A new dynamic security enhancement method via genetic algorithms integrated with neural network based tools

Kucuktezcan C. F., Genç V. M. İ.

ELECTRIC POWER SYSTEMS RESEARCH, vol.83, no.1, pp.1-8, 2012 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 83 Issue: 1
  • Publication Date: 2012
  • Doi Number: 10.1016/j.epsr.2011.09.004
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.1-8
  • Keywords: Dynamic security assessment, Generation rescheduling, Preventive control, Genetic algorithm, Probabilistic neural network
  • Istanbul Technical University Affiliated: Yes


In this paper, a new dynamic security assessment and generation rescheduling method utilizing genetic algorithms (GAs) which are integrated with probabilistic neural networks (PNNs) and adaptive neuro fuzzy inference systems (ANFISs) is proposed for the preventive control of large power systems against transient instabilities. By the proposed approach, PNNs are employed in a feasible manner to calculate the security regions accurately during the assessment and control. The security constrained generation rescheduling is implemented through a GA which optimizes the total fuel cost or the generation shifting during the preventive control. The steady-state solutions of the variables required for the GA are smoothly performed by the use of an ANFIS. The proposed methods are demonstrated on the 17-generator 163-bus Iowa power system and on the 50-generator 145-bus IEEE test system successfully and the effectiveness of the approaches is discussed. (C) 2011 Elsevier B.V. All rights reserved.