A novel robust fault detection method for induction motor rotor by using unknown input observer

Soleymani, Reza; Nekoui, Mohammad Ali; Moarefianpour, Ali

doi:10.1080/21642583.2019.1578296

Cited by 12 publications

(5 citation statements)

References 15 publications

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“…Applying of observer-based approach for asynchronous fault detection for separately excited DC Motor introduced in [18]. The work of [19] presents robust observer-based open-circuit faults for industrial permanent-magnet synchronous motor, and then, [20] introduces a new robust observer to detect the faults in an induction motor. In [21,22] Fuzzy logic is used to improve the performance of observer-based fault detection approaches.…”

Section: Iiliterature Surveymentioning

confidence: 99%

Observer-Based Fault Detection Approach Using Fuzzy Adaptive Poles Placement System With Real-Time Implementation

et al. 2021

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The conventional methods of observer poles placement in sensor fault detection usually adopt the trial-and-error methods. These methods cannot achieve global optimal performance because of their fixed poles placement and it leads to an observer with constant parameters, which could be reducing the system performance. Therefore, this paper proposes a fuzzy-based observer tuning method to optimize and adapt the selection of poles locations to determine the optimal gains of the observer, and it is experimentally applied to a composite sensor fault detection. Fuzzy logic is a promising method that could overcome the trial-and-error method challenges by introducing better adaptation and system robustness. The proposed observer structure includes adaptive tuning corresponding to an unknown input. Utilizing self-tuning for the observer correction stage, the gain is going to be updated online using the proposed fuzzy adaptive poles placement (FAPP) system. This paper validated the system simulation by implementing fault detection algorithms by using a real-time embedded observer-based system. The experimental results demonstrate the effectiveness of the proposed fuzzy-based observer schemes at detecting sensor faults in the Brushless DC (BLDC) motors, with significantly better performance than conventional counterparts' methodologies. The experiments indicate that the average estimation error is 0.146, which less by 43.8% than was obtained for high levels of noise and disturbances compared with the traditional Luenberger observer approach.

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Section: Iiliterature Surveymentioning

confidence: 99%

Observer-Based Fault Detection Approach Using Fuzzy Adaptive Poles Placement System With Real-Time Implementation

et al. 2021

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“…Multiphase motors are also fault tolerant because such machines remain functional even if failures affect certain phases (Riveros et al , 2018; Arahal et al , 2010). For this reason, multiphase motors are often used for actuation in industrial systems and harsh operating conditions (Lu et al , 2016; Mochammad et al , 2021; Soleymani et al , 2019).…”

Section: Introductionmentioning

confidence: 99%

Nonlinear optimal control for the five-phase induction motor-based traction system of electric vehicles

Rigatos,

Siano,

Al-Numay

et al. 2024

COMPEL

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Purpose The purpose of this article is to treat the nonlinear optimal control problem in EV traction systems which are based on 5-phase induction motors. Five-phase permanent magnet synchronous motors and five-phase asynchronous induction motors (IMs) are among the types of multiphase motors one can consider for the traction system of electric vehicles (EVs). By distributing the required power in a large number of phases, the power load of each individual phase is reduced. The cumulative rates of power in multiphase machines can be raised without stressing the connected converters. Multiphase motors are also fault tolerant because such machines remain functional even if failures affect certain phases. Design/methodology/approach A novel nonlinear optimal control approach has been developed for five-phase IMs. The dynamic model of the five-phase IM undergoes approximate linearization using Taylor series expansion and the computation of the associated Jacobian matrices. The linearization takes place at each sampling instance. For the linearized model of the motor, an H-infinity feedback controller is designed. This controller achieves the solution of the optimal control problem under model uncertainty and disturbances. Findings To select the feedback gains of the nonlinear optimal (H-infinity) controller, an algebraic Riccati equation has to be solved repetitively at each time-step of the control method. The global stability properties of the control loop are demonstrated through Lyapunov analysis. Under moderate conditions, the global asymptotic stability properties of the control scheme are proven. The proposed nonlinear optimal control method achieves fast and accurate tracking of reference setpoints under moderate variations of the control inputs. Research limitations/implications Comparing to other nonlinear control methods that one could have considered for five-phase IMs, the presented nonlinear optimal (H-infinity) control approach avoids complicated state-space model transformations, is of proven global stability and its use does not require the model of the motor to be brought into a specific state-space form. The nonlinear optimal control method has clear implementation stages and moderate computational effort. Practical implications In the transportation sector, there is progressive transition to EVs. The use of five-phase IMs in EVs exhibits specific advantages, by achieving a more balanced distribution of power in the multiple phases of the motor and by providing fault tolerance. The study’s nonlinear optimal control method for five-phase IMs enables high performance for such motors and their efficient use in the traction system of EVs. Social implications Nonlinear optimal control for five-phase IMs supports the deployment of their use in EVs. Therefore, it contributes to the net-zero objective that aims at eliminating the emission of harmful exhaust gases coming from human activities. Most known manufacturers of vehicles have shifted to the production of all-electric cars. The study’s findings can optimize the traction system of EVs thus also contributing to the growth of the EV industry. Originality/value The proposed nonlinear optimal control method is novel comparing to past attempts for solving the optimal control problem for nonlinear dynamical systems. It uses a novel approach for selecting the linearization points and a new Riccati equation for computing the feedback gains of the controller. The nonlinear optimal control method is applicable to a wider class of dynamical systems than approaches based on the solution of state-dependent Riccati equations.

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“…To this end, the use of model-predictive control (MPC), backstepping control, and sliding-mode control methods has been proposed (Echeikh et al, 2020;Xiang and Li, 2022). Moreover, fault diagnosis methods for gas compressors have been developed (Lu et al, 2016;Mochammad et al, 2021;Soleymani et al, 2019). In the present article, a novel nonlinear optimal control approach has been developed for an integrated gas compression system that is electrically actuated by a five-phase induction motor (5-phase IM).…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Optimal Control for a Gas Compressor Actuated by a Five-Phase Induction Motor

Rigatos,

Abbaszadeh,

Sari

et al. 2023

Power Electronics and Drives

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The article proposes a nonlinear optimal control method for the dynamic model of a gas centrifugal compressor being actuated by a five-phase induction motor (5-phase IM). To achieve high torque and high power in the functioning of gas compressors, 5-phase IM appear to be advantageous in comparison to three-phase synchronous or asynchronous electric machines. The dynamic model of the integrated compression system, which comprises the gas compressor and the 5-phase IM, is first written in a nonlinear and multivariable state-space form. It is proven that the electrically driven gas-compression system is differentially flat. Next, this system is approximately linearised around a temporary operating point that is recomputed at each sampling interval. The linearisation is based on first-order Taylor series expansion and uses the computation of the Jacobian matrices of the state-space model of the integrated system. For the linearised state-space description of the compressor and 5-phase IM, a stabilising optimal (H-infinity) feedback controller is designed. This controller achieves a solution to the nonlinear optimal control problem of the compressor and 5-phase IM system under model uncertainty and external perturbations. The feedback gains of the controller are computed by solving an algebraic Riccati equation at each iteration of the control method. Lyapunov analysis is used to demonstrate global stability for the control loop. Additionally, the H-infinity Kalman filter is used as a robust state estimator, which allows for implementing sensorless control for the gas compression system.

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A novel robust fault detection method for induction motor rotor by using unknown input observer

Cited by 12 publications

References 15 publications

Observer-Based Fault Detection Approach Using Fuzzy Adaptive Poles Placement System With Real-Time Implementation

Observer-Based Fault Detection Approach Using Fuzzy Adaptive Poles Placement System With Real-Time Implementation

Nonlinear optimal control for the five-phase induction motor-based traction system of electric vehicles

Nonlinear Optimal Control for a Gas Compressor Actuated by a Five-Phase Induction Motor

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