Optimal Switch Placement in Distribution Systems Using Trinary Particle Swarm Optimization Algorithm

Electric Power Systems Research

“…Therefore, the placement of RCSs must take into account the functional requirements and cost benefit [67]. Usually, switch placement is formulated as a constrained nonlinear mix-integer optimization problem [67][68][69][70][71][72][73][74][75]. Heuristics, such as fuzzy logic approach [68], genetic algorithm [69], and immune algorithm [70], are used to obtain a near-optimal solution with acceptable computational performance.…”

Smart Distribution Systems

“…Therefore, the placement of RCSs must take into account the functional requirements and cost benefit [67]. Usually, switch placement is formulated as a constrained nonlinear mix-integer optimization problem [67][68][69][70][71][72][73][74][75]. Heuristics, such as fuzzy logic approach [68], genetic algorithm [69], and immune algorithm [70], are used to obtain a near-optimal solution with acceptable computational performance.…”

A Comprehensive Survey of Optimal Placement and Coordinated Control Techniques of FACTS Controllers in Multi-Machine Power System Environments

“…A Trinary Particle Swarm Optimization Technique has been proposed for optimal switch placement in distribution systems for achieving high distribution reliability levels and con-currently minimizing capital costs can be considered as the main issues. A novel three state approaches has been proposed for inspired from the discrete version of a powerful heuristic algorithm, PSO is developed and presented to determine the optimal number and locations of two types of switches (sectionalizes and breakers) in radial power systems automation is an important issue from the reliability and economical point of view [73]. In [74]- [76], an Artificial Intelligence Based Techniques has been addressed for optimal placement of FACTS controllers in large scale power system.…”

“…The various artificial intelligence (AI) based methods proposed in literature includes genetic algorithms (GA) [49]- [64], [175]- [176], [180], tabu search algorithms [65], [66], simulated annealing (SA) based approach [69]- [70], [177], particle swarm optimization (PSO) techniques [71]- [73], [80], artificial neural network (ANN) based algorithms [74]- [76], ant colony optimization (ACO) algorithms [77]- [78], graph search algorithms [79], fuzzy logic based approach [81]- [82], other techniques such as norm forms of diffeomorphism techniques [83], evolution strategies algorithms [84], [86], improved evolutionary programming [68], gravitational optimization techniques [85], benders decomposition techniques [42], augmented Lagrange multiplier approach [67], hybrid meta-heuristic approach [172], heuristic and algorithmic approach [178], energy approach [179].…”