New Efficient Evolutionary Algorithm Applied to Optimal Reactive Power Dispatch

Evolutionary Algorithms (EAs) are well-known optimization techniques to deal with nonlinear and complex optimization problems. However, most of these population-based algorithms are computationally expensive due to the slow nature of the evolutionary process. To overcome this drawback and to improve the convergence rate, this chapter employs Quasi-Opposition-Based Learning (QOBL) in conventional Biogeography-Based Optimization (BBO) technique. The proposed Quasi-Oppositional BBO (QOBBO) is comprehensively developed and successfully applied for solving the Optimal Reactive Power Dispatch (ORPD) problem by minimizing the transmission loss when both equality and inequality constraints are satisfied. The proposed QOBBO algorithm’s performance is studied with comparisons of Canonical Genetic Algorithm (CGA), five versions of Particle Swarm Optimization (PSO), Local Search-Based Self-Adaptive Differential Evolution (L-SADE), Seeker Optimization Algorithm (SOA), and BBO on the IEEE 30-bus, IEEE 57-bus, and IEEE 118-bus power systems. The simulation results show that the proposed QOBBO approach performed better than the other listed algorithms and can be efficiently used to solve small-, medium-, and large-scale ORPD problems.

[1]  Chaohua Dai,et al.  Dynamic multi-group self-adaptive differential evolution algorithm for reactive power optimization , 2010 .

[2]  Sharmistha Bhattacharya Halder,et al.  Attribute Reduction Using Bayesian Decision Theoretic Rough Set Models , 2014, Int. J. Rough Sets Data Anal..

[3]  Sakti Prasad Ghoshal,et al.  Optimal VAR control for improvements in voltage profiles and for real power loss minimization using Biogeography Based Optimization , 2012 .

[4]  Mohammad Shahidehpour,et al.  A decentralized approach for optimal reactive power dispatch using a Lagrangian decomposition method , 2012 .

[5]  Chuangxin Guo,et al.  A multiagent-based particle swarm optimization approach for optimal reactive power dispatch , 2005 .

[6]  Haiping Ma,et al.  Oppositional ant colony optimization algorithm and its application to fault monitoring , 2010, Proceedings of the 29th Chinese Control Conference.

[7]  S. Baskar,et al.  An improved generalized differential evolution algorithm for multi-objective reactive power dispatch , 2012 .

[8]  Shahryar Rahnamayan,et al.  Quasi-oppositional Differential Evolution , 2007, 2007 IEEE Congress on Evolutionary Computation.

[9]  Kenichi Aoki,et al.  Optimal VAr planning by approximation method for recursive mixed-integer linear programming , 1988 .

[10]  S. S. Thakur,et al.  Biogeography based optimization for multi-constraint optimal power flow with emission and non-smooth cost function , 2010, Expert Syst. Appl..

[11]  Kenji Iba.  Reactive power optimization by genetic algorithm , 1993 .

[12]  Wei Zhang,et al.  Multiagent-Based Reinforcement Learning for Optimal Reactive Power Dispatch , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[13]  Zhiwei Ni,et al.  A Novel Swarm Model With Quasi-oppositional Particle , 2009, 2009 International Forum on Information Technology and Applications.

[14]  Provas Kumar Roy,et al.  Optimal reactive power dispatch using quasi-oppositional teaching learning based optimization , 2013 .

[15]  Y.-Y. Hsu,et al.  Reactive power/voltage control in a distribution substation using dynamic programming , 1995 .

[16]  Shahryar Rahnamayan,et al.  Opposition versus randomness in soft computing techniques , 2008, Appl. Soft Comput..

[17]  Q. H. Wu,et al.  Power system optimal reactive power dispatch using evolutionary programming , 1995 .

[18]  Charles Møller,et al.  Encyclopedia of Information Science and Technology , 2005 .

[19]  Yi Liu,et al.  Dynamic optimal reactive power dispatch based on parallel particle swarm optimization algorithm , 2009, Comput. Math. Appl..

[20]  Chaohua Dai,et al.  Seeker Optimization Algorithm for Optimal Reactive Power Dispatch , 2009, IEEE Transactions on Power Systems.

[21]  J.G. Vlachogiannis,et al.  A Comparative Study on Particle Swarm Optimization for Optimal Steady-State Performance of Power Systems , 2006, IEEE Transactions on Power Systems.

[22]  Eduardo N. Asada,et al.  Penalty-based nonlinear solver for optimal reactive power dispatch with discrete controls , 2014 .

[23]  R. A. El-Sehiemy,et al.  Optimal reactive power dispatch using ant colony optimization algorithm , 2011 .

[24]  Loi Lei Lai,et al.  Evolutionary programming approach to reactive power planning , 1996 .

[25]  P. K. Chattopadhyay,et al.  Solution of Optimal Reactive Power Flow using Biogeography-Based Optimization , 2010 .

[26]  M. Kalantar,et al.  Optimal reactive power dispatch based on harmony search algorithm , 2011 .

[27]  K. Mahadevan,et al.  Comprehensive learning particle swarm optimization for reactive power dispatch , 2010, Appl. Soft Comput..

[28]  Ashish Saini,et al.  Multi-objective optimal reactive power dispatch considering voltage stability in power systems using HFMOEA , 2013, Eng. Appl. Artif. Intell..

[29]  Sakti Prasad Ghoshal,et al.  Biogeography-based Optimization for Economic Load Dispatch Problems , 2009 .

[30]  Dan Simon,et al.  Biogeography-Based Optimization , 2022 .

[31]  K. Lee,et al.  A United Approach to Optimal Real and Reactive Power Dispatch , 1985, IEEE Transactions on Power Apparatus and Systems.

[32]  Wei Yan,et al.  A new optimal reactive power flow model in rectangular form and its solution by predictor corrector primal dual interior point method , 2006, IEEE Transactions on Power Systems.

[33]  Hamid R. Tizhoosh,et al.  Opposition-Based Learning: A New Scheme for Machine Intelligence , 2005, International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06).

[34]  Serhat Duman,et al.  Optimal reactive power dispatch using a gravitational search algorithm , 2012 .

[35]  Nicoletta Dessì,et al.  Improving spatial decision making in cloud computing , 2014 .

[36]  Ponnuthurai N. Suganthan,et al.  Efficient constraint handling for optimal reactive power dispatch problems , 2012, Swarm Evol. Comput..

[37]  Chaohua Dai,et al.  Reactive power dispatch considering voltage stability with seeker optimization algorithm , 2009 .

[38]  K.P. Wong,et al.  Parallel Optimal Reactive Power Flow Based on Cooperative Co-Evolutionary Differential Evolution and Power System Decomposition , 2007, IEEE Transactions on Power Systems.

[39]  Yoshikazu Fukuyama,et al.  A particle swarm optimization for reactive power and voltage control considering voltage security assessment , 2000 .

[40]  Kwang Y. Lee,et al.  Optimization method for reactive power planning by using a modified simple genetic algorithm , 1995 .

[41]  Zou Yiqin.  Optimal Reactive Power Planning Based on Improved Tabu Search Algorithm , 2010, 2010 International Conference on Electrical and Control Engineering.

[42]  Sanjay N. Talbar,et al.  Multimodality Medical Image Fusion using M-Band Wavelet and Daubechies Complex Wavelet Transform for Radiation Therapy , 2015, Int. J. Rough Sets Data Anal..

[43]  K. S. Swarup,et al.  Differential evolution approach for optimal reactive power dispatch , 2008, Appl. Soft Comput..

[44]  S. Granville.  Optimal reactive dispatch through interior point methods , 1994 .

[45]  Taher Niknam,et al.  Multiobjective Optimal Reactive Power Dispatch and Voltage Control: A New Opposition-Based Self-Adaptive Modified Gravitational Search Algorithm , 2013 .