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Improved genetic algorithm for voltage security constrained optimal power flow problem

Author

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  • D. Devaraj
  • J. Preetha Roselyn

Abstract

Voltage stability has become an important issue in the planning and operation of many power systems. Contingencies such as unexpected line outages in a stressed system may often result in voltage instability which may lead to voltage collapse. This paper presents a Genetic Algorithm (GA) approach for solving the Voltage Security Constrained Optimal Power Flow (VSC-OPF) problem. Base-case generator power output, voltage magnitude of generator buses, transformer tap position and reactive power generation of capacitor banks are taken as the control variables. Maximum L-index of load buses is used to specify the voltage stability level of the system. An improved GA which permits the control variables to be represented in their natural form is proposed to solve this combinatorial optimisation problem. For effective genetic operation, crossover and mutation operators which can directly operate on floating point numbers and integers are used. The proposed approach has been evaluated on the IEEE 30-bus test system. Simulation results show the effectiveness of the proposed approach for improving the voltage security of the system.

Suggested Citation

  • D. Devaraj & J. Preetha Roselyn, 2007. "Improved genetic algorithm for voltage security constrained optimal power flow problem," International Journal of Energy Technology and Policy, Inderscience Enterprises Ltd, vol. 5(4), pages 475-488.
    • Handle: RePEc:ids:ijetpo:v:5:y:2007:i:4:p:475-488
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    Cited by:

    1. El Sehiemy, Ragab A. & Selim, F. & Bentouati, Bachir & Abido, M.A., 2020. "A novel multi-objective hybrid particle swarm and salp optimization algorithm for technical-economical-environmental operation in power systems," Energy, Elsevier, vol. 193(C).

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