Particle swarm optimization-based Fast Relevance Victor Machine for forecasting dissolved gases content in Power transformer oil

• Autorzy: Liang Y. , Li K. , Zhao B. , Zhao J. , Niu L.

Warianty tytułu

PL

Prognozowanie zawartości gazów rozpuszczonych w oleju transformatorowym przy wykorzystaniu metody RVM – Optymalizacja Stadna Cząsteczek

• Języki publikacji: EN

Abstrakty

EN

Forecasting of dissolved gases concentration in power transformer is very significant to detect incipient failures of transformer early and ensure hassle free operation of entire power system. A forecasting model based on Particle Swarm Optimization –Fast Relevance Vector Machine (PSO-FRVM) is proposed in this paper. PSO is utilized to optimize the free parameter of the Gaussian kernel function to improve the forecasting performance. The Matlab program testify the correctness and validity of the model.

PL

W artykule przedstawiono metodę prognozowania rozpływu gazów w transformatorze elektrycznym, opartą na zbudowanym modelu. W tworzeniu modelu wykorzystano Optymalizację Stadną Cząsteczek z maszyną opartą na wektorach istotnych (ang. PSO-FRVM). Metoda PSO wykorzystana została do optymalizacji doboru parametru wolnego w funkcji jądra Gaussa dla polepszenia jakości prognozowania. Weryfikację przeprowadzono w programie Matlak.

Słowa kluczowe

EN

relevance vector machine; particle swarm optimization (PSO); dissolved gas; Gaussian kernel function

PL

optymalizacja stadna cząsteczek; gaz rozpuszczony; funkcja jądra Gaussa

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2013
• Tom: R. 89, nr 1b
• Strony: 290--293
• Opis fizyczny: Bibliogr. 15 poz., schem., tab.

Twórcy

autor

Liang Y.

• Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education (Shandong University)

autor

Li K.

• Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education (Shandong University)

autor

Zhao B.

• Laiwu Power Supply Company of Shandong Electric Power Corporation

autor

Zhao J.

• Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education (Shandong University)
• State Grid of China Technology Colleague

autor

Niu L.

Bibliografia

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• [2] Wang W., Liu Y., Pedrycz W., A Two-Stage Online Prediction Method for a Blast Furnace Gas System and Its Application, IEEE Trans. on Control Systems Technology, 19 (2011), No. 3, 507-520
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• [5] Shengwei Fei, Mingjun Wang, Yubin Miao, et al, Particle swarm optimization-based support vector machine for forecasting dissolved gases content in power transformer oil. Energy Conversion and Management, 50(2009), 1604-1609
• [6] Shengwei Fei, YuSun, Forecasting dissolved gases content in power transformer oil based on support vector machine with genetic algorithm, Electric Power Systems Research, 78(2008), 507-514
• [7] Michael E. Tipping. Sparse Bayesian Learning and the Relevance Vector Machine. Journal of Machine Learning Research, 1(2001), 211-244
• [8] Fereidoun A. Mianji, Ye Zhang, Robust Hyperspectral Classification Using Relevance Vector Machine, IEEE Trans. on Geosc. and Remote Sensing, 49(2011), No. 6, 2100-2112
• [9] Ioannis Psorakis, Theodoros Damoulas, Mark A., Multiclass Relevance Vector Machines: Sparsity and Accuracy, IEEE Trans. on Neural Networks, 21(2000), No. 10, 1588-1598
• [10] Liyang Wei, Yongyi Yang, Robert M. Nishikawa, et al. Relevance Vector Machine for Automatic Detection of Clustered Microcalcifications, IEEE Trans. on Medical Imaging, 24(2005), No. 10, 1278-1285
• [11] Michael E. Tipping, Anita Faul, Fast Marginal Likelihood Maximisation for Sparse Bayesian Models. Proc. 9th Int. Workshop Artif. Intell. Statist., Key West, FL, 2003
• [12] Fernandez-Martinez, J.L., Garcia-Gonzalo E., Stochastic Stability Analysis of the Linear Continuous and Discrete PSO Models, IEEE Trans. on Evolutional Computing, 15(2011), No. 3, 405-423
• [13] Das Sharma, K.; Chatterjee, A.; Rakshit, A., A Random Spatial lbest PSO-Based Hybrid Strategy for Designing Adaptive Fuzzy Controllers for a Class of Nonlinear Systems, IEEE Trans. on Instrumentation and Measurement, 61(2012), No. 6, 1605-1612
• [14] Bo Liu, Ling Wang, Yihui Jin, An Effective PSO-Based Memetic Algorithm for Flow Shop Scheduling, IEEE Trans. on Systems, Man, and Cybernetics, Part B: Cybernetics, 24(2007), No. 1, 18-27
• [15] Chia-Nan Ko; Ying-Pin Chang; Chia-Ju Wu, A PSO Method With Nonlinear Time-Varying Evolution for Optimal Design of Harmonic Filters, IEEE Trans. on Power Systems, 24(2009), No. 1, 437-444