ESTIMATION OF SYNCHRONOUS MOTOR EXCITATION CURRENT USING MULTIPLE LINEAR REGRESSION MODEL OPTIMIZED BY SYMBIOTIC ORGANISMS SEARCH ALGORITHM

Abstract

In this paper, an effective and simpler means of estimating the excitation current of a synchronous motor (SM) is presented for power factor correction task. First, a multiple linear regression model with four predictor variables such as motor load current, actual power factor, power factor error and excitation current change is formed to estimate the SM excitation current. Then, recently introduced symbiotic organisms search (SOS) algorithm is employed in the hope of searching better values of regression coefficients in that model using the data collected from the prepared experimental setup. The supremacy of SOS over some recently published algorithms such as genetic algorithm, artificial bee colony and gravitational search algorithm is widelyattested through comparative computer simulations for the similar compensation system. The results exhibited in this article show that the proposed SOS algorithm outperforms the other reported popular algorithms from the aspects of simplicity, robustness and accuracy. In view of this, the suggested tuning of regression coefficients of the multiple linear regression model yields a better estimating performance of SM excitation current than the earlier studies.

Keywords

• Synchronous motor,power factor correction,multiple linear regression model,symbiotic organisms search algorithm,optimization,optimization

SİMBİYOTİK ORGANİZMALAR ARAMA ALGORİTMASI İLE OPTİMİZE EDİLMİŞ ÇOKLU DOĞRUSAL REGRESYON MODELİ KULLANILARAK SENKRON MOTOR UYARTIM AKIMININ TAHMİNİ

Abstract

Bu belgede güç faktörü düzeltme işlemi için senkron motor (SM) uyartım akımının tahminine yönelik etkili ve basit bir yol sunulmuştur. Bu işlem için ilk olarak motor yük akımı, gerçek güç faktörü, güç faktörü hatası ve uyartım akımının değişimi karar değişkenleri olarak ele alınarak çoklu doğrusal regresyon modeli oluşturulmuştur. Ardından hazırlanan deneysel düzenekten toplanan veriler kullanılarak bu modeldeki regresyon katsayılarının iyileştirilmesi amacıyla yeni ortaya konulan simbiyotik organizmalar arama algoritmasından faydalanılmıştır. Bu algoritmanın benzer kompanzasyon işlemi için genetik algoritma, yapay arı kolonisi ve yerçekimi algoritması gibi yakın zamanda yayınlanan algoritmalara olan üstünlüğü karşılaştırmalı bilgisayar simülasyonları ile gösterilmiştir. Bu makalede sergilenen sonuçlar, sunulan tekniğin bahsi geçen literatürdeki algoritmalara göre basitlik, gürbüzlük ve doğruluk açılarından daha iyi performans verdiğini göstermiştir. Bu bağlamda çoklu doğrusal regresyon model katsayıların önerilen şekilde ayarı önceki çalışmalardan daha iyi SM uyartım akımı tahmin performansı sağlamıştır.

Keywords

• Senkron motor,güç faktörü düzeltme,çoklu doğrusal regresyon modeli,simbiyotik organizmalar arama algoritması,optimizasyon

References

1. 1. Vaez-Zadeh S, Ghasemi AR (2005) Design optimization of permanent magnet synchronous motors for high torque capability and low magnet volume. Electr Pow Syst Res 74: 307-313
2. 2. Gani A, Keçecioğlu ÖF, Açıkgöz H, Yıldız C, Şekkeli M (2016) Simulation study on power factor correction controlling excitation current of synchronous motor with fuzzy logic controller. International Journal of Intelligent Systems and Applications in Engineering 4: 229-233.
3. 3. Chai W, Lipo TA, Kwon B (2018) Design and optimization of a novel wound field synchronous machine for torque performance enhancement. Energies 11: 1-15.
4. 4. Khaing MKT (2014) Power factor correction with synchronous condenser for power quality improvement in industrial load. International Journal of Science and Engineering Applications 3: 39-43.
5. 5. Saha O, Sayed R, Ali M (2016) Automatic power factor correction by using synchronous condenser with continuous monitoring. Journal of PU: Part B 3: 1-9.
6. 6. Deaconu ID, Chırıla AI, Ghıta C, Navrapescu V. Maximal reactive power compensation using loaded synchronous motors. International Conference on Renewable Energies and Power Quality, 4-6 April 2017, Malaga, Spain.
7. 7. Stancu C, Ward T, Rahman KM, Dawsey R, Savagian P (2018) Separately excited synchronous motor with rotary transformer for hybrid vehicle application. IEEE Trans Indust Appl 54: 223-232.
8. 8. Moradi AR, Alinejad-Beromi Y, Parsa M, Mohammadi M (2018) Optimal locating and sizing of unified power quality conditioner- phase angle control for reactive power compensation in radial distribution network with wind generation. International Journal of Engineering 31: 299-306.

9. 9. Bayindir R, Sagiroglu S, Colak I (2009) An intelligent power factor corrector for power system using artificial neural networks. Electr Pow Syst Res 79:152-160.
10. 10. Çolak İ, Bayindir R, Sefa İ (2004) Experimental study on reactive power compensation using a fuzzy logic controlled synchronous motor. Energ Convers Manage 45: 2371-2391.
11. 11. Sagiroglu S, Colak I, Bayindir R (2006) Power factor correction technique based on artificial neural networks. Energ Convers Manage 47:3204-3215.
12. 12. Kahraman HT, Bayindir R., Sagiroglu S (2012) A new approach to predict the excitation current and parameter weightings of synchronous machines based on genetic algorithm-based k-NN estimator. Energ Convers Manage 64: 129-138.
13. 13. Bayindir R, Colak I, Sagiroglu Seref, Kahraman HT. Application of adaptive artificial neural network method to model the excitation currents of synchronous motors. 11th International Conference on Machine Learning and Applications, 12-15 December 2012, Boca Raton, Florida, USA.
14. 14. Kahraman HT (2014) Metaheuristic linear modeling technique for estimating the excitation current of a synchronous motor. Turk J Elec Eng & Comp Sci 22: 1637-1652.
15. 15. Liu L, Wenxin L, David A, Cartes A (2008) Particle swarm optimization-based parameter identification applied to permanent magnet synchronous motors. Eng Appl Artif Intell 21: 1092-1100.
16. 16. Leon AE, Solsona JA, Figueroa JL, Valla MI (2011) Optimization with constraints for excitation control in synchronous generators. Energy 36: 5366-5373.
17. 17. Dehghani M, Karrari M, Rosehart W, Malik OP (2010) Synchronous machine model parameters estimation by a time-domain identification method. Int J Elec Power 32: 524-529.
18. 18. Tun MZ, Swe PL (2014) Power factor correction with synchronous motor for rice mill. International Journal of Scientific Engineering and Technology Research 3: 1-5.
19. 19. Bayındır R, Vadi S (2017) A web-based educational tool for simulation of reactive power compensation with synchronous motor. Journal of Polytechnic 20: 61-69.
20. 20. Cheng MY, Prayogo D (2014) Symbiotic Organisms Search: A new metaheuristic optimization algorithm. Comput and Struct 139: 98-112.
21. 21. Guha D, Roy PK, Banerjee S (2018) Symbiotic organism search algorithm applied to load frequency control of multi-area power system. Energy Syst 9: 439-468.
22. 22. Guha D, Roy PK, Banerjee S (2017) Quasi-oppositional symbiotic organism search algorithm applied to load frequency control. Swarm Evol Comput 33: 46-67.
23. 23. Guvenç U, Duman S, Sonmez Y, Kahraman HT, Dosoglu K (2017) Symbiotic Organisms Search algorithm for economic load dispatch problem with valve-point effect. Scientia Iranica, doi: 10.24200/SCI.2017.4378.
24. 24. Çelik E, Öztürk N (2018) First application of symbiotic organisms search algorithm to off-line optimization of PI parameters for DSP-based DC motor drives. Neural Comput Appl 30: 1689-1699.
25. 25. Do D, Lee J (2017) A modified symbiotic organisms search (mSOS) algorithm for optimization of pin-jointed structures. Appl Soft Comput 61: 683-699.
26. 26. Das D, Bhattacharya A, Ray R. Symbiotic organisms search algorithm for economic dispatch problems. International Conference on Electrical, Computer and Communication Technologies, 22-24 Feb. 2017, Coimbatore, India.
27. 27. Dib N (2017) Design of planar concentric circular antenna arrays with reduced side lobe level using symbiotic organisms search. Neural Comput Appl, doi: 10.1007/s00521-017-2971-2.
28. 28. Çelik E, Öztürk N (2018) A hybrid symbiotic organisms search and simulated annealing technique applied to efficient design of PID controller for automatic voltage regulator. Soft Computing, doi: 10.1007/s00500-018-3432-2.
29. 29. Çelik E, Durgut R (2018) Performance enhancement of automatic voltage regulator by modified cost function and symbiotic organisms search algorithm. Int J Eng Sci, doi: 10.1016/j.jestch.2018.08.006.
30. 30. Saha A, Chakraborty AK, Das P (2018) Quasi-reflection based symbiotic organisms search algorithm for solving static optimal power flow problem. Scientia Iranica, doi: 10.24200/SCI.2018.20179.
31. 31. Saha S, Mukherjee V (2016) Optimal placement and sizing of DGs in RDS using chaos embedded SOS algorithm. IET Gener Transm Dıs 10: 3671-3680.