Güç Sistemi Kararlı Kılıcısı Parametrelerinin Uygunluk Mesafe Dengesi Tabanlı Parçacık Sürü Optimizasyonu Kullanılarak Belirlenmesi
Öz
Güç sistemlerinde yer alan senkron generatörlerin talep edilen gücün karşılanabilmesi amacıyla maksimum limitlerde çalıştırılması, hat arızaları veya çeşitli mekanik problemlere neden olur. Bu durum, generatörlerin rotor tarafında düşük frekanslı salınımlar meydana gelmesine yol açar. Sistemde oluşan salınımların sönümlenmesi amacıyla sıklıkla kullanılan denetleyici yapılarının başında, güç sistemi kararlı kılıcısı gelmektedir. Bu denetleyicilerin en uygun parametre değerlerinin belirlenmesi, salınımların etkili bir şekilde sönümlenmesi ve sistem kararlılığının sağlanması açısından oldukça önemlidir. Bu çalışmada, güç sistemi kararlı kılıcısı parametrelerinin en uygun değerlerinin belirlenmesi amacıyla uygunluk mesafe dengesi tabanlı parçacık sürü optimizasyonu kullanılmıştır. Önerilen algoritmanın çok makineli bir güç sistemindeki etkisini test edebilmek amacıyla, farklı arıza senaryoları için elde edilen sistem yanıtlarına ve performans indekslerine bağlı olarak karşılaştırmalar yapılmıştır. Sonuçlar, uygunluk mesafesi dengesine dayalı parçacık sürü optimizasyonu ile elde edilen güç sistemi kararlı kılıcısı parametrelerinin, diğer algoritmalarla belirlenen parametrelere göre sistem kararlılığı açısından daha etkili sonuçlar verdiğini göstermektedir.
Anahtar Kelimeler
Uygunluk Mesafe Dengesi Parçacık Sürü Optimizasyonu Güç Sistemi Kararlı Kılıcısı.
Etik Beyan
Bu çalışma Düzce Üniversitesi Bilimsel Araştırma Projesi (Proje No:2022.06.03.1281 ) tarafından desteklenmiştir.
Destekleyen Kurum
Düzce Üniversitesi
Proje Numarası
2022.06.03.1281
Teşekkür
Bu çalışma Düzce Üniversitesi Bilimsel Araştırma Projesi (Proje No:2022.06.03.1281 ) tarafından desteklenmiştir.
Kaynakça
- D. Mondal, A. Chakrabarti, and A. Sengupta, “Power system small signal stability analysis and control”, Academic Press, 2020.
- D. Chitara, K.R. Niazi, A. Swarnkar and N. Gupta, ‘‘Cuckoo search optimization algorithm for designing of a multimachine power system stabilizer, ’’ IEEE Transactions on Industry Applications, vol .54, no.4, pp. 3056-3065, 2018..
- M. Jokarzadeh, M. Abedini, and A. Seifi, ‘‘Improving power system damping using a combination of optimal control theory and differential evolution algorithm, ’’ ISA transactions, vol. 90, pp. 169-177, 2019.
- F.P. Demello and C. Concordia, “Concepts of synchronous machine stability as affected by excitation control,” IEEE Transactions on power apparatus and systems, vol. 88, no. 4, pp. 316-329, 1969.
- E. Larsen and D. Swann, “Applying power system stabilizers part I: general concepts, ” IEEE Trans Power Appar Syst., vol.100, no.6, pp. 3017-3024,1981.
- E. Larsen and D. Swann, “Applying power system stabilizers part III: practical considerations,” IEEE Trans Power Appar Syst., vol.100, no.6, pp. 3034-3046, 1981.
- M.J. Gibbard, “Coordinated design of multimachine power system stabilisers based on damping torque concepts,” IEE Proceedings - Generation, Transmission and Distribution., vol.135, no.4, pp.276,1988.
- P. Kundur, M. Klein, G.J. Rogers and M.S. Zywno, “Application of power system stabilizers for enhancement of overall system stability,” IEEE Transactions on Power Systems, vol. 4, no. 2, pp. 614–626, 1989.
- L.Abualigah, D. Yousri, and M.A Elaziz, “Aquila optimizer: a novel meta-heuristic optimization algorithm,” Comput Ind Eng. https:// doi. org/ 10. 1016/j. cie. 2021. 107250, 2021.
- L.H. Hassan , M. Moghavvemi, H.A. Almurib, K.M. Muttaqi, V.G. Ganapathy, “Optimization of power system stabilizers using participation factor and genetic algorithm,” International Journal of Electrical Power & Energy Systems, vol. 55, pp. 668-679, 2014.
- K. Sebaa and M. Boudour, “Optimal allocations and tuning of robust power system stabilizer using genetic algorithms,” Electr Power Syst Res.,vol.79, no.2, pp. 406–416, 2009.
- M.A. Abido, “Optimal design of power-system stabilizers using particle swarm optimization, ” IEEE transactions on energy conversion, vol.17, no. 3, pp. 406-413, 2002.
- S.M. Abd-Elazim and E.S. Ali, “Power system stability enhancement via bacteria foraging optimization algorithm,” Arabian Journal for Science and Engineering, vol.38, no. 3, pp.599-611, 2013.
- M. Mohammadi and N. Ghadimi, “Optimal location and optimized parameters for robust power system stabilizer using honeybee mating optimization,” Complexity, vol.21, no.1,pp. 242-258, 2015.
- S.M. Abd-Elazim and E.S. Ali, “Optimal power system stabilizers design via cuckoo search algorithm,” International Journal of Electrical Power & Energy Systems, vol.75, pp. 99-107, 2016.
- S. Ekinci, “Optimal design of power system stabilizer using sine cosine algorithm,” J Fac Eng Archit Gazi Univ., vol.34, no.3, pp. 1329-1350, 2019.
- D. Butti, S.K. Mangipudi, and S.R. Rayapudi, “An improved whale optimization algorithm for the design of multi‐machine power system stabilizer, ” International Transactions on Electrical Energy Systems, vol. 30, no. 5, pp. e12314, 2020.
- R. Devarapalli, B. Bhattacharyya, N.K. Sinha, and B. Dey, “Amended GWO approach based multi-machine power system stability enhancement, ” ISA transactions, vol. 109, pp. 152-174, 2021.
- B. Morales-Castañeda, D.Zaldivar, E. Cuevas, F. Fausto and A. Rodríguez, “A better balance in metaheuristic algorithms: Does it exist? , ” Swarm and Evolutionary Computation, vol. 54, pp. 100671, 2020.
- J. Xu and J. Zhang, “Exploration-exploitation tradeoffs in metaheuristics: survey and analysis, ” in: Proc. 33rd Chinese Control Conf, (CCC), pp. 8633–8638, 2014.
- M.Z. Ali, N.H. Awad, R.G. Reynolds, and P.N. Suganthan, “A balanced fuzzy cultural algorithm with a modified levy flight search for real parameter optimization, ” Inform. Sci. vol. 447, pp. 12–35, 2018.
- H.T Kahraman, S.Aras, and E.Gedikli, “Fitness-distance balance (FDB): a new selection method for meta-heuristic search algorithms,” Knowledge-Based Systems, vol. 190, pp. 105169, 2020.
- S Ekinci, “Çok makinalı güç sisteminde açısal kararlılık analizi ve kontrolör parametre Optimizasyonu,” (Doktora tezi Fen Bilimleri Enstitüsü), 2015.
- A. Farah, T.Guesmi, H.H. Abdallah, and A. Ouali. “A novel chaotic teaching–learning-based optimization algorithm for multi-machine power system stabilizers design problem,” International Journal of Electrical Power & Energy Systems, vol. 77, pp. 197-209, 2016.
- P.W.Sauer and M.A. Pai, “Power System Dynamics and Stability, ” Urbana: Pearson Education, 1998.
- J. Kennedy and R. Eberhart, “Particle swarm optimization, ” in: Proceedings of the Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan, pp. 39–43, 1995.
- Y. Shi and R. Eberhart, “A modified particle swarm optimizer,” in: The 1998 IEEE International Conference on Evolutionary Computation Proceedings, Anchorage, pp. 69–73, 1998.
- J.J. Liang, A.K. Qin, P.N. Suganthan, and S. Baskar, “Comprehensive learning particle swarm optimizer for global optimization of multimodal functions,” IEEE Trans.Evol. Comput., pp. 281–295, 2006.
- H.T Kahraman, S. Aras, and E.Gedikli, “Fitness-distance balance (FDB): a new selection method for meta-heuristic search algorithms, ” Knowledge-Based Systems, vol. 190, pp. 105169, 2020.
Determination of Power System Stabilizer Parameters Using Fitness Distance Balance Based Particle Swarm Optimization
Öz
Operating synchronous generators in power systems at maximum limits in order to meet the requested power causes line failures or various mechanical problems. This situation leads to low-frequency oscillations on the rotor side of the generators. Power system stabilizers are among the controller structures frequently used to dampen oscillations in the system. Determining the optimum parameter values of these controllers is extremely important in terms of effectively damping oscillations and ensuring system stability. In this study, fitness distance balance-based particle swarm optimization was used to determine the optimum parameter values of the power system stabilizer. In order to test the effect of the proposed algorithm on a multi-machine power system, comparisons were made based on the system responses and performance indices obtained for different fault scenarios. Results show that the power system stabilizer parameters obtained by fitness distance balance-based particle swarm optimization provide more effective system stability than those obtained with other algorithms.
Anahtar Kelimeler
Fitness Distance Balance Particle Swarm Optimization Power System Stabilizer.
Proje Numarası
2022.06.03.1281
Kaynakça
- D. Mondal, A. Chakrabarti, and A. Sengupta, “Power system small signal stability analysis and control”, Academic Press, 2020.
- D. Chitara, K.R. Niazi, A. Swarnkar and N. Gupta, ‘‘Cuckoo search optimization algorithm for designing of a multimachine power system stabilizer, ’’ IEEE Transactions on Industry Applications, vol .54, no.4, pp. 3056-3065, 2018..
- M. Jokarzadeh, M. Abedini, and A. Seifi, ‘‘Improving power system damping using a combination of optimal control theory and differential evolution algorithm, ’’ ISA transactions, vol. 90, pp. 169-177, 2019.
- F.P. Demello and C. Concordia, “Concepts of synchronous machine stability as affected by excitation control,” IEEE Transactions on power apparatus and systems, vol. 88, no. 4, pp. 316-329, 1969.
- E. Larsen and D. Swann, “Applying power system stabilizers part I: general concepts, ” IEEE Trans Power Appar Syst., vol.100, no.6, pp. 3017-3024,1981.
- E. Larsen and D. Swann, “Applying power system stabilizers part III: practical considerations,” IEEE Trans Power Appar Syst., vol.100, no.6, pp. 3034-3046, 1981.
- M.J. Gibbard, “Coordinated design of multimachine power system stabilisers based on damping torque concepts,” IEE Proceedings - Generation, Transmission and Distribution., vol.135, no.4, pp.276,1988.
- P. Kundur, M. Klein, G.J. Rogers and M.S. Zywno, “Application of power system stabilizers for enhancement of overall system stability,” IEEE Transactions on Power Systems, vol. 4, no. 2, pp. 614–626, 1989.
- L.Abualigah, D. Yousri, and M.A Elaziz, “Aquila optimizer: a novel meta-heuristic optimization algorithm,” Comput Ind Eng. https:// doi. org/ 10. 1016/j. cie. 2021. 107250, 2021.
- L.H. Hassan , M. Moghavvemi, H.A. Almurib, K.M. Muttaqi, V.G. Ganapathy, “Optimization of power system stabilizers using participation factor and genetic algorithm,” International Journal of Electrical Power & Energy Systems, vol. 55, pp. 668-679, 2014.
- K. Sebaa and M. Boudour, “Optimal allocations and tuning of robust power system stabilizer using genetic algorithms,” Electr Power Syst Res.,vol.79, no.2, pp. 406–416, 2009.
- M.A. Abido, “Optimal design of power-system stabilizers using particle swarm optimization, ” IEEE transactions on energy conversion, vol.17, no. 3, pp. 406-413, 2002.
- S.M. Abd-Elazim and E.S. Ali, “Power system stability enhancement via bacteria foraging optimization algorithm,” Arabian Journal for Science and Engineering, vol.38, no. 3, pp.599-611, 2013.
- M. Mohammadi and N. Ghadimi, “Optimal location and optimized parameters for robust power system stabilizer using honeybee mating optimization,” Complexity, vol.21, no.1,pp. 242-258, 2015.
- S.M. Abd-Elazim and E.S. Ali, “Optimal power system stabilizers design via cuckoo search algorithm,” International Journal of Electrical Power & Energy Systems, vol.75, pp. 99-107, 2016.
- S. Ekinci, “Optimal design of power system stabilizer using sine cosine algorithm,” J Fac Eng Archit Gazi Univ., vol.34, no.3, pp. 1329-1350, 2019.
- D. Butti, S.K. Mangipudi, and S.R. Rayapudi, “An improved whale optimization algorithm for the design of multi‐machine power system stabilizer, ” International Transactions on Electrical Energy Systems, vol. 30, no. 5, pp. e12314, 2020.
- R. Devarapalli, B. Bhattacharyya, N.K. Sinha, and B. Dey, “Amended GWO approach based multi-machine power system stability enhancement, ” ISA transactions, vol. 109, pp. 152-174, 2021.
- B. Morales-Castañeda, D.Zaldivar, E. Cuevas, F. Fausto and A. Rodríguez, “A better balance in metaheuristic algorithms: Does it exist? , ” Swarm and Evolutionary Computation, vol. 54, pp. 100671, 2020.
- J. Xu and J. Zhang, “Exploration-exploitation tradeoffs in metaheuristics: survey and analysis, ” in: Proc. 33rd Chinese Control Conf, (CCC), pp. 8633–8638, 2014.
- M.Z. Ali, N.H. Awad, R.G. Reynolds, and P.N. Suganthan, “A balanced fuzzy cultural algorithm with a modified levy flight search for real parameter optimization, ” Inform. Sci. vol. 447, pp. 12–35, 2018.
- H.T Kahraman, S.Aras, and E.Gedikli, “Fitness-distance balance (FDB): a new selection method for meta-heuristic search algorithms,” Knowledge-Based Systems, vol. 190, pp. 105169, 2020.
- S Ekinci, “Çok makinalı güç sisteminde açısal kararlılık analizi ve kontrolör parametre Optimizasyonu,” (Doktora tezi Fen Bilimleri Enstitüsü), 2015.
- A. Farah, T.Guesmi, H.H. Abdallah, and A. Ouali. “A novel chaotic teaching–learning-based optimization algorithm for multi-machine power system stabilizers design problem,” International Journal of Electrical Power & Energy Systems, vol. 77, pp. 197-209, 2016.
- P.W.Sauer and M.A. Pai, “Power System Dynamics and Stability, ” Urbana: Pearson Education, 1998.
- J. Kennedy and R. Eberhart, “Particle swarm optimization, ” in: Proceedings of the Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan, pp. 39–43, 1995.
- Y. Shi and R. Eberhart, “A modified particle swarm optimizer,” in: The 1998 IEEE International Conference on Evolutionary Computation Proceedings, Anchorage, pp. 69–73, 1998.
- J.J. Liang, A.K. Qin, P.N. Suganthan, and S. Baskar, “Comprehensive learning particle swarm optimizer for global optimization of multimodal functions,” IEEE Trans.Evol. Comput., pp. 281–295, 2006.
- H.T Kahraman, S. Aras, and E.Gedikli, “Fitness-distance balance (FDB): a new selection method for meta-heuristic search algorithms, ” Knowledge-Based Systems, vol. 190, pp. 105169, 2020.
Ayrıntılar
| Birincil Dil | Türkçe |
|---|---|
| Konular | Elektrik Tesisleri |
| Bölüm | Araştırma Makaleleri |
| Yazarlar | |
| Proje Numarası | 2022.06.03.1281 |
| Erken Görünüm Tarihi | 25 Ekim 2024 |
| Yayımlanma Tarihi | 29 Ekim 2024 |
| Gönderilme Tarihi | 3 Nisan 2024 |
| Kabul Tarihi | 21 Mayıs 2024 |
| Yayımlandığı Sayı | Yıl 2024 Cilt: 6 Sayı: 2 |
