Optimal reactive power dispatch using water wave optimization algorithm

This paper presents water wave optimization (WWO) algorithm to solve the optimal reactive power dispatch (ORPD) problem with the continuous and discrete control variables in power system. The ORPD problem is defined as a complex, discrete, constrained nonlinear combinatorial optimization problem. The WWO algorithm is utilized to find the optimized values of control variables such as generator voltages, tap positions of tap changing transformers and the amount of reactive compensation devices to achieve minimized value of active power losses. The WWO algorithm not only effectively avoids the shortcomings of local search and poor calculation accuracy, but also accelerates the convergence rate to find the global optimal solution. The WWO algorithm is implemented on standard IEEE 30-bus power system that is to verify the effectiveness and feasibility of the WWO algorithm to tackle with the ORPD problem. Compared with other algorithms, the WWO algorithm can find the set of the optimal solutions of control variables. The simulation experiment indicates that the WWO algorithm has better overall performance to reduce the real power losses.

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

[2]  Mohd Herwan Sulaiman,et al.  Using the gray wolf optimizer for solving optimal reactive power dispatch problem , 2015, Appl. Soft Comput..

[3]  O. Alsac,et al.  Optimal Load Flow with Steady-State Security , 1974 .

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

[5]  N. Grudinin.  Reactive power optimization using successive quadratic programming method , 1998 .

[6]  Tarek Bouktir,et al.  Ant lion optimizer for solving optimal reactive power dispatch problem in power systems , 2017 .

[7]  Xin-She Yang,et al.  Bat algorithm: literature review and applications , 2013, Int. J. Bio Inspired Comput..

[8]  Francisco Herrera,et al.  A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC’2005 Special Session on Real Parameter Optimization , 2009, J. Heuristics.

[9]  Ali Reza Seifi,et al.  Fuzzy optimal reactive power control , 1994 .

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

[11]  Seyedali Mirjalili,et al.  SCA: A Sine Cosine Algorithm for solving optimization problems , 2016, Knowl. Based Syst..

[12]  Yujun Zheng.  Water wave optimization: A new nature-inspired metaheuristic , 2015, Comput. Oper. Res..

[13]  Sakti Prasad Ghoshal,et al.  Solution of reactive power dispatch of power systems by an opposition-based gravitational search algorithm , 2014 .

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

[15]  S. M. Shahidehpour,et al.  Linear reactive power optimization in a large power network using the decomposition approach , 1990 .

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

[17]  Y.P. Lu,et al.  Swarm Intelligence for Optimal Reactive Power Dispatch , 2005, 2005 IEEE/PES Transmission & Distribution Conference & Exposition: Asia and Pacific.

[18]  F. Wilcoxon.  Individual Comparisons by Ranking Methods , 1945 .

[19]  Mahdi Taghizadeh,et al.  Colonial competitive differential evolution: An experimental study for optimal economic load dispatch , 2016, Appl. Soft Comput..

[20]  Sahand Ghavidel,et al.  Modified teaching learning algorithm and double differential evolution algorithm for optimal reactive power dispatch problem: A comparative study , 2014, Inf. Sci..

[21]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[22]  Xin-She Yang,et al.  Flower Pollination Algorithm for Global Optimization , 2012, UCNC.

[23]  R. Adapa,et al.  The quadratic interior point method solving power system optimization problems , 1994 .

[24]  R. E. Marsten,et al.  A direct nonlinear predictor-corrector primal-dual interior point algorithm for optimal power flows , 1993 .

[25]  Tarek Bouktir,et al.  Artificial Bee Colony Algorithm for Discrete Optimal Reactive Power Dispatch , 2015, 2015 International Conference on Industrial Engineering and Systems Management (IESM).

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

[27]  M. Bjelogrlic,et al.  Application of Newton's optimal power flow in voltage/reactive power control , 1989, Conference Papers Power Industry Computer Application Conference.

[28]  Alireza Askarzadeh,et al.  A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm , 2016 .

[29]  Mahdi Taghizadeh,et al.  Solving optimal reactive power dispatch problem using a novel teaching-learning-based optimization algorithm , 2015, Eng. Appl. Artif. Intell..