Enhanced Red Wolf Optimization Algorithm for Reduction of Real Power Loss

This paper projects enhanced red wolf optimization (ERWO) algorithm for solving optimal reactive power problem. Projected ERWO algorithm hybridizes the wolf optimization (WO) algorithm with particle swarm optimization (PSO) algorithm. Each red wolf has a flag vector, in the algorithm, and length is equivalent to the whole sum of numbers which features in the dataset of the wolf optimization (WO). Due to the hybridization of both WO with PSO exploration, the ability of the proposed red wolf optimization algorithm has been enhanced. Efficiency of the projected enhanced red wolf optimization (ERWO) algorithm has been evaluated in standard IEEE 118 bus test system. Results indicate that enhanced red wolf optimization (ERWO) algorithm performs well in solving the problem. Actual power losses are reduced, and control variables are well within the limits.

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

[2]  Eric Hcbson,et al.  Network Constrained Reactive Power Control Using Linear Programming , 1980, IEEE Transactions on Power Apparatus and Systems.

[3]  Amin Kargarian,et al.  Probabilistic reactive power procurement in hybrid electricity markets with uncertain loads , 2012 .

[4]  Kwang Y. Lee,et al.  Optimal Real and Reactive Power Control Using Linear Programming , 1993 .

[5]  D.C. Yu,et al.  A novel optimal reactive power dispatch method based on an improved hybrid evolutionary programming technique , 2004, IEEE Transactions on Power Systems.

[6]  Zechun Hu,et al.  Stochastic optimal reactive power dispatch: Formulation and solution method , 2010 .

[7]  Cristian Bovo,et al.  A GA approach to compare ORPF objective functions including Secondary Voltage Regulation , 2012 .

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

[9]  A.C.Z. de Souza,et al.  Comparison of performance indices for detection of proximity to voltage collapse , 1996 .

[10]  Fuli Wang,et al.  An Improved Biogeography-based Optimization Algorithm for Optimal Reactive Power Flow , 2014 .

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

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

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

[14]  Fang Liu,et al.  A hybrid genetic algorithm-interior point method for optimal reactive power flow , 2006, IEEE Transactions on Power Systems.

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

[16]  Ching-Tzong Su,et al.  Optimal setting of reactive compensation devices with an improved voltage stability index for voltage stability enhancement , 2012 .

[17]  A. Monticelli,et al.  Security-Constrained Optimal Power Flow with Post-Contingency Corrective Rescheduling , 1987, IEEE Transactions on Power Systems.

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

[19]  F. Capitanescu,et al.  Assessing Reactive Power Reserves With Respect to Operating Constraints and Voltage Stability , 2011, IEEE Transactions on Power Systems.

[20]  Juan Yu,et al.  An Unfixed Piecewise-Optimal Reactive Power-Flow Model and its Algorithm for AC-DC Systems , 2008, IEEE Transactions on Power Systems.

[21]  Ali Kaveh,et al.  APPLICATION OF GREY WOLF OPTIMIZER IN DESIGN OF CASTELLATED BEAMS , 2016 .

[22]  Bala Venkatesh,et al.  A new optimal reactive power scheduling method for loss minimization and voltage stability margin maximization using successive multi-objective fuzzy LP technique , 2000 .