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Special Protection and Control Scheme for Transmission Line Overloading Elimination Based on Hybrid Differential Evolution/Electromagnetism-Like Algorithm

  • Hadi, Mahmood Khalid (Dept. of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia UPM) ;
  • Othman, Mohammad Lutfi (Dept. of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia UPM) ;
  • Wahab, Noor Izzri Abd (Dept. of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia UPM)
  • Received : 2016.08.01
  • Accepted : 2017.05.11
  • Published : 2017.09.01

Abstract

In designing System Protection Schemes (SPSs) in power systems, protecting transmission network against extreme undesired conditions becomes a significant challenge in mitigating the transmission line overloading. This paper presents an intelligent Special Protection and Control Scheme (SPCS) using of Differential Evolution with Adaptive Mutation (DEAM) approach to obtain the optimum generation rescheduling to solve the transmission line overloading problem in system contingency conditions. DEAM algorithm employs the attraction-repulsion idea that is applied in the electromagnetism-like algorithm to support the mutation process of the conventional Differential Evolution (DE) algorithm. Different N-1 contingency conditions under base and increase load demand are considered in this paper. Simulation results have been compared with those acquired from Genetic Algorithm (GA) application. Minimum severity index has been considered as the objective function. The final results show that the presented DEAM method offers better performance than GA in terms of faster convergence and less generation fuel cost. IEEE 30-bus test system has been used to prove the effectiveness and robustness of the proposed algorithm.

Keywords

Special protection scheme;N-1 contingency condition;Transmission line overloading;Generation rescheduling;Differential Evolution;Electromagnetism-like algorithm

Acknowledgement

Supported by : Universiti Putra Malaysia (UPM)

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