Genetic algorithm for optimal sectionalizing in radial distribution systems with alternative supply

Application benefits of Distribution Automation and AMI systems convergence methodology for distribution power restoration analysis

“…Consider that distribution utility 'A' has implemented power restoration in the DA system based on proposed algorithm in this paper with AMI integration in place and distribution utility 'B' has implemented with any of the existing methods in the literature [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19] without AMI integration in place. With the fault condition simulated and depicted in RBTS test system of Fig.…”

“…Most of the restoration schemes are usually analyzed offline and are based on optimization based techniques [4][5][6][7][8][9][10][11][12][13][14], which utilize pre-programmed rules & assumptions. Later, online based restoration schemes were proposed [18,19] which embed short-term load forecasting into restoration analysis to estimate maximum demand during the next load profile interval.…”

“…The process of obtaining an automatic solution which meets the target of optimal network reconfiguration strategy after an emergency situation like the fault or outage is defined as power restoration, which gained importance since the late eighties. In the literature several methods were proposed to obtain power restoration solution based on evolving new technologies such as Heuristic search [4], Expert system based [5], Knowledge based system [6], Artificial neural networks [7], Fuzzy logic control [8], Genetic algorithm [9], Artificial intelligence [10], Petri net [11], Tabu search [12], Optimization technique [13] and Particle swarm optimization [14]. These methods faced problems such as the difficulty in obtaining a realistic solution every time due to pre-defined rules or time required to obtain the solution was high.…”

A nonlinear binary programming model for electric distribution systems reliability optimization

“…Solving implicit models usually requires the use of stochastic search methods, at the expense of at most being able to provide a probabilistic convergence guarantee [3]. These approaches include the use of genetic algorithms [4], simulated annealing [5], evolutionary algorithms [6] and ant colony optimization [7] for optimal allocation of sectionalizing switches in distribution systems. Additionally, the tabu search is used in [8] and the multiobjective ant colony optimization method is used in [9] to solve the problem of optimal allocation of both protective devices and switches.…”

Power Restoration in Distribution Network Using MST Algorithms

“…Due to the advancement in mathematics, new algorithms were developed to solve the restoration problem in distribution network. It mainly consisted of Artificial Neural Networks (Hoyong Kim et al 1993), Fuzzy Logic control (Han-Ching kuo and Yuan Yih Hsu 1993), Genetic Algorithm (Gregory Levitin et al 1995), Artificial Intelligence (Rahman 1993), Petri net (Fountas et al 1997), Tabu search (Toune 1998), Optimization (Nagata and Sasaki 2002), Ant colony search algorithm (Mohanty et al 2003) and Particle Swarm Optimization (Si-Qing Sheng et al 2009).…”