A fuzzy type-2 fault detection methodology to minimize false alarm rate in induction motor monitoring applications

Rocha, Erick Melo; Junior, WalterBarra; Marcillo, Kevin Eduardo Lucas; Júnior, Carlos Tavares da Costa; Júnior, José Gracildo de Carvalho; Medeiros, Renan Landau Paiva de; Nogueira, Fabrício G.

doi:10.1016/j.asoc.2020.106373

Cited by 9 publications

(1 citation statement)

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“…Nearly 80% of the energy utilized in industries is produced by three-phase induction motors. A three-phase induction motor has a wide number of applications in areas such as Electric Vehicles, energy saving, and monitoring systems [1][2][3]. This article centered on the nonlinear speed control of a three-phase induction motor.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Backstepping-Super Twisting Algorithm for Robust Speed Control of a Three-Phase Induction Motor

2023

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This paper proposes a Hybrid Backstepping Super Twisting Algorithm for robust speed control of a three-phase Induction Motor in the presence of load torque uncertainties. First of all, a three-phase squirrel cage Induction Motor is modeled in MATLAB/Simulink. This is then followed by the design of different non-linear controllers, such as sliding mode control (SMC), super twisting SMC, and backstepping control. Furthermore, a novel controller is designed by the synergy of two methods, such as backstepping and super twisting SMC (Back-STC), to obtain the benefits of both techniques and, thereby, improve robustness. The sigmoid function is used with an exact differentiator to minimize the high-speed discontinuities present in the input channel. The efficacy of this novel design and its performance were evidenced in comparison with other methods, carried out by simulations in MATLAB/Simulink. Regression parameters, such as ISE (Integral Square error), IAE (Integral Absolute error) and ITAE (Integral Time Absolute error), were calculated in three different modes of operation: SSM (Start-Stop Mode), NOM (Normal Operation Mode) and DRM (Disturbance Rejection Mode). In the end, the numerical values of the regression parameters were quantitatively analyzed to draw conclusions regarding the tracking performance and robustness of the implemented non-linear control techniques.

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Section: Introductionmentioning

confidence: 99%