(Publisher of Peer Reviewed Open Access Journals)
Navigation

International Journal of Advanced Technology and Engineering Exploration (IJATEE)

ISSN (Print):2394-5443    ISSN (Online):2394-7454
Volume-8 Issue-75 February-2021
Full-Text PDF
Paper Title : Design of optimal multi-objective-based facts component with proportional-integral-derivative controller using swarm optimization approach
Author Name : Abdul Waheed Khawaja, Nor Azwan Mohamed Kamari, Ismail Musirin, Mohd Asyraf Zulkifley and Muhamad Zahim Sujod
Abstract :

This study proposes a multi-objective-based swarm intelligence method to improve angle stability. An optimization operation with single objective function only improves the performance of one perspective and ignores the other. The combination of two objective functions which derived from real and imaginary components of eigenvalue are able to provide better performance beyond the optimization capabilities of single objective function. Tested using MATLAB, the simulation is performed using a single machine attached to the infinite bus (SMIB) system equipped with static var compensator (SVC) that attached with PID controller (SVC-PID). The objective of this experiment is to explore the excellent parameters in SVC-PID to produce a more stable system. In addition to the comparison of objective functions, this study also compares particle swarm optimization (PSO) capabilities with evolutionary programming (EP) and artificial immune system (AIS) techniques.
Keywords : Single machine, Static var compensator, Particle swarm optimization, Multi objective function.
Cite this article : Khawaja AW, Kamari NA, Musirin I, Zulkifley MA, Sujod MZ. Design of optimal multi-objective-based facts component with proportional-integral-derivative controller using swarm optimization approach. International Journal of Advanced Technology and Engineering Exploration. 2021; 8(75):391-404. DOI:10.19101/IJATEE.2020.762132.
References :
[1]Eladany MM, Eldesouky AA, Sallam AA. Power system transient stability: An algorithm for assessment and enhancement based on catastrophe theory and FACTS devices. IEEE Access. 2018; 6:26424-37.
[Crossref] [Google Scholar]
[2]Zhao Q, Li C. Two-stage multi-swarm particle swarm optimizer for unconstrained and constrained global optimization. IEEE Access. 2020; 8:124905-27.
[Crossref] [Google Scholar]
[3]Ling AW, Shareef H, Mohamed A, Ibrahim AA. An enhanced opposition-based firefly algorithm for solving complex optimization problems. J. Kejuruter. 2014; 26(1):89-96.
[Google Scholar]
[4]Rao HG, Prabhu N, Mala RC. Adaptive distance protection for transmission lines incorporating SSSC with energy storage device. IEEE Access. 2020; 8:156017-26.
[Crossref] [Google Scholar]
[5]Shafik MB, Chen H, Rashed GI, El-Sehiemy RA. Adaptive multi objective parallel seeker optimization algorithm for incorporating TCSC devices into optimal power flow framework. IEEE Access. 2019; 7:36934-47.
[Crossref] [Google Scholar]
[6]Eroglu F, Kazmi HU, Vural AM. Modelling and control of a three-level diode-clamped medium voltage distribution static synchronous compensator using space vector pulse width modulation. Gazi University Journal of Science. 2020; 33(1):106-18.
[Google Scholar]
[7]Keshta HE, Ali AA, Saied EM, Bendary FM. Application of Static Var Compensator (SVC) with PI controller for grid integration of wind farm using harmony search. International Journal of Emerging Electric Power Systems. 2016; 17(5):555-66.
[Crossref] [Google Scholar]
[8]Wan Y, Murad MA, Liu M, Milano F. Voltage frequency control using SVC devices coupled with voltage dependent loads. IEEE Transactions on Power Systems. 2018; 34(2):1589-97.
[Crossref] [Google Scholar]
[9]Kamari NA, Musirin I, Ibrahim AA. Swarm intelligence approach for angle stability improvement of PSS and SVC-based SMIB. Journal of Electrical Engineering & Technology. 2020; 15(3):1001-14.
[Crossref] [Google Scholar]
[10]Abdulkhader HK, Jacob J, Mathew AT. Fractional-order lead-lag compensator-based multi-band power system stabiliser design using a hybrid dynamic GA-PSO algorithm. IET Generation, Transmission & Distribution. 2018; 12(13):3248-60.
[Crossref] [Google Scholar]
[11]Lu CF, Hsu CH, Juang CF. Coordinated control of flexible AC transmission system devices using an evolutionary fuzzy lead-lag controller with advanced continuous ant colony optimization. IEEE Transactions on Power Systems. 2012; 28(1):385-92.
[Crossref] [Google Scholar]
[12]Kamari NA, Musirin I, Othman Z, Halim SA. PSS based angle stability improvement using whale optimization approach. Indonesian Journal of Electrical Engineering and Computer Science. 2017; 8(2):382-90.
[Google Scholar]
[13]Jan MU, Xin A, Abdelbaky MA, Rehman HU, Iqbal S. Adaptive and fuzzy PI controllers design for frequency regulation of isolated microgrid integrated with electric vehicles. IEEE Access. 2020; 8:87621-32.
[Crossref] [Google Scholar]
[14]Sönmez S, Ayasun S. Stability region in the parameter space of PI controller for a single-area load frequency control system with time delay. IEEE Transactions on Power Systems. 2015; 31(1):829-30.
[Crossref] [Google Scholar]
[15]Kamari NA, Musirin I, Hamid ZA, Ibrahim AA. Optimal tuning of SVC-PI controller using whale optimization algorithm for angle stability improvement. Indonesian Journal of Electrical Engineering and Computer Science. 2018; 12(2):612-9.
[Google Scholar]
[16]Chaiyatham T, Ngamroo I. Improvement of power system transient stability by PV farm with fuzzy gain scheduling of PID controller. IEEE Systems Journal. 2014; 11(3):1684-91.
[Crossref] [Google Scholar]
[17]Mohapatra TK, Dey AK, Sahu BK. Employment of quasi oppositional SSA-based two-degree-of-freedom fractional order PID controller for AGC of assorted source of generations. IET Generation, Transmission & Distribution. 2020; 14(17):3365-76.
[Crossref] [Google Scholar]
[18]Kamari NM, Musirin I, Othman MM. IPSO based SVC-PID for angle stability enhancement. International Journal of. Simulation. Systems, Science & Technology. 2017; 17:20-1.
[Crossref] [Google Scholar]
[19]Zamani MK, Musirin I, Omar MS, Suliman SI, Ghani NA, Kamari NA. Gravitational search algorithm based technique for voltage stability improvement. Indonesian Journal of Electrical Engineering and Computer Science. 2018; 9(1):123-30.
[Crossref] [Google Scholar]
[20]Satapathy P, Dhar S, Dash PK. Stability improvement of PV-BESS diesel generator-based microgrid with a new modified harmony search-based hybrid firefly algorithm. IET Renewable Power Generation. 2017; 11(5):566-77.
[Crossref] [Google Scholar]
[21]Hasanien HM. Whale optimisation algorithm for automatic generation control of interconnected modern power systems including renewable energy sources. IET Generation, Transmission & Distribution. 2017; 12(3):607-14.
[Crossref] [Google Scholar]
[22]Kang LJ, Halim SA, Rosli HM, Kamari NAM, Awalin LA. Optimal distributed generations placement in radial distribution network using whale optimization algorithm. International Journal of Advanced Trends in Computer Science and Engineering. 2020; 9(5):7680-9.
[Google Scholar]
[23]Zhou J, Wang C, Li Y, Wang P, Li C, Lu P, Mo L. A multi-objective multi-population ant colony optimization for economic emission dispatch considering power system security. Applied Mathematical Modelling. 2017; 45:684-704.
[Crossref] [Google Scholar]
[24]Ram JP, Rajasekar N. A novel flower pollination based global maximum power point method for solar maximum power point tracking. IEEE Transactions on Power Electronics. 2016; 32(11):8486-99.
[Crossref] [Google Scholar]
[25]Ramli NF, Kamari NA, Halim SA, Musirin I. Solving non-smooth economic load dispatch problem via flower pollination algorithm. International Journal of Emerging Trends in Engineering Research. 2020; 8(1 1.1):158-65.
[Google Scholar]
[26]Elsakaan AA, El-Sehiemy RA, Kaddah SS, Elsaid MI. An enhanced moth-flame optimizer for solving non-smooth economic dispatch problems with emissions. Energy. 2018; 157:1063-78.
[Crossref] [Google Scholar]
[27]Ramli NF, Kamari NA, Zulkifley MA, Musirin I. Optimal power scheduling for economic dispatch using moth flame optimizer. Indonesian Journal of Electrical Engineering and Computer Science. 2020; 20(1):379-84.
[Google Scholar]
[28]Liang H, Liu Y, Li F, Shen Y. A multiobjective hybrid bat algorithm for combined economic/emission dispatch. International Journal of Electrical Power & Energy Systems. 2018; 101:103-15.
[Crossref] [Google Scholar]
[29]Ng XW, Ramli NF, Kamari NAM, Zulkifley MA, Musirin I. Optimal energy scheduling strategy in power system using bat algorithm optimization approach. International Journal of Emerging Trends in Engineering Research. 2020; 8(10):7510-5.
[Crossref]
[30]Khatod DK, Pant V, Sharma J. Evolutionary programming based optimal placement of renewable distributed generators. IEEE Transactions on Power systems. 2012; 28(2):683-95.
[Crossref] [Google Scholar]
[31]Kamari NA, Musirin I, Othman MM. EP based optimization for estimating synchronizing and damping torque coefficients. Australian Journal of Basic and Applied Sciences. 2010; 4(8):3741-54.
[Google Scholar]
[32]Alonso FR, Oliveira DQ, De Souza AZ. Artificial immune systems optimization approach for multiobjective distribution system reconfiguration. IEEE Transactions on Power Systems. 2014; 30(2):840-7.
[Crossref] [Google Scholar]
[33]Patel SK, Sharma AK. Improved PSO based job scheduling algorithm for resource management in grid computing. International Journal of Advanced Technology and Engineering Exploration. 2019; 6(54):152-61.
[Crossref] [Google Scholar]
[34]Sato M, Fukuyama Y, Iizaka T, Matsui T. Total optimization of energy networks in a smart city by multi-swarm differential evolutionary particle swarm optimization. IEEE Transactions on Sustainable Energy. 2018; 10(4):2186-200.
[Crossref] [Google Scholar]
[35]Mohamed Kamari NA, Musirin I, Dagang AN, Mohd Zaman MH. PSO-based oscillatory stability assessment by using the torque coefficients for SMIB. Energies. 2020; 13(5):1-15.
[Crossref] [Google Scholar]