صرفه‌جویی در مصرف انرژی با جایگزینی به‌موقع موتور القایی سه فاز به کمک تخمین دقیق بازده آن توسط الگوریتم زنبورعسل اصلاح‌شده
محورهای موضوعی : انرژی های تجدیدپذیرمهدی بیگدلی 1 , داود عزیزیان 2 , محمد جمادی 3
1 - استادیار - گروه مهندسی برق، واحد زنجان، دانشگاه آزاد اسلامی، زنجان، ایران
2 - استادیار - گروه مهندسی برق، واحد ابهر، دانشگاه آزاد اسلامی، ابهر، ایران
3 - کارشناس ارشد - گروه مهندسی برق، واحد زنجان، دانشگاه آزاد اسلامی، زنجان، ایران
کلید واژه: موتور القایی, اندازه‌گیری, الگوریتم زنبورعسل اصلاح‌شده, تخمین بازده, صرفه‌جویی در مصرف انرژی,
چکیده مقاله :
امروزه بیش‌ترین میزان مصرف انرژی در صنعت مربوط به موتورهای القایی است. بنابراین تعیین بازده موتورهای القایی به‌منظور انجام اقدامات پیشگیرانه، عملیات تعمیر و نگهداری و درنهایت جایگزینی آن‌ها با موتورهای راندمان بالا از اهمیت ویژه‌ای برخوردار است. این مقاله روشی کارآمد مبتنی بر الگوریتم زنبورعسل اصلاح‌شده برای تخمین بازده موتور القایی ارائه می‌کند. مهم‌ترین مزیت روش ارائه‌شده، تعیین راندمان موتور القایی بدون انجام هرگونه آزمایش تهاجمی است و یک روش بدون مزاحمت و با دقت بالا را ارائه می‌دهد. برای اثبات قابلیت‌های روش پیشنهادی، نتایج آن با سایر الگوریتم‌های بهینه‌سازی هوشمند مقایسه شده‌اند. پس از تخمین راندمان، یکی از کاربردهای مهم تخمین راندمان که جایگزینی موتور راندمان بالا به جای موتور راندمان معمولی می‌باشد، تشریح و در مورد میزان صرفه‌جویی در مصرف انرژی به واسطه دانستن راندمان موتور القایی، بحث می‌شود. نتایج حاصل از محاسبه میزان صرفه‌جویی انرژی نشان می‌دهد که در صورت جایگزینی موتور استاندارد در حال کار با یک موتور پربازده، صرفه‌جویی قابل توجهی در مصرف انرژی صورت خواهد گرفت.
Today, most energy consumption in industry is related to induction motors. Evaluation of induction motor’s efficiency is an important issue for life estimation, extend the life and energy saving managements. Using the estimated efficiency of the induction motor, its performance can be judged and replacing the existing low efficiency motor by a high efficiency motor could be decided. In this paper, a novel and efficient method based on Modified Artificial Bee Colony (MABC) Algorithm is presented for efficiency estimation in the induction motors. The main advantage of the proposed method is efficiency evaluation of induction motor without any intrusive test. In order to demonstrate the capabilities of the proposed method, a comparison with other intelligent optimization algorithms is performed. Then, one of the important applications of efficiency estimation, which replaces the high efficiency induction motors instead of conventional motors, is discussed. The results of the calculation of energy savings show that if a standard motor is replaced with a high efficiency motor, energy savings will be significant.
[1] J.S. Hsu, J.D. Kueck, M. Olszewski, D.A. Casada, P. J. Otaduy, “Comparison of induction motor field efficiency evaluation methods”, IEEE Trans. on Industry Applications, Vol. 34, No. 1, pp. 117-125, Jan./Feb. 1998.
[2] B. Lu, T.G. Habetler, R.G. Harley, “A survey of efficiency-estimation methods for in-service induction motors”, IEEE Trans. on Industry Applications, Vol. 42, No. 4, pp. 924-933, July/Aug. 2006.
[3] C.S. Gajjar, J.M. Kinyua, M.A. Khan, P.S. Barendse, “Analysis of a non-intrusive efficiency estimation technique for induction machines compared to the IEEE 112B and IEC 34-2-1 standards”, IEEE Trans. on Industry Applications, Vol. 51, No. 6, pp. 4541-4553, 2006.
[4] M. Chirindo, M. A. Khan, P. S. Barendse, “Considerations for non-intrusive efficiency estimation of inverter-fed induction motors”, IEEE Trans. on Industrial Electronics, Early Access, Published Online, 2015.
[5] IEEE Standard Test Procedure for Polyphase Induction Motors and Generators, IEEE Standard 112, IEEE power Engineering society, New York, 1996.
[6] B. Lu, T.G. Habetler, R.G. Harley, “A nonintrusive and in-service motor-efficiency estimation method using air-gap torque with considerations of condition monitoring”, IEEE Trans. on Industry Applications, Vol. 44, No. 6, pp. 1666-1674, 2008.
[7] Y. EI-Ibiary, “An accurate low cost method for determining electric motor’s efficiency for the purpose of plant energy management”, IEEE Trans. on Industry Applications, Vol. 39, No. 4, pp. 12-19, 2003.
[8] A.G. Siraki, P. Pillay, P. Angers, “Full load efficiency estimation of refurbished induction machines from no-load testing,” IEEE Trans. on Energy Conversion, Vol. 28, No. 2, pp. 317-326, 2013.
[9] M. Al-Badri, P. Pillay, P. Angers, “A novel algorithm for estimating refurbished three-phase induction motors efficiency using only no-load tests”, IEEE Trans. on Energy Conversion, Vol. 30, No. 2, pp. 615-625, 2015.
[10] V. Dlamini, R. Naidoo, M. Manyage, “A non-intrusive method for estimating motor efficiency using vibration signature analysis”, International Journal of Electrical Power and Energy Systems, Vol. 45, No. 1, pp. 384-390, 2013.
[11] J. R. Holmquist, M. A. Rooks, “Richter practical approach for determining motor efficiency in the field using calculated and measured values”, IEEE Trans. on Industry Applications, Vol. 40, No. 1, pp. 242-248, 2004.
[12] T. Phumiphak, C. Chat-Uthai, “Estimation of induction motor parameters based on field test coupled with genetic algorithm,” Proceedings of the IEEE/ICPST, pp. 1199- 1203, Kunming, China, China, Oct. 2002.
[13] A. Charette, J. Xu, A. Ba-Razzouk, P. Pillay, V. Rajagopalan, “The use of the genetic algorithm for in-situ efficiency measurement of an induction motor”, Proceedings of the IEEE/PESW, pp. 392-397, Singapore, Singapore, Jan. 2000.
[14] M. Cunkas, T. Sag, “Efficiency determination of induction motors using multi-objective evolutionary algorithms”, Advances in Engineering Software, Vol. 41, No. 2, pp. 255–261, 2010.
[15] P. Nangsue, P. Pillay, S. E. Conry, “Evolutionary algorithms for induction motor parameter determination”, IEEE Trans. on Energy Conversion, Vol. 14, No. 3, pp. 447-453, 1999.
[16] B. Lu, C. Wenping, I. French, K. J. Bradley, T. G. Habetler, “Non-intrusive efficiency determination of in-service induction motors using genetic algorithm and air-gap torque methods”, Proceedings of the IEEE/IAS, pp. 1186-1192, New Orleans, LA, USA, Sep. 2007.
[17] M. Al-Badri, P. Pillay, P. Angers, “A novel in situ efficiency estimation algorithm for three-phase IM using GA, IEEE method F1 calculations, and pretested motor data”, IEEE Trans. on Energy Conversion, Vol. 30, No. 3, pp. 1092-1102, 2015.
[18] I. Kostov, V. Vasil Spasov, V. Rangelova, “Application of genetic algorithm for determining the parameters of induction motors”, Technical Gazette, Vol. 16, No. 2, pp. 49-53, 2009.
[19] V.P. Sakthivel, S. Subramanian, “On-site efficiency evaluation of three-phase induction motor based on particle swarm optimization”, Energy, Vol. 36, No. 3, pp. 1713- 1720, 2011.
[20] C.P. Salomon, W.C. Sant’Ana, L.E. Borges da Silva, G. Lambert-Torres, E.L. Bonaldi, L.E.L. Oliveira, J.G.B. Silva, “Motor efficiency evaluation using a new concept of stator resistance”, IEEE Trans. on Instrumentation and Measurement, Vol. 64, No. 11, pp. 2908-2917, 2015.
[21] V.P. Sakthivel, R. Bhuvaneswari, S. Subramanian, “Non-intrusiveefficiency estimation method for energy auditing and management of in service induction motor using bacterial foraging algorithm”, IET Electric Power Applications, Vol. 4, No. 8, pp. 579–590, 2010.
[22] V.S. Santos, P.R.V. Felipe, J.R.G. Sarduy, N.A. Lemozy, A. Jurado, E.C. Quispe, “Procedure for determining induction motor efficiency working under distorted grid voltages”, IEEE Trans. on Energy Conversion, Vol. 30, No. 1, pp. 331-339, 2015.
[23] V.S. Santos, P.V. Felipe, J.G., Sarduy, “Bacterial foraging algorithm application for induction motor field efficiency estimation under unbalanced voltages”, Measurement, Vol. 46, No. 7, pp. 2232-2237, 2013.
[24] V. P. Sakthivel, R. Bhuvaneswari, S. Subramanian, “An accurate and economical approach for induction motor field efficiency estimation using bacterial foraging algorithm”, Measurement, Vol. 44, No. 4, pp. 674–684, 2011.
[25] D. Karaboga, “An idea based on honey bee swarm for numerical optimization, Technical report”, TR06, Turkey, Computer Engineering Department University, 2005.
[26] D. Karaboga, B. Akay, “A Comparative Study ofArtificial Bee Colony Algorithm”, Appl Math Comput, Vol. 214, No.1, pp.108–132, 2009.
[27] D. Karagoba, B. Basturk, “On the performance of artificial bee colony (ABC) algorithm”, Applied Soft Computing, Vol. 8, No.1, pp. 687-697, 2008.
[28] D. Karaboga, B. Akay, “A modified artificial bee colony algorithm for real-parameter optimization”, Inform Sciences, Vol. 192, pp. 120- 142, 2012.
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