Optimal Placement of Phasor Measurement Units Using Immunity Genetic Algorithm
By F. Aminifar, Caro Lucas, Amin Khodaei, M. Fotuhi‐Firuzabad. IEEE Transactions on Power Delivery, 2009
327 citations recorded by the source corpus.
Subfields: Civil and Structural Engineering, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/5ffd1436bdffd7a493f35638a443d940b7510b97
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In Civil and Structural Engineering
In Electrical and Electronic Engineering
- Nikolaos P. Theodorakatos
- Nikolaos M. Manousakis
- George N. Korres
- Feng Lin
- Rajesh G. Kavasseri
- Kai Sun
- Wenyuan Li
- Xiao Li
Citations in the corpus
- Reliability-Based Incremental PMU Placement (2014, background): “Neither weighting approaches which subjectively choose weighting factors [6], nor considering reliability in the constrained conditions [20] can perfectly solve the contradiction.”
- 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 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].”
- Joint Placement of Phasor and Power Flow Measurements for Observability of Power Systems (2011, methodology): “Computational intelligence based methods such as genetic algorithms [16]–[18] have also been proposed to solve the placement problem.”