ارزیابی تأثیر پارامترهای روغن ترانسفورماتور بر شاخص سلامت ترانسفورماتور با استفاده از روش رگرسیون تخمین منحنی
محورهای موضوعی : مهندسی برق و کامپیوتر
مرتضی سعید
1
,
حامد زين الديني ميمند
2
1 - دانشکده مهندسی برق و کامپیوتر دانشگاه تحصیلات تکمیلی صنعتی و فناوری پیشرفته کرمان
2 - دانشکده مهندسی برق و کامپیوتر دانشگاه تحصیلات تکمیلی صنعتی و فناوری پیشرفته کرمان
کلید واژه: رگرسیون تخمین منحنی, شاخص سلامت ترانسفورماتور, گازهای محلول در روغن,
چکیده مقاله :
ترانسفورماتورها از گران‌ترین و مهم‌ترین تجهیزات در سیستم‌های قدرت هستند كه تحت تأثیر تنش‌های الكتریكی، حرارتی و واكنش‌های شیمیایی می‌باشند. شاخص سلامت ترانسفورماتور معیاریست كه با استفاده از داده‌های آزمایشگاهی و بازرسی‌های میدانی برای ارزیابی وضعیت و تعیین عمر باقیمانده ترانسفورماتور از آن استفاده می‌شود. هدف از این مقاله به‌صورت یک ایده جدید، تعیین روابط بین پارامترهای الکتریکی، فیزیکی، شیمیایی روغن، گازهای محلول در روغن و شاخص سلامت ترانسفورماتور می‌باشد. یکی از مزایای استفاده از روش رگرسیون در تحلیل داده‌های ترانسفورماتور نسبت به روش‌های دیگر برای تعیین شاخص سلامت ترانسفورماتور، تعیین تأثیرپذیری پارامترهایی است که بیشترین تأثیر را بر یکدیگر دارند از جمله: 1) معرفی رطوبت به عنوان پارامتری که بیشترین نقش را در کاهش ولتاژ شکست روغن دی‌الکتریک و شاخص سلامت ترانسفورماتور دارد. 2) تعیین وجود رابطه معکوس بین مؤلفه اسید و مؤلفه فورفورال. 3) تعیین فورفورال به عنوان پارامتری که بیشترین نقش را در کاهش کشش سطحی (به‌هم‌پیوستگی مولکول‌های) روغن دارد. 4) تعیین گاز CO به عنوان گازی که بیشترین نقش را در تولید مؤلفه فورفورال دارد. 5) تعیین گاز 2H2C به عنوان گازی که بیشترین نقش را در تولید مؤلفه اسید دارد. در این مقاله از روش رگرسیون تخمین منحنی استفاده شده و نتایج با رسم نمودارها توسط نرم‌افزار آماری SPSS برای تحلیل پارامترها ترسیم گردیده است. برای انجام شبیه‌سازی‌ها داده‌های آزمایشگاهی مربوط به 120 عدد ترانسفورماتور در نظر گرفته شده است.
Transformers are one of the most expensive and important equipment in power systems that are under the influence of electrical, thermal and chemical reactions The transformer health index is a standard that is used to evaluate the condition and determine the remaining life of the transformer by using laboratory data and field inspections. The purpose of this article is to determine the relationships between electrical, physical, chemical parameters of oil, dissolved gases in oil and transformer health index. One of the advantages of using the regression method in the analysis of transformer data compared to other methods for determining the transformer health index is determining the influence of the parameters that have the greatest impact on each other. In this article, Curve Estimation Regression method is used and the results are drawn by drawing graphs by SPSS statistical software to analyze the parameters. To carry out the simulations, the laboratory data of some transformers have been considered.
[1] CIGRE A2.49, Condition Assessment of Power Transformers, Technical Brochure CIGRE, no. 761, 2019.
[2] N. A. Baka, A. Abu-Siada, S. Islam, and M. F. El-Naggar, "A new technique to measure interfacial tension of transformer oil using UV-Vis spectroscopy," IEEE Trans. on Dielectrics and Electrical Insulation, vol. 22, no. 2, pp. 1275-1282, Apr. 2015.
[3] R. Soni and B. Mehta, "Diagnosis and prognosis of incipient faults and insulation status for asset management of power transformer using fuzzy logic controller & fuzzy clustering means," Electric Power Systems Research, vol. 220, Article ID: 109256, Jul. 2023.
[4] W. Chen, Z. Gu, J. Zou, F. Wan, and Y. Xiang, "Analysis of furfural dissolved in transformer oil based on confocal laser Raman spectroscopy," IEEE Trans. on Dielectrics and Electrical Insulation, vol. 23, no. 2, pp. 915-921, Apr. 2016.
[5] Q. Chen, W. Sun, S. Cheng, and G. Huang, "A review on a novel method for aging evaluation of transformer insulating paper based on methanol," IET Generation, Transmission & Distribution, vol. 17, no. 9, pp. 1955-1971, May 2023.
[6] A. M. Abd-Elhady, M. E. Ibrahim, T. A. Taha, and M. A. Izzularab, "Effect of temperature on AC breakdown voltage of nanofilled transformer oil," IET Science, Measurement & Technology, vol. 12, no. 1, pp. 138-144, Jan. 2018.
[7] A. Maher, D. E. A. Mansour, K. Helal, and R. A. Abd El Aal, "Dissolved gas analysis and dissipation factor measurement of mineral oil‐based nanofluids under thermal and electrical faults," High Voltage, vol. 8, no. 3, pp. 455-465, Jun. 2023.
[8] D. Peng, D. Yang, C. Wang, and M. Li, "The influence of transformer oil aging to dielectric dissipation factor and its insulating lifetime," in Proc. Asia-Pacific Power and Energy Engineering Conf., 4 pp., Wuhan, China, 27-31 Mar. 2009.
[9] S. Zandbaaf, M. R. K. Khorrami, and M. G. Afshar, "Prediction of dielectric dissipation factor by ATR-FTIR spectroscopy based on multivariate calibration methods for transformer oil samples in power industry," Infrared Physics & Technology, vol. 128, Article ID: 104528, Jan. 2023.
[10] CIGRE A2.30, Moisture Equilibrium and Moisture Migration within Transformer Insulation Systems, Technical Brochure CIGRE, no. 349, 2008.
[11] S. Forouhari and A. Abu-Siada, "Remnant life estimation of power transformer based on IFT and acidity number of transformer oil," in Proc. IEEE 11th Int. Conf. on the Properties and Applications of Dielectric Materials, ICPADM'15, pp. 552-555, Sydney, Australia, 19-22 Jul. 2015.
[12] Y. Kittikhuntharadol, et al., "Physical and chemical properties' comparison of natural ester and palm oil used in a distribution transformer," Energy Reports, vol. 9, Sup. 1, pp. 549-556, Mar. 2023.
[13] H. Zeinoddini-Meymand, S. Kamel, and B. Khan, "An efficient approach with application of linear and nonlinear models for evaluation of power transformer health index," IEEE Access, vol. 9, pp. 150172-150186, 2021.
[14] E. Baker, S. V. Nese, and E. Dursun, "Hybrid condition monitoring system for power transformer fault diagnosis," Energies, vol. 16, no. 3, Article ID:. 1151, 2023.
[15] S. Li, et al., "Review of condition monitoring and defect inspection methods for composited cable terminals," High Voltage, vol. 8, no. 3, pp. 431-444, Jun. 2023.
[16] Y. Luo, et al., "Dynamic state evaluation method of power transformer based on Mahalanobis-Taguchi system and health index," Energies, vol. 16, no. 6, Article ID: 2765, 2023.
[17] N. Islam, et al., "Power transformer health condition evaluation: a deep generative model aided intelligent framework," Electric Power Systems Research, vol. 218, Article ID: 109201, May 2023.
[18] I. G. N. et al., "Application of health index method for transformer condition assessment," in Proc. IEEE Region 10 Conf., TENCON'14, 6 pp., Bangkok, Thailand, 22-25 Oct. 2014.
[19] M. Augusta Martins, "Condition and risk assessment of power transformers: a general approach to calculate a health index," Ciência & Tecnologia dos Materiais, vol. 26, no. 1, pp. 9-16, Jan./Jun. 2014.
[20] G. Brandtzaeg, Health Indexing of Norwegian Power Transformers, MS Thesis, NTNU, 2015.
[21] A. Azmi, J. Jasni, N. Azis, and M. A. Kadir, "Evolution of transformer health index in the form of mathematical equation," Renewable and Sustainable Energy Reviews, vol. 76, pp. 687-700, Sept. 2017.
[22] J. I. Aizpurua, B. G. Stewart, S. D. J. Mc Arthur, B. Lambert, J. G. Cross, and V. M. Catterson, "Improved power transformer condition monitoring under uncertainty through soft computing and probabilistic health index," Applied Soft Computing, vol. 85, Article ID: 105530, Dec. 2019.
[23] H. Zeinoddini-Meymand and B. Vahidi, "Health index calculation for power transformers using technical and economical parameters," IET Science, Measurement & Technology, vol. 10, no. 7, pp. 823-830, Jun. 2016.
[24] A. Dehghani Ashkezari, H. Ma, T. K. Saha, and C. Ekanayake, "Application of fuzzy support vector machine for determining the health index of the insulation system of in-service power transformers," IEEE Trans. on Dielectrics and Electrical Insulation, vol. 20, no. 3, pp. 965-973, Jun. 2013.
[25] R. A. Prasojo, K. Diwyacitta, Suwarno, and H. Gumilang, "Transformer paper expected life estimation using ANFIS based on oil characteristics and dissolved gases (case study: indonesian transformers)," Energies, vol. 10, no. 8, Article ID: 1135, 2017.
[26] F. R. Barbosa, et al., "Artificial neural network application in estimation of dissolved gases in insulating mineral oil from physical-chemical datas for incipient fault diagnosis," in Proc. IEEE 15th Conf. on Intelligent System Applications to Power Systems, 5 pp., Curitiba, Brazil, 8-12 Nov. 2009.
[27] G. C. Jaiswal, M. S. Ballal, H. M. Surywanshi, and M. Wath, "Diagnostic approach and condition monitoring methods to boost up the reliability of transformer," in Proc. IEEE First Int.Conf. on Smart Technologies for Power, Energy and Control, STPEC'20, 5 pp., Nagpur, India, 25-26 Sept. 2020.
[28] A. J. C. Trappey, C. V. Trappey, L. Ma, and J. C. M. Chang, "Intelligent engineering asset management system for power transformer maintenance decision supports under various operating conditions," Computers & Industrial Engineering, vol. 84, pp. 3-11, Jun. 2015.
[29] Y. Lin, L. Yang, R. Liao, W. Sun, and Y. Zhang, "Effect of oil replacement on furfural analysis and aging assessment of power transformers," IEEE Trans. on Dielectrics and Electrical Insulation, vol. 22, no. 5, pp. 2611-2619, Oct. 2015.
[30] J. Brady, T. Dürig, P. I. Lee, and J. X. Li, Polymer properties and characterization, Developing solid oral dosage forms, Academic Press, pp. 181-223, 2017.
[31] T. Nakajima, K. Kajiwara, and J. E. Mclntyre, Advanced Fiber Spinning Technology, Woodhead Publishing, 1994.
[32] K. Benhmed, A. Mooman, A. Younes, K. Shaban, and A. El-Hag, "Feature selection for effective health index diagnoses of power transformers," IEEE Trans. on Power Delivery, vol. 33, no. 6, pp. 3223-3226, Dec. 2018.
[33] CIGRE A2.18, Guidelines for Life Management Techniques for Power Transformers, Technical Brochure, no. 227, 2002.
[34] Q. Zou, J. Zhao, and J. Wen, "Robust quantile regression analysis for probabilistic modelling of SN curves," International J. of Fatigue, pt. A, vol. 167, Article ID: 107326, Feb. 2023.
[35] T. Z. Keith, Multiple Regression and Beyond: An Introduction to Multiple Regression and Structural Equation Modeling, 3rd Edition, New York: Routledge, 2019.
[36] A. Bouzida, et al., "Fault diagnosis in industrial induction machines through discrete wavelet transform," IEEE Trans. on Industrial Electronics, vol. 58, no. 9, pp. 4385-4395, Sept. 2011. [37] A. Abu Siada, M. Bagheri, and T. Phung, Power Transformer Condition Monitoring and Diagnosis: Chapter 3: Frequency Response Analysis, IET, United Kingdom, 2018. [38] S. A. Khan, M. D. Equbal, and T. Islam, "ANFIS based identification and location of paper insulation faults of an oil immersed transformer," in Proc. IEEE 6th Power India Int. Conf., PIICON'14, 6 pp., Delhi, India, 5-7 Dec. 2014.
