Güç sisteminde meta-sezgisel algoritmalarla güç kaybı ve gerilim kararlılığı optimizasyonu

Abstract

Güç sistemi alanında en belirgin problemlerden biri olan güç akışı, kararlı durum gerilim genlikleri ve güç değerleri bilinen bara verileri kullanılarak her bir baranın gerilim genliklerinin, faz açılarının ve güç kayıplarının hesaplanması işlemidir. Artan talep ve merkezi olmayan yeni enerji kaynaklarının güç sistemine çeşitli noktalardan bağlanması güç akış problemini daha karmaşık hale getirmektedir. Güç akışı problemi hem elektrik üretimi hem de iletimi için büyük önem taşımaktadır. Gelecekte sisteme bağlanabilecek yeni yüklerin planlanması ve mevcut iletim hatlarının tam kapasite ile kullanılması güç akışı sorununun çözümüne dayanmaktadır. Doğrusal olmayan bir problem olan güç akışı geleneksel olarak Newton-Raphson ve Gauss Seidel gibi nümerik yöntemler kullanılarak çözülmüştür. Ancak güç sisteminin şartlarına bağlı olarak klasik çözüm algoritmalarının başarısı azalmaktadır. Son yıllarda geliştirilen meta-sezgisel optimizasyon teknikleri ve arama algoritmaları güç akışı probleminin çözümünde daha iyi sonuçların elde edilebileceğini göstermektedir. Bu çalışmada, Matlab yazılımı kullanılarak oluşturulan IEEE-14 bara test güç sisteminde güç akışı problemini optimize etmek için Yapay Arı Kolonisi (ABC), Gri Kurt (GWO), Parçacık Sürüsü Optimizasyonu (PSO) ve Newton Raphson algoritmaları uygulanmıştır. Algoritmaların performansı model güç sisteminden elde edilen gerilim genlikleri, gerilim sapması, faz açıları, güç kayıpları ve hesaplama süreleri göz önünde bulundurularak karşılaştırılmıştır.

Keywords

• Güç akışı
• Meta-sezgisel algoritmalar
• Gerilim sapması

Power loss and voltage stability optimization with meta-heuristic algorithms in power system

Abstract

Power flow, which is one of the most prominent problems in the field of power system, is the calculation of the voltage amplitudes and phase angles of each bus and the power losses by using the bus data with known steady state voltage amplitudes and power values. Increasing demand and the connection of decentralized energy sources to the power system at various points make more complicated power flow problem. The power flow problem is of great importance for both electricity generation and transmission. Planning new loads that can be connected to the system in the future and using the existing transmission lines at full capacity are based on the solution of the power flow problem. Power flow, which is a nonlinear problem, has traditionally been solved using numerical methods such as Newton-Raphson and Gauss Seidel. However, the success of classical solution algorithms decreases depending on the conditions of the power system. Meta-heuristic optimization techniques and search algorithms developed in recent years show that better results can be obtained in solving the power flow problem. In this study, Artificial Bee Colony (ABC), Gray Wolf (GWO), Particle Swarm Optimization (PSO) and Newton Raphson algorithms have been applied to optimize the power flow problem in the IEEE-14 bus test power system created using Matlab software. The performance of the algorithms has been compared by considering the voltage amplitudes, voltage deviation, phase angles, power losses and calculation times obtained from the model power system.

Keywords

• Power Flow
• Meta-heuristic algorithms
• Voltage deviation

References

1. [1] Tahir M, Nassar M, El-Shatshat R, Salama M. “A review of Volt/Var control techniques in passive and active power distribution networks”. 4th IEEE International Conference on Smart Energy Grid Engineering, Toronto, Canada, 21-24 August 2016.
2. [2] Prakash P, Khatod D. “An analytical approach for optimal sizing and placement of distributed generation in radial distribution systems”. 1st IEEE International Conference on Power Electronics Intelligent Control and Energy Systems, Delhi, India, 4-6 July 2016.
3. [3] Gutiérrez D, Lopez JM, Vill WM. “Metaheuristic techniques applied to the optimal reactive power dispatch: A review”. IEEE Latin America Transactions, 14(5), 2253-2263, 2016.
4. [4] Kothari D. “Power system optimization”. National Conference on Computational Intelligence and Signal Processing, Assam, India, 2-3 March 2012.
5. [5] Mirjali S, Mirjali SM, Lewis A. “Grey wolf optimizer”. Advances in Engineering Software, 69, 46-61, 2014.
6. [6] Ahmed AAE, Germano LT, de Souza ACZ. “A hybrid particle swarm optimization applied to loss power minimization”. IEEE Transactions On Power Systems, 20(2), 859-866, 2005.
7. [7] Akay B. Nümerik Optimizasyon Problemlerinde Yapay Arı Kolonisi (Artificial Bee Colony) Algoritmasının Performans Analizi. Doktora Tezi, Erciyes Üniversitesi, Kayseri, Türkiye, 2009.
8. [8] Abu-Mouti FS, El-Hawary ME. “Optimal distributed generation allocation and sizing in distribution systems via artificial bee colony”. IEEE Transactions on Power Delivery, 26(4), 2090-2101, 2011.

9. [9] Mala D, Swapan KG. “Optimal reactive power procurement with voltage stability consideration in deregulated power system”. IEEE Transactions on Power Systems, 29(5), 2078-2086, 2014.
10. [10] Ibrahim BMT, Ehab EE. “Optimal reactive power resources sizing for power system operations enhancement based on improved grey wolf optimiser”. IET Generation, Transmission & Distribution, 12(14), 3421-3434, 2018.
11. [11] Sanjay R, Jayabarathi T, Raghunattan T, Ramesh V, Mithulananthan N. “Optimal allocation of distributed generation using hybrid grey wolf optimizer”. IEEE Access, 5, 14807-14818, 2017.
12. [12] Kennedy J, Eberhart R. “Particle swarm optimization”. International Conference on Neural Networks, WA, Australia, 27 November-1 December, 1995.
13. [13] Hu X, Eberhart RC, Shi Y. “Engineering optimization with particle swarm”. IEEE Swarm Intelligence Symposium. Indianapolis, USA, 26-26 April 2003.
14. [14] Shi Y, Eberhart RC. “Parameter selection in particle swarm optimization”. International Conference on Evolutionary Programming, San Diego, USA, 25-27 March 1998.
15. [15] AlRashidi MR, El-Hawary ME. “A survey of particle swarm optimization applications in electric power systems”. IEEE Transactions on Evolutionary Computation, 13(4), 913-918, 2009.
16. [16] Zambroni de Souza AC. “Tangent vector applied to voltage collapse and loss sensivity studies”. Electric Power Systems Research, 47(1), 65-70, 1998.
17. [17] Ferreira LCA, Zambroni de Souza AC, Granville S, Lima JWM. “Interior point method applied to voltage collapse problems and losses reduction”. IEE Proceedings-Generation, Transmission and Distribution, 149(2), 165-170, 2002.
18. [18] Yoshida H, Kawata K, Fukuyama Y, Nakanishi Y, Takayama S. “A particle swarm optimization for reactive power and voltage control considering voltage security assessment”. IEEE Transactions On Power Systems, 15(4), 1232-1239, 2000.
19. [19] Rashidi E, Nezabadi H, Saryazdi S. “GSA: A gravitational search algorithm”. Information Sciences, 179(13), 2232-2248, 2009.
20. [20] Jayakumar N, Subramanian S, Ganesan S, Elanchezhian EB. “Grey wolf optimization for combined heat and power dispatch with cogeneration systems”. International Journal Of Electrical Power&Energy Systems, 74, 252-264, 2016.
21. [21] Hagh MT, Amiyan P, Galvani S, Valizadeh N. “Probabilistic load flow using the particle swarm optimisation clustering method”. IET Generation, Transmission & Distribution, 12(3), 781-789, 2018.
22. [22] Shi Y, Eberhart RC. “A modified particle swarm optimizer”. 8th International Conference on Electronic Measurement and Instruments, Xi'an, China, 16-18 August 2007.
23. [23] Shi Y, Eberhart RC. “Fuzzy adaptive particle swarm optimization”. Proceedings of the 2001 Congress on Evolutionary Computation, Seoul, South Korea, 27-30 May 2001.
24. [24] M’zoughi F, Bouallegue S, Garrido AJ, Garrido I, Ayadi M. “Stalling-free control strategies for oscillating-water-column-based wave power generation plants”. IEEE Transactions on Energy Conversion, 33(1), 209-222, 2018.
25. [25] Benidris M, Elsaiah S, Mitra J. “Power system reliability evaluation using a state space classification technique and particle swarm optimisation search method”. IET Generation, Transmission & Distribution, 9(4), 1865-1873, 2015.
26. [26] Sun Q, Shi Y, Eberhart RC, Bauson WA. “Utilizing particle swarm optimization to label a structured beam matrix”. IEEE Swarm Intelligence Symposium, Indianapolis, USA, 26-26 April 2003.
27. [27] DJGJ. KW, Eberhart RC. “Deep swarm: Nested particle swarm optimization”. IEEE Symposium Series on Computational Intelligence, Hı, USA, 27 November- 1 December 2017.
28. [28] Ganguly S. “Multi-objective planning for reactive power compensation of radial distribution networks with unified power quality conditioner allocation using particle swarm optimization”. IEEE Transactions On Power Systems, 29(4), 1801-1810, 2014.
29. [29] Hu X, Eberhart RC, Shi Y. “Particle swarm with extended memory for multiobjective optimization”. IEEE Swarm Intelligence Symposium, Indianapolis, USA, 26-26 April 2003.
30. [30] Eberhart R, Kennedy J. “A new optimizer using particle swarm theory”. Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan, 4-6 October 1995.
31. [31] Eberhart RC, Shi Y. “Particle swarm optimization: Developments, applications and resources”. Congress on Evolutionary Computation, Seoul, South Korea, 27-30 May 2001.
32. [32] University of Washington. “IEEE 14 Bus Test System Data”. https://labs.ece.uw.edu/pstca/pf14/pg_tca14bus.htm (09.03.2021).
33. [33] Hu K, Cao S, Li W, Zhu F. “An ımproved particle swarm optimization algorithm suitable for photovoltaic power tracking under partial shading conditions”. IEEE Access, 7, 143217-143232, 2019.
34. [34] Parashar S, Swarnkar A, Niazi KR, Gupta N. “Multiobjective optimal sizing of battery energy storage in grid-connected microgrid”. The Journal of Engineering, 2019(18), 5281-5283, 2018.
35. [35] Rosa W, Gerez C, Belati E. “Optimal distributed generation allocating using particle swarm optimization and linearized ac load flow”. IEEE Latin America transactions, 16(10), 2665-2670, 2018.
36. [36] Fontenele NRM, Melo LS, LEAo RPS, Sampaio RF. “Application of multi-objective evolutionary algorithms in automatic restoration of radial power distribution systems”. IEEE Conference on Evolving and Adaptive Intelligent Systems, Natal, Brazil, 23-25 May 2016.
37. [37] Prakash DB, Laksminarayana C. “Optimal siting of capacitors in radial distribution network using whale optimization algorithm”. Alexandria Engineering Journal, 56(4), 499-509, 2017.
38. [38] Esmin AAA, Torres GL. “Application of particle swarm optimization to optimal power systems”. International Journal of Innovative Computing, Information and Control, 8(3A), 1705-1716, 2012.