15 8 2011 8 ELECTRI C MACHINES AND CONTROL Vol. 15 No. 8 Aug. 2011 LS-SVM 1 2 1 1 1 1. 210016 2. 232001 least squares support vector machine LS-SVM Buck LS-SVM LS-SVM 2% TP 206 A 1007-449X 2011 08-0064- 05 Research on fault prediction method of power electronic circuits based on least squares support vector machine JIANG Yuan-yuan 1 2 WANG You-ren 1 CUI Jiang 1 SUN Feng-yan 1 1. College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing 210016 China 2. College of Electric and Information Engineering Anhui University of Science and Technology Huainan 232001 China Abstract Aiming at the issue of fault prediction technique of power electronic circuits a method based on characteristic parameter data and least squares support vector machine LS-SVM for the prediction of power electronic circuits was proposed. Taking the Buck converter circuit as an example the fault prediction of power electronic circuits was achieved. Firstly the output voltage was selected as monitoring signal and then the average voltage and ripple voltage were extracted as characteristic parameters. Lastly LS-SVM algorithm was used to predict Buck converter circuit. The experimental results show that the LS- SVM algorithm is especially accurate in predicting the average voltage and ripple voltage with the relative error less than 2%. The new method can trace the characteristic parameters trend and can be effectively applied in fault prediction of power electronic circuits. Key words power electronic circuits fault prediction characteristic parameter data driving least squares support vector machine LS-SVM 2010-07 - 24 60871009 2009ZD52045 CXLX11-0183 NS2010063 1982 1963 1977 1983
8 LS-SVM 65 0 1-2 3 1 Buck Fig. 1 Buck converter circuit 1 Buck DC-DC 4 5 6 3 Buck Buck 2 a u o 7-9 1 U o N u o i i U o = 1 u least squares support N N o i 1 i = 1 vector machine LS-SVM b - LS-SVM - Δu 1 1. 1 LS - SVM SVM 10 11 support vector machine regression SVR LS-SVM 1 x Buck Hilbert 1 MOSFET IRF151 SVM f = 50 khz D = 0. 22 L = 43 μh f x = w T x + b w R n b R 2 D1 MUR405 C = 220 μf ESR R L = 1. 25 Ω 1. 2 LS-SVM f x
66 15 x w w T w b R n n R support vector SV 7 2 minj w e = 1 w b e 2 w 2 + γ n e 2 i } i = 1 3 1 Buck S. t. y i = w T x i + b + e i Pspice e i γ x i x = u o x 1 x i x n i y i y = Matlab7. 6 y 1 y i y n i Lagrange Lagrange α i L w e b α = J w e - n i = 1 α i w T x i + b + e i - y i 4 Karush-Kuhn-Tucker LS- SVM f x = n i = 1 α i K x i x + b 5 K x y = x y Mercer Sigmoid 1. 3 LS-SVM 2 2 Fig. 2 Flowchart of the fault prediction 1 2. 1 Buck 1 h 5 h 24 h 2 8 12 C 6 LS-SVM 2 LS-SVMlab Buck ESR L MOSFET R ds 3 V th g m 1 1 4 1 Buck 5 LS-SVM 2
8 LS-SVM 67 Table 1 1 Buck LS-SVM The parameters of the components in Buck Buck /h C /μf R ESR /mω L /μh R ds /mω V th /V g m /μ 1 220 512 43 1. 6 2. 83 20. 5 2 218 523 42. 9 3. 6 2. 85 20. 4 3 215 532 42. 8 5. 7 2. 87 20. 3 4 212 544 42. 6 8. 0 2. 89 20. 2 5 208 557 42. 4 11 2. 91 20. 1 6 205 570 42. 2 15 2. 93 20 7 200 583 42 19 2. 95 19. 9 8 194 608 41. 7 24 2. 97 19. 7 9 189 621 41. 5 29 3. 0 19. 5 10 183 637 41. 1 34 3. 03 19. 2 Table 3 3 Prediction results of characteristic parameters Table 2 2 The characteristic parameters data /h /V /V 1 5. 137 0. 768 2 5. 135 0. 782 3 5. 132 0. 793 4 5. 130 0. 809 5 5. 126 0. 825 6 5. 123 0. 842 7 5. 118 0. 858 8 5. 112 0. 889 9 5. 106 0. 905 10 5. 100 0. 928 Fig. 3 3 Prediction curves of average voltage 2. 2 LS-SVM 1 2 1 ~ 6 7 7 1 2 ~ 7 6 8 4 LS-SVM 2 Buck gam = Buck 1003 3 LS-SVM Buck 2% U o Δu 3 4 + 1 ~ 6 7 ~ 10 4 3 4 5. 137 8 V Fig. 4 4 Predicted curves of ripple voltage 0. 5 V 10% 1 V
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