Fault diagnosis and fault tolerant control for electrical drive
The most used solutions for the detection and the isolation of failures in electrical drives are the data-based and the observer-based methods. A non-exhaustive list of the main faults in an energy conversion chain is given in Table 5.
For converter faults, [Zhou2010] shows a post-fault control strategy for a hexagram inverter. The hexagram inverter motor drive system can realize real two-phase motor operation without modifying the power circuit topology, which is a distinct advantage over conventional threephase inverter motor drives or other multilevel motor drive systems. When an outer leg fails, since the three output currents in the hexagram motor drive system are independent, it is possible to precisely control the remaining two-phase currents.
For sensor faults, observer-based approach has shown his effectiveness [Akr2011] even if it requires robust observers. Observers are used with a reference model to generate residuals, which may lead to fault detection. However, the observer-based approach to the detection problem has a number of drawbacks. First, it requires the observability of the signal to diagnose (not always guaranteed) and an observer for the entire states variable because the correction part needs full states estimation. Second, while it is clear that in the absence of disturbances and faults, such a residual is typically zero, it is a fact that its dynamics is controlled in part by the fault and can lead to undetected faulty events. Several observers have been designed to track the rotor position, the speed, phase currents and the dc-link voltage. However, the operation of the observers in the whole speed range is still an open problem. Also, functional safety rules require uncorrelated sources for data reconstruction, which limits the reconfiguration options. Signal processing method performs time, frequency or time-frequency analysis to extract relevant features [Xue2013, Yin 2014]. Examples based on the behavior of the current-vector trajectory and the instantaneous frequency tracking in faulty mode can be found in [Dia2004, Delp2008].Neural network architecture with a selection of training parameters and trainingdata can also be used. The input space is defined as a feature space with the features extracted from torque, voltage and current signals in the electric drive system. For a given input feature vector, the neural network system generates the output vector and the diagnostic decision can be derived from this output vector using rules based on unknown faulty condition, normal operating condition, and known faulty condition.
Fault Tolerant Control or Design
For certain faults, the two-step classical approach, that consists in designing the process and then build the controller (including diagnosis and reconfiguration) has to be thought differently. So, in some cases, the question is how to design the component (electrical machine or inverter) so as to facilitate the diagnosis and improve the reconfiguration and availability capability. In aerospace applications, machines that have been designed with fault tolerance in mind are found in [Atk2005, Ram2011 and Ben2011]. The case of automotive (electric cars) industry is also mentioned by [Bia2003, Wel2004].
For the design of the actuators, increase the number of stator phases to achieve high reliability for the power stage has shown his effectiveness and in addition, a functional post-fault structure may lead to generate the references for the switching patterns [Bau2012]. Modern technological systems rely heavily on sophisticated control systems to meet safety requirements. This is particularly the case of safety critical applications such as automobile industry where a minor and often fault could potentially lead to unwanted events. To prevent fault induced losses and to minimize the potential risks, new control techniques and design approaches need to be developed to cope with system component malfunctions while maintaining a good limp mode.
Combined architecture for traction and battery charging
The objective of the SOFRACI (high efficiency Inverter with integrated charge function) project was to develop an innovative architecture for achieving the functions of traction, regeneration and charging [Sil2009]. To do so, a unique powertrain is used for the charging and the traction of the vehicle, allowing to:
– Reduce the global number of components.
– Integrate charging function without additional electronic component.
– Provide a limp mode to the user in case of critical faults.
Because the traction and charging modes cannot be simultaneous, the inductances of the machine and the power converter can be used to perform the battery charging. The level of current is enforced by the traction design, which requires high power to drive the vehicle; then it is also possible to sustain high power in charging mode. The power converter used for traction has already been used for battery charging [Shi1994]. However, high current relays were required to pass through one mode to the other. In addition, these electromechanical components increase the design complexity.
Figure 2.1 shows the proposed topology. In charging mode, the three-phase grid is connected to the midpoints a’’, b’’ and c’’ of each machine’s coils and no relay is necessary. It could be possible to connect the grid to the extremities a, b, c or a’, b’, c’ but the charging three-phase currents would generate an undesirable rotating magnetic field [Bru2010]. By connecting the grid to the midpoints of each phase, the current will be split in two equal and opposite components and therefore nullify the Magneto-Motive Force at the stator level. This cancellation ensures a magnetic decoupling between the rotor and the stator. Another consequence is that the same amount of current flows in each leg of the same bridge and is in phase with the 50Hz or 60Hz input voltage [Lac2013]. The ac current is therefore rectified to source the DC link (Figure 2.1b). Then each power switch can be sized with the half maximum current.
PMSM and Sensors
The energy conversion chain is composed of: an electrical machine (PMSM), a power supply, a power converter and sensors for the measurements required by the control loop. Synchronous motors with rare earth permanent magnets have higher power density than comparable DC motors because there is no limiting effect due to the mechanical commutator [Ref2006]. The SOFRACI electrical machine is a poly-phase Permanent Magnet Synchronous Machine (PMSM) designed by Leroy SOMER. The machine is a radial flux type and has been chosen for its robustness and reliability properties. It can develop high torque density and has the ability to reach high speeds [San2013]. This makes it practical to magnetise the pole pieces separately rather than magnetising the complete assembly and requires also much less space.
Table of contents :
Chapter I: State of the art of electrical drives fault tolerant control
1.1 Electrical drives in automotive systems
1.2 Fault Management
1.2.1 Fault Detection and Diagnosis principle
1.2.2 Fault Tolerant Control principle
1.3 Application to an electrical system
1.3.1 Fault types in electrical drive
126.96.36.199 Actuator faults
188.8.131.52 Sensor faults
184.108.40.206 Electrical Machine faults
1.3.2 Fault Diagnosis and Fault Tolerant Control for electrical drives
220.127.116.11 Fault Diagnosis
18.104.22.168 Fault Tolerant Control or Design
Chapter II: SOFRACI Platform modelling and description
2.1 SOFRACI Structure: A system designed for Fault Tolerance
2.1.1 Combined architecture for traction and battery charging
2.1.2 Diagnosis capabilities
2.2 Bench description
2.2.1 PMSM and Sensors
2.2.2 Power supply and Inverter
2.2.3 Real-time hardware implementation of the controller
2.3 PMSM modelling for control
2.3.1 Electrical equations
2.3.2 Mechanical equations
2.3.3 Nonlinear model for state space representation
2.4 Electrical Drive Control in traction mode
2.4.1 PI controller synthesis
22.214.171.124 Synthesis by identification to a 2nd order system
126.96.36.199 Speed Controller
2.4.2 3H bridge Inverter model
2.4.3 Simulation and Experimental results of the Torque Control
188.8.131.52 Simulation Results
184.108.40.206 Experimental Results
Chapter III: Position/Speed Sensor Fault Tolerant Control
3.1 Impact of a sensor failure on the PMSM Control
3.2 Position /speed Estimators and their various uses
3.2.1 Extended Kalman Filter
3.2.2 Back-EMF based Observer
3.2.3 High Frequency Signal Injection
3.3 New Estimator based on a Differential Algebraic Approach
3.3.1 Observation Principle
3.3.2 Differential Algebraic Estimation in PMSM
220.127.116.11 Position/Speed Estimator Synthesis
18.104.22.168 Stability Analysis
3.3.3 Position/Speed Estimation Results of the Differential Algebraic Estimator
22.214.171.124 Operation with sensor
126.96.36.199 Sensorless Control
188.8.131.52 Robustness Issues
3.3.4 Comparison of the three estimators
3.4 Position /Speed Sensor Fault Detection, Isolation and Reconfiguration
3.4.1 Sensor Fault Enabling based on Observers
Chapter IV: Phase Current and DC bus voltage sensors Fault Detection and Diagnosis
4.1 Needs on phase currents sensors and DC link voltage sensors diagnosis
4.1.1 Current Sensor fault origins and consequences
4.1.2 DC Voltage Measurement
4.1.3 Existing methods based on Observers and Signal processing
4.2 Developed methods for Phase Currents Sensors Fault Detection and Isolation
4.2.1 Diagnosis by an algebraic approach of fault estimation
184.108.40.206 Design and Simulation Results
220.127.116.11 Experimental Results in FDI scheme
4.2.2 Current vector analysis for Fault Detection and Diagnosis
18.104.22.168 Current residuals analysis in the (d,q) frame
22.214.171.124 Simulation Results
126.96.36.199 Experimental Results
4.3 DC Link Voltage Obserer
Conclusions and Perspectives