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Power system reliability assessment using intelligent state space pruning techniques: A comparative study

Power system reliability assessment using intelligent state space pruning techniques: A comparative study,10.1109/POWERCON.2010.5666062,Robert C. Gree

Power system reliability assessment using intelligent state space pruning techniques: A comparative study   (Citations: 5)
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State space pruning is a methodology that has been used to improve the computational efficiency and convergence of Monte Carlo Simulation (MCS) when computing the reliability indices of power systems. This methodology improves performance of MCS by pruning state spaces in such a manner that a new state space with a higher density of failure states than the original state space is created. We have previously proposed using Population-based Intelligent Search (PIS), specifically Genetic Algorithms (GA) and Binary Particle Swarm Optimization (BPSO), to prune the state space. This paper reexamines these techniques, suggests improvements, examines the extension of these techniques to a larger test system, and extends the method to include both Repulsive Binary Particle Swarm Optimization (RBPSO) and Binary Ant Colony Optimization (BACO). These methods are tested using the single and three area IEEE Reliability Test Systems.
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    • ...The model used here is based upon models given by [3], [4], [19] and has previously been described in [12]–[14]...

    Robert C. Green IIet al. An examination of artificial immune system optimization in intelligent...

    • ...Several methods that have been developed for the reliability analysis of composite power systems include analytical methods [1], Monte Carlo simulation (MCS) [2], analytical methods for decomposition [3], [4], [5], population-based intelligent search (PIS) methods [6], [7], [8], [9], [10], [11], and state space pruning methods [12], [13], [14], [15], [16], [17]...
    • ...PIS methods, and MCS have recently resulted in intelligent state space pruning techniques that have been shown to increase the rate of convergence and decrease the computational time of MCS [12], [13], [14], [15], [16], [17]...

    Robert C. Green IIet al. Intelligent state space pruning using multi-objective PSO for reliabil...

    • ...Some of these new and computationally superior methods include Monte Carlo Simulation (MCS) [2], Population-based Intelligent Search (PIS) [3], [4], [5], analytical methods for state space pruning [6], [7], [8], and PIS methods for state space pruning [9], [10], [11]...
    • ...The need to reduce the computational efforts for convergence in complex systems has been the driver behind much of the work that has previously been accomplished in the area of state space pruning including analytical [6], [7], [8] and PIS based [9], [10], [11] techniques...
    • ...based upon models given by [18], [6], [7] and has previously been described in [9], [10], [11]...
    • ...The details of the implementation of all PIS algorithms used regarding state space pruning and some discussion regarding them can be found in [9], [10], [11]...
    • ...Detail regarding this implementation can be found in [9], [10], [11]...

    Robert C. Green IIet al. Intelligent and parallel state space pruning for power system reliabil...

    • ...The model used here is based upon models given in [9]–[11] and has previously been described in [12]–[14]...

    Robert C. Green IIet al. Evaluating the impact of Plug-in Hybrid Electric Vehicles on composite...

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