods developed on the basis of graph theory (GT), methods that utilize multivariable regression (MVR), converting and packing (CP)-based methods, and methods that use the concept of monitor reach area (MRA) [6]. In graph-based approaches, the electrical network is presented as a graph, and then the grid convergence matrix is determined.…”

Hybrid Approach for Constrained Optimal Control of Nonlinear Switched Systems

“…PSO is based on a simplified social model (Kennedy and Eberhart 1995;Kennedy and Spears 1998;Matsui et al 2008;Parsopoulos and Varahatis 2002;Shi and Eberhart 1998;Lari et al 2014;Junqueira et al 2019). This method was proposed by Kennedy in 1995 and has attracted considerable attention as one of the promising optimization methods with higher speed and higher accuracy than those of existing solution methods.…”