Abstract
This paper presents an effective method of transmission line management in power systems. Two conflicting objectives 1) generation cost and 2) transmission line overload are optimized to provide non-dominated Pareto-optimal solutions. A fuzzy ranking-based multi-objective differential evolution (MODE) is used to solve this complex nonlinear optimization problem. The generator real power and generator bus voltage magnitude is taken as control variables to minimize the conflicting objectives. The fuzzy ranking method is employed to extract the best compromise solution out of the available non-dominated solutions depending upon its highest rank. N-1 contingency analysis is carried out to identify the most severe lines and those lines are selected for outage. The effectiveness of the proposed method has been analyzed on standard IEEE 30 bus system with smooth cost functions and their results are compared with non-dominated sorting genetic algorithm-II (NSGA-II) and Differential evolution (DE). The results demonstrate the superiority of the MODE as a promising multi-objective evolutionary algorithm to solve the power system multi-objective optimization problem.
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Keywords
- Particle Swarm Optimization
- Differential Evolution
- Gravitational Search Algorithm
- Optimal Power Flow
- Optimal Power Flow Problem
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Pandiarajan, K., Babulal, C.K. (2013). Transmission Line Management Using Multi-objective Evolutionary Algorithm. In: Panigrahi, B.K., Suganthan, P.N., Das, S., Dash, S.S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2013. Lecture Notes in Computer Science, vol 8297. Springer, Cham. https://doi.org/10.1007/978-3-319-03753-0_29
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DOI: https://doi.org/10.1007/978-3-319-03753-0_29
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