Application of NSGA - II to power system topology based multiple contingency scrutiny for risk analysis

Authors:
Nalluri Madhusudana Rao;Diptendu Sinha Roy;Dusmanta K. Mohanta
Affiliations:
School of Computing, Sastra University, Thanjavur, India;Department of Computer Science and Engineering, National Institute of Science and Technology, Berhampur, India;Department of Electrical and Electronics Engineering, Birla Institute of Technology, Mesra, India
Venue:
SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part I
Year:
2011

Citing 3
Cited 0

Multi-Objective Optimization Using Evolutionary Algorithms

Multi-Objective Optimization Using Evolutionary Algorithms
Multi-objective evolutionary algorithms based on the summation of normalized objectives and diversified selection

Information Sciences: an International Journal
A fast and elitist multiobjective genetic algorithm: NSGA-II

IEEE Transactions on Evolutionary Computation

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Abstract

The Incorporation of deregulation and increase in renewable sources of generation has shifted the nature of existing power systems to a more geographically distributed system. This had led to significant challenges towards on-line monitoring and control. Contingency set identification is an essential step in monitoring the power system security level. Multiple contingency analysis forms the basis of security issues, particularly of large, interconnected power systems. The difficulty of multiple contingency selections for on-line security analysis lies in its inherent combinatorial nature. In this paper, an approach for identification of power system vulnerability to avoid catastrophic failures is put forward, as a multi objective optimization problem that partitions its topology graph, accounts for maximizing the imbalance between generation and load in each island and at the same time minimizes the number of lines cut to realize the partitions. The Nondominated Sorted Genetic Algorithm, version II (NSGA II) has been applied to obtain the optimal solutions and the methodology involved has been applied to an IEEE 30 bus test system and results are presented.