Nondominated sorting genetic algorithm for optimal phasor measurement placement
By B. Milosevic, Miroslav M. Begovic. IEEE Power Engineering Society General Meeting, 2002
427 citations recorded by the source corpus.
Subfields: Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/cb70d5d4c83f821ca9d5ba9e84214a0488847565
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In Electrical and Electronic Engineering
- Rajesh G. Kavasseri
- George N. Korres
- Nikolaos M. Manousakis
- Nikolaos P. Theodorakatos
- Kai Sun
- Wei Kang
- Xiao Li
- Junjian Qi
Citations in the corpus
- Generalized Integer Linear Programming Formulation for Optimal PMU Placement (2008, methodology): “The authors in [5] developed an optimal placement algorithm for PMUs by using integer”
- Reliability-Based Incremental PMU Placement (2014, background): “In [12], a non-dominated sorting genetic algorithm was proposed to balance two conflicting objectives, i.”
- Optimal Placement of Phasor Measurement Units with Linear and Non-linear Models (2015, methodology): “A simple nondominated sorting genetic algorithm (NSGA) [23], a binary particle swarm optimization (BPSO) methodology [24], a modified discrete BPSO algorithm [25], a global optimization tabu search (TS) algorithm [26, 27], a recursive Tabu search (RTS) method [28], an immunity genetic algorithm (IGA”
- A Framework for Phasor Measurement Placement in Hybrid State Estimation Via Gauss–Newton (2013, methodology): “Numerical techniques such as genetic algorithms [22], [23], simulated-annealing [14], and integer linear programming [24] have also been applied in similar placement problems.”
- Optimal Placement of PMUs by Integer Linear Programming (2008, methodology): “In [ 5 ] a special tailored nondominated sorting genetic algorithm is developed for the PMU placement problem.”
- Unified PMU Placement for Observability and Bad Data Detection in State Estimation (2014, methodology): “The authors in [9] applied the nondominated sorting genetic-based algorithm (NSGA) to successfully solve the PMU placement problem with two competing objectives, such as minimization of the number of PMUs and maximization of the measurement redundancy.”
- Optimal PMU Placement for Power System Dynamic State Estimation by Using Empirical Observability Gramian (2014, methodology): “Under this framework, many approaches have been proposed, such as mixed-integer programming [24], [25], binary search [26], metaheuristics [27], [28], particle swarm optimization [29], and eigenvalue-eigenvector-based approaches [30], [31].”