@article {8752207,
	title = {Adjacent Graph Based Vulnerability Assessment for Electrical Networks Considering Fault Adjacent Relationships Among Branches},
	journal = {IEEE Access},
	volume = {7},
	year = {2019},
	pages = {88927-88936},
	abstract = {Security issues related to vulnerability assessment in electrical networks are necessary for operators to identify the critical branches. At present, using complex network theory to assess the structural vulnerability of the electrical network is a popular method. However, the complex network theory cannot be comprehensively applicable to the operational vulnerability assessment of the electrical network because the network operation is closely dependent on the physical rules not only on the topological structure. To overcome the problem, an adjacent graph (AG) considering the topological, physical, and operational features of the electrical network is constructed to replace the original network. Through the AG, a branch importance index that considers both the importance of a branch and the fault adjacent relationships among branches is constructed to evaluate the electrical network vulnerability. The IEEE 118-bus system and the French grid are employed to validate the effectiveness of the proposed method.},
	keywords = {adjacent graph, branch importance index, complex network theory, Complex networks, electrical network, fault adjacent relationships, French grid, IEEE 118-bus system, Indexes, Load modeling, Loading, Measurement, network operation, network theory (graphs), power grids, power system faults, power system security, Transient analysis, Vulnerability},
	doi = {10.1109/ACCESS.2019.2926148},
	author = {T. Zang and J. Lei and X. Wei and T. Huang and T. Wang and Mario J. P{\'e}rez-Jim{\'e}nez and H. Lin}
}
@article {8611442,
	title = {Modeling Fault Propagation Paths in Power Systems: A New Framework Based on Event SNP Systems With Neurotransmitter Concentration},
	journal = {IEEE Access},
	volume = {7},
	year = {2019},
	pages = {12798-12808},
	keywords = {Biocomputing, Computational modeling, Ev-SNP system, event SNP systems, ex-ante fault propagation path prediction, Fault diagnosis, fault propagation path, fault propagation relationship, inference mechanisms, large-scale power systems, large-scale systems, Membrane computing, modeling fault propagation paths, neural nets, neural P systems, Neurons, neurotransmitter concentration, Neurotransmitters, power engineering computing, power system, power system security, Power system stability, Probability, reduced order systems, reduction-SNP system, RSNP system, Spiking neural P system, synapses},
	doi = {10.1109/ACCESS.2019.2892797},
	author = {T. Wang and X. Wei and T. Huang and J. Wang and H. Peng and Mario J. P{\'e}rez-Jim{\'e}nez and Luis Valencia-Cabrera}
}
