Ключові слова
діагностика
достовірність розпізнавання
аналіз розчинених у маслі газів
дефекти, що розвиваються
локальні перегріви
часткові розряди
іскрові розряди
дугові розряди
комбіновані дефекти
номограми дефектів
графічні області
Як цитувати
Анотація
У статті наведено результати аналізу номограм (графічних областей) дефектів маслонаповненого обладнання електричних мереж. Як вихідні дані було використано результати аналізу розчинених у маслі газів для 2997 трансформаторів і шунтувальних реакторів із дефектами різного типу, тобто навчальну вибірку. Для нівелювання можливих суперечностей під час використання різних критеріїв, навчальну вибірку було розбито на окремі масиви даних не тільки з одним і тим самим типом дефекту, а й з ідентичними значеннями характерних відношень газів, відсоткового вмісту газів і відношень газів до газу з максимальною концентрацією. З метою врахування дрейфу значень координат окремих номограм в отриманих масивах, номограми дефектів запропоновано представляти у вигляді еталонних областей. Як значення меж еталонних областей використовуються максимальні та мінімальні значення координат (відношень кожного з газів до газу з максимальною концентрацією), отримані для однорідних масивів результатів аналізу розчинених в маслі газів. При цьому центр графічної області збігається з еталонною номограмою. У результаті було побудовано 115 номограм, характерних для дефектів термічного типу, електричних розрядів, а також перегрівів з різною температурою гарячої точки, що супроводжуються розрядами з різною щільністю енергії, розрядами з різною щільністю енергії, а також розрядами з різною щільністю енергії, що супроводжуються перегрівами з різною температурою гарячої точки. Наведено короткий аналіз отриманих графічних областей, розглянуто найхарактерніші ушкодження, які відповідають тій чи іншій графічній області, проаналізовано значення співвідношення характерних газів, які відповідають аналізованим областям. У процесі аналізу встановлено, що найбільш максимальне значення достовірності розпізнавання типу дефекту може бути забезпечено завдяки одночасному використанню усіх трьох діагностичних критеріїв, а саме значень відношень газів, відсоткового вмісту газів і номограм (графічних областей) дефектів. Отримані результати дають змогу істотно збільшити кількість еталонних номограм, що дасть змогу істотно збільшити кількість дефектів, які можна розпізнати, і, як наслідок, знизити ризик аварійного пошкодження маслонаповненого обладнання через пропуск дефекту, спричинений відмовою від розпізнавання.
Посилання
E. Dörnenburg and W. Strittmater, “Monitoring oil-cooled transformers by gas analysis,” Brown Boveri Review, vol. 61, pp. 238–274, 1974.
R. R. Rogers, “IEEE and IEC codes to interpret incipient faults in transformers, using gas in oil analysis,” IEEE Transactions on Electrical Insulation, vol. EI-13, no. 5, pp. 349–354, Oct. 1978, doi: https://doi.org/10.1109/TEI.1978.298141.
Mineral oil-filled electrical equipment in service – Guidance on the interpretation of dissolved and free gases analysis, IEC 60599:2015, 2015.
M. Duval, “The duval triangle for load tap changers, non-mineral oils and low temperature faults in transformers,” IEEE Electrical Insulation Magazine, vol. 24, no. 6, pp. 22–29, Nov. 2008, doi: https://doi.org/10.1109/mei.2008.4665347.
S.-j. Lee, Y.-m. Kim, H.-d. Seo, J.-r. Jung, H.-j. Yang, and M. Duval, “New methods of DGA diagnosis using IEC TC 10 and related databases Part 2: Application of relative content of fault gases,” IEEE Transactions on Dielectrics and Electrical Insulation, vol. 20, no. 2, pp. 691–696, Apr. 2013, doi: https://doi.org/10.1109/tdei.2013.6508774.
IEEE Guide for the Interpretation of Gases Generated in Mineral Oil-Immersed Transformers, IEEE Std C57.104–2019, 2019, doi: https://doi.org/10.1109/IEEESTD.2019.8890040.
M. M. Emara, G. D. Peppas, and I. F. Gonos, “Two graphical shapes based on DGA for power transformer fault types discrimination,” IEEE Transactions on Dielectrics and Electrical Insulation, vol. 28, no. 3, pp. 981–987, Jun. 2021, doi: https://doi.org/10.1109/tdei.2021.009415.
D.-E. A. Mansour, “Development of a new graphical technique for dissolved gas analysis in power transformers based on the five combustible gases,” IEEE Transactions on Dielectrics and Electrical Insulation, vol. 22, no. 5, pp. 2507–2512, Oct. 2015, doi: https://doi.org/10.1109/TDEI.2015.004999.
O. E. Gouda, S. H. El-Hoshy, and H. H. E.L.-Tamaly, “Condition assessment of power transformers based on dissolved gas analysis,” IET Generation, Transmission & Distribution, vol. 13, no. 12, pp. 2299–2310, Jun. 2019, doi: https://doi.org/10.1049/iet-gtd.2018.6168.
S.-w. Kim, S.-j. Kim, H.-d. Seo, J.-r. Jung, H.-j. Yang, and M. Duval, “New methods of DGA diagnosis using IEC TC 10 and related databases Part 1: Application of gas-ratio combinations,” IEEE Transactions on Dielectrics and Electrical Insulation, vol. 20, no. 2, pp. 685–690, Apr. 2013, doi: https://doi.org/10.1109/tdei.2013.6508773.
S.-J. Kim, H.-D. Seo, and S.-W. Kim, “Internal defect diagnosis method of an oil immersed transformer through a dissolved gas composition ratio,” South Korean Patent 1020130074674, Jul. 4, 2013. (in Korean)
O. E. Gouda, S. H. El-Hoshy, and H. H. El-Tamaly, “Proposed heptagon graph for DGA interpretation of oil transformers,” IET Generation, Transmission & Distribution, vol. 12, no. 2, pp. 490–498, Jan. 2018, doi: https://doi.org/10.1049/iet-gtd.2017.0826.
S.-A. Ahmadi and M. Sanaye-Pasand, “A robust multi-layer framework for online condition assessment of power transformers,” IEEE Transactions on Power Delivery, vol. 37, no. 2, pp. 947–954, Apr. 2021, doi: https://doi.org/10.1109/tpwrd.2021.3074545.
I. B.M. Taha and D.-E. A. Mansour, “Novel power transformer fault diagnosis using optimized machine learning methods,” Intelligent Automation & Soft Computing, vol. 28, no. 3, pp. 739–752, 2021. Accessed: Nov. 5, 2022, doi: https://doi.org/10.32604/iasc.2021.017703.
L. Zhang, G. Sheng, H. Hou, N. Zhou, and X. Jiang, “An adaptive fault diagnosis method of power transformers based on combining oversampling and cost‐sensitive learning,” IET Smart Grid, vol. 4, no. 6, pp. 623–635, Jun. 2021, doi: https://doi.org/10.1049/stg2.12044.
L. Wang, T. Littler, and X. Liu, “Gaussian process multi-class classification for transformer fault diagnosis using dissolved gas analysis,” IEEE Transactions on Dielectrics and Electrical Insulation, vol. 28, no. 5, pp. 1703–1712, Oct. 2021, doi: https://doi.org/10.1109/tdei.2021.009470.
L. Bouchaoui, K. E. Hemsas, H. Mellah, and S. Benlahneche, “Power transformer faults diagnosis using undestructive methods (Roger and IEC) and artificial neural network for dissolved gas analysis applied on the functional transformer in the Algerian north-eastern: A comparative study,” Electrical Engineering & Electromechanics, no. 4, pp. 3–11, Jul. 2021, doi: https://doi.org/10.20998/2074-272x.2021.4.01.
H. A. Illias, C. Kai Choon, W. Z. Liang, H. Mokhlis, A. M. Ariffin, and M. Fairouz Mohd Yousof, “Fault identification in power transformers using dissolve gas analysis and support vector machine,” in 2021 IEEE International Conference on the Properties and Applications of Dielectric Materials (ICPADM), Johor Bahru, Malaysia, Jul. 12–14, 2021, pp. 33–36, doi: https://doi.org/10.1109/icpadm49635.2021.9493970.
C. Saha, N. Baruah, and S. K. Nayak, “Implementation of self-organizing map and logistic regression in dissolved gas analysis of transformer oils,” in 2021 IEEE International Conference on the Properties and Applications of Dielectric Materials (ICPADM), Johor Bahru, Malaysia, Jul. 12–14, 2021, pp. 131–134, doi: https://doi.org/10.1109/icpadm49635.2021.9493899.
O. Kherif, Y. Benmahamed, M. Teguar, A. Boubakeur, and S. S. M. Ghoneim, “Accuracy improvement of power transformer faults diagnostic using KNN classifier with decision tree principle,” IEEE Access, vol. 9, pp. 81693-81701, 2021, doi: https://doi.org/10.1109/ACCESS.2021.3086135.
I. B. M. Taha, S. Ibrahim, and D.-E. A. Mansour, “Power transformer fault diagnosis based on DGA using a convolutional neural network with noise in measurements,” IEEE Access, vol. 9, pp. 111162–111170, 2021, doi: https://doi.org/10.1109/access.2021.3102415.
T. Kawamura, N. Kawada, K. Ando, M. Yamaoka, T. Maeda, and T. Takatsu, “Analyzing gases dissolved in oil and its application to maintenance of transformers,” in International Conference on Large High Voltage Electric Systems, Paris, Apr. 27–Sep. 4, 1986.
“Guideline for the refurbishement of electric power transformers,” Electrical Cooperative Research Association, vol. 65, no. 1, 2009. (in Japanese)
O. Shutenko and O. Kulyk, “Comparative analysis of the defect type recognition reliability in high-voltage power transformers using different methods of DGA results interpretation,” in 2020 IEEE Problems of Automated Electrodrive. Theory and Practice (PAEP), Kremenchuk, Ukraine, Sep. 21–25, 2020, pp. 1–6, doi: https://doi.org/10.1109/paep49887.2020.9240911.
O. Shutenko and I. Yakovenko, “Fault diagnosis of power transformer using method of graphic images,” in 2017 IEEE International Young Scientists Forum on Applied Physics and Engineering, Lviv, Ukraine, 17–20 October 2017, pp. 66–69, doi: https://doi.org/10.1109/YSF.2017.8126594.
O. Shutenko and O. Kulyk, “Recognition of low-temperature overheating in power transformers by dissolved gas analysis,” Electrical Engineering, vol. 104, no. 4, pp. 2109–2121, Jan. 2022, doi: https://doi.org/10.1007/s00202-021-01465-5.
Diahnostyka Maslonapovnenoho Transformatornoho Obladnannia Za Rezultatamy Khromatohrafichnoho Analizu Vilnykh Haziv, Vidibranykh Iz Hazovoho Rele, I Haziv, Rozchynenykh U Izoliatsiinomu Masli. Metodychni Vkazivky [Diagnosis of Oil-Filled Transformer Equipment by Chromatographic Analysis of Free Gases Sampled From the Gas Relay and Gases Dissolved in the Insulating Oil. Methodological Guidelines], SOU-N EE 46.501:2006, Naukovo-inzhenernyj centr “ZTZ-Servis” [Scientific and engineering centre “ZTZ-service”], Kyiv, 2007. (in Ukrainian)
O. Shutenko and O. Kulyk, “Recognition of mid-temperature overheating in high-voltage power transformers by dissolved gas analysis,” in 2021 IEEE 2nd KhPI Week on Advanced Technology (KhPIWeek), Kharkiv, Ukraine, Sep. 13–17, 2021, pp. 401–406, doi: https://doi.org/10.1109/khpiweek53812.2021.9570059.
K. Shrivastava and A. Choubey, ‘A Novel Association Rule Mining with IEC Ratio Based Dissolved Gas Analysis for Fault Diagnosis of Power Transformers”", International Journal of Advanced Computer Research, vol. 2, no. 4, pp. 34-44, 2012.
A.-P. Chen and C.-C. Lin, “Fuzzy approaches for fault diagnosis of transformers,” Fuzzy Sets and Systems, vol. 118, no. 1, pp. 139–151, Feb. 2001, doi: https://doi.org/10.1016/s0165-0114(99)00115-3.
F. Wang, J. Bi, B. Zhang, and S. Yuan, “Research of transformer intelligent evaluation and diagnosis method based on DGA,” MATEC Web of Conferences, vol. 77, p. 01002, 2016, doi: https://doi.org/10.1051/matecconf/20167701002.
O. Shutenko and O. Kulyk, “Recognition of high-temperature overheating in high-voltage power transformers by dissolved gas analysis,” in 2021 IEEE International Conference on Modern Electrical and Energy Systems (MEES), Kremenchuk, Ukraine, Sep. 21–24, 2021, pp. 1–6, doi: https://doi.org/10.1109/mees52427.2021.9598575.
M. G. Niasar, “Partial discharge signatures of defects in insulation systems consisting of oil and oil-impregnated paper,” Licentiate thesis, KTH School of Electrical Engineering, Stockholm, Sweden, 2012.
K. I. Lundgaard et al., “Partial discharges in transformer insulation,” CIGRE Task Force 15.04, 2000.
O. Shutenko and I. Yakovenko, “Analysis of gas content in high voltage equipment with partial discharges,” in 2018 IEEE 3rd International Conference on Intelligent Energy and Power Systems (IEPS), Kharkiv, Sep. 10–14, 2018, pp. 347–352, doi: https://doi.org/10.1109/ieps.2018.8559534.
A. S. Kulkarni, P. S. Swami, and A. G. Thosar, “Dissolved gas analysis of transformer oil using adaptive neuro-fuzzy inference system,” International Journal of Scientific & Engineering Research, vol. 7, no. 5, pp. 1487–1491, 2016.
R. Soni and K. Chaudhari, “An approach to diagnose incipient faults of power transformer using dissolved gas analysis of mineral oil by ratio methods using fuzzy logic,” in 2016 International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES), Paralakhemundi, Odisha, India, Oct. 3–5, 2016, pp. 1894–1899, doi: https://doi.org/10.1109/scopes.2016.7955775.
V. G. M. Cruz, A. L. H. Costa, and M. L. L. Paredes, “Simulation of thermal decomposition of mineral insulating oil,” Brazilian Journal of Chemical Engineering, vol. 32, no. 3, pp. 781–794, Sep. 2015, doi: https://doi.org/10.1590/0104-6632.20150323s00003531.
E. Li, L. Wang, B. Song, and S. Jian, “Improved fuzzy c-means clustering for transformer fault diagnosis using dissolved gas analysis data,” Energies, vol. 11, no. 9, p. 2344, Sep. 2018, doi: https://doi.org/10.3390/en11092344.
O. Shutenko and O. Kulyk, “Diagnosis of oil-filled equipment with x-wax deposition based on dissolved gas analysis,” in 2021 IEEE 3rd Ukraine Conference on Electrical and Computer Engineering (UKRCON), Lviv, Ukraine, Aug. 26–28, 2021. pp. 1–6, doi: https://doi.org/10.1109/ukrcon53503.2021.9575623.
O. S. Kulyk and O. V. Shutenko, “Analysis of gas content in oil-filled equipment with spark discharges and discharges with high energy density,” Transactions on Electrical and Electronic Materials, vol. 20, no. 5, pp. 437–447, Jul. 2019, doi: https://doi.org/10.1007/s42341-019-00124-8.
O. Shutenko and O. Kulyk, “Analysis of gas content in oil-filled equipment with low energy density discharges,” International Journal on Electrical Engineering and Informatics, vol. 12, no. 2, pp. 258–277, Jun. 2020, doi: https://doi.org/10.15676/ijeei.2020.12.2.6.
O. Shutenko, “Analysis of gas composition in oil-filled faulty equipment with acetylene as the key gas,” Energetika, vol. 65, no. 1, pp. 21–38, May 2019, doi: https://doi.org/10.6001/energetika.v65i1.3973.
M. Duval and L. Lamarre, “The duval pentagon-a new complementary tool for the interpretation of dissolved gas analysis in transformers,” IEEE Electrical Insulation Magazine, vol. 30, no. 6, pp. 9–12, Nov. 2014, doi: https://doi.org/10.1109/mei.2014.6943428.
O. Shutenko and O. Kulyk, “Recognition of overheating with temperatures of 150-300°C by analysis of dissolved gases in oil,” in 2020 IEEE 4th International Conference on Intelligent Energy and Power Systems (IEPS), Istanbul, Turkey, Sep. 7–11, 2020, pp. 71–76, doi: https://doi.org/10.1109/ieps51250.2020.9263145.
O. Shutenko and O. Kulyk, “Combined defects recognition in the low and medium temperature range by results of dissolved gas analysis,” in 2020 IEEE KhPI Week on Advanced Technology (KhPIWeek), Kharkiv, Ukraine, Oct. 5–10, 2020, pp. 65–70, doi: https://doi.org/10.1109/khpiweek51551.2020.9250131.
O. Shutenko and O. Kulyk, “Analysis of gas content in oil-filled equipment with defects for which ethane is the key gas,” Lighting Engineering and Power Engineering, vol. 2, no. 58, pp. 33–42, Sep. 2020, doi: https://doi.org/10.33042/2079-424x-2020-2-58-33-42.
A. R. Gouda, “Image processing based analysis of transformer oil,” Bachelor thesis, Department of Electrical Engineering National Institute Of Technology, Rourkela, 2014.
Y. Liu, B. Song, L. Wang, J. Gao, and R. Xu, “Power transformer fault diagnosis based on dissolved gas analysis by correlation coefficient-DBSCAN,” Applied Sciences, vol. 10, no. 13, p. 4440, Jun. 2020, doi: https://doi.org/10.3390/app10134440.
O. Shutenko and O. Kulyk, “Recognition of combined defects with high-temperature overheating based on the dissolved gas analysis,” Sādhanā, vol. 47, no. 3, p. 146, Jul. 2022, doi: https://doi.org/10.1007/s12046-022-01919-x.
J. B. DiGiorgio. “Dissolved gas analysis of miniral oil insulating fluids.” NORTHERN TECHNOLOGY & TESTING. https://nttworldwide.com/wp-content/uploads/2019/08/dga2102.pdf.
H. Ahadpour, “A novel approach for diagnosis of power transformers internal faults using an electronic nose,” Journal of Basic and Applied Scientific Research, no. 7, pp. 808–815, 2011.
G. Mani and J. Jerome, “Intuitionistic fuzzy expert system based fault diagnosis using dissolved gas analysis for power transformer,” Journal of Electrical Engineering and Technology, vol. 9, no. 6, pp. 2058–2064, Nov. 2014, doi: https://doi.org/10.5370/jeet.2014.9.6.2058.
D. V. S. S. S. Sarma and G. N. S. Kalyani, “Ann approach for condition monitoring of power transformers using DGA,” in 2004 IEEE Region 10 Conference TENCON 2004, Chiang Mai, Thailand, Nov. 24, 2004. pp. 444–447. doi: https://doi.org/10.1109/tencon.2004.1414803.
O. Shutenko and O. Kulyk, “Recognition of discharges that are accompanied by low-temperature overheating based on the analysis of gases dissolved in the oil of high-voltage transformers,” Energy Saving. Power Engineering. Energy Audit, no. 3–4 (157–158), pp. 20–33, Sep. 2021, doi: https://doi.org/10.20998/2313-8890.2021.03.02.
O. V. Shutenko, “Analysis of the content of gases in oil-filled equipmentwith electrical defects,” Problemele Energeticii Regionale, no. 3(38), pp. 1–16, 2018, doi: https://doi.org/10.5281/zenodo.2222331. (in Russian)
I. A. Hameed and S. R. Farag, “Monitoring power transformer using fuzzy logic,” Journal of Engineering and Development, vol. 17, no. 6, pp. 146–163, 2013.
H. Shang, J. Xu, Z. Zheng, B. Qi, and L. Zhang, “A novel fault diagnosis method for power transformer based on dissolved gas analysis using hypersphere multiclass support vector machine and improved D–S evidence theory,” Energies, vol. 12, no. 20, p. 4017, Oct. 2019, doi: https://doi.org/10.3390/en12204017.
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