Abstract image of a woman sitting on books and studying on a laptop

3271829.ppt

Download as PPT, PDF
A
AhmedHeskol2

This document describes a sensor fault diagnosis scheme for a DC/DC converter used in hybrid electric vehicles. The scheme uses a bank of extended Kalman filters to generate residuals by comparing estimated and actual sensor measurements. A generalized likelihood ratio test evaluates the residuals to detect faults. The diagnosis scheme was tested on a hardware prototype of a bidirectional DC/DC converter. Modeling of the power converter system and details of the proposed residual-based fault diagnosis algorithm are provided.

Engineering
1 of 60
Download to read offline
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
A Sensor Fault Diagnosis Scheme for a DC/DC Converter used in Hybrid Electric Vehicles Hiba Al-SHEIKH Ghaleb HOBLOS Nazih MOUBAYED
2 Overview  Examined power converter system  Hardware prototype  Converter Modelling  Proposed residual-based fault diagnosis scheme  Bank of extended Kalman filters  Generalized likelihood ratio test  Tuning using receiver operating characteristic curve  Conclusion and future perspectives
3 Recent advances in power electronics encouraged the development of new initiatives for Hybrid Electric Vehicles (HEVs) with advanced multi-level power electronic systems. Power converters are intensively used in HEVs • convert power at different levels • drive various load • electric drives
4 Intensive use of power converters in modern hybrid vehicles Need for efficient methods of condition monitoring and fault diagnosis Reliability of the automotive electrical power system
5 Controller Power Converters Sensors Machine AC Side Common Electrical Faults in Electric Drive Systems Connectors/ DC Bus Power Converters • high power • relatively low voltage high current increase thermal and electric stresses on the converter components and monitoring sensors
6 Controller Power Converters Sensors Machine AC Side Common Electrical Faults in Electric Drive Systems Connectors/ DC Bus • AC current sensor • DC bus voltage sensor Power Converters Sensors Sensor faults in a DC/DC power converter system used in HEV
7 7 Observer-based Fault diagnosis methods Knowledge-based methods Analytical model-based methods Signal-based methods Fault Diagnosis Techniques for Power Converters Analytical model-based methods For HEV applications where converters operate under variable load conditions, model-based diagnosis is of particular interest.
8 Examined Power Converter System
9 Automotive Electrical System DC Main System DC Distribution AC Distribution
10 Power Converters  DC/DC Choppers  DC/AC Inverters  AC/DC Rectifiers Automotive Electrical System
11
12
13 Parallel DC-linked Multi-input DC/DC Converter consisting of two bidirectional half-bridge cells DC bus Energy Storage System AC Drive Battery PM UC Multi-port DC/DC Converter Inverter Examined Power Converter System
14 Isolated topologies boost-half bridge half-bridge full-bridge Non-isolated topologies SEPIC cuk buck-boost Bidirectional DC/DC Converter Topologies
15 Source voltage 200V DC-link voltage 300V Rated Power 30kW Switching frequency 15kHz Source voltage ripple 2% p/p DC-link voltage ripple 4.5% p/p Inductor current ripple ±10% Design Requirements Examined Power Converter System Converter Parameters Parameter Symbol Value Input Capacitance Cin 80µF Input Capacitor ESR RCin 100mΩ Inductance L 146µH Inductor ESR RL 5mΩ Output Capacitance Co 5mF Output Capacitor ESR RCo 80mΩ Transistor ON resistance RON 1mΩ
16 Examined Power Converter System State variables 𝑣𝐶𝑖𝑛, 𝑖𝐿, 𝑣𝐶𝑜 s (duty cycle)
17 0 0.02 0.04 0.06 0.08 0.1 194 196 198 200 x 1 =v Cin 0 0.02 0.04 0.06 0.08 0.1 0 200 400 600 x 2 =i L 0 0.02 0.04 0.06 0.08 0.1 100 200 300 400 time (sec) x 3 =v Co 0 0.02 0.04 0.06 0.08 0.1 0 200 400 600 y 1 =i in 0 0.02 0.04 0.06 0.08 0.1 150 200 250 300 350 y 2 =v o time (sec) State variables during healthy boost operation Observed variables during healthy boost operation
18 Hardware Prototype of Converter System
19 Hardware Prototype Experimental test bench Hardware prototype of bidirectional DC/DC converter
20 Measurement of sensor 1 (measuring load voltage 𝒗𝒐) Measurement of sensor 2 (measuring source current 𝒊𝒊𝒏) Hardware Prototype
21 Sensor 2 Sensor 1 Hardware Prototype
22 Modelling of Power Converter
23 Converter State-Space Model  The examined converter is a nonlinear and time-varying system DC bus Battery PM UC Multi-input DC/DC Converter Inverter Boost operation
24 Converter State-Space Model  The examined converter is a nonlinear and time-varying system DC bus Battery PM UC Multi-input DC/DC Converter Inverter Buck operation
25 Converter State-Space Model  The examined converter is a nonlinear and time-varying system  The converter state-space model is obtained in three steps: 1. Piece-wise linear state-space model 2. Continuous-time nonlinear state-space model 3. Discrete-time nonlinear state-space model
26 Switching configuration 2 (T1 OFF; D2 ON) Switching configuration 2 (T2 OFF; D1 ON) Switching configuration 1 (T1 ON; D2 OFF) Switching configuration 1 (T2 ON; D1 OFF) Converter State-Space Model Boost mode Buck mode 1. During each switching configuration, the converter is linear and possesses a piece-wise switched linear state-space model
27 Converter State-Space Model 1. During each switching configuration, the converter is linear and possesses a piece-wise switched linear state-space model 𝒙 = 𝐀𝐢 𝐣 𝒙 + 𝐁𝐢 𝐣 𝒖 𝒚 = 𝐂𝐢 𝐣 𝒙 + 𝐃𝐢 𝐣 𝒖 Operation Mode Switching State T1 D1 T2 D2 j = 1 (Boost) i = 1 ON OFF OFF OFF i = 2 OFF OFF OFF ON j = 2 (Buck) i = 1 OFF OFF ON OFF i = 2 OFF ON OFF OFF
28 Converter State-Space Model Operation Mode Switching State T1 D1 T2 D2 j = 1 (Boost) i = 1 ON OFF OFF OFF i = 2 OFF OFF OFF ON j = 2 (Buck) i = 1 OFF OFF ON OFF i = 2 OFF ON OFF OFF 𝐀𝐚𝐯 𝐣 = 𝐀𝟏 𝐣 𝑑 + 𝐀𝟐 𝐣 1 − 𝑑 𝐁𝐚𝐯 𝐣 = 𝐁𝟏 𝐣 𝑑 + 𝐁𝟐 𝐣 1 − 𝑑 𝐂𝐚𝐯 𝐣 = 𝐂𝟏 𝐣 𝑑 + 𝐂𝟐 𝐣 1 − 𝑑 𝐃𝐚𝐯 𝐣 = 𝐃𝟏 𝐣 𝑑 + 𝐃𝟐 𝐣 1 − 𝑑 where averaged using 𝒅 as control variable 2. Averaged continuous-time model 𝒙 = 𝐀𝐚𝐯 𝐣 𝒙 𝒙 + 𝐁𝐚𝐯 𝐣 𝒙 𝒖 𝒚 = 𝐂𝐚𝐯 𝐣 𝒙 𝒙 + 𝐃𝐚𝐯 𝐣 𝒙 𝒖
29 Converter State-Space Model 2. Averaged continuous-time model  The continuous-time model is nonlinear  The duty cycle is a function of the state variables, 𝒅 = 𝑓(𝒙)  𝑓 is obtained from the converter dynamics during steady state 𝒙 = 𝐀𝐚𝐯 𝐣 𝒙 𝒙 + 𝐁𝐚𝐯 𝐣 𝒙 𝒖 𝒚 = 𝐂𝐚𝐯 𝐣 𝒙 𝒙 + 𝐃𝐚𝐯 𝐣 𝒙 𝒖 𝐀𝐚𝐯 𝐣 = 𝐀𝟏 𝐣 𝑑 + 𝐀𝟐 𝐣 1 − 𝑑 𝐁𝐚𝐯 𝐣 = 𝐁𝟏 𝐣 𝑑 + 𝐁𝟐 𝐣 1 − 𝑑 𝐂𝐚𝐯 𝐣 = 𝐂𝟏 𝐣 𝑑 + 𝐂𝟐 𝐣 1 − 𝑑 𝐃𝐚𝐯 𝐣 = 𝐃𝟏 𝐣 𝑑 + 𝐃𝟐 𝐣 1 − 𝑑 where
30                                            0 1 0 1 1 0 1 o iCin iCin Co iCin ON iCin L Cin in iCin in iCin in in iCin in C f L f LR R R f R R f R R R R LR R R C R R C x x x x x Aav                            o Co iCin Cin iCin in C L R f LR R R C 1 0 1 0 1 x x Bav                  1 1 0 0 1 Co iCin Cin iCin R f R R R x x Cav           Co iCin R R 0 0 1 av D . Converter State-Space Model
31 Converter State-Space Model 3. The continuous-time model is discretized using first order hold with sampling period 𝑇 = 1𝜇 seconds.  Including process noise and measurement noise, the discrete-time state- space model becomes  𝒘 and 𝒗 are white Gaussian, zero-mean, independent random processes with constant auto-covariance matrices Q and R. 𝒙 𝑘 + 1 = 𝐀𝐝 𝐣 𝒙 𝒙 𝑘 + 𝐁𝐝 𝐣 𝒙 𝒖 𝑘 + 𝒘 𝑘 𝒚 𝑘 = 𝐂𝐝 𝐣 𝒙 𝒙 𝑘 + 𝐃𝐝 𝐣 𝒙 𝒖 𝑘 + 𝒗 𝑘
32 Proposed Fault Diagnosis Algorithm
33 Fault Diagnosis of Converter Sensor Faults Sensor 2 Sensor 1 Model-Based Residual Approach
34 Output variables Input variables Power Converter System Residual Generation Fault/No fault Residual Evaluation Residuals Fault Diagnosis of Converter Sensor Faults
35 Residual Generation using Bank of Extended Kalman Filters
36 Converter state- space model + + Converter input signals Sensor measured signals The Extended Kalman Filter (EKF) Estimates of the measured signals + - Residual signals “Innovations”
37 The Extended Kalman Filter (EKF)  Recursive application of prediction and correction cycles  At the end of sampling period, the nonlinearity of the converter system is approximated by a linear model around the last predicted and corrected estimate
38 The EKF Algorithm Initialization 𝑘 = 0, 𝐱 0|0 = 𝑬 𝐱(𝟎) and P 0|0 = P(0) Prediction Cycle 𝐱(𝑘 + 1|𝑘) = 𝐀𝐝 x(𝑘|𝑘) x(𝑘|𝑘) + 𝐁𝐝 x(𝑘|𝑘) 𝑢(𝑘) 𝐏(𝑘 + 1|k) = 𝐀𝐣(𝑘)𝐏(𝑘|𝑘)𝐀𝐣 𝐓 (k) + 𝐐 𝐲 𝑘 + 1|𝑘 = 𝐂𝐝 x 𝑘 + 1 𝑘 𝐱(𝑘 + 1|𝑘) + 𝐃𝐝𝑢(𝑘) where 𝐀𝐣(𝑘) is the jacobian matrix of 𝐀𝐝 x(𝑘|𝑘) x(𝑘|𝑘) Correction Cycle A new measurement is obtained 𝑦 𝑘 + 1 𝐱(𝑘 + 1|𝑘 + 1) = 𝐱(k + 1|𝑘) + 𝐊 𝑘 + 1 𝐫(𝑘 + 1) 𝐏 𝑘 + 1|𝑘 + 1 = I − 𝐊 𝑘 + 1 𝐂𝐣 𝑘 + 1 𝐏 𝑘 + 1|𝑘 where 𝐊(𝑘 + 1) = 𝐏(𝑘 + 1|𝑘)𝐂𝐣 𝐓 (𝑘 + 1) 𝐂𝐣 𝑘 + 1 𝐏 k + 1 𝑘 𝐂𝐣 𝐓 (k + 1) + 𝐑 −1 𝐫 𝑘 + 1 = 𝐲 𝑘 + 1 − 𝐲 𝑘 + 1|𝑘 𝐂𝐣(𝑘) is the jacobian matrix of 𝐂𝐝 x(𝑘|𝑘) x(𝑘|𝑘) 𝒌 increments Prediction and correction repeat with corrected estimates used to predict new estimates
39 0 0.01 0.02 0.03 0.04 0.05 0 100 200 300 400 Observer 1 time (s) Residual r 1 e y1 0 0.01 0.02 0.03 0.04 0.05 -50 0 50 100 150 time (s) Residual r 1 e y2 0 0.01 0.02 0.03 0.04 0.05 -100 0 100 200 300 Observer 2 time (s) Residual r 2 e y1 0 0.01 0.02 0.03 0.04 0.05 -4 -2 0 2 4 time (s) Residual r 2 e y2 Residuals Generated by the Bank of EKF Instant of fault Standardized residuals with fault on sensor 1 occurring at 0.03s
40 0 0.01 0.02 0.03 0.04 0.05 -4 -2 0 2 4 Observer 1 time (s) Residual r 1 e y1 0 0.01 0.02 0.03 0.04 0.05 0 500 1000 1500 2000 2500 time (s) Residual r 1 e y2 0 0.01 0.02 0.03 0.04 0.05 0 500 1000 1500 2000 2500 Observer 2 time (s) Residual r 2 e y1 0 0.01 0.02 0.03 0.04 0.05 0 500 1000 1500 2000 2500 time (s) Residual r 2 e y2 Standardized residuals with fault on sensor 2 occurring at 0.03s Instant of fault Residuals Generated by the Bank of EKF
41 Residuals Generated by the Bank of EKF Advantage of Kalman Filtering  independent residuals  with white Gaussian, zero-mean and unit-covariance characteristics  in case of faultless operation  with altered statistical characteristics  in case of sensor faults Statistical change detection approaches
42 Residual Evaluation using Generalized Likelihood Ratio Test
43 Residuals Evaluation Approaches  Statistical data processing  Correlation  Pattern recognition  Fuzzy logic  Fixed threshold  Adaptive threshold Stochastic envirmonent Likelihood ratio tests Generalized Likelihood Ratio (GLR) Test
44 Residuals Evaluation using GLR Test  sensor is faultless  residuals are Gaussain with 𝜇0 = 0 and 𝜎0 2 = 1  sensor is faulty  𝜇0 is altered into 𝜇1 and 𝜎0 2 into 𝜎1 2 Statistical Hypothesis Testing Problem Ho and H1
45 Statistical Hypothesis Testing Problem Ho and H1 Residuals Evaluation using GLR Test  Maximizing the likelihhod ratio  𝜇1 is the Maximum Likelihood Estimate (MLE) of 𝜇1  𝜇0 is the MLE of 𝜇0               o o y y y H e p H e p e L i i i , ˆ ; , ˆ ; ln 1 1  
46 At every time step t Apply the GLR statistic on the recent W residual values Generate a detection function 𝑔 𝑡 = 𝑚𝑎𝑥 𝐺𝐿𝑅𝑡(𝑘) for each residual Is residual variance known? Evaluate 𝐺𝐿𝑅𝑡(𝑘) for all 1 ≤ 𝑘 ≤ 𝑊 using   2 ) ( 2         k x k k GLR t t Evaluate 𝐺𝐿𝑅𝑡(𝑘) for all 1 ≤ 𝑘 ≤ 𝑊 using                     2 ) ( ) ( 1 ln 2 k k x k k GLR t t t  Is 𝑔(𝑡) > 𝛾? Decide H1 (fault) Decide H0 (No fault) Yes No Yes No GLR Algorithm
47 Detection Function Generated by GLR Test Detection function with fault on sensor 1 0 0.01 0.02 0.03 0.04 0.05 0 100 200 300 Residual r1 ey1 0 0.01 0.02 0.03 0.04 0.05 -5 0 5 Residual r2 ey2 0 0.01 0.02 0.03 0.04 0.05 0 10 20 30 GLRt for r1 ey1 0 0.01 0.02 0.03 0.04 0.05 0 10 20 GLRt for r2 ey2 0 0.01 0.02 0.03 0.04 0.05 0 10 20 30 time (s) GLRt for r1 ey1 0 0.01 0.02 0.03 0.04 0.05 0 10 20 time (s) GLRt for r2 ey2 instant of fault  unknown  known  known  unknown
48 Detection Function Generated by GLR Test Detection function with fault on sensor 2 0 0.01 0.02 0.03 0.04 0.05 -5 0 5 Residual r1 ey1 0 0.01 0.02 0.03 0.04 0.05 0 2 4 GLRt for r1 ey1 0 0.01 0.02 0.03 0.04 0.05 0 20 40 GLRt for r2 ey2 0 0.01 0.02 0.03 0.04 0.05 0 1 2 3 time (s) GLRt for r1 ey1 0 0.01 0.02 0.03 0.04 0.05 0 20 40 time (s) GLRt for r2 ey2 0 0.01 0.02 0.03 0.04 0.05 0 1000 2000 Residual r2 ey2 instant of fault  unknown  known  unknown  known
49 Tuning using Receiver Operating Characteristic Curve
50 false positives rate (tpr) true positives rate (fpr) (0, 0) (1, 1) as 𝛾 increase 0 1 1 + optimal 𝛾 ROC Analysis An evaluation tool to measure the performance of the residual- based GLR test.
51 Three ROC Plots:  W = 30  For each W, 𝛾 is varied from 0 to 𝛾𝑚𝑎𝑥  For each 𝛾, a test set of 1000 simulations is used  Healthy and faulty trials  During faulty trials, different fault amplitudes were injected  At the end of every trial, the detection function 𝑔 𝑡 is generated using 𝐺𝐿𝑅𝑡 and compared the corresponding 𝛾  At the end of the 1000 trials, the tpr and fpr are calculated and the corresponding point is located on the ROC curve. ROC Analysis  W = 50  W = 70
52 -1 0 1 2 3 4 5 6 7 x 10 -3 0.97 0.98 0.99 1 28.17 28.16 28.14 28.13 28.11 28.1 28.08 28.05 21.56 21.24 20.31 19.8 17.88 17.63 17.39 16.35 14.9 14.8 14.49 0 5 10 15 20 x 10 -3 0.94 0.96 0.98 1 X: 0 Y: 1 ROC curve (Observer 1/ Residual 1) r1 ey1 false positive rate true positive rate X: 0.002894 Y: 1 X: 0.0152 Y: 0.9942 W=30 W=50 W=70 optimal point (0,1) -1 0 1 2 3 4 5 6 7 x 10 -3 0 0.2 0.4 0.6 0.8 1 35.35 35.34 35.33 35.32 35.31 34.83 31.43 30.66 29.71 -0.005 0 0.005 0.01 0.015 0.02 0.025 0.03 0.035 0.04 0.6 0.7 0.8 0.9 1 X: 0 Y: 1 ROC curve (Observer 2/ Residual 2) r2 ey2 false positive rate true positive rate X: 0.01333 Y: 1 X: 0.0304 Y: 1 W=30 W=50 W=70 optimal point (0,1) ROC Curve for Residual r1ey1 ROC Curve for Residual r2ey2 false positive rate false positive rate true positive rate true positive rate optimal point for 𝜸=28.05 and 𝑾=70 optimal point for 𝜸=35.31 and 𝑾=70
53 Conclusion and Future Perspectives
54 Proposed Fault Diagnosis Algorithm Output variables Input variables Power Converter System Bank of Kalman Filters GLR Test Residuals 𝒓𝟏, 𝒓𝟐 Decision 𝒈(𝒕) ≷ 𝜸 Fault/No fault Tuning of W Tuning of 𝜸 ROC curve Detection function 𝒈(𝒕) Residual Generation Residual Evaluation
55 Conclusion “Combining several disciplines to achieve an efficient comprehensive fault diagnosis scheme” Battery PM UC DC/DC Converter Inverter DC bus sensor faults
56 Conclusion GLR Test + + Model-based Residual generation Power Converter Process ROC Curves
57 « Power electronics interface configurations for hybrid energy storage in hybrid electric vehicles » 17th IEEE MELECON’14 Mediterranean Electrotechnical Conference « Modeling, design and fault analysis of bidirectional DC-DC converter for hybrid electric vehicles » 23rd IEEE ISIE’14 International Symposium on Industrial Electronics « Study on power converters used in hybrid vehicles with monitoring and diagnostics techniques » 17th IEEE MELECON’14 Mediterranean Electrotechnical Conference « Condition Monitoring of Bidirectional DC-DC Converter for Hybrid Electric Vehicles » 22nd MED’14 Mediterranean Conference on Control & Automation
58 « A Sensor fault diagnosis scheme for a DC/DC converter used in hybrid electric vehicles » 9th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes SAFEPROCESS'15
59 Future Perspectives Future work will utilize the proposed model-based approach to detect/diagnose component faults in the converter such as  open-circuited transistor  short-circuited diode  degraded capacitor
60 Thank you

More Related Content

PPTX
A single loop repetitive voltage controller for a four
Wesam Rohouma
 
PPT
PPT FINAL (1)-1 (1).ppt
tariqqureshi33
 
PDF
type2_and_type3_compensator_analysis_for_power_supplies.pdf
mohsen salehi
 
PDF
Modelling design and control of an electromechanical mass lifting system usin...
Mustefa Jibril
 
PDF
Modified Bidirectional Converter with Current Fed Inverter
IJPEDS-IAES
 
PPTX
CONTROL MATLAB1.pptx
KASHYAPMOKARIYA1
 
PDF
Chapter 6 - Modelling and Control of Converters.pdf
benson215
 
PPT
14 activefilters
You-Yin Chen
 
A single loop repetitive voltage controller for a four
Wesam Rohouma
 
PPT FINAL (1)-1 (1).ppt
tariqqureshi33
 
type2_and_type3_compensator_analysis_for_power_supplies.pdf
mohsen salehi
 
Modelling design and control of an electromechanical mass lifting system usin...
Mustefa Jibril
 
Modified Bidirectional Converter with Current Fed Inverter
IJPEDS-IAES
 
CONTROL MATLAB1.pptx
KASHYAPMOKARIYA1
 
Chapter 6 - Modelling and Control of Converters.pdf
benson215
 
14 activefilters
You-Yin Chen
 

Similar to 3271829.ppt (20)

PPTX
OPERATION & MAINTENANCE OF 33/11 kV SUBSTATION AT DHAKA PALLI BIDYUT SAMITY-1
md muzahidul haque
 
PPT
ECE4762011_Lect10.ppt
ThomasNikola
 
PPT
ECE4762011_Lect10.ppt
babu717541
 
PDF
IRJET- Auto Selection of any Available Phase, in Three Phase Supply System
IRJET Journal
 
PPTX
Anees ppt on hcc
anees ansari
 
PDF
G1013238
IJERD Editor
 
PDF
5 Filter Part 2 (Lecture 19, 20, 21, 22).pdf
deepaanmol2002
 
PDF
Power System Analysis and Design
ZainUlAbdeen41
 
PDF
17.pmsm speed sensor less direct torque control based on ekf
Mouli Reddy
 
PDF
Modelling design and control of an electromechanical mass lifting system usin...
Mustefa Jibril
 
PPTX
Power Electronics - Phase Controlled Converters.pptx
Poornima D
 
PDF
REPORT
Anirudh Shandilya
 
PDF
Journal jpe 7-3_1299739594
Venkat Mandadhi
 
PPT
Ahmed.ppt
Safwanazb
 
PDF
PG Project
mathew1122
 
PDF
Work
mathew1122
 
PDF
International Journal of Engineering Research and Development
IJERD Editor
 
PDF
A Predictive Control Strategy for Power Factor Correction
IOSR Journals
 
PDF
15 47-58
idescitation
 
PPTX
15.02.2024.pptxDSD3E23DSDSDQWE23EWQDSDSDQWD
suresh386785
 
OPERATION & MAINTENANCE OF 33/11 kV SUBSTATION AT DHAKA PALLI BIDYUT SAMITY-1
md muzahidul haque
 
ECE4762011_Lect10.ppt
ThomasNikola
 
ECE4762011_Lect10.ppt
babu717541
 
IRJET- Auto Selection of any Available Phase, in Three Phase Supply System
IRJET Journal
 
Anees ppt on hcc
anees ansari
 
G1013238
IJERD Editor
 
5 Filter Part 2 (Lecture 19, 20, 21, 22).pdf
deepaanmol2002
 
Power System Analysis and Design
ZainUlAbdeen41
 
17.pmsm speed sensor less direct torque control based on ekf
Mouli Reddy
 
Modelling design and control of an electromechanical mass lifting system usin...
Mustefa Jibril
 
Power Electronics - Phase Controlled Converters.pptx
Poornima D
 
REPORT
Anirudh Shandilya
 
Journal jpe 7-3_1299739594
Venkat Mandadhi
 
Ahmed.ppt
Safwanazb
 
PG Project
mathew1122
 
Work
mathew1122
 
International Journal of Engineering Research and Development
IJERD Editor
 
A Predictive Control Strategy for Power Factor Correction
IOSR Journals
 
15 47-58
idescitation
 
15.02.2024.pptxDSD3E23DSDSDQWE23EWQDSDSDQWD
suresh386785
 
Ad

More from AhmedHeskol2 (8)

PPTX
How to Connect UltraSonic with ArduinoAVR MCU Course 18.pptx
AhmedHeskol2
 
PPT
5732650.ppt
AhmedHeskol2
 
PPT
Sigma Mini Presentation.ppt
AhmedHeskol2
 
PPT
7694441.ppt
AhmedHeskol2
 
PPT
10408668.ppt
AhmedHeskol2
 
PPT
10731617.ppt
AhmedHeskol2
 
PPT
dokumen.tips_vhdl-0-introduction-to-vhdl.ppt
AhmedHeskol2
 
PPT
5150174.ppt
AhmedHeskol2
 
How to Connect UltraSonic with ArduinoAVR MCU Course 18.pptx
AhmedHeskol2
 
5732650.ppt
AhmedHeskol2
 
Sigma Mini Presentation.ppt
AhmedHeskol2
 
7694441.ppt
AhmedHeskol2
 
10408668.ppt
AhmedHeskol2
 
10731617.ppt
AhmedHeskol2
 
dokumen.tips_vhdl-0-introduction-to-vhdl.ppt
AhmedHeskol2
 
5150174.ppt
AhmedHeskol2
 
Ad

Recently uploaded (20)

PPT
INTRODUCTION -Data Warehousing and Mining-M.Tech- VTU.ppt
Subramanyam Natarajan
 
PPTX
ASME PCC-02 TRAINING -DESKTOP-NLE5HNP.pptx
laxmikantvjawale
 
PDF
EXPLORING LEARNING ENGAGEMENT FACTORS INFLUENCING BEHAVIORAL, COGNITIVE, AND ...
johnmathew9417
 
PDF
PREDICTION OF DIABETES FROM ELECTRONIC HEALTH RECORDS
ijaia
 
PDF
Exploratory_Data_Analysis_Fundamentals.pdf
Ashutosh Satapathy
 
PPTX
CyberSecurity Mobile and Wireless Devices
RobinRohit2
 
PPTX
Information Storage and Retrieval Techniques Unit III
jeyamohan2519
 
PPTX
AUTOMOTIVE ENGINE MANAGEMENT (MECHATRONICS).pptx
joanaabban6
 
PDF
Artificial Superintelligence (ASI) Alliance Vision Paper.pdf
SonaliPatil325517
 
PPTX
tack Data Structure with Array and Linked List Implementation, Push and Pop O...
usham61
 
PPTX
Graph Data Structures with Types, Traversals, Connectivity, and Real-Life App...
usham61
 
PPTX
Chemical Technological Processes, Feasibility Study and Chemical Process Indu...
Raihan87
 
PPTX
Fundamentals of Mechanical Engineering.pptx
MdMowdudAhmed
 
PPTX
Management Information system : MIS-e-Business Systems.pptx
ShankarGowda22
 
PDF
Accra-Kumasi Expressway - Prefeasibility Report Volume 1 of 7.11.2018.pdf
JeorgeWilsonKingson1
 
PPTX
Current and future trends in Computer Vision.pptx
Subramanyam Natarajan
 
PPTX
6ME3A-Unit-II-Sensors and Actuators_Handouts.pptx
anukaranugi
 
PDF
Improvement effect of pyrolyzed agro-food biochar on the properties of.pdf
LasFernandaJuchem
 
PPTX
Feature types and data preprocessing steps
deepalishinkar1
 
PPTX
communication and presentation skills 01
CdtAfrazAttaullah
 
INTRODUCTION -Data Warehousing and Mining-M.Tech- VTU.ppt
Subramanyam Natarajan
 
ASME PCC-02 TRAINING -DESKTOP-NLE5HNP.pptx
laxmikantvjawale
 
EXPLORING LEARNING ENGAGEMENT FACTORS INFLUENCING BEHAVIORAL, COGNITIVE, AND ...
johnmathew9417
 
PREDICTION OF DIABETES FROM ELECTRONIC HEALTH RECORDS
ijaia
 
Exploratory_Data_Analysis_Fundamentals.pdf
Ashutosh Satapathy
 
CyberSecurity Mobile and Wireless Devices
RobinRohit2
 
Information Storage and Retrieval Techniques Unit III
jeyamohan2519
 
AUTOMOTIVE ENGINE MANAGEMENT (MECHATRONICS).pptx
joanaabban6
 
Artificial Superintelligence (ASI) Alliance Vision Paper.pdf
SonaliPatil325517
 
tack Data Structure with Array and Linked List Implementation, Push and Pop O...
usham61
 
Graph Data Structures with Types, Traversals, Connectivity, and Real-Life App...
usham61
 
Chemical Technological Processes, Feasibility Study and Chemical Process Indu...
Raihan87
 
Fundamentals of Mechanical Engineering.pptx
MdMowdudAhmed
 
Management Information system : MIS-e-Business Systems.pptx
ShankarGowda22
 
Accra-Kumasi Expressway - Prefeasibility Report Volume 1 of 7.11.2018.pdf
JeorgeWilsonKingson1
 
Current and future trends in Computer Vision.pptx
Subramanyam Natarajan
 
6ME3A-Unit-II-Sensors and Actuators_Handouts.pptx
anukaranugi
 
Improvement effect of pyrolyzed agro-food biochar on the properties of.pdf
LasFernandaJuchem
 
Feature types and data preprocessing steps
deepalishinkar1
 
communication and presentation skills 01
CdtAfrazAttaullah
 

3271829.ppt

  • 1. A Sensor Fault Diagnosis Scheme for a DC/DC Converter used in Hybrid Electric Vehicles Hiba Al-SHEIKH Ghaleb HOBLOS Nazih MOUBAYED
  • 2. 2 Overview  Examined power converter system  Hardware prototype  Converter Modelling  Proposed residual-based fault diagnosis scheme  Bank of extended Kalman filters  Generalized likelihood ratio test  Tuning using receiver operating characteristic curve  Conclusion and future perspectives
  • 3. 3 Recent advances in power electronics encouraged the development of new initiatives for Hybrid Electric Vehicles (HEVs) with advanced multi-level power electronic systems. Power converters are intensively used in HEVs • convert power at different levels • drive various load • electric drives
  • 4. 4 Intensive use of power converters in modern hybrid vehicles Need for efficient methods of condition monitoring and fault diagnosis Reliability of the automotive electrical power system
  • 5. 5 Controller Power Converters Sensors Machine AC Side Common Electrical Faults in Electric Drive Systems Connectors/ DC Bus Power Converters • high power • relatively low voltage high current increase thermal and electric stresses on the converter components and monitoring sensors
  • 6. 6 Controller Power Converters Sensors Machine AC Side Common Electrical Faults in Electric Drive Systems Connectors/ DC Bus • AC current sensor • DC bus voltage sensor Power Converters Sensors Sensor faults in a DC/DC power converter system used in HEV
  • 7. 7 7 Observer-based Fault diagnosis methods Knowledge-based methods Analytical model-based methods Signal-based methods Fault Diagnosis Techniques for Power Converters Analytical model-based methods For HEV applications where converters operate under variable load conditions, model-based diagnosis is of particular interest.
  • 9. 9 Automotive Electrical System DC Main System DC Distribution AC Distribution
  • 10. 10 Power Converters  DC/DC Choppers  DC/AC Inverters  AC/DC Rectifiers Automotive Electrical System
  • 11. 11
  • 12. 12
  • 13. 13 Parallel DC-linked Multi-input DC/DC Converter consisting of two bidirectional half-bridge cells DC bus Energy Storage System AC Drive Battery PM UC Multi-port DC/DC Converter Inverter Examined Power Converter System
  • 14. 14 Isolated topologies boost-half bridge half-bridge full-bridge Non-isolated topologies SEPIC cuk buck-boost Bidirectional DC/DC Converter Topologies
  • 15. 15 Source voltage 200V DC-link voltage 300V Rated Power 30kW Switching frequency 15kHz Source voltage ripple 2% p/p DC-link voltage ripple 4.5% p/p Inductor current ripple ±10% Design Requirements Examined Power Converter System Converter Parameters Parameter Symbol Value Input Capacitance Cin 80µF Input Capacitor ESR RCin 100mΩ Inductance L 146µH Inductor ESR RL 5mΩ Output Capacitance Co 5mF Output Capacitor ESR RCo 80mΩ Transistor ON resistance RON 1mΩ
  • 16. 16 Examined Power Converter System State variables 𝑣𝐶𝑖𝑛, 𝑖𝐿, 𝑣𝐶𝑜 s (duty cycle)
  • 17. 17 0 0.02 0.04 0.06 0.08 0.1 194 196 198 200 x 1 =v Cin 0 0.02 0.04 0.06 0.08 0.1 0 200 400 600 x 2 =i L 0 0.02 0.04 0.06 0.08 0.1 100 200 300 400 time (sec) x 3 =v Co 0 0.02 0.04 0.06 0.08 0.1 0 200 400 600 y 1 =i in 0 0.02 0.04 0.06 0.08 0.1 150 200 250 300 350 y 2 =v o time (sec) State variables during healthy boost operation Observed variables during healthy boost operation
  • 18. 18 Hardware Prototype of Converter System
  • 19. 19 Hardware Prototype Experimental test bench Hardware prototype of bidirectional DC/DC converter
  • 20. 20 Measurement of sensor 1 (measuring load voltage 𝒗𝒐) Measurement of sensor 2 (measuring source current 𝒊𝒊𝒏) Hardware Prototype
  • 21. 21 Sensor 2 Sensor 1 Hardware Prototype
  • 23. 23 Converter State-Space Model  The examined converter is a nonlinear and time-varying system DC bus Battery PM UC Multi-input DC/DC Converter Inverter Boost operation
  • 24. 24 Converter State-Space Model  The examined converter is a nonlinear and time-varying system DC bus Battery PM UC Multi-input DC/DC Converter Inverter Buck operation
  • 25. 25 Converter State-Space Model  The examined converter is a nonlinear and time-varying system  The converter state-space model is obtained in three steps: 1. Piece-wise linear state-space model 2. Continuous-time nonlinear state-space model 3. Discrete-time nonlinear state-space model
  • 26. 26 Switching configuration 2 (T1 OFF; D2 ON) Switching configuration 2 (T2 OFF; D1 ON) Switching configuration 1 (T1 ON; D2 OFF) Switching configuration 1 (T2 ON; D1 OFF) Converter State-Space Model Boost mode Buck mode 1. During each switching configuration, the converter is linear and possesses a piece-wise switched linear state-space model
  • 27. 27 Converter State-Space Model 1. During each switching configuration, the converter is linear and possesses a piece-wise switched linear state-space model 𝒙 = 𝐀𝐢 𝐣 𝒙 + 𝐁𝐢 𝐣 𝒖 𝒚 = 𝐂𝐢 𝐣 𝒙 + 𝐃𝐢 𝐣 𝒖 Operation Mode Switching State T1 D1 T2 D2 j = 1 (Boost) i = 1 ON OFF OFF OFF i = 2 OFF OFF OFF ON j = 2 (Buck) i = 1 OFF OFF ON OFF i = 2 OFF ON OFF OFF
  • 28. 28 Converter State-Space Model Operation Mode Switching State T1 D1 T2 D2 j = 1 (Boost) i = 1 ON OFF OFF OFF i = 2 OFF OFF OFF ON j = 2 (Buck) i = 1 OFF OFF ON OFF i = 2 OFF ON OFF OFF 𝐀𝐚𝐯 𝐣 = 𝐀𝟏 𝐣 𝑑 + 𝐀𝟐 𝐣 1 − 𝑑 𝐁𝐚𝐯 𝐣 = 𝐁𝟏 𝐣 𝑑 + 𝐁𝟐 𝐣 1 − 𝑑 𝐂𝐚𝐯 𝐣 = 𝐂𝟏 𝐣 𝑑 + 𝐂𝟐 𝐣 1 − 𝑑 𝐃𝐚𝐯 𝐣 = 𝐃𝟏 𝐣 𝑑 + 𝐃𝟐 𝐣 1 − 𝑑 where averaged using 𝒅 as control variable 2. Averaged continuous-time model 𝒙 = 𝐀𝐚𝐯 𝐣 𝒙 𝒙 + 𝐁𝐚𝐯 𝐣 𝒙 𝒖 𝒚 = 𝐂𝐚𝐯 𝐣 𝒙 𝒙 + 𝐃𝐚𝐯 𝐣 𝒙 𝒖
  • 29. 29 Converter State-Space Model 2. Averaged continuous-time model  The continuous-time model is nonlinear  The duty cycle is a function of the state variables, 𝒅 = 𝑓(𝒙)  𝑓 is obtained from the converter dynamics during steady state 𝒙 = 𝐀𝐚𝐯 𝐣 𝒙 𝒙 + 𝐁𝐚𝐯 𝐣 𝒙 𝒖 𝒚 = 𝐂𝐚𝐯 𝐣 𝒙 𝒙 + 𝐃𝐚𝐯 𝐣 𝒙 𝒖 𝐀𝐚𝐯 𝐣 = 𝐀𝟏 𝐣 𝑑 + 𝐀𝟐 𝐣 1 − 𝑑 𝐁𝐚𝐯 𝐣 = 𝐁𝟏 𝐣 𝑑 + 𝐁𝟐 𝐣 1 − 𝑑 𝐂𝐚𝐯 𝐣 = 𝐂𝟏 𝐣 𝑑 + 𝐂𝟐 𝐣 1 − 𝑑 𝐃𝐚𝐯 𝐣 = 𝐃𝟏 𝐣 𝑑 + 𝐃𝟐 𝐣 1 − 𝑑 where
  • 30. 30                                            0 1 0 1 1 0 1 o iCin iCin Co iCin ON iCin L Cin in iCin in iCin in in iCin in C f L f LR R R f R R f R R R R LR R R C R R C x x x x x Aav                            o Co iCin Cin iCin in C L R f LR R R C 1 0 1 0 1 x x Bav                  1 1 0 0 1 Co iCin Cin iCin R f R R R x x Cav           Co iCin R R 0 0 1 av D . Converter State-Space Model
  • 31. 31 Converter State-Space Model 3. The continuous-time model is discretized using first order hold with sampling period 𝑇 = 1𝜇 seconds.  Including process noise and measurement noise, the discrete-time state- space model becomes  𝒘 and 𝒗 are white Gaussian, zero-mean, independent random processes with constant auto-covariance matrices Q and R. 𝒙 𝑘 + 1 = 𝐀𝐝 𝐣 𝒙 𝒙 𝑘 + 𝐁𝐝 𝐣 𝒙 𝒖 𝑘 + 𝒘 𝑘 𝒚 𝑘 = 𝐂𝐝 𝐣 𝒙 𝒙 𝑘 + 𝐃𝐝 𝐣 𝒙 𝒖 𝑘 + 𝒗 𝑘
  • 33. 33 Fault Diagnosis of Converter Sensor Faults Sensor 2 Sensor 1 Model-Based Residual Approach
  • 35. 35 Residual Generation using Bank of Extended Kalman Filters
  • 36. 36 Converter state- space model + + Converter input signals Sensor measured signals The Extended Kalman Filter (EKF) Estimates of the measured signals + - Residual signals “Innovations”
  • 37. 37 The Extended Kalman Filter (EKF)  Recursive application of prediction and correction cycles  At the end of sampling period, the nonlinearity of the converter system is approximated by a linear model around the last predicted and corrected estimate
  • 38. 38 The EKF Algorithm Initialization 𝑘 = 0, 𝐱 0|0 = 𝑬 𝐱(𝟎) and P 0|0 = P(0) Prediction Cycle 𝐱(𝑘 + 1|𝑘) = 𝐀𝐝 x(𝑘|𝑘) x(𝑘|𝑘) + 𝐁𝐝 x(𝑘|𝑘) 𝑢(𝑘) 𝐏(𝑘 + 1|k) = 𝐀𝐣(𝑘)𝐏(𝑘|𝑘)𝐀𝐣 𝐓 (k) + 𝐐 𝐲 𝑘 + 1|𝑘 = 𝐂𝐝 x 𝑘 + 1 𝑘 𝐱(𝑘 + 1|𝑘) + 𝐃𝐝𝑢(𝑘) where 𝐀𝐣(𝑘) is the jacobian matrix of 𝐀𝐝 x(𝑘|𝑘) x(𝑘|𝑘) Correction Cycle A new measurement is obtained 𝑦 𝑘 + 1 𝐱(𝑘 + 1|𝑘 + 1) = 𝐱(k + 1|𝑘) + 𝐊 𝑘 + 1 𝐫(𝑘 + 1) 𝐏 𝑘 + 1|𝑘 + 1 = I − 𝐊 𝑘 + 1 𝐂𝐣 𝑘 + 1 𝐏 𝑘 + 1|𝑘 where 𝐊(𝑘 + 1) = 𝐏(𝑘 + 1|𝑘)𝐂𝐣 𝐓 (𝑘 + 1) 𝐂𝐣 𝑘 + 1 𝐏 k + 1 𝑘 𝐂𝐣 𝐓 (k + 1) + 𝐑 −1 𝐫 𝑘 + 1 = 𝐲 𝑘 + 1 − 𝐲 𝑘 + 1|𝑘 𝐂𝐣(𝑘) is the jacobian matrix of 𝐂𝐝 x(𝑘|𝑘) x(𝑘|𝑘) 𝒌 increments Prediction and correction repeat with corrected estimates used to predict new estimates
  • 39. 39 0 0.01 0.02 0.03 0.04 0.05 0 100 200 300 400 Observer 1 time (s) Residual r 1 e y1 0 0.01 0.02 0.03 0.04 0.05 -50 0 50 100 150 time (s) Residual r 1 e y2 0 0.01 0.02 0.03 0.04 0.05 -100 0 100 200 300 Observer 2 time (s) Residual r 2 e y1 0 0.01 0.02 0.03 0.04 0.05 -4 -2 0 2 4 time (s) Residual r 2 e y2 Residuals Generated by the Bank of EKF Instant of fault Standardized residuals with fault on sensor 1 occurring at 0.03s
  • 40. 40 0 0.01 0.02 0.03 0.04 0.05 -4 -2 0 2 4 Observer 1 time (s) Residual r 1 e y1 0 0.01 0.02 0.03 0.04 0.05 0 500 1000 1500 2000 2500 time (s) Residual r 1 e y2 0 0.01 0.02 0.03 0.04 0.05 0 500 1000 1500 2000 2500 Observer 2 time (s) Residual r 2 e y1 0 0.01 0.02 0.03 0.04 0.05 0 500 1000 1500 2000 2500 time (s) Residual r 2 e y2 Standardized residuals with fault on sensor 2 occurring at 0.03s Instant of fault Residuals Generated by the Bank of EKF
  • 41. 41 Residuals Generated by the Bank of EKF Advantage of Kalman Filtering  independent residuals  with white Gaussian, zero-mean and unit-covariance characteristics  in case of faultless operation  with altered statistical characteristics  in case of sensor faults Statistical change detection approaches
  • 43. 43 Residuals Evaluation Approaches  Statistical data processing  Correlation  Pattern recognition  Fuzzy logic  Fixed threshold  Adaptive threshold Stochastic envirmonent Likelihood ratio tests Generalized Likelihood Ratio (GLR) Test
  • 44. 44 Residuals Evaluation using GLR Test  sensor is faultless  residuals are Gaussain with 𝜇0 = 0 and 𝜎0 2 = 1  sensor is faulty  𝜇0 is altered into 𝜇1 and 𝜎0 2 into 𝜎1 2 Statistical Hypothesis Testing Problem Ho and H1
  • 45. 45 Statistical Hypothesis Testing Problem Ho and H1 Residuals Evaluation using GLR Test  Maximizing the likelihhod ratio  𝜇1 is the Maximum Likelihood Estimate (MLE) of 𝜇1  𝜇0 is the MLE of 𝜇0               o o y y y H e p H e p e L i i i , ˆ ; , ˆ ; ln 1 1  
  • 46. 46 At every time step t Apply the GLR statistic on the recent W residual values Generate a detection function 𝑔 𝑡 = 𝑚𝑎𝑥 𝐺𝐿𝑅𝑡(𝑘) for each residual Is residual variance known? Evaluate 𝐺𝐿𝑅𝑡(𝑘) for all 1 ≤ 𝑘 ≤ 𝑊 using   2 ) ( 2         k x k k GLR t t Evaluate 𝐺𝐿𝑅𝑡(𝑘) for all 1 ≤ 𝑘 ≤ 𝑊 using                     2 ) ( ) ( 1 ln 2 k k x k k GLR t t t  Is 𝑔(𝑡) > 𝛾? Decide H1 (fault) Decide H0 (No fault) Yes No Yes No GLR Algorithm
  • 47. 47 Detection Function Generated by GLR Test Detection function with fault on sensor 1 0 0.01 0.02 0.03 0.04 0.05 0 100 200 300 Residual r1 ey1 0 0.01 0.02 0.03 0.04 0.05 -5 0 5 Residual r2 ey2 0 0.01 0.02 0.03 0.04 0.05 0 10 20 30 GLRt for r1 ey1 0 0.01 0.02 0.03 0.04 0.05 0 10 20 GLRt for r2 ey2 0 0.01 0.02 0.03 0.04 0.05 0 10 20 30 time (s) GLRt for r1 ey1 0 0.01 0.02 0.03 0.04 0.05 0 10 20 time (s) GLRt for r2 ey2 instant of fault  unknown  known  known  unknown
  • 48. 48 Detection Function Generated by GLR Test Detection function with fault on sensor 2 0 0.01 0.02 0.03 0.04 0.05 -5 0 5 Residual r1 ey1 0 0.01 0.02 0.03 0.04 0.05 0 2 4 GLRt for r1 ey1 0 0.01 0.02 0.03 0.04 0.05 0 20 40 GLRt for r2 ey2 0 0.01 0.02 0.03 0.04 0.05 0 1 2 3 time (s) GLRt for r1 ey1 0 0.01 0.02 0.03 0.04 0.05 0 20 40 time (s) GLRt for r2 ey2 0 0.01 0.02 0.03 0.04 0.05 0 1000 2000 Residual r2 ey2 instant of fault  unknown  known  unknown  known
  • 49. 49 Tuning using Receiver Operating Characteristic Curve
  • 50. 50 false positives rate (tpr) true positives rate (fpr) (0, 0) (1, 1) as 𝛾 increase 0 1 1 + optimal 𝛾 ROC Analysis An evaluation tool to measure the performance of the residual- based GLR test.
  • 51. 51 Three ROC Plots:  W = 30  For each W, 𝛾 is varied from 0 to 𝛾𝑚𝑎𝑥  For each 𝛾, a test set of 1000 simulations is used  Healthy and faulty trials  During faulty trials, different fault amplitudes were injected  At the end of every trial, the detection function 𝑔 𝑡 is generated using 𝐺𝐿𝑅𝑡 and compared the corresponding 𝛾  At the end of the 1000 trials, the tpr and fpr are calculated and the corresponding point is located on the ROC curve. ROC Analysis  W = 50  W = 70
  • 52. 52 -1 0 1 2 3 4 5 6 7 x 10 -3 0.97 0.98 0.99 1 28.17 28.16 28.14 28.13 28.11 28.1 28.08 28.05 21.56 21.24 20.31 19.8 17.88 17.63 17.39 16.35 14.9 14.8 14.49 0 5 10 15 20 x 10 -3 0.94 0.96 0.98 1 X: 0 Y: 1 ROC curve (Observer 1/ Residual 1) r1 ey1 false positive rate true positive rate X: 0.002894 Y: 1 X: 0.0152 Y: 0.9942 W=30 W=50 W=70 optimal point (0,1) -1 0 1 2 3 4 5 6 7 x 10 -3 0 0.2 0.4 0.6 0.8 1 35.35 35.34 35.33 35.32 35.31 34.83 31.43 30.66 29.71 -0.005 0 0.005 0.01 0.015 0.02 0.025 0.03 0.035 0.04 0.6 0.7 0.8 0.9 1 X: 0 Y: 1 ROC curve (Observer 2/ Residual 2) r2 ey2 false positive rate true positive rate X: 0.01333 Y: 1 X: 0.0304 Y: 1 W=30 W=50 W=70 optimal point (0,1) ROC Curve for Residual r1ey1 ROC Curve for Residual r2ey2 false positive rate false positive rate true positive rate true positive rate optimal point for 𝜸=28.05 and 𝑾=70 optimal point for 𝜸=35.31 and 𝑾=70
  • 53. 53 Conclusion and Future Perspectives
  • 54. 54 Proposed Fault Diagnosis Algorithm Output variables Input variables Power Converter System Bank of Kalman Filters GLR Test Residuals 𝒓𝟏, 𝒓𝟐 Decision 𝒈(𝒕) ≷ 𝜸 Fault/No fault Tuning of W Tuning of 𝜸 ROC curve Detection function 𝒈(𝒕) Residual Generation Residual Evaluation
  • 55. 55 Conclusion “Combining several disciplines to achieve an efficient comprehensive fault diagnosis scheme” Battery PM UC DC/DC Converter Inverter DC bus sensor faults
  • 57. 57 « Power electronics interface configurations for hybrid energy storage in hybrid electric vehicles » 17th IEEE MELECON’14 Mediterranean Electrotechnical Conference « Modeling, design and fault analysis of bidirectional DC-DC converter for hybrid electric vehicles » 23rd IEEE ISIE’14 International Symposium on Industrial Electronics « Study on power converters used in hybrid vehicles with monitoring and diagnostics techniques » 17th IEEE MELECON’14 Mediterranean Electrotechnical Conference « Condition Monitoring of Bidirectional DC-DC Converter for Hybrid Electric Vehicles » 22nd MED’14 Mediterranean Conference on Control & Automation
  • 58. 58 « A Sensor fault diagnosis scheme for a DC/DC converter used in hybrid electric vehicles » 9th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes SAFEPROCESS'15
  • 59. 59 Future Perspectives Future work will utilize the proposed model-based approach to detect/diagnose component faults in the converter such as  open-circuited transistor  short-circuited diode  degraded capacitor

Editor's Notes

  • #3: I will proceed
  • #6: Failures can occur almost anywhere in automotive electrical power systems, however, converters used in electric traction systems undergo some of the highest stresses. The converter high power and relatively low voltage (hundreds of volts) cause high currents (hundreds of amperes) which increase thermal and electric stresses on the converter components and monitoring sensors
  • #7: This work deals with sensor faults in a high power bidirectional DC/DC converter used in HEVs. The aim is to design a comprehensive diagnostic approach to detect and isolate ……
  • #8: In general, for power electronic converters, reported fault diagnosis methods in literature can be categorized into knowledge-based, signal-based and model-based techniques. Nevertheless, for HEV applications where power converters operate under variable load conditions, model-based is of particular interest. In particular, observer-based methods are most commonly used for the detection of sensor faults in dynamic processes.
  • #9: Before describing our proposed observer-based fault diagnosis scheme; lets first examine the power electronics system under study.
  • #10: In general, the automotive electrical system consists of a DC main system and hybrid DC and AC distributions. With such architecture the use of power electronic converters is essential onboard of the HEV
  • #11: For this purpose a HEV contains choppers, inverters and possibly rectifiers
  • #12: This figure shows the main electric power architecture in a series HEV. So basically, there are two bidirectional DC/DC converters, two inverters and a rectifier.
  • #13: Our work focuses on the main electric subsystem marked in red as it contains the main power converters controlling electric traction. In addition, the majority of faults that affect the electric powertrain appear in this subsystem. In particular, we are interested in the DC/DC converter in this subsystem.
  • #14: Our examined system is a multi-port bidirectional DC/DC converter interfacing a HESS composed of a battery unit and an UltraCapacitor (UC) pack and the AC drive which consists of a three-phase bridge voltage source inverter and a permanent magnet synchronous motor in a HEV. Our converter is a ….
  • #15: There exist several DC/DC converter topologies for the bidirectional interface of energy/power sources in HEVs.
  • #16: Sizing of the converter components was done based on the requirements of a HEV with …… Accordingly the converter parameters were calculated as shown in this table.
  • #17: The examined converter is driven by three inputs or controls; the source voltage, vin, the load current, io, and the duty cycle, d, which is used as a control variable that will appear inside the matrices of the state-space model rather than in the input vector. The converter state variables are the inductor current iL, the voltage across the input capacitor, vCin, and the voltage across the output capacitor, vCo. The observed or output variables are the source current, iin, and the load voltage, vo, which are usually measured in the electric drive for control purposes.
  • #18: The converter operation during flawless operation is illustrated using Matlab/Simulink. vin and io are assumed constant with values 200V and 100A respectively. In order to obtain real data measurements of the observed signals, to be used in the proposed fault diagnosis scheme, …….
  • #19: a hardware prototype of the power converter system is realized.
  • #20: Due to safety reasons and cost limitations, the voltage and current ratings of the converter prototype are attained at 20 times reduced scale. The input and output voltages and currents are measured by a DAQ device (NI USB 6008) from National Instruments with a 12-bit resolution and the resulting values are displayed and saved via Labview.
  • #22: To inject a fault on these measurements, the voltage and current signals are artificially degraded using biasing circuits. The Labview program, the DAQ and the PIC microcontroller cooperate to control the converter circuit and the injected fault as shown in Fig. 4. At the end of the experiment, a log file of the measured voltages and currents is generated for use as input data to the EKF algorithm.
  • #24: The power converter system is nonlinear and time-varying due to the fact that it contains switches which alter the system topology with every commutation mode.
  • #27: As we have said, our power converter system is nonlinear nevertheless,
  • #29: For each of the boost and buck modes, a continuous-time state-space model can be obtained by taking a linearly weighted average of the state equations in both states. Accordingly, the averaged matrices are obtained from the piecewise-switched matrices using the duty cycle as a control variable.
  • #30: The resulting continuous average model is nonlinear basically because
  • #32: Finally,
  • #33: Now that we have a prototype and a model ready of the examined system, we can design our
  • #34: The proposed fault diagnosis system is based on a residual approach capable of detecting and isolating faults on the converter sensors
  • #35: This is mainly achieved in two stages, a residual generation stage and a residual evaluation stage. The first stage is based on a state estimation approach, specifically the EKF. Residuals of measured observations are generated by employing a bank of Extended Kalman Filters (EKF) on a stochastic nonlinear model of the converter. The Generalized Likelihood Ratio (GLR) test is used as a statistical change detection method to evaluate the residuals and generate a detection function which is compared with a decision threshold to detect the occurrence of a fault (Gustafsson, 2007; Harrou et al., 2013; Seo et al., 2009). The Receiver Operating Characteristic (ROC) curve is then used to tune the detection threshold value and sliding window width of the statistical test in order to achieve maximum correct detection and minimum false alarm rates.
  • #37: The EKF estimates the converter measured signals based on knowledge of the input signals, the observed measurements and the system state-space model. A so-called innovation signal or output residual is generated from comparison between the estimated output and the real measurement.
  • #38: The predictor-corrector version of Kalman Filter is used. Estimation of the measured signals is achieved through …
  • #42: The advantage of Kalman filtering over other estimation or identification approaches is its ability to generate ….. Which when standardized
  • #44: Residual evaluation can be done in several ways such as statistical data processing, correlation, pattern recognition, fuzzy logic, fixed threshold, or adaptive thresholds depending whether a deterministic or stochastic environment is assumed. In a stochastic setting, it is common to use statistical approaches; in particular likelihood ratio tests. In this work, the GLR test is used in a statistical hypothesis testing framework to detect changes in the residuals due to a fault.
  • #46: The origin of the GLR test resides in maximizing the likelihood ratio L of the probability distributions of the faulty and faultless residuals
  • #49: It is observed that at the instance of occurrence of a fault, the test statistic obtained using known residual variance grows exponentially into larger scores as compared to that assuming unknown residual variance which increases linearly. Moreover, for low threshold values, detection of faults occur earlier when assuming unknown σ than when assuming known σ. In the next section, ROC curves are generated based on the GLR statistic in (17) since when implementing the proposed algorithm in real-time applications, the residual variance is usually unknown and can only be calculated for previous time steps.
  • #51: The ROC plots the true positives rate as a function of the false positives rate for different threshold values
  • #55: This is mainly achieved in two stages, a residual generation stage and a residual evaluation stage. The first stage is based on a state estimation approach, specifically the EKF. Residuals of measured observations are generated by employing a bank of Extended Kalman Filters (EKF) on a stochastic nonlinear model of the converter. The Generalized Likelihood Ratio (GLR) test is used as a statistical change detection method to evaluate the residuals and generate a detection function which is compared with a decision threshold to detect the occurrence of a fault (Gustafsson, 2007; Harrou et al., 2013; Seo et al., 2009). The Receiver Operating Characteristic (ROC) curve is then used to tune the detection threshold value and sliding window width of the statistical test in order to achieve maximum correct detection and minimum false alarm rates.