تشخیص گرمای بیش از حد در سیستم‌های قدرت با استفاده از مواد ترموکرومیک و پردازش تصویر
محورهای موضوعی : انرژی های تجدیدپذیر
1 - کارشناس ارشد – گروه برق و مکاترونیک، دانشکده فنی مهندسی، واحد سمنان، دانشگاه آزاد اسلامی، سمنان، ایران
2 - استادیار – باشگاه پژوهشگران جوان و نخبگان، واحد سمنان، دانشگاه آزاد اسلامی، سمنان، ایران
کلید واژه: ترموگرافی, گرمای بیش از حد, اتصالات سست, ترموکرومیک, LBP,
چکیده مقاله :
با توجه به اینکه تشخیص نقص در تجهیزات الکتریکی باعث جلوگیری از بروز حادثه، خسارت و تلفات میشود، ضرورت ایجاب می‌کند تا کارهای موثر در تشخیص دادن نقص انجام شود تا بتوانیم خطا را پیش‌بینی و پیش‌گیری نماییم. در حال حاضر در سیستم‌های قدرت نقص‌های حرارتی توسط ابزار ترموگرافی شناسایی میشود که دارای محدودیت‌هایی از قبیل نیاز داشتن به تجهیزات گران قیمت ترموگرافی می‌باشد. در این مقاله، یک روش جدید برای شناسایی نقص در تجهیزات برقی ارائه میشود. در این روش استفاده از مواد ترموکرومیک در سیستم‌های قدرت پیشنهاد شده است. ترموکرومیک‌ها نوعی از مواد هوشمند می‌باشند که به صورت برگشت پذیر رنگشان با دما تغییر می‌کنند. با توجه به اینکه اغلب نقص‌ها در تجهیزات باعث تولید حرارت می‌شوند، در صورتی که مواد ترموکرومیک روی تجهیزات پوشش داده شوند، با افزایش دما در محل نقص، تغییر رنگ حاصل میشود. در این مقاله، تجهیزات الکتریکی با مواد ترموکرومیک پوشش داده شدند. سپس با معرفی ویژگی‌های نوین مربوط به هیستوگرام در مرحله اول و همچنین DRLBP و شبکه عصبی در مرحله دوم به دو دسته دارای نقص و بدون نقص طبقه‌بندی شدند. نتایج نشان داد که در روش پیشنهادی با افزایش دما در محل دارای نقص تغییر رنگ مشهود حاصل میشود و شناسایی نقص به سادگی و با دقت بالایی قابل تشخیص است.
According to the diagnosis of defects in electrical equipment to prevent accidents, damage and losses, it is necessary to work effectively in identifying defects so that we can predict and prevent errors. Nowadays, thermal defects detect in power systems by thermography. However, there is limitation such as the need to have expensive thermography equipment. In this paper, a new method for detecting defects in the electrical equipment is presented. In this method, the use of thermochromic materials has been suggested in power systems for the first time. Thermochromic is a kind of smart materials, which is returnable with temperature change. Since most of the defects in the equipment produce heat, if the equipment covers with thermochromic material, the color change is obtained with temperature rising. In this paper, the equipment was covered with thermochromic materials. Then, with introducing the novel feature regarding the histogram in the first level and DRLBP in the second level, the equipment was classified into two categories, with defect and without defect, by a neural network. The results showed that with increasing temperature, color changed in the location of defects and defect identification recognized easily and with high accuracy.
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[27] N.A. Mat Isa, M.S. Al-Batah, K.Z. Zamli, K.A. Azizli, A. Joret, N.R. Mat Nor, “Suitable features selection for HMLP and MLP networks to identify the shape of aggregate”, Construction and Building Materials, Vol. 22, No. 3, pp. 402–410, March 2008.
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[30] T. Dutta, J. Sil, P. Chottopadhyay, “Condition monitoring of electrical equipment using thermal image processing”, Proceeding of the IEEE/CMI, pp. 311-315, Kolkata, India, Jan. 2016.
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[32] J.V.B. Soares, J.J.G. Leandro, R.M. Cesar, H.F. Jelinek, M.J. Cree, “Intelligent Thermographic Diagnostic Applied to Surge Arresters: A new approach”, IEEE Trans. on Power Delivery, Vol. 24, No. 2, pp. 751 – 757, April 2009.
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[34] S. Rouhani, Z. Bahrami niya, “Review on smart thermochromics and their application”, Journal of Studies in Color World, Vol. 3, No. 3, pp. 23-32, Autumn 2013.
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[1] N. Hou, “The infrared thermography diagnostic technique of high-voltage electricalequipments with internal faults”, Proceeding of the IEEE/POWERCON, Vol. 1, pp. 110–115, Beijing, China, Aug. 1998.
[2] S.P. Garnaik, “Infrared thermography: a versatile technology for condition monitoring and energy conservation”, . (accessed11.08.11).
[3] Z. Korendo, M. Florkowski, “Thermography based diagnostic of power equipment”, Power Engineering Journal, Vol. 15, No. 1, pp. 33-42, Feb. 2001.
[4] R.A. Epperly, G.E. Heberlein, L.G. Eads, “A tool for reliability and safety: predict and prevent equipment failures with thermography”, Proceeding of the IEEE/, pp. 59–68, Banff, Alta., Canada, Sep. 1997.
[5] M.A. Kregg, “Benefits of using infrared thermography in utility substations”, Proceeding of the SPIE, pp. 249–257, 2004.
[6] R. Salisbury, “Thermal image and predictive maintenance: what the future has store”, Proceeding of the IEEE/PCA, Salt Lake City, Utah, pp. 277–287, May 2000.
[7] F. Lizak, M. Kolcun, “Improving reliability and decreasing losses of electrical system with infrared thermography”, Acta Electrotechnica et Informatica, Vol. 8, No. 1, pp. 60–63, 2008.
[8] A.S.N. Huda, S. Taib, D. Ishak, “Analysis and prediction of temperature of electrical equipment for infrared diagnosis considering emissivity and object to camera distance setting effect”, Proceeding of the PIERS., Kuala Lumpur, Malaysia, March 27–30, March 2012.
[9] A.S.N. Huda, S. Taib, “Suitable features selection for monitoring thermal condition of electrical equipment using infrared thermography”, Infrared Physics and Technology, Vol. 61, pp. 184–191, Nov. 2013.
[10] E.T.W. Neto, E.G. da Costa, M.J.A. Maia, “Influence of emissivity and distance in high voltage equipments thermal imaging”, Proceeding of the IEEE/PES, pp. 1-4, Caracas, Venezuela, Aug. 2006.
[11] B.B. Lahiri, S. Bagavathiappan, T. Jayakumar, J. Philip, “Medical applications of infrared thermography: A review”, Infrared Phys. Technol, Vol.. 55, No. 4, pp. 221–235, July 2012.
[12] M.S. Jadin, S. Taib, “Recent progress in diagnosing the reliability of electrical equipments by using infrared thermography”, Infrared Phys. Technol, Vol. 55, No. 4, pp. 236–245, July 2012.
[13] M.A. Shafi’i, N. Hamzah, “Internal fault classification using artificial neural network”, Proceeding of the IEEE/PEOCO, pp. 352–357, Shah Alam, Malaysia, June 2010.
[14] M. Negnevitsky, Artificial intelligence: A guidetoIntelligent system, Second ed., Addison-Weslay, England, 2004.
[15] K. Mahmood, A. Zidouri, A. Zerguine, “Performance analysis of a RLS-based MLP-DFE in time-invariant and time-varying channels”, Digital Signal Process, Vol. 18, No. 3, pp. 307–320, May 2008.
[16] K. Levenberg, “A method for the solution of certain non-linear problems in least squares”, Quart. Appl. Math. Vol. 2, No. 2, pp. 164–168, 1944.
[17] D.W. Marquardt, “An algorithm for least-squares estimation of nonlinear parameters”, Journal of the Society for Industrial and Applied Mathematics, Vol. 11, No. 2, pp. 431–441, 1963.
[18] C.M. Bishop, Neural networks for pattern recognition, Oxford University Press, Oxford, pp. pp. 253–424, 2004.
[19] M.S. Jadin, S. Taib, S. Kabir, M.A.B. Yousuf, “Image processing methods for evaluating infrared thermographic image of electrical equipments”, in: Progress of Electromagnetics Research Symposium Proc., Marrakesh, Moroccco, Mar. 20–23, 2011.
[20] Standard for Infrared Inspection of Electrical Systems & Rotating Equipment, Infraspection Institute, 2008.
[21] ASTM, ASTM E 1934: Standard Guide for Examining Electrical and Mechanical Equipment with Infrared Thermography, West Conshohocken, Pennsylvania, ASTM International, 2005.
[22] NFPA, NFPA 70B: Recommended Practice for Electrical Equipment Maintenance, Quincy, Massachusetts, National Fire Protection Association, 2006.
[23] Y. Chou, L. Yao, “Automatic diagnostic system of electrical equipment using infrared thermography”, Proceeding of the IEEE/SOCPAR, pp. 155–160, Malacca, Malaysia, Dec. 2009.
[24] M.S. Jadin, S. Kabir, S. Taib, “Thermal imaging for qualitative-based measurements of thermal anomalies in electrical components”, Proceeding of the IEEE/ECPC, Saudi Arab, 24–26, June 2011.
[25] J.N. Kapur, P.K. Sahoo, A.K.C. Wong, “A new method for grey-level picture thresholding using the entropy of the histogram”, Comput. Vision Graphics Image Process. 29, pp. 273–285, 1985.
[26] M. Azarbad, A. Ebrahimzade,V. Izadian, “Segmentation of infrared images and objectives detection using maximum entropy method based on the bee algorithm”, International Journal of Computer Information Systems and Industrial Management Applications, Vol. 3, pp. 26-33, 2011.
[27] N.A. Mat Isa, M.S. Al-Batah, K.Z. Zamli, K.A. Azizli, A. Joret, N.R. Mat Nor, “Suitable features selection for HMLP and MLP networks to identify the shape of aggregate”, Construction and Building Materials, Vol. 22, No. 3, pp. 402–410, March 2008.
[29] R. Mehta,K. Egiazarian, “Dominant rotated local binary patterns (DRLBP) for texture classification”, Pattern Recognition Letters, Vol. 71, pp. 16-22, Feb. 2016.
[30] T. Dutta, J. Sil, P. Chottopadhyay, “Condition monitoring of electrical equipment using thermal image processing”, Proceeding of the IEEE/CMI, pp. 311-315, Kolkata, India, Jan. 2016.
[31] M. Suguna, S. Mohamed Mansoor Roomi, I. Sanofer, “Fault localisation of electrical equipments using thermal imaging technique”, Proceeding of the IEEE/ICETT, pp. 1-3, Kollam, India, Oct. 2016.
[32] J.V.B. Soares, J.J.G. Leandro, R.M. Cesar, H.F. Jelinek, M.J. Cree, “Intelligent Thermographic Diagnostic Applied to Surge Arresters: A new approach”, IEEE Trans. on Power Delivery, Vol. 24, No. 2, pp. 751 – 757, April 2009.
[33] A. Rahmani, J. Haddadnia, O. Seryasat, “Intelligent fault detection of electrical equipment in ground substations using thermo vision technique”, Proceeding of the IEEE/ICMEE,, Vol. 2, pp. 150-154, Kyoto, Japan, Aug. 2010.
[34] S. Rouhani, Z. Bahrami niya, “Review on smart thermochromics and their application”, Journal of Studies in Color World, Vol. 3, No. 3, pp. 23-32, Autumn 2013.