Reliability analysis of bulk power systems using swarm intelligence

Robinson, Duane

doi:10.1109/rams.2005.1408345

Cited by 13 publications

(6 citation statements)

References 3 publications

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“…For the reliability analysis of the bulk power system, a transfer function based on swarm intelligence was used [40]. A binary coded firefly algorithm that solves the set covering problem was performed using the same transfer function [37].…”

Section: Binarization Methodsmentioning

confidence: 99%

A Binary Cuckoo Search Big Data Algorithm Applied to Large‐Scale Crew Scheduling Problems

et al. 2018

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The progress of metaheuristic techniques, big data, and the Internet of things generates opportunities to performance improvements in complex industrial systems. This article explores the application of Big Data techniques in the implementation of metaheuristic algorithms with the purpose of applying it to decision-making in industrial processes. This exploration intends to evaluate the quality of the results and convergence times of the algorithm under different conditions in the number of solutions and the processing capacity. Under what conditions can we obtain acceptable results in an adequate number of iterations? In this article, we propose a cuckoo search binary algorithm using the MapReduce programming paradigm implemented in the Apache Spark tool. The algorithm is applied to different instances of the crew scheduling problem. The experiments show that the conditions for obtaining suitable results and iterations are specific to each problem and are not always satisfactory.

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Section: Binarization Methodsmentioning

confidence: 99%

A Binary Cuckoo Search Big Data Algorithm Applied to Large‐Scale Crew Scheduling Problems

et al. 2018

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“…Analyzing the reliability indices of a power system is historically done using contingency analysis, which in a large network, considering multiple failures, can be extremely complicated and time consuming. Robinson successfully applied binary PSO for identifying the set of network elements which if disrupted, would possibly lead to a cascading series of events resulting in a widespread damage to the network [181].…”

Section: Power System Reliability and Securitymentioning

confidence: 99%

Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems

Valle

Venayagamoorthy

Mohagheghi

et al. 2008

IEEE Trans. Evol. Computat.

1,978

851

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Many areas in power systems require solving one or more nonlinear optimization problems. While analytical methods might suffer from slow convergence and the curse of dimensionality, heuristics-based swarm intelligence can be an efficient alternative. Particle swarm optimization (PSO), part of the swarm intelligence family, is known to effectively solve large-scale nonlinear optimization problems. This paper presents a detailed overview of the basic concepts of PSO and its variants. Also, it provides a comprehensive survey on the power system applications that have benefited from the powerful nature of PSO as an optimization technique. For each application, technical details that are required for applying PSO, such as its type, particle formulation (solution representation), and the most efficient fitness functions are also discussed.Index Terms-Classical optimization, particle swarm optimization (PSO), power systems applications, swarm intelligence.

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“…PSO is meta-heuristic used in reliability analysis as well as optimization of systems in general, and electrical power systems in particular. Robinson et al [113] have used PSO to identify critical elements in an electrical grid system. Their method is applicable in reliability analysis of bulk supply systems.…”

Section: B Evolutionary Computation and Meta-heuristicsmentioning

confidence: 99%

Soft Computing Techniques for Dependable Cyber-Physical Systems

et al. 2019

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Cyber-Physical Systems (CPS) allow us to manipulate objects in the physical world by providing a communication bridge between computation and actuation elements. In the current scheme of things, this sought-after control is marred by limitations inherent in the underlying communication network(s) as well as by the uncertainty found in the physical world. These limitations hamper fine-grained control of elements that may be separated by large-scale distances. In this regard, soft computing is an emerging paradigm that can help to overcome the vulnerabilities, and unreliability of CPS by using techniques including fuzzy systems, neural network, evolutionary computation, probabilistic reasoning and rough sets. In this paper, we present a comprehensive contemporary review of soft computing techniques for CPS dependability modeling, analysis, and improvement. This paper provides an overview of CPS applications, explores the foundations of dependability engineering, and highlights the potential role of soft computing techniques for CPS dependability with various case studies, while identifying common pitfalls and future directions. In addition, this paper provides a comprehensive survey on the use of various soft computing techniques for making CPS dependable.

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Reliability analysis of bulk power systems using swarm intelligence

Cited by 13 publications

References 3 publications

A Binary Cuckoo Search Big Data Algorithm Applied to Large‐Scale Crew Scheduling Problems

A Binary Cuckoo Search Big Data Algorithm Applied to Large‐Scale Crew Scheduling Problems

Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems

Soft Computing Techniques for Dependable Cyber-Physical Systems

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