Minimization of Active Power Losses Using Harris Hawks Optimization Algorithm

Year 2020, Volume: 22 Issue: 65, 481 - 490, 15.05.2020

Ozan Akdağ , Abdullah Ates , Celaleddin Yeroglu

https://doi.org/10.21205/deufmd.2020226516

Cited By: 1

Abstract

Optimum Power Flow (OPF) is a nonlinear optimization
problem that allows the optimum determination of steady state variables in the
power system. The OPF problem aims to minimize the cost of production, active/reactive
power losses and fuel emissions in the power system by keeping the parameters
such as generator/bus voltage, busbar shunt current reactor/capacitor value,
generator active/reactive power, transformer tap changer and line capacity
within safe limits. In this paper, a new algorithm, Harris Hawk Optimization
(HHO) algorithm, is used to minimize active power losses for OPF. This
algorithm was applied to a section of 13 bar 
Eastern Anatolia power system of Turkey. Then, the results of the tests
were compared with the Vector evaluated Particle Swarm Optimization (VPSO) and Fractional Order Darwinian Particle Swarm Optimization
with Constraint Threshold (FODPSO-CT)  and the effectiveness of HHO was discussed.

Keywords

Optimum Power Flow, Minimization of Active Power Losses, Harris Hawk optimization algorithm

Harris Şahini Optimizasyon Algoritması ile Aktif Güç Kayıplarının Minimizasyonu

Abstract

Optimum  Yük Akış (OYA)
problemi, güç sistemindeki sabit durum değişkenlerinin  optimum şekilde belirlenmesini sağlayan doğrusal
olmayan bir optimizasyon problemidir. OYA problemi  jeneratör/bara
gerilimi, bara şönt akımı,  reaktör/kapasitör değeri, jeneratör
aktif/reaktif güç, trafo kademe değiştirici ve hat kapasitesi gibi verileri
güvenli sınırlar içinde tutarak güç sisteminde üretim maliyeti, aktif/reaktif
güç kayıpları, yakıt emisyon miktarı gibi çıktıları minimize etmeyi amaçlar. Bu
çalışmada OYA için aktif güç kayıplarının minimizasyonunda yeni bir algoritma
olan Harris Şahini  Optimizasyon (HŞO)
algoritması kullanılmıştır. Bu algoritma 13 baralık Türkiye Doğu Anadolu güç
sisteminin bir kesitine uygulanmıştır. Sonrasında  elde edilen test sonuçları literatürde bulunan
Vektörel Parçacık Sürü Optimizasyonu (VPSO) ve Eşik Değer kısıtlamalı Kesir Dereceli
Darwinian Parçacık Sürü Optimizasyonu (ED-KDDPSO) algoritmaları ile
karşılaştırılarak, HŞO’nun etkinliği tartışılmıştır.

Keywords

Optimum Güç Akışı, Aktif Güç Kayıplarının minimizasyonu, harris şahini optimizasyon algoritması

References

• [1] Dommel, H.W., Tinney, W.F. 1968. Optimal power flow solutions, IEEE Trans: PowerAppar. Syst. PAS–87, s. 1866–1876. DOI: 10.1109/TPAS.1968.292150.
• [2] Singh, R.P., Mukherjee, V., Ghoshal, S.P. 2016. Particle swarm optimization with an aging leader and challengers algorithm for the solution of optimal power flow problem: Applied Soft Computing, Cilt. 40, s. 161-177. DOI: 10.1016/j.asoc.2015.11.027.
• [3] Maria, G.A., Findlay, J.A., 1987. A Newton optimal power flow program for Ontariohydro EMS: IEEETrans. Power Syst. 2 (3), s. 576–582.DOI:10.1109/TPWRS.1987.4335171.
• [4] Abido, M.A., 2002. Optimal power flow using tabu search algorithm. Electric power components and systems, Cilt. 30(5), s. 469-483.DOI: 10.1080/15325000252888425.
• [5] Kirchmayer, L.K., Stagg, G.W., 1951. Analysis of total and incremental losses in transmission systems: Transactions of the American Institute of Electrical Engineers, Cilt. 70(2), s. 1197-1205.DOI: 10.1109/T-AIEE.1951.5060547.
• [6] Mota-Palomino, R., Quintana, V.H., 1986. Sparse reactive power scheduling by a penalty function-linear programming technique: IEEE Transactions on Power Systems, Cilt. 1(3), s. 31-39. DOI: 10.1109/TPWRS.1986.4334951.
• [7] Momoh, J.A., El-Hawary, M.E., Adapa, R.A., 1993. review of selected optimal power flow literature to 1993, II. Newton, linear programming and interior point methods: IEEE Transactions on Power Systems, Cilt. 14(1), s. 105-111. DOI: 10.1109/59.744495.
• [8] Wei, H., Sasaki, H., Kubokawa, J., Yokoyama, R., 1998. An interior point nonlinear programming for optimal power flow problems with a novel data structure: IEEE Transactions on Power Systems, Cilt. 13(3), s. 870-877.
• [9] Wu, Y.C., Debs, A.S., Marsten, R.E., 1994. A direct nonlinear predictor-corrector primal-dual interior point algorithm for optimal power flows: IEEE Transactions on power systems, Cilt. 9(2), s. 876-883. DOI: 10.1109/59.317660
• [10] Habibollahzadeh, H., Luo, G.X., Semlyen, A., 1989. Hydrothermal optimal power flow based on a combined linear and nonlinear programming methodology: IEEE Transactions on Power Systems, Cilt. 4(2), s. 530-537. DOI: 10.1109/59.193826
• [11] Burchett, R.C., Happ, H.H., Vierath, D.R., 1984. Quadratically convergent optimal power flow: IEEE Transactions on Power Apparatus and Systems, Cilt. (11) s. 3267-3275.
• [12] Momoh, J.A., Guo, S.X., Ogbuobiri, E.C., Adapa, R., 1994. The quadratic interior point method solving power system optimization problems: IEEE Transactions on Power Systems, Cilt. 9(3), s. 1327-1336.
• [13] Fan, J.Y., Zhang, L., 1998. Real-time economic dispatch with line flow and emission constraints using quadratic programming, IEEE Transactions on Power Systems, Cilt. 13(2), s. 320-325.
• [14] Abido, M.A., 2002. Optimal power flow using particle swarm optimization: Electrical Power and, Energy Systems, Cilt. (24), s. 563–571. DOI: 10.1016/S0142-0615(01)00067-9.
• [15] Reddy, M.L., Reddy, M.R., Reddy, V.V., 2012. Optimal Power flow using particle swarm optimization. Journal of Engineering Sciences & Emerging Technologies, Cilt. 4(1), s. 116-124.
• [16] Kahourzade, S., Mahmoudi, A., Mokhlis, H.B., A comparative study of multi-objective optimal power flow based on particle swarm, evolutionary programming, and genetic algorithm: Electrical Engineering, Cilt. 97(1), s. 1-12.
• [17] Ganguly, S., Samajpati, D., 2015. Distributed generation allocation on radial distribution networks under uncertainties of load and generation using genetic algorithm. IEEE Transactions on Sustainable Energy, Cilt. 6(3), s. 688-697.
• [18] Abido, M.A., 2002. Optimal power flow using tabu search algorithm: Electric Power Components and Systems.DOI: 10.1080/15325000252888425.
• [19] Kulworawanichpong, T., Sujitjorn, S., 2002. Optimal power flow using tabu search: IEEE Power Engineering Review, Cilt. 22.6, s. 37-39.
• [20] Awasthi, A., Venkitusamy, K., Padmanaban, S., Selvamuthukumaran, R., Blaabjerg, F., Singh, A.K., 2017. Optimal planning of electric vehicle charging station at the distribution system using hybrid optimization algorithm: Energy, Cilt. 133, s. 70-78. DOI: 10.1016/j.energy.2017.05.094
• [21] Baydar, B., Gozde, H., Taplamacioglu, M.C., Kucuk, A.O., 2019. Resilient Optimal Power Flow with Evolutionary Computation Methods: Short Survey. In Power Systems Resilience Springer, Cham, s. 163-189.
• [22] Bouchekara, H.R.E.H., Chaib, A.E., Abido, M.A., El-Sehiemy, R.A., 2016. Optimal powerflow using an improved colliding bodies optimization algorithm: Appl. SoftComput. Cilt. 42, s. 119–131. DOI: 10.1016/j.asoc.2016.01.041.
• [23] Heidari, A.A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., Chen, H., 2019. Harris Hawks optimization: Algorithm and applications: Future Generation Computer Systems, Cilt. 97, s. 849-872. DOI: 10.1016/j.future.2019.02.028.
• [24] Akdağ, O., Okumuş, F., Kocamaz, A.F., Yeroğlu, C., 2018. Fractional Order Darwinian PSO with Constraint Threshold for Load Flow Optimization of Energy Transmission System: Gazi University