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Fault diagnosis of power transformer based on improved particle swarm optimization OS-ELM

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
A transformer is an important part of power transmission and transformation equipment. Once a fault occurs, it may cause a large-scale power outage. The safety of the transformer is related to the safe and stable operation of the power system. Aiming at the problem that the diagnosis result of transformer fault diagnosis method is not ideal and the model is unstable, a transformer fault diagnosis model based on improved particle swarm optimization online sequence extreme learning machine (IPSO-OS-ELM) algorithm is proposed. The improved particle swarmoptimization algorithm is applied to the transformer fault diagnosis model based on the OS-ELM, and the problems of randomly selecting parameters in the hidden layer of the OS-ELM and its network output not stable enough, are solved by optimization. Finally, the effectiveness of the improved fault diagnosis model in improving the accuracy is verified by simulation experiments.
Rocznik
Strony
161--172
Opis fizyczny
Bibliogr. 21 poz., rys., tab., wz.
Twórcy
autor
  • School of Control and Computer Engineering, North China Electric Power University 2 Beinong Rd, 102206 Beijing, China
autor
  • School of Control and Computer Engineering, North China Electric Power University 2 Beinong Rd, 102206 Beijing, China
Bibliografia
  • [1] Shanker T.B., Nagamani H.N., Antony D., Punekar G.S., Case studies on transformer fault diagnosis using dissolved gas analysis, 2017 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC), Bangalore, pp. 1–3 (2017).
  • [2] Ghoneim S.S.M., Taha I.B.M., A new approach of DGA interpretation technique for transformer fault diagnosis, International Journal of Electrical Power & Energy Systems, vol. 81, pp. 265–274 (2016).
  • [3] Lin H., Tang W.H., Ji T.Y., Wu Q.H., A novel approach to power transformer fault diagnosis based on ontology and Bayesian network, 2014 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC), Hong Kong, pp. 1–6 (2014).
  • [4] Li S.,Wu G., Gao B., Hao C., Xin D.,Yin X., Interpretation ofDGA for transformer fault diagnosis with complementary SaE-ELM and arctangent transform, IEEE Transactions on Dielectrics and Electrical Insulation, vol. 23, no. 1, pp. 586–595 (2016).
  • [5] Razmjooy N., Sheykhahmad F.R., Ghadimi N., A Hybrid Neural Network – World Cup Optimization Algorithm for Melanoma Detection, Open medicine (Warsaw, Poland), vol. 13, pp. 9–16 (2018).
  • [6] Yu S., Zhao D., ChenW., Hou H., Oil-immersed Power Transformer Internal Fault Diagnosis Research Based on Probabilistic Neural Network, Procedia Computer Science, vol. 83, pp. 1327–1331 (2016).
  • [7] Luo Y., Hou Y., Liu G., Tang C., Transformer fault diagnosis method based on QIA optimization BP neural network, 2017 IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), Chengdu, pp. 1623–1626 (2017).
  • [8] MoW., Kari T.,Wang H., Luan L., GaoW., Power Transformer Fault Diagnosis Using Support Vector Machine and Particle Swarm Optimization, 2017 10th International Symposium on Computational Intelligence and Design (ISCID), Hangzhou, pp. 511–515 (2017).
  • [9] Dai J., Song H., Sheng G., Jiang X., Dissolved gas analysis of insulating oil for power transformer fault diagnosis with deep belief network, IEEE Transactions on Dielectrics and Electrical Insulation, vol. 24, no. 5, pp. 2828–2835 (2017).
  • [10] Ji X., Zhang Y., Sun H., Liu J., Zhuang Y., Lei Q., Fault diagnosis for power transformer using deep learning and softmax regression, 2017 Chinese Automation Congress (CAC), Jinan, pp. 2662–2667 (2017).
  • [11] Mirza B., Lin Z., Cao J., Lai X., Voting based weighted online sequential extreme learning machine for imbalance multi-class classification, 2015 IEEE International Symposium on Circuits and Systems (ISCAS), Lisbon, pp. 565–568 (2015).
  • [12] Wang F., Shao S., Dong P., Research on transformer fault diagnosis method based on artificial immune network and fuzzy c-means clustering algorithm, Applied Mechanics and Materials, vol. 574, pp. 468–473 (2014).
  • [13] Niţu M.C., Aciu A.M., Nicola C.I., Nicola M., Power transformer fault diagnosis using fuzzy logic technique based on dissolved gas analysis and furan analysis, 2017 International Conference on Optimization of Electrical and Electronic Equipment (OPTIM) & 2017 Intl Aegean Conference on Electrical Machines and Power Electronics (ACEMP), Brasov, pp. 184–189 (2017).
  • [14] Ning Q., Quan Y., Wang D., The application of fuzzy mathematics to transformer diagnosis expert system, International Conference on the Properties and Applications of Dielectric Materials, Icpadm, pp. 161–164 (2009).
  • [15] Wu K., Kang J., Chi K., Power Transformer Fault Diagnosis Based on Improved Multi-classification Algorithm and Correlation Vector Machine, High Voltage Engineering, vol. 42, no. 9, pp. 3011–3019 (2016).
  • [16] Wu G., Yuan H., Song Z., Power Transformer Fault Diagnosis Based on Rough Sets and Support Vector Machines, High Voltage Engineering, vol. 11, pp. 3668–3674 (2017).
  • [17] Zhang Y., Jiao J., Wang K., Power Transformer Fault Diagnosis Model Based on Support Vector Machines Optimized by Empire Colonization Competition Algorithm, Electric Power Automation Equipment, vol. 1, pp. 99–104 (2018).
  • [18] Jiang C., Etorre B., A hybrid method of chaotic particle swarm optimization and linear interior for reactive power optimisation, Mathematics and Computers in Simulation, vol. 68, no. 1, pp. 57–65 (2005).
  • [19] Kennedy J., Eberhart R., Particle swarm optimization, IEEE International Conference on Neural Networks, pp. 1942–1948 (2002).
  • [20] Liang J., Qin A., Suganthan P., Comprehensive learning particle swarm optimizer for global optimization of multimodal functions, IEEE Transactions on Evolutionary Computation, vol. 10, no. 3, pp. 281–295 (2006).
  • [21] Kiranyaz S., Yildirim A., Fractional particle swarm optimization in multidimensional search space, IEEE Transactions on Systems Man & Cybernetics, vol. 40, no. 2, pp. 298–319 (2010).
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-31fb6c8b-12b6-403c-8b51-5b4e1a44fd28
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