[1] Aggarwal, C.C., Hinneburg, A., Keim, D.A.: On the surprising behavior of distance metrics in high dimensional space. Proc. of the 8th Int. Conf. on Database Theory. LNCS 1973. Springer-Verlag, Berlin Heidelberg, 420–430. (2001).
[2] Aggarwal, C.C., Reddy, C.K.: Data Clustering. Algorithms and Applications. CRC Data Mining, Knowledge Discovery Series, Chapman and Hall/CRC, Boca Raton. (2013).
[3] Akbari, A., Setayeshmehr, A., Borsi, H., Gockenbach, E.: Intelligent agent-based system using dissolved gas analysis to detect incipient faults in power transformers. IEEE Electrical Insulation Magazine, 26, 6, 27–40. (2010).
[4] Andersen, R., Borgs, Ch., Chayes, J., Hopcraft, J., Mirrokni ,V.S., Teng, S.H.: Local computation of PageRank contributions. Internet Mathematics 5(1-2), 23–45. (2008).
[5] Baranwal, M., Salapaka, S.: Clustering and supervisory voltage control in power systems.
International Journal of Electrical Power & Energy Systems, Volume 109, 641–651. (2019).
[6] Basak, A.: Condition monitoring of power transformers. Engineering Science and Education Journal, 8, 1, 41–46. (1999).
[7] Berkhin, P.: A survey of clustering data mining techniques. In Kogan, J., Nicholas, Ch., Teboulle, M., eds : Grouping Multidimensional Data. Springer, 25–72. (2006).
[8] Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York, London. (1981).
[9] Bezdek, J.C., Pal, S.K.: Fuzzy Models for Pattern Recognition: Methods that Search for Structures in Data. IEEE, New York. (1992).
[10] Bishop, C.M.: Neural Networks for Pattern Recognition. Clarendon Press, London. (1995).
[11] Boczar T.: Identification of Fundamental Forms of Partial Discharges Based of the Results of Frequency Analysis of Their Acoustic Emission. Proceedings of the International Conference AE, 13–19, Brno. (1999).
[12] Boczar T.: Identification of Fundamental Forms of PDs Based of the Results of Frequency Analysis of Their AE, Journal of Acoustic Emission, Vol. 17, No. 3-4, 7–12, Los Angeles, USA.
(1999)
[13] Boczar, T.: Identification of a specific type of PD form acoustics emission frequency spectra.
IEEE Transactions on Dielectrics and Electrical Insulation, 8, 4, 598–606. (2001).
[14] Boczar, T.: Identyfikacja formy wyładowania niezupełnego na podstawie jego widm częstotliwościowych emisji akustycznej. Rozprawa doktorska, Wydział El. Pol. Śl. w Gliwicach, Gliwice. (1998).
[15] Boczar, T.: Możliwości identyfikacji formy wyładowania na podstawie widma częstotliwościowego jego EA. VI Symp. EUI, 35–40, Zakopane. (1997).
[16] Boczar, T.: Obiektywizacja wyników akustycznej metody oceny wyładowań niezupełnych przy zastosowaniu do opisu sygnałów analizy statystycznej i cyfrowych metod przetwarzania. Of.
Wyd. Pol. Op. (2003)
41 [17] Boczar, T.: Widma emisji akustycznej generowanej przez wyładowania niezupełne w izolacji
olejowej. S i M z. 114, Politechnika Opolska. (2000).
[18] Boczar, T., Cichon, A., Borucki, S.: Diagnostic expert system of transformer insulation systems using the acoustic emission method. IEEE Transactions on Dielectrics and Electrical Insulation, 21, 2, 854–865. (2014).
[19] Boczar, T., Borucki, S., Cichoń, A., Lorenc, M.: Rozpoznawanie wyładowań elektrycznych rejestrowanych metodą emisji akustycznej przy wykorzystaniu sieci neuronowych. Przegląd Elektrotechniczny – Konferencja 1’2005, Jubileuszowe X Sympozjum EUI’2005, Problemy eksploatacji układów izolacyjnych wysokiego napięcia, 30–33, Krynica, 27-30 września (2005).
[20] Boczar, T., Borucki, S., Cichoń, A., Lorenc, M.: Recognizing partial discharge forms measured by the acoustic emission method using the spectrum power density as a parameter of the artificial neuron network. The Molecular and Quantum Acoustics, Annual Journal, Vol. 26, 35–44. (2005).
[21] Borucki, S., Boczar, T., Cichoń, A., Lorenc, M.: The evaluation of neural networks application for recognizing single-source PD forms generated in paper-oil insulation systems based on the AE signal analysis. European Physical Journal Special Topics, 154, 1, 23–29. (2007).
[22] Borucki, S.: Możliwości rozpoznawania form WNZ rejestrowanych metodą EA przy zastosowaniu sieci neuronowych. Rozprawa Doktorska, WEAiI Politechniki Opolskiej, Opole.
(2006).
[23] Borucki, S., Boczar, T: The Influence of the Temperature of Insulation Oil on the Results of Measurements of the Acoustic Emission Signals Generated by Partial Discharges. Journal de Physique IV Proceedings, 34th Winter School on Wave and Quantum Acoustics, 89–93, ISSN:1155-4339. (2005).
[24] Borucki, S., Cichoń, A.: Wpływ zmiany obciążenia transformatora energetycznego na wyniki analizy sygnałów wibroakustycznych. Przegląd Elektrotechniczny, 86, 7, 45–47. (2010).
[25] Borucki, S., Łuczak, J.: Ocena wpływu doboru częstotliwości widmowej gęstości mocy sygnału akustycznego na efektywność klasyfikacji podstawowych form wyładowań niezupełnych z użyciem metody klasteryzacji, Energetyka, 7/2017, 448–452. (2017).
[26] Borucki, S., Łuczak, J., Zmarzły, D.: Using Clustering Methods for the Identification of Acoustic Emission Signals Generated by the Selected Form of Partial Discharge in Oil-Papier Insulation.
Archives of Acoustics, vol. 43, No. 2, 207–215. (2018).
[27] Castro Heredia, L.C., Rodrigo Mor, A.: Density-based clustering methods for unsupervised separation of partial discharge sources. International Journal of Electrical Power & Energy Systems, Volume 107, 224–230. (2019).
[28] Chen, L., Yue, D., Dou, C., Cheng, Z., Chen, J.: Robustness of cyber-physical power systems in cascading failure: Survival of interdependent clusters. International Journal of Electrical Power
& Energy Systems, Volume 114. (2019).
[29] Chia-Hung, L., Chien-Hsien, W., Ping-Zan, H.: Grey clustering analysis for incipient fault diagnosis in oil-immersed transformers. Expert Systems with Applications, Volume 36, Issue 2, Part 1, 1371–1379. (2009).
[30] Cichoń, A.: Nowa metoda diagnostyki stanu technicznego podobciążeniowych przełączników zaczepów. Politechnika Opolska, Of. Wyd. P.O., Opole. (2011).
[31] Cichoń, A., Boczar ,T.: Comparison Analysis of Acoustic Emission Signals Generated by Electrical Discharges Measured by the Hydrophone and the Wide Wideband Contact Transducer.
Journal de Physique IV Proceedings, 34th Winter School on Wave and Quantum Acoustics, 93–
96, ISSN:1155-4339. (2005).
[32] Cichoń, A., Borucki, S., Kunicki, M., Łuczak, J.: Evaluation of the effectiveness of selected clustering methods for identification of acoustic emission signals generated by partial discharges
42 occurring in paper and oil insulation, Book of Abstract and Poster, 47th Winter School on Wave and Quantum Acoustics, Szczyrk (2018).
[33] Duda, R.O., Hart, P.E., Stork, G.: Pattern Classification. 2nd ed., Wiley, New York. (2000).
[34] Eke, S., Clerc, G., Aka-Ngnui, T., Fofana, I.: Transformer Condition Assessment Using Fuzz C-means Clustering Techniques, 0883-7544/19/C2019/IEEE March/April – Vol.35, No. 2, DEIS.
(2019).
[35] Everitt, B.S.: Cluster Analysis, Halsted Press. (1993).
[36] Fang, R., Shang, R., Wu, M., Peng, C., Guo, X.: Application of gray relational analysis to k-means clustering for dynamic equivalent modeling of wind farm. International Journal of Hydrogen Energy, Volume 42, Issue 31, 20154 – 20163. (2017).
[37] Filippone, M., Camastra, F., Masulli, F., Rovetta, S.: A survey on spectral and kernel methods for clustering. Pattern Recognition, 41(1), 179–190. Jan. (2008).
[38] Fuhr, J.: Procedure for identification and localization of dangerous partial discharges sources in power transformers. IEEE Transactions on Dielectrics and Electrical Insulation, 12, 5, 1005–
1014. (2005).
[39] Han, J., Kamber ,M., Pei, J.: Data mining: concepts and techniques. Morgan Kaufmann Pub., 3rd ed. (2012).
[40] Hao, J., Liu, D., Li, Z., Chen, Z., Kong, L.: Power System Load Forecasting Based on Fuzzy Clustering and Gray Target Theory. Energy Procedia, Volume 16, Part C, 1852–1859. (2012).
[41] Hermosa González-Carrato de la, R.R.: Wind farm monitoring using Mahalanobis distance and fuzzy clustering. Renewable Energy, Volume 123, 526–540. (2018).
[42] Jain, A., Dubes, R.: Algorithms for Clustering Data. Prentice Hall, New Jersey. (1998).
[43] Jain, A., Murty, M., Flynn, P.: Data clustering: A review. ACM Computing Surveys, 31, 264–
323. (1999).
[44] Jasiński, M., Sikorski, T., Borkowski, K.: Clustering as a tool to support the assessment of power quality in electrical power networks with distributed generation in the mining industry. Electric Power Systems Research, Volume 166, 52–60. (2019).
[45] Jiang, Z., Wu, H., Zhan, Z.: Compound substation characteristics analysis based on multi-objective model and cluster-correct algorithm. Electric Power Systems Research, Volume 175.
(2019).
[46] Kalyani, S., Swarup, K.S.: Particle swarm optimization based K-means clustering approach for security assessment in power systems. Expert Systems with Applications, Volume 38, Issue 9, 10839–10846. (2011).
[47] Kapinos, J., Glinka, T., Drak, B.: Typowe przyczyny uszkodzeń eksploatacyjnych transformatorów energetycznych, Przegląd Elektrotechniczny. 90, 1, 186-189. (2014).
[48] Kazmierski, M., Olech, W.: Technical diagnostics and monitoring of transformers [in Polish], Printing house of ZPBE Energopomiar-elektryka Sp. z o.o., Gliwice. (2013).
[49] Kogan, J.: Introduction to Clustering Large and High-Dimensional Data. Cambridge University Press, Cambridge. (2007).
[50] Korzeniewski, J.: Metody selekcji zmiennych w analizie skupień. Nowe procedury.
Wydawnictwo Uniwersytetu Łódzkiego, Łódź. (2012).
[51] Kowolik, S.: Klasteryzacja z wykorzystaniem procedury cluster Matlaba.
https://www.researchgate.net/publication/305494816, Dąbrowa Górnicza. (2014).
[52] Krzyśko, M. Wołyński, W. Górecki, T. Skorzybut, M.: Systemy uczące się – rozpoznawanie wzorców, analiza skupień i redukcja wymiarowości. WNT, Warszawa. (2008).
[53] Kucharska, B.: Parametry EA emitowane przez WNZ. Rozprawa Doktorska, Wydz. El. Pol. Śl.
w Gliwicach, Gliwice. (1995).
43 [54] Kurtasz, P.: Zastosowanie algorytmu multikomparacyjnego do klasyfikacji sygnałów emisji akustycznej generowanych przez wyładowania niezupełne. Rozprawa Doktorska, WEAiI Politechniki Opolskiej, Opole. (2011).
[55] Kurtasz, P., Boczar, T.: The application of the optimized multicomparative algorithm for classifying acoustic signals, generated by partial discharges, cataloged in the modified database.
Pomiary Automatyka Robotyka, Vol. 12, No 12, 73–79. (2010).
[56] Lalitha, E.M., Satish, L.: Wavelet analysis for classification of multi-source PD patterns. IEEE Transactions on Dielectrics and Electrical Insulation, 7, 1, 40–47. (2002).
[57] Li, P., Gu, W., Wang, L., Xu, B., Wu, M., Shen, W.: Dynamic equivalent modeling of two-staged photovoltaic power station clusters based on dynamic affinity propagation clustering algorithm.
International Journal of Electrical Power & Energy Systems, Volume 95, 463–475, (2018).
[58] Liu, G., Zhu, L., Wu, X., Wang, J.: Time series clustering and physical implication for photovoltaic array systems with unknown working conditions, Solar Energy, Volume 180, 401–
411. (2019).
[59] Łuczak, J.: Use of clustering method to analyse acoustic emission generated by forms of partial discharges. Zeszyty Naukowe Politechniki Opolskiej seria: „Elektryka” z.75, nr kol. 365/2017, 49, Opole. (2017).
[60] Łuczak, J.: Verification of the effectiveness of the selected clustering method used to analyse acoustic emission from partial discharges. Zeszyty Naukowe Politechniki Opolskiej seria:
„Elektryka” z.75, nr kol. 365/2017, 51, Opole. (2017).
[61] Malecki, J., Ranachowski, J. – praca zbiorowa: Emisja akustyczna – Źródła, Metody, Zastosowania. PAN–KBN, Warszawa. (1994).
[62] Malecki, I., Witos, Z.: Deskryptory emisji akustycznej. Prace IPPT–PAN, Nr 39, Warszawa.
(1993).
[63] Ming-Shou, S., Chung-Chu, C., Chien-Yi, C., Jiann-Fuh, C.: Classification of partial discharge events in GILBS using probabilistic neural networks and the fuzzy c-means clustering approach.
International Journal of Electrical Power & Energy Systems, Volume 61, 173–179. (2014).
[64] Mohan Rao, U., Sood, Y.R., Jarial, R.K.: Subtractive Clustering Fuzzy Expert System for Engineering Applications. Procedia Computer Science, Volume 48, 77–83. (2015).
[65] Morzy, T.: Eksploracja danych. Metody i algorytmy, PWN, Warszawa. (2013).
[66] Muthamizh Selvam, M., Gnanadass, R., Padhy, N.P.: Fuzzy based clustering of smart meter data using real power and THD patterns. Energy Procedia, Volume 117, 401–408. (2017).
[67] Oliveira de, J.V., Pedrycz, W., eds : Advances in Fuzzy Clustering and its Applications. Wiley, New York. (2007).
[68] Olszewska, A., Witos, F.: Location of partial discharge sources and analysis of signals in chosen power oil transformers by means of acoustic emission method. Acta Physica Polonica A, 122, 5, 921–926. (2012).
[69] Om Prakash, M., Abdul Gafoor, S.: Power quality recognition in distribution system with solar energy penetration using S-transform and Fuzzy C-means clustering. Renewable Energy, Volume 106, 37–51. (2017).
[70] Panapakidis, I. P., Christoforidis, G. C.: Implementation of modified versions of the K-means algorithm in power load curves profiling. Sustainable Cities and Society, Volume 35. (2017).
[71] Panapakidis, I. P., Christoforidis, G. C.: Optimal Selection of Clustering Algorithm via Multi-Criteria Decision Analysis (MCDA) for Load Profiling Applications. Appl. Sci. 2018, 8, 237, www.mdpi.com/journal/applsci. Published 4 February (2018).
[72] Pedrycz, W.: Knowledge-based Clustering. Wiley, Hoboken. (2005).
44 [73] Radionov, A.A., Evdokimov, S.A., Sarlybaev, A.A., Karandaeva, O.I.: Application of Subtractive Clustering for Power Transformer Fault Diagnostics. Procedia Engineering, Volume 129, 22–28.
(2015).
[74] Rajaraman, A., Ullman, J.D.: Mining of Massive Data Sets. Stanford University. (2010).
[75] Rodrigo, A.. Llovera, P., Fuster, V., Quijano, A.: Influence of high frequency current transformers bandwidth on charge evaluation in partial discharge measurements. IEEE Transactions on Dielectrics and Electrical Insulation, 18, 5, 1798–1802. (2011).
[76] Rodrigo Mor, A., Castro Heredia, L.C., Muñoz, F.A.: Effect of acquisition parameters on equivalent time and equivalent bandwidth algorithms for partial discharge clustering.
International Journal of Electrical Power & Energy Systems, Volume 88, 141–149. (2017).
[77] Rubio-Serrano, J., Rojas-Moreno, M.V., Posada, J., Martienez-Tarifa, J.M., Robles, G., Garcia-Souto, J.A.: Electro-acoustic detection, identification and location of PD sources in oil-paper insulation systems. IEEE Transactions on Dielectrics and Electrical Insulation, 19, 5, 1569–1578.
(2012).
[78] Sneath, P.H., Sokal, R.R.: Numerical Taxonomy. Freeman, San Francisco. (1973).
[79] Soltani, A., Haghjoo, F., Shahrtash, S.M.: Compensation of the effects of electrical sensors in measuring PD signals. IET Science, Measurement &Technology, 6, 6, 4, 494–501. (2012).
[80] Späth, H.: Cluster Analysis Algorithms for Data Reductions and Classification of Objects. Ellis Harwood, Chichester. (1980).
[81] Tan, P.N., Steinbach, M., Kumar, V.: Introduction to Data Mining. Addison Wesley Longman.
(2006).
[82] Tou, J.T., Gonzalez, R.C.: Pattern Recognition Principles. Addison-Wesley Pub. Co. (1974).
[83] Wang, K.: Power System Critical Cutset Identification based on Rolling Double Level Hierarchical Clustering. Energy Procedia, Volume 159, 148–153. (2019).
[84] Wen, L., Zhou, K., Yang, S.: A shape-based clustering method for pattern recognition of residential electricity consumption. Journal of Cleaner Production, Volume 212, 475–488. (2019).
[85] Wierzchoń, S. Kłopotek, M.: Algorytmy analizy skupień, WNT, Warszawa. (2015).
[86] Xu, R., Wunsch, II D.: Survey of clustering algorithms. IEEE Trans. on Neur. Netw., 16(3), 645–
678. May (2005).
[87] Zheng, J., Ma, Z., Wang, Z., Wang, X. Zhu, S., Wei, L.: Feature distance based online cluster modeling of LVRT controlled PV power plants. Electric Power Systems Research, Volume 154, 223–233. (2018).
[88] http://www.obliczeniastatystyczne.pl