Transient stability constrained optimal power flow using teaching learning based optimization

Transient Stability Constrained Optimal Power Flow (TSCOPF) constitutes one of the most computational-intensive applications; it is used for power system preventive control against blackouts triggered by transient instability after a contingency. In this paper, a novel Optimal Power Flow (OPF) is proposed by adding the Transient Stability (TS) constraints into the conventional OPF problem, a Teaching-Learning-Based Optimization (TLBO) is proposed to solve the OPF problem. The objective function is to minimize the total cost of fuel for all generators. The proposed methodology has been tested on standard test systems the IEEE 30-bus network model. The simulation results are compared to those obtained with other conventional and new methods found in recent works.

[1]  M. A. Abido,et al.  Optimal power flow using Teaching-Learning-Based Optimization technique , 2014 .

[2]  R. Venkata Rao,et al.  Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems , 2011, Comput. Aided Des..

[3]  Vassilios Petridis,et al.  Optimal power flow by enhanced genetic algorithm , 2002 .

[4]  R. Venkata Rao,et al.  Review of applications of TLBO algorithm and a tutorial for beginners to solve the unconstrained and constrained optimization problems , 2016 .

[5]  Kit Po Wong,et al.  A Hybrid Method for Transient Stability-Constrained Optimal Power Flow Computation , 2012, IEEE Transactions on Power Systems.

[6]  Heder S. Bernardino,et al.  A hybrid genetic algorithm for constrained optimization problems in mechanical engineering , 2007, 2007 IEEE Congress on Evolutionary Computation.

[7]  C.A. Roa-Sepulveda,et al.  A solution to the optimal power flow using simulated annealing , 2001, 2001 IEEE Porto Power Tech Proceedings (Cat. No.01EX502).

[8]  Matej Crepinsek,et al.  A note on teaching-learning-based optimization algorithm , 2012, Inf. Sci..

[9]  Yu Liu,et al.  A novel bat algorithm with habitat selection and Doppler effect in echoes for optimization , 2015, Expert Syst. Appl..

[10]  Abdullah Abusorrah,et al.  Optimal Power Flow Using Adapted Genetic Algorithm with Adjusting Population Size , 2012 .

[11]  A. Rezaee Jordehi,et al.  Parameter selection in particle swarm optimisation: a survey , 2013, J. Exp. Theor. Artif. Intell..

[12]  P. N. Suganthan,et al.  Differential Evolution: A Survey of the State-of-the-Art , 2011, IEEE Transactions on Evolutionary Computation.

[13]  M. A. Abido,et al.  Optimal power flow using differential evolution algorithm , 2009 .

[14]  Deqiang Gan,et al.  Stability-constrained optimal power flow , 2000 .

[15]  M. A. Abido,et al.  Optimal power flow using particle swarm optimization , 2002 .

[16]  Amir Hossein Gandomi,et al.  Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems , 2011, Engineering with Computers.

[17]  A. Gandomi,et al.  Mixed variable structural optimization using Firefly Algorithm , 2011 .

[18]  R. Venkata Rao,et al.  A comparative study of a teaching-learning-based optimization algorithm on multi-objective unconstrained and constrained functions , 2014, J. King Saud Univ. Comput. Inf. Sci..

[19]  Hiroshi Sasaki,et al.  A solution of optimal power flow with multicontingency transient stability constraints , 2003 .

[20]  Samir Sayah,et al.  Modified differential evolution algorithm for optimal power flow with non-smooth cost functions , 2008 .