Intelligent State Space Pruning with local search for power system reliability evaluation

Abstract

A methodology called Intelligent State Space Pruning (ISSP) has recently been developed and applied in order to reduce the computational resources necessary to achieve convergence when using non-sequential Monte Carlo Simulation (MCS). The main application of this algorithm has been the probabilistic evaluation of composite power system reliability. ISSP has been shown to perform differently when implemented using different population based metaheuristic algorithms, though computation resources are typically reduced by more than 50%. This reduction in computation resources is particularly important when considering the smart grid -a system whose complexity will be far beyond that of the present power grid. In order to further this line of research, this paper focuses on four contributions: 1) The presentation of a binary version of the deterministic Central Force Optimization (CFO) optimization algorithm, 2) The role of this new algorithm regarding ISSP, 3) The integration of a local search technique with three flavors of the ISSP algorithm in order to improve performance, and 4) A discussion of the role that ISSP may play in the reliability evaluation of the smart grid.

Metaheuristic searching genetic algorithm based reliability assessment of hybrid power generation system

“…Meta-heuristic algorithms, such as genetic algorithm and ant colony algorithm, etc., have become more and more effective methods for solving complex optimization problems (Bonabeau et al., 2000; Dorigo et al., 1996). Most meta-heuristic algorithms are derived from the simulation of biological behavior or physical properties or chemical processes, for example, Ant colony algorithm is to simulate the actual ant colony foraging process (Gambardella and Dorigo, 1997), particle swarm algorithm is derived from the bird and fish groups (Benidris and Mitra, 2014; Green et al., 2010, 2012; Hadow et al., 2010; Huang and Liu, 2013), Evolutionary Computation (EC) and Smart State Space Pruning (ISSP). Despite the series of researches on the reliability of generation system, more appropriate techniques are needed which are computationally scalable and more practical to reflect the soundness of power generation (Almutairi et al., 2015; Kadhem et al., 2017; Athraa et al, 2017).…”

Reliability Assessment of Power Generation Systems Using Intelligent Search Based on Disparity Theory

“…Examples of these algorithms include the ant colony system (ACS), genetic algorithm (GA), particle swarm optimization (PSO), intelligent state space pruning (ISSP), and evolutionary computation (EC) [6][7][8][9][10].…”