Deciding Optimal Location of DPFC in Transmission Line Using Artificial Algae Algorithm

J. Chakravorty, J. Saraswat

Abstract


In this paper, the application of artificial algae algorithm (AAA) in optimal placement distributed power flow controller (DPFC) with MCFC in transmission networks has been proposed The proposed method is tested on IEEE 14- bus system and the results are discussed. The biggest advantage of DPFC is that it can control the active and reactive power flow and bus voltages, simultaneously. In this paper, the optimal placement of one DPFC in IEEE-14 bus system and then optimal placement of two DPFCs in IEEE-14 bus system has been proposed. Optimal placement of DPFC in power system by AAA leads to increased stability and capacity of the power transmission in lines. The proposed model has been simulated in Matlab/Simulink and the performance results are tabulated.


Keywords


artifical algae algorithm; DPFC; MCFC

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References


S. V. Ravi Kumar, S. S. N. Raju, “Loss Minimization by incorporation of UPFC in Load Flow Studies”, International Journal of Electrical and Power Engineering, Vol. 1, No. 3, pp. 321-327, 2007

A. M. Vural, M. Tumay, “Mathematical modelling and analysis of a unified power flow controller: A comparison of two approaches in power flow studies and effects of UPFC location”, Electrical Power and Energy Systems, Vol. 29, pp. 617-629, 2007

B. V. Rao, G. V. N. Kumar, M. R. Priya, P. V. S. Sobhan, “Implementation of Static VAR Compensator for Improvement of Power System Stability”, 2009 International Conference on Advances in Computing, Control, and Telecommunication Technologies, Trivandrum, India, December 28-29, 2009

M. D. Reddy, K. D. Babu, “Optimal placement of SVC using fuzzy and PSO algorithm”, International Journal of Engineering Research and Applications, Vol. 3, No. 1, pp. 485-490, 2013

V. K. Shende, P. Jagtap, “Optimal Location and sizing of SVC by PSO technique for Voltage Stability Enhancement and Power loss minimization”, International journal of Engineering Trends and Technology, Vol. 4, No. 6, pp. 2278-2282, 2013

R. S. Rao, V. S. Rao, “A generalized approach for determination of optimal location and performance analysis of FACTs devices”, International Journal of Electrical Power & Energy Systems, Vol. 73, pp. 711-724, 2015

B. Bhattacharyya, V. K. Gupta, S. Kumar, “UPFC with series and shunt FACTS controllers for the economic operation of a power system”, Ain Shams Engineering Journal, Vo. 5, No. 3, pp. 775-787, 2014

N. Karuppiah, V. Malathi, G. Selvalakshmi, “Optimal Placement and Sizing of Multi-type Facts Devices Using PSO and HSA”, in: Lecture Notes in Computer Science, Vol. 8947, Springer, 2014

S. Frank I. Steponavice, S. Rebennack, “Optimal power flow: a bibliographic survey II Nondeterministic and hybrid methods”, Energy Systems, Vol. 3, No. 3, pp. 259-289, 2012

S. Akumalla, S. Peddakotla, S. R. A. Kuppa, “A Modified Cuckoo Search Algorithm for Improving Voltage Profile and to Diminish Power Losses by Locating Multi-type FACTS Devices”, Journal of Control, Automation and Electrical Systems, Vol. 27, No. 1, pp. 93-104, 2016

S. Dutta, P. K. Roy, D. Nandi, “Optimal location of UPFC controller in transmission network using hybrid chemical reaction optimization algorithm”, International Journal of Electrical Power & Energy Systems, Vol. 64, pp. 194-211, 2015

S. A. Taher, M. K. Amooshahi, “New approach for optimal UPFC placement using hybrid immune algorithm in electric power systems”, International Journal of Electrical Power & Energy Systems, Vol. 43, No. 1, pp. 899-909, 2012

J. Chakravorty, J. Saraswat, V. Bhatia, “Modeling a Distributed Power Flow Controller with a PEM Fuel Cell for Power Quality Improvement”, Engineering, Technology & Applied Science Research, Vol. 8, No. 1, pp. 2585-2589, 2018

S. Ali, G. Tezel, E. Yel, “Artificial Algae Algorithm (AAA) for nonlinear global optimization”, Applied Soft Computing, Vol. 31, pp. 153-171, 2015

S. A. Uymaz, G. Tezel, E. Yel, “Artificial Algae Algorithm with multi-light source for numerical optimization and application”, Biosystems, Vol. 138, pp. 25-38, 2015

M. P. Aghababa, M. E. Akbari, A. M. Shotorbani, “An Efficient Modified Shuffled Frog Leaping Optimization Algorithm”, International Journal of Computer Applications, Vol. 32, pp. 26-30, 2011




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