Solving multi-contingency transient stability constrained optimal power flow problems with an improved GA

In this paper, an improved genetic algorithm has been proposed for solving multi-contingency transient stability constrained optimal power flow (MC-TSCOPF) problems. The MC-TSCOPF problem is formulated as an extended optimal power flow (OPF) with additional generator rotor angle constraints and is converted into an unconstrained optimization problem, which is suitable for genetic algorithms to deal with, using a penalty function. The improved genetic algorithm is proposed by incorporating an orthogonal design in exploring solution spaces. A case study indicates that the improved genetic algorithm outperforms the existing genetic algorithm-based method in terms of robustness of solutions and the convergence speed while the solution quality can be kept.

[1]  Zwe-Lee Gaing,et al.  Real-coded mixed-integer genetic algorithm for constrained optimal power flow , 2004, 2004 IEEE Region 10 Conference TENCON 2004..

[2]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

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

[4]  Christopher R. Houck,et al.  A Genetic Algorithm for Function Optimization: A Matlab Implementation , 2001 .

[5]  M. A. Pai,et al.  A new approach to dynamic security assessment using trajectory sensitivities , 1997 .

[6]  H. Wang,et al.  Approach for optimal power flow with transient stability constraints , 2004 .

[7]  Luonan Chen,et al.  Optimal operation solutions of power systems with transient stability constraints , 2001 .

[8]  J. Carpentier,et al.  Optimal Power Flows , 1979, VSC-FACTS-HVDC.

[9]  B. Yegnanarayana,et al.  Genetic-algorithm-based optimal power flow for security enhancement , 2005 .

[10]  Rajarshi Das,et al.  A Study of Control Parameters Affecting Online Performance of Genetic Algorithms for Function Optimization , 1989, ICGA.

[11]  Shengwei Mei,et al.  Multicontingency transient stability constrained optimal power flow by genetic algorithm , 2006 .

[12]  Kalyanmoy Deb,et al.  A Comparative Analysis of Selection Schemes Used in Genetic Algorithms , 1990, FOGA.

[13]  Jonathan A. Wright,et al.  Self-adaptive fitness formulation for constrained optimization , 2003, IEEE Trans. Evol. Comput..

[14]  Tony B. Nguyen,et al.  Dynamic security-constrained rescheduling of power systems using trajectory sensitivities , 2003 .

[15]  C. R. Joyner,et al.  Propulsion system design optimization using the Taguchi method , 1993 .

[16]  Ka Wing Chan,et al.  Direct nonlinear primal–dual interior-point method for transient stability constrained optimal power flow , 2005 .

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

[18]  A. Bendell,et al.  Taguchi methods : applications in world industry , 1989 .

[19]  R.T.F. Ah King,et al.  Genetic algorithms for economic dispatch with valve point effect , 2004, IEEE International Conference on Networking, Sensing and Control, 2004.

[20]  Kit Po Wong,et al.  Evolutionary programming based optimal power flow algorithm , 1999, 1999 IEEE Power Engineering Society Summer Meeting. Conference Proceedings (Cat. No.99CH36364).

[21]  M. Todorovski,et al.  An initialization procedure in solving optimal power flow by genetic algorithm , 2006, IEEE Transactions on Power Systems.

[22]  Xin Yao,et al.  Stochastic ranking for constrained evolutionary optimization , 2000, IEEE Trans. Evol. Comput..

[23]  A. Bakirtzis,et al.  Optimal Power Flow by Enhanced Genetic Algorithm , 2002, IEEE Power Engineering Review.

[24]  S. R. Paranjothi,et al.  Optimal Power Flow Using Refined Genetic Algorithm , 2002 .

[25]  R.K. Aggarwal,et al.  Genetic algorithms for optimal reactive power compensation on the national grid system , 2005, IEEE Transactions on Power Systems.

[26]  Heinz Mühlenbein,et al.  Predictive Models for the Breeder Genetic Algorithm I. Continuous Parameter Optimization , 1993, Evolutionary Computation.

[27]  L. Darrell Whitley,et al.  The GENITOR Algorithm and Selection Pressure: Why Rank-Based Allocation of Reproductive Trials is Best , 1989, ICGA.