An optimal reactive power dispatch (ORPD) for voltage security using particle swarm optimization (PSO) in graph theory

The stochastic nature of the wind and the highly non linear transform from wind speed to electrical energy makes it more difficult to determine how to dispatch the power in order to guarantee both operational cost reduction and power system security. From a network constraint perspective for the economic dispatch problem one of the factors to be accounted for is voltage security, which impacts both active and/or reactive power dispatch. In this paper, an Optimal Reactive Power Dispatch based on Particle Swarm Optimization (PSO) using Graph Theory has been proposed to overcome the above-mentioned problem. Graph Theory has been used since it becomes very useful in cases of fault detection and isolation or to shed unbalanced nodes in case of excessive or insufficient supply. Simulation studies on the modified IEEE-14 Bus System have been conducted to show the effectiveness of the proposed method.

[1]  R. Minguez,et al.  Component failure simulation tool for optimal electrical configuration and repair strategy design of off-shore wind farms , 2011, OCEANS 2011 IEEE - Spain.

[2]  Q.Y. Jiang,et al.  Distributed optimal reactive power dispatch based on parallel particle swarm optimisation algorithm , 2008, 2008 43rd International Universities Power Engineering Conference.

[3]  M. Negnevitsky,et al.  Short term wind power forecasting using hybrid intelligent systems , 2007, 2007 IEEE Power Engineering Society General Meeting.

[4]  Han-lin Liu,et al.  Notice of RetractionOptimal reactive power dispatch based on dynamic readjusting cost , 2011, 2011 IEEE Power Engineering and Automation Conference.

[5]  Vo Ngoc Dieu,et al.  Optimal reactive power dispatch by pseudo-gradient guided particle swarm optimization , 2012, 2012 10th International Power & Energy Conference (IPEC).

[6]  R D Zimmerman,et al.  MATPOWER: Steady-State Operations, Planning, and Analysis Tools for Power Systems Research and Education , 2011, IEEE Transactions on Power Systems.

[7]  G. Lambert-Torres,et al.  A hybrid particle swarm optimization applied to loss power minimization , 2005, IEEE Transactions on Power Systems.

[8]  S. Leva,et al.  Small signal stability of power system with SCIG, DFIG wind turbines , 2014, 2014 Annual IEEE India Conference (INDICON).

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

[10]  Ken-ichi Kawarabayashi,et al.  Algorithmic graph minor theory: Decomposition, approximation, and coloring , 2005, 46th Annual IEEE Symposium on Foundations of Computer Science (FOCS'05).

[11]  Shin-Ju Chen,et al.  Comparative study of evolutionary computation methods for active-reactive power dispatch , 2012 .

[12]  T.S. Oepomo A step-by-step method for Z-loop construction using graph theory and topology for power system studies , 2008, 2008 IEEE Region 8 International Conference on Computational Technologies in Electrical and Electronics Engineering.

[13]  Wei Wei,et al.  Short-term forecasting for wind speed based on wavelet decomposition and LMBP neural network , 2011, 2011 4th International Conference on Electric Utility Deregulation and Restructuring and Power Technologies (DRPT).

[14]  G. Evensen Data Assimilation: The Ensemble Kalman Filter , 2006 .

[15]  Shilpa S. Shrawane,et al.  Optimal reactive power dispatch by furnishing UPFC using multi-objective hybrid GAPSO approach for transmission loss minimisation and voltage stability , 2015, 2015 International Conference on Nascent Technologies in the Engineering Field (ICNTE).

[16]  G.A. Bakare,et al.  Reactive power and voltage control of the Nigerian grid system using micro-genetic algorithm , 2005, IEEE Power Engineering Society General Meeting, 2005.

[17]  Y. Amrane,et al.  Optimal reactive power dispatch based on particle swarm optimization approach applied to the Algerian electric power system , 2014, 2014 IEEE 11th International Multi-Conference on Systems, Signals & Devices (SSD14).

[18]  Laxmi Srivastava,et al.  Hybrid multi-swarm particle swarm optimisation based multi-objective reactive power dispatch , 2015 .

[19]  Laxmi Srivastava,et al.  Evolutionary Computing Techniques for Optimal Reactive Power Dispatch: An Overview and Key Issues , 2014, 2014 Fourth International Conference on Communication Systems and Network Technologies.

[20]  Yongdong Li,et al.  Modeling and control of doubly fed induction generator wind turbines by using Causal Ordering Graph during voltage dips , 2008, 2008 International Conference on Electrical Machines and Systems.

[21]  B. Tyagi,et al.  A method for optimal placement of reactive sources & reactive power procurement in competitive electricity markets , 2006, 2006 IEEE Power India Conference.

[22]  Behrooz Vahidi,et al.  Hybrid shuffled frog leaping algorithm and Nelder-Mead simplex search for optimal reactive power dispatch , 2011 .

[23]  Xiaoming Jin,et al.  Optimal reactive power dispatch considering wind turbines , 2014, 2014 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC).

[24]  Amit Konar,et al.  Differential Evolution Using a Neighborhood-Based Mutation Operator , 2009, IEEE Transactions on Evolutionary Computation.

[25]  Khidir M. Ali,et al.  Applications of Graph Theory in Computer Science , 2011, 2011 Third International Conference on Computational Intelligence, Communication Systems and Networks.

[26]  Li Hui,et al.  One hour ahead prediction of wind speed based on data mining , 2010, 2010 2nd International Conference on Advanced Computer Control.

[27]  Tek Tjing Lie,et al.  Wind Speed Forecasting Using Hybrid Wavelet Transform- ARMA Techniques , 2015 .

[28]  H. Yoshida,et al.  A particle swarm optimization for reactive power and voltage control considering voltage security assessment , 1999, 2001 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.01CH37194).

[29]  Tao Yu,et al.  Optimal Reactive Power Dispatch Based on Modified Particle Swarm Optimization Considering Voltage Stability , 2007, 2007 IEEE Power Engineering Society General Meeting.

[30]  Vladimir Terzija,et al.  A graph theory based new approach for power system restoration , 2013, 2013 IEEE Grenoble Conference.

[31]  Yanguang Sun,et al.  Comparison of multiobjective particle swarm optimization and evolutionary algorithms for optimal reactive power dispatch problem , 2014, 2014 IEEE Congress on Evolutionary Computation (CEC).

[32]  M. Velez-Reyes,et al.  A reconfiguration algorithm for a DC Zonal Electric Distribution System based on graph theory methods , 2009, 2009 IEEE Electric Ship Technologies Symposium.

[33]  I. Erlich,et al.  Reactive Power Management in Offshore Wind Farms by Adaptive PSO , 2007, 2007 International Conference on Intelligent Systems Applications to Power Systems.

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

[35]  Yongjun Zhang,et al.  Optimal reactive power dispatch considering costs of adjusting the control devices , 2005 .

[36]  Salman Mohagheghi,et al.  Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems , 2008, IEEE Transactions on Evolutionary Computation.

[37]  Worawat Nakawiro,et al.  A novel optimization algorithm for optimal reactive power dispatch: A comparative study , 2011, 2011 4th International Conference on Electric Utility Deregulation and Restructuring and Power Technologies (DRPT).

[38]  Abdelghani Bekrar,et al.  Hybrid PSO-tabu search for the optimal reactive power dispatch problem , 2014, IECON 2014 - 40th Annual Conference of the IEEE Industrial Electronics Society.

[39]  Shan Gao,et al.  Wind speed forecast for wind farms based on ARMA-ARCH model , 2009, 2009 International Conference on Sustainable Power Generation and Supply.

[40]  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.

[41]  Norihiko Shinomiya,et al.  Distributed control based on tie-set graph theory for smart grid networks , 2010, International Congress on Ultra Modern Telecommunications and Control Systems.

[42]  Kaamran Raahemifar,et al.  Reactive power optimization based on hybrid particle swarm optimization algorithm , 2012, 2012 25th IEEE Canadian Conference on Electrical and Computer Engineering (CCECE).

[43]  Manoj Kumar Maharana,et al.  Graph theory based corrective control strategy during single line contingency , 2009, 2009 International Conference on Power Systems.