طبقه‌بندی عیوب ترانسفورماتور با استفاده از تحلیل پاسخ فرکانسی بر پایه تکنیک همبستگی متقابل و ماشین بردار پشتیبان

نویسنده

دانشگاه آزاد اسلامی

چکیده

یکی از مهم‌ترین روش‌های تشخیص عیب در ترانسفورماتورها (خصوصاً عیوب مکانیکی) روش تحلیل پاسخ فرکانسی (FRA) است. مهم‌ترین گام در فرآیند تشخیص عیب به کمک FRA، متمایز کردن عیوب و قرار دادن آن‌ها در کلاسهای متفاوت است. در این مقاله از روش هوشمند ماشین بردار پشتیبان (SVM) برای طبقه‌بندی عیوب ترانسفورماتور استفاده می‌شود. برای این منظور، دو گروه از ترانسفورماتورها مورد آزمایش قرار گرفته است. ابتدا آزمایش‌های لازم بر روی ترانسفورماتورهای مدل تحت شرایط سالم و تحت شرایط عیوب مختلف (جابجایی محوری، تغییر شکل شعاعی، تغییر فاصله بین بشقاب‌ها، اتصال کوتاه بین بشقاب‌ها و تغییر شکل هسته) انجام می‌شود. سپس با تقسیم‌بندی بازه‌های فرکانسی توابع تبدیل اندازه‌گیری شده از ترانسفورماتور، یک مشخصه جدید مبتنی بر تکنیک همبستگی متقابل برای آموزش و اعتبارسنجی SVM پیشنهاد می‌شود. بعد از انجام فرآیند آموزش، با اعمال داده‌های به‌دست‌آمده از ترانسفورماتورهای واقعی، عملکرد SVM در حالت‌های مختلف مورد ارزیابی و مقایسه قرار گرفته و مناسب‌ترین شاخص ارائه می‌شود.

کلیدواژه‌ها


عنوان مقاله [English]

Classification of transformer faults using frequency response analysis based on cross-correlation technique and support vector machine

نویسنده [English]

  • Mehdi Bigdeli
چکیده [English]

One of the most important methods for transformers fault diagnosis (especially mechanical defects) is the frequency response analysis (FRA) method. The most important step in the FRA diagnostic process is to differentiate the faults and classify them in different classes. This paper uses the intelligent support vector machine (SVM) method to classify transformer faults. For this purpose, two groups of transformers have been tested. First, the necessary measurements are performed on the model transformers under healthy conditions and under various fault conditions (axial displacement, radial deformation, disc space variation, short-circuits, and core deformation). Then, by dividing the frequency ranges of the measured transfer functions of the transformer, a new feature based on the cross-correlation technique is proposed for SVM training and validation. After the training process, by applying the data obtained from real transformers, the performance of SVM in different modes is evaluated and compared. Finally, the most appropriate feature has been provided.

کلیدواژه‌ها [English]

  • Transformer
  • Frequency Response Analysis
  • Support Vector Machine
  • Fault Detection
  • Cross-Correlation Technique
  1. [1] Rahimpour, E., Christian, J., Feser, K., and Mohseni, H., “Transfer function method to diagnose axial displacement and radial deformation of transformer winding”, IEEE Transaction on Power Delivery, Vol. 18, No. 2, pp. 493–505, 2003. [2] IEEE Guide for the Application and Interpretation of Frequency Response Analysis for Oil-Immersed Transformers, IEEE Std C57.149, 2012. [3] IEC Standard on Power Transformers, Part 18: Measurement of Frequency Response, IEC 60076-18, 2012. [4] Abu-Siada, A., Mosaad, M. I., Kim, D. W., El-Naggar, M., F., “Estimating Power Transformer High frequency Model Parameters using Frequency Response Analysis”, IEEE Transactions on Power Delivery (Early Access), 2020. [5] Samimi, M. H., Hillenbrand, Ph., Tenbohlen, S., Shayegani Akmal, A. A., Mohseni, Faiz, J., “Investigating the applicability of the finite integration technique for studying the frequency response of the transformer winding”, International journal of Electrical Power and Energy Systems, Vol. 110, pp. 411–418, 2019. [6] Jianqiang, Ni., Zhongyong, Zh., Shan, T., Yu, Ch., Chenguo, Y., Chao, T., “The actual measurement and analysis of transformer winding deformation fault degrees by FRA using mathematical indicators”, Electric Power Systems Research, Vol. 184, pp. 1-11, 2020. [7] Zhao, X., Yao, Ch., Zhou, Z., Li, Ch., Wang, X., Zhu, t., Abu-Siada, A., “Experimental Evaluation of Transformer Internal Fault Detection Based on V–I Characteristics”, IEEE Transactions on Industrial Electronics, Vol. 67, No. 5, pp. 4108-4119, 2020. [8] Shamlou, A., Feyzi, M., R., Behjat, V., “Interpretation of frequency response analysis of power transformer based on evidence theory”, IET Generation, Transmission & Distribution, Vol. 13, No. 17, pp. 3879-3887, 2019. [9] Samimi, M. H., Tenbohlen, S., Shayegani Akmal, A. A., Mohseni, H., “Evaluation of numerical indices for the assessment of transformer frequency response”, IET Generation, Transmission & Distribution, Vol. 11, No. 1, pp. 218-227, 2017. [10] Behjat, V., Mahvi, M., “Statistical approach for interpretation of power transformers frequency response analysis results”, IET Science, Measurement & Technology, Vol. 9, No. 3, pp. 367-375, 2015. [11] Pourhossein, K., Gharehpetian, G. B., Rahimpour, E., Araabi, B. N., “A Probabilistic Feature to Determine Type and Extent of Winding Mechanical Defects in Power Transformers”, Electric Power Systems Research, Vol. 82, pp. 1-10, 2012. [12] Ghanizadeh, A. J., Gharehpetian, G. B., “Application of Characteristic Impedance and Wavelet Coherence Technique to Discriminate Mechanical Defects of Transformer Winding”, Electric Power Components and Systems, Vol. 41, pp.868-878, 2013. [13] Rahbarimagham, H., Esmaeili, S., and Gharehpetian, G. B., “Discrimination between Radial Deformation and Axial Displacement in Power Transformers Using Analysis of Electromagnetic Waves”, IEEE Sensors Journal, Vol. 17, No. 16, pp. 5324-5331, 2017. [14] Pourhossein, K., Gharehpetian, G. B., Rahimpour, E., Araabi, B. N., “A Vector-based Approach to Discriminate Radial Deformation and Axial Displacement of Transformer Winding and Determine Defect Extent”, Electric Power Components and Systems, Vol. 40, pp. 597-612, 2012. [15] Karimifard, P., Gharehpetian, G. B., Ghanizadeh, A. J., “Estimation of simulated transfer function to discriminate axial displacement and radial deformation of transformer winding”, COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, Vol. 31 No. 4, pp. 1277-1292, 2012. [16] Rahimpour, E., Jabbari, M., Tenbohlen, S., “Mathematical Comparison Methods to Assess Transfer Functions of Transformers to Detect Different Types of Mechanical Faults”, IEEE Transactions on Power Delivery, Vol. 25, No. 4, pp. 2544-2555, 2010. [17] Abbasi, A. R., Mahmoudi, M. R., Avazzadeh, Z., “Diagnosis and Clustering of Power Transformer Winding Fault Types by Cross-Correlation and Clustering Analysis of FRA Results”, IET Generation, Transmission & Distribution, Vol. 12, No. 19, pp. 4301-4309, 2018. [18] Bigdeli, M., Azizian, D., Gharehpetian, G. B., “Detection of Probability of Occurrence, Type and Severity of Faults in Transformer Using Frequency Response Analysis Based Numerical Indices”, Measurement, Vol. 168, pp. 108322, 2021. [19] Bigdeli, M., Vakilian, M., Rahimpour, E., “A Probabilistic Neural Network Classifier Based Method for Transformer Winding Fault Identification through its Transfer Function Measurement”, International Transactions on Electrical Energy Systems, Vol. 23, No. 3, 2013. [20] Bigdeli, M., Vakilian, M., Rahimpour, E., “Transformer Winding Faults Classification Based on Transfer Function Analysis by Support Vector Machine”, IET Electric Power Applications, Vol. 6, No. 5, 2012. [21] Bagheri, S., Moravej, Z., Gharehpetian, G. B., “Classification and Discrimination among Winding Mechanical Defects, Internal and External Electrical Faults, and Inrush Current of Transformer”, IEEE Transactions on Industrial Informatics, Vol. 14, No. 2, pp. 484-493, 2018. [22] Zhao, Zh., Yao, Ch, Tang, Ch., Li, Ch., Yan, F., Islam, S., “Diagnosing Transformer Winding Deformation Faults Based on the Analysis of Binary Image Obtained From FRA Signature”, IEEE Access, Vol. 7, pp. 40463- 40474, 2019. [23] Liu, J., Zhao, Zh., Tang, Ch., Yao, Ch., Li, Ch., and Islam, S., “Classifying Transformer Winding Deformation Fault Types and Degrees using FRA based on Support Vector Machine”, IEEE Access, Vol. 7, pp. 112494 – 112504, 2019. [24] Tarimoradi, H., Gharehpetian, G. B., “A Novel Calculation Method of Indices to Improve Classification of Transformer Winding Fault Type, Location and Extent”, IEEE Transactions on Industrial Informatics, Vol. 13, No. 4, pp. 1531-1540, 2017. [25] Ghanizadeh, A. J., Gharehpetian, G. B., “ANN and Cross-correlation based Features for Discrimination between Electrical and Mechanical Defects and their Localization in Transformer Winding”, IEEE Transactions on Dielectrics and Electrical Insulation, Vol. 21, No. 5, pp. 2374-2382, 2014. [26] Haykin, S., “Neural Networks and Learning Machines Third Edition”, Pearson, Prentice Hall publication, 2009. [27] Vapnik, V. N, “Statistical Learning Theory”, Wiley Publication, 1998. [28] Wu, X., et al., “Top 10 algorithms in data mining”, Knowledge and Information Systems, Vol. 14, No. 1, pp. 1-37, 2008. [29] شیخان، جعفری نسب «آموزش شبکه عصبی مصنوعی با نسخه آشوب‌گونه الگوریتم جستجوی گرانشی و کاربرد آن در پیش‌بینی آلاینده‌های هوا: مطالعه قیاسی» مجله محاسبات نرم، دوره 5، شماره 2، ص. ۴۸-۶۵، 1395. [30] وثیقی ذاکر، جلیلی «پیش بینی ژن‌های بیماری با استفاده از دسته‌بند تک‌کلاسی ماشین بردار پشتیبان» مجله محاسبات نرم، دوره 4، شماره 1، ص. 83-74، 1394. [31] ویسی، قایدشرف، ابراهیمی «بهبود کارایی الگوریتم‌های یادگیری ماشین در تشخیص بیماری‌های قلبی با بهینه‌سازی داده‌ها و ویژگی‌ها » دوره ۸، شماره ۱، ص 85-70، ۱۳۹۸.