Abstract

This paper presents the implementation of Artificial Immune System (AIS) for total system loss minimization and Voltage Deviation Index (VDI) reduction, along with the identification of the Static VAR Compensator (SVC) sizing. The objective of this optimization problem is to find the best location and sizing of SVC units that result in low total system loss and low value of VDI. Two case studies have been introduced in solving the problem: (N-1) line contingency and reactive power loading. It is found that the proposed approach is suitable to be used to solve optimal SVC placement problem as it has managed to achieve minimum total system loss and lower down the value of VDI by finding the best location and size of the SVC units in the transmission network. For example, for case 1 ((N-1) line contingency), AIS found bus 11 and bus 13 are the suitable locations to install SVC1 and SVC2 with the corresponding sizes of 55.25 MVAR (injecting) and 53.04 MVAR (injecting), respectively for total system loss minimization. The loss has reduced from 17.68 MW to 15.84 MW after the installation of the SVC units.

Keyword

FACTS devices, Static VAR compensator, Voltage deviation index, Artificial immune system.

Cite this article

Aziz MH, Mansor MH, Musirin I, Jelani S, Ismail SA

Refference

[1][1]Zamani MK, Musirin I, Suliman SI, Mustaffa SA, Ali NZ, Mansor MH, Hamid ZA. Optimal TCSC allocation via chaotic immune symbiotic organisms search for voltage profile improvement. In E3S web of conferences 2020 (p. 03002). EDP Sciences.

[2][2]Kumar BS, Suryakalavathi M, Kumar GN. Optimal power flow with static VAr compensator using galaxy based search algorithm to minimize real power losses. Procedia Computer Science. 2016; 92:42-7.

[3][3]Malatji EM, Twala B, Mbuli N. Optimal placement model of multi-type FACTS devices in power system networks on a limited budget. In AFRICON 2017 (pp. 1296-300). IEEE.

[4][4]Bhattacharyya B, Goswami SK. Optimal planning for the placement of facts devices by differential evolution technique for the increased loadabilty of a power system. In Asia-pacific power and energy engineering conference 2012 (pp. 1-4). IEEE.

[5][5]Razali NS, Roslan N, Mansor MH, Musirin I, Shaaya SA, Jelani S. Optimal reactive power control using compensating capacitor based on artificial immune system. International Journal of Advanced Trends in Computer Science and Engineering. 2019; 8(1.3):381-6.

[6][6]Gitizadeh M, Kalantar M. A new approach for congestion management via optimal location of FACTS devices in deregulated power systems. In 2008 third international conference on electric utility deregulation and restructuring and power technologies 2008 (pp. 1592-7). IEEE.

[7][7]Ghorbani A, Khederzadeh M, Mozafari B. Impact of SVC on the protection of transmission lines. International Journal of Electrical Power & Energy Systems. 2012; 42(1):702-9.

[8][8]Nguyen KP, Fujita G, Dieu VN. Cuckoo search algorithm for optimal placement and sizing of static var compensator in large-scale power systems. Journal of Artificial Intelligence and Soft Computing Research. 2016; 6(2):59-68.

[9][9]Silva MC, Belati EA. Allocation of static VAr compensators using optimal reactive power flow and branch & bound algorithm. IEEE Latin America Transactions. 2016; 14(5):2194-200.

[10][10]Qi J, Huang W, Sun K, Kang W. Optimal placement of dynamic var sources by using empirical controllability covariance. IEEE Transactions on Power Systems. 2016; 32(1):240-9.

[11][11]Huang JS, Jiang ZH, Negnevitsky M. Loadability of power systems and optimal SVC placement. International Journal of Electrical Power & Energy Systems. 2013; 45(1):167-74.

[12][12]Aziz T, Saha TK, Mithulananthan N. Identification of the weakest bus in a distribution system with load uncertainties using reactive power margin. In Australasian universities power engineering conference 2010 (pp. 1-6). IEEE.