Energy Efficiency Improvement by Tap Adjustment in Transformer Using Genetic Algorithms

Abstract

Main purpose in this study is to minimize the active power losses of the distribution systems as an optimization problem. To achieve this, the tap position of the ULTC (Under Load Tap Changer) is used as the system variable. As the processual flow of the method, the tap setting of the ULTC in the IEEE 13 Bus test system, was obtained by power flow with OpenDSS software according to the predetermined load condition for 24 hours, on the first step. The step value of the transformer's output voltage of the transformer is adjusted to the desired level (usually 1 per-unit (pu) in the middle point of the feeder), in OpenDSS software. In the next step, as the main objective, an implementation is designed in MATLAB to compute the transformer tap values, which will minimize the active power losses in distribution systems by using genetic algorithms which is a heuristic method. As a constraint, the voltage level in the system is kept between 0.95 - 1.05 pu. Simulation studies have shown, loss minimization provides more effectiveness and better energy efficiency by using the step values obtained by genetic algorithms.

Keywords

• Energy efficiency
• Optimization
• Genetic algorithms
• Transformer tap changing

Genetik Algoritmalar Kullanılarak Transformatörde Kademe Ayarı ile Enerji Verimliliği İyileştirme

Abstract

Bu çalışmada amaç, bir optimizasyon problemi olarak, dağıtım sisteminin aktif güç kayıplarını minimum yapmaktır. Bunun için, sistem değişkeni olarak kademe değiştiricili transformatörün kademe pozisyonu kullanılmıştır. Yöntem adımları olarak önce, IEEE 13 Baralı test sisteminde bulunan kademe değiştiricili transformatörün kademe ayarı, 24 saat için önceden öngörülen yük durumuna göre OpenDSS yazılımı ile güç akışı yapılarak elde edilmiştir. OpenDSS yazılımında kademe değeri transformatörün çıkış gerilimi istenilen seviyeye (genellikle fiderin tam ortasında 1 per-unit (pu) olacak şekilde) ayarlanmaktadır. Sonra esas amaç olan, dağıtım sistemindeki aktif güç kayıplarını minimum yapacak transformatör kademe değerleri MATLAB programında, sezgisel yöntemlerden olan genetik algoritmalar kullanılarak bulunmuştur. Kısıt olarak sistemdeki gerilim seviyesi 0.95 - 1.05 pu arasında tutulmuştur. Yapılan Simülasyon çalışmaları, genetik algoritmalarla elde edilen kademe değerleri ile kayıp minimizasyonun daha etkin daha iyi enerji verimliliği sağladığını göstermiştir.

Keywords

• Enerji verimliliği
• Optimizasyon
• Transformatör kademe ayarı
• Genetik algoritmalar

References

1. Zongo O A ve Oonsiviai A, 2017. Optimal placement of distributed generator for power loss minimization and voltage stability improvement. Energy Procedia, 138: 134-139.
2. Mahdad B, Bouktir T, Srairi K, Benbouzid ME, 2010. Dynamic strategy based fast decomposed GA coordinated with FACTS devices to enhance the optimal power flow. Energy Convers Manage, 51: 1370–1380.
3. Sayah S, Zehar K, 2008. Modified differential evolution algorithm for optimal power flow with non-smooth cost functions. Energy Convers Manage, 49: 3036–3042.
4. Gonggui C, Lilan L, Peizhu S, Yangwei D, 2014. Chaotic improved PSO-based multi-objective optimization for minimization of power losses and L index in power systems. Energy Conversion and Management, 86: 548–560.
5. Castro JR, Saad M, Lefebvre S, Asber D, Lenoir L, 2016. Optimal voltage control in distribution network in the presence of DGs. Electrical Power and Energy Systems, 78: 239–247.
6. Araujo L R, Penido D R R, Carneiro S, Pereira J L R, 2017. Optimal unbalanced capacitor placement in distribution systems for voltage control and energy losses minimization. Electric Power Systems Research, 154:110-121.
7. Aryanezhad M, Management and coordination of LTC, SVR, shunt capacitor and energy storage with high PV penetration in power distribution system for voltage regulation and power loss minimization, International Journal of Electrical Power & Energy Systems, 100: 178-192.
8. Leisse I, Samuelsson O, Svensson J, 2010. Electricity Meters for Coordinated Voltage Control in Medium Voltage Networks with Wind Power, 2010 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe), pp: 1-7.

9. Bravo RJ, Robles SA, Bialek T, 2014. VAr support from solar PV inverters, 2014 IEEE 40th Photovolt. Spec. Conf. PVSC, pp: 2672–2676.
10. IEEE Standards Coordinating Committee 21, 1547TM IEEE Standard for Interconnecting Distributed Resources with Electric Power Systems. New York, NY, USA, 2003.
11. Hussain M, Zuhaina Z, Rizman Z, Yasin M, 2018 Power loss estimation due to difference transformer tap changer position at interface. Journal of Fundamental and Applied Sciences. 9. 685-697.
12. Ettappan M, Virmala V, Ramesh S, Kesavan V T, 2020. Optimal reactive power dispatch for real power loss minimization and voltage stability enhancement using Artificial Bee Colony Algorithm, 76:1-7.
13. Kumar KS, Jayabarathi T, 2012. Power system reconfiguration and loss minimization for an distribution systems using bacterial foraging optimization algorithm. Int J Electric Power Energy Syst, 36: 13–17.
14. Torres J, Guardado JL, Rivas-Dávalos F, Maximov S, Melgoza E, 2013. A genetic algorithm based on the edge window decoder technique to optimize power distribution systems reconfiguration. Int J Electric Power Energy System, 45(1): 28–34.
15. Varadarajan M, Swarup KS, 2008. Differential evolutionary algorithm for optimal reactive power dispatch. Electrical Power and Energy Systems, 30: 435–441. Qiao F, Ma J, 2020.Voltage/Var Control for Hybrid Distribution Networks Using Decomposition-Based Multiobjective Evolutionary Algorithm,” IEEE Access, 8: 12015-12024.
16. Emiroglu S , Uyaroglu Y , Ozdemir G, Distributed Reactive Power Control based Conservation Voltage Reduction in Active Distribution Systems. Advances in Electrical and Computer Engineering. 17:99-106.
17. Melanie M, 1996. An Introduction to Genetic Algorithms. Cambridge, MA: MIT Press. ISBN 9780585030944.
18. Öztürk A, Tosun S, Erdoğmuş P, Hasırcı U, 2009. Elektrik enerji dağıtım sisteminde ekonomik aktif güç dağıtımının genetik algoritma ile belirlenmesi. Eskişehir Osmangazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 22(3): 185-197.
19. Nassar IA, Omara MA, Abdella MM, 2019. Enhancement of Voltage Profile in Power Systems by Using Genetic Algorithm. 21st International Middle East Power Systems Conference (MEPCON-2019), Cairo, Egypt, pp: 459-464.