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Fault Tolerant Control (FTC) systems are crucial in industry to ensure safe and reliable operation, especially of motor drives. This paper proposes the use of multiple controllers for a FTC system of an induction motor drive, selected based on a switching mechanism. The system switches between sensor vector control, sensorless vector control, closed-loop voltage by frequency (V/f) control and open loop V/f control. Vector control offers high performance, while V/f is a simple, low cost strategy with high speed and satisfactory performance. The faults dealt with are speed sensor failures, stator winding open circuits, shorts and minimum voltage faults. In the event of compound faults, a protection unit halts motor operation. The faults are detected using a wavelet index. For the sensorless vector control, a novel Boosted Model Reference Adaptive System (BMRAS) to estimate the motor speed is presented, which reduces tuning time. Both simulation results and experimental results with an induction motor drive show the scheme to be a fast and effective one for fault detection, while the control methods transition smoothly and ensure the effectiveness of the FTC system. The system is also shown to be flexible, reverting rapidly back to the dominant controller if the motor returns to a healthy state.

Practical control systems are susceptible to component malfunctions which may cause significant performance degradation and even instability of the system. The past two decades have therefore seen considerable research on Fault Tolerant Control (FTC). FTC systems are designed to allow recovery from damage and system faults. When it comes to electrical drives used in safety critical applications or industrial processes where system faults may lead to enormous costs, FTC systems are crucial [

Fourier Transform (FT) techniques, such as those using high resolution frequency estimation [

In this paper, a fault tolerant control strategy which deals with a wide range of induction motor faults is implemented. A vector control drive with an encoder is the dominant control scheme. In the event of an encoder fault, the system switches to sensorless vector control. If the stator winding is open circuited or shorted, a closed loop V/f controller takes over. If a minimum voltage fault occurs, the system goes to open loop V/f control. Even further deterioration activates a protection circuit which halts the motor. Faults are detected using a wavelet index.

The four different controllers ensure the effectiveness and availability of the control scheme. The wavelet index is shown to be an excellent fault indicator. Additionally, the system has the ability to revert back to the dominant controller if the motor resumes normal operation, thus ensuring its availability at all times. Moreover, the protection circuit requires no extra hardware, thus reducing the cost of the drive. Additionally, the sensorless vector control features a novel Boosted Model Reference Adaptive System (BMRAS) to estimate the speed that eliminates the need for a PI controller and thus of much tuning. The fault tolerant algorithm was executed initially through Matlab/Simulink and then was verified experimentally.

This paper is organized as follows. Section 2 describes the motor control strategies used in this work. The BMRAS controller is presented in Section 3. Section 4 explains the wavelet transform. The fault tolerant control strategy is described in Section 5. The experimental results are presented in Section 6. Finally, concluding remarks are given in Section 7.

Vector control decouples flux and torque currents so as to linearly control the output torque of a nonlinear induction motor. The three phases of voltage and current are transformed to two-phase dq axes. The dq frame rotates synchronously with the rotor flux space vector. The expression for torque in an induction motor is [

According to the orientation of _{rq}_{m}, L_{r}, p, Φ_{rd}, Φ_{rq}, i_{sd}, i_{sq}_{e}

As is clear from _{sq}

Vector control with a sensor is the dominant controller in this work, due to its straightforward implementation. The following calculations are carried out in the vector control according to the Park transformation:

This operation can be illustrated in

dq to abc transformation is:

Therefore, the rotor flux and the torque can be independently controlled to obtain a linear current/torque relationship through the stator current in the

The Simulink model is shown in

The encoder used for position and speed measurement may lead to problems. Faults such as loss of output information, offset, disturbances, measure deviation and channel mismatch may occur [

The V/f control is one of the most popular control techniques due to the following reasons:

It is a simple algorithm

There is no need of current sensors

There is no requirement of speed measurement

The following equations can explain the principle of V/f:

The stator flux remains constant if the ratio V/F remains constant despite the change in the frequency.

The stator flux in an induction motor is proportional to the ratio of applied voltage and supply frequency. Varying the frequency changes the speed. With the voltage to frequency maintained at the same ratio, flux and torque can be kept constant throughout the speed range. The speed is adjusted by varying frequency (

For constant air gap flux (_{airgap}

It is a much simpler control strategy than vector control and does not require high performance digital processing [

Model Reference Adaptive Systems (MRAS) are used to estimate quantities using a reference model and an adaptive model. The difference between the outputs of the two models drives an adaptive mechanism that provides the quantity that is to be estimated. Conventional MRAS use a simple fixed gain linear PI controller to generate the estimated rotor speed. This PI controller consumes time for tuning. In this work, the PI controller is replaced with a ‘booster’, which cuts down on tuning time while providing a good response. The booster is constructed using a rate limiter and zero order hold.

Taking the system shown in [

The adaptive model can be expressed in the following equations:
_{s}, L_{s}, V_{ds}, V_{qs}, T_{r}, w_{r}

The error between the reference and adaptive outputs, along with the reference speed (_{ref}

The initial condition of both signals is kept to zero. The rate limiter restricts the change of the signal passed to it by limiting the slope. The upper limit is called the rising slew parameter (

Finally, the estimated speed is calculated as follows:

The BMRAS was tested in both simulation and experiments (The experimental setup is described in Section 6).

The

According to the computer simulation and experimental results shown above, the system shows fast response with higher accuracy than the conventional MRAS in the literature [

A wavelet is an orthogonal function that can be applied to a finite group of data [

It must also have a square norm of one, as is seen in

A general equation of the mother wavelet, shown in

Wavelet coefficients are obtained using a low pass filter to obtain what is called an ‘approximation’ signal, while a high pass filter provides ‘details’. The approximation signal is progressively decomposed into further approximations and details, till a desired level of decompositions is obtained [

The energy is calculated according to

A Daubechies wavelet (db10) is the mother wavelet function using which the wavelet index is generated. The Simulink implementation is shown in

The wavelet decomposition levels used in _{s}

The optimal levels of decomposition are gauged through the optimum mother wavelet. The Shannon entropy orientates the route in the selection of this optimal level by determining the entropy of each original (parent) subspace of the (DWT) and also views it in comparison to its new (children) subspace.

Fault tolerant control is indispensable, especially taking into consideration the formidable costs of unplanned stops in industrial system operations. The mechanism to switch between controllers in the event of fault and the overall fault tolerant control scheme used in this work is shown in

In the

In this work, four control strategies are used. In normal operation, sensor vector control runs the drive. When an encoder fault occurs, sensorless vector control takes over. An open circuit in the stator winding or a short reverts the system to closed loop V/f control. V/F controlled drives are very reliable due to the restriction to low dynamic performance and the absence of closed loop control, while a minimum voltage fault enables open loop V/f control to maintain acceptable level of operation due to the degradation of the system performance and the difficulties of keeping good performance with the closed loop.

If a slight noise is wrongly interpreted as a fault, the system quickly reverts back to sensor vector control. Finally, the protection circuit is enabled in the event that two or more faults occur at once. Digital motor control blocks (DMC) are used to simulate the proposed algorithm due to their easy compilation from Simulink/Matlab to C++ or C through the Texas Instruments F28335 DSP. The Simulink model is shown in

Experimental setup of the induction motor drive is based on the TMS320F28335 DSP. The induction motor parameters are listed in

The hardware scheme is depicted in

The wavelet decompositions of stator current in the healthy induction motor are shown in

The amplitude of the wavelet index for healthy operation, as seen in

The monitoring of the system parameters can be obtained through a serial communication cable between the DSP and the PC using SCI transmit and receive blocks as is shown in

To demonstrate the effectiveness of the fault tolerant algorithm, three faults are investigated: Short and open circuits in the stator winding and sensor faults. At each fault, the appropriate wavelet index is calculated, as is demonstrated in the following sections.

To simulate this fault, the stator resistance was decreased 10 times in steps of 0.1 Ω. The motor has a delta connection. The variable resistance serves to reduce the stator resistance according to the equation for equivalent resistance of two parallel resistors. For each shunt resistance value, the mean wavelet index is calculated. The wavelet decomposition details are shown in

Experimental responses of the drive at 450 rpm, 900 rpm and 1,600 rpm were obtained with this fault. At each speed, the wavelet index was recorded and compared to the simulation results as is detailed below:

The first test was with a speed of 450 rpm. The wavelet index comparison between experimental and simulation results at this speed is listed in

The second test is with a maximum speed of 900 rpm. As its clear from

The wavelet index lies between 1.8 and 2 for a stator winding short at 1,600 rpm as is seen in

To introduce the open circuit fault, the stator resistance was increased 10 times the original (20 Ω) in steps of 2 Ω. The wavelet decomposition of the faulty stator current is shown in

The wavelet index was recorded to be 1.5 for 450 rpm, between 1.2 and 1.6 for 900 rpm and 1.8 at 1,600 rpm as is shown in

Two types of speed sensor (encoder) faults are presented in this work. The first is complete speed sensor failure as is depicted in

The second type of sensor fault was a partial sensing error in the position, which was created by introducing noise in the encoder LED. The encoder output in

The fault tolerant algorithm was tested with these faults at different speeds. Before starting the induction motor, the cables of the encoder channels were disconnected. As is seen in

The recovery from a fault occurs rapidly and the transition from one control scheme to the other is seen to be smooth. The performance does not degrade considerably even as the control strategy changes.

The flexibility of the control strategy is depicted in

A fault tolerant control system incorporating (sensor and sensorless) vector control and (closed loop and open loop) V/f control has been presented. The wavelet index used for fault detection has been shown to be both fast and effective. The index detected complete sensor failures, partial sensor errors, stator winding shorts and open circuits and compound faults. The transitions from one controller to the other were both quick and smooth. The threshold of the WI is set according to the amplitude of the stator current, which differs for every fault.

The Boosted Model Reference Adaptive System (BMRAS) used in sensorless vector control was shown to be effective for rotor speed estimation. It saved time otherwise consumed in tuning the conventional PI controller, while maintaining excellent performance.

The system has been shown to be flexible, in that if a fault is removed and the system returns to a healthy state, the drive reverts back to the dominant sensor vector control. The protection unit was implemented successfully, not requiring additional hardware and thus saving cost.

Future work may consider adding strategies such as Direct Torque Control (DTC) to the control scheme. Additionally, a thorough analysis of the switching mechanism, such as time delays, would be useful. The inclusion of prognostic mechanisms, for an early prediction of faults before they occur, is also a very good prospect.

The authors acknowledge the financial support of the University of Malaya, Provision of High Impact Research, Grant No. D000022-16601, Hybrid Solar Energy Research Suitable for Rural Electrification.

Reference frame for vector control.

Park transformation principle.

Simulink implementation of sensor vector control.

Simulink implementation of sensorless vector controller.

Simulink implementation BMRAS.

Simulation results of the speed tracking by the BMRAS.

Experimental results of the speed tracking by the BMRAS.

Simulink wavelet index implementation.

Switching mechanism between the controllers.

Fault tolerant control algorithm.

Simulink model of FTC system.

Hardware implementation scheme.

Circuits of the induction motor drive.

Induction motor setup.

Wavelet decomposition in healthy motor.

Experimental (red) and simulation comparison of the healthy I.M wavelet index.

Serial communication Interface to show the experimental output.

Wavelet decomposition.

Wavelet decomposition Rs = 200 Ω.

Experimental and simulation Wavelet index Ohm at 450 rpm.

Experimental and simulation wavelet index for series Rs (2–200) Ohm at 900 rpm.

Wavelet index for series Rs (2–200) Ohm at 1,600 rpm.

Experimental rotor position: Complete sensor failure.

Experimental rotor position with partial sensing error.

Experimental speed transition with different controllers.

Control system recovery.

Expanded flow chart of the work.

Induction motor parameters.

Power | 1 kW |

Current | 2.5 A |

Voltage (delta) | 400 V |

Rated Speed | 2,780 rpm |

No. of poles | 2 |

Moment of Inertia | 2.4e−4 kgm^{2} |

Stator Resistance | 20.9 ohm |

Rotor Resistance | 19.5 ohm |

Stator Inductance | 50e−3 Henry |

Rotor Inductance | 50e−3 Henry |

Wavelet index (WI) for shunt Rs (0.1–2) for different speeds.

_{sh}(Ω) |
||||||
---|---|---|---|---|---|---|

| ||||||

0.10 | 1.10 | 1.00 | 1.78 | 1.76 | 1.80 | 1.70 |

0.20 | 1.40 | 1.40 | 1.80 | 1.80 | 1.81 | 1.72 |

0.40 | 1.43 | 1.42 | 1.80 | 1.80 | 1.85 | 1.76 |

0.80 | 1.43 | 1.42 | 1.82 | 1.80 | 1.90 | 1.88 |

1.60 | 1.50 | 1.50 | 1.84 | 1.86 | 1.92 | 1.91 |

1.80 | 1.50 | 1.50 | 1.84 | 1.88 | 1.95 | 1.93 |

2.00 | 1.50 | 1.50 | 1.84 | 1.90 | 2.00 | 1.96 |