Three-phase pulse width modulation converters using insulated gate bipolar transistors(IGBTs)have been widely used in industrial application.However,faults in IGBTs can severely affect the operation and safety of the ...Three-phase pulse width modulation converters using insulated gate bipolar transistors(IGBTs)have been widely used in industrial application.However,faults in IGBTs can severely affect the operation and safety of the power electronics equipment and loads.For ensuring system reliability,it is necessary to accurately detect IGBT faults accurately as soon as their occurrences.This paper proposes a diagnosis method based on data-driven theory.A novel randomized learning technology,namely extreme learning machine(ELM)is adopted into historical data learning.Ensemble classifier structure is used to improve diagnostic accuracy.Finally,time window is defined to illustrate the relevance between diagnostic accuracy and data sampling time.By this mean,an appropriate time window is achieved to guarantee a high accuracy with relatively short decision time.Compared to other traditional methods,ELM has a better classification performance.Simulation tests validate the proposed ELM ensemble diagnostic performance.展开更多
This paper describes an adaptive gain sliding mode observer for brushless DC motor for large variations in speed. Sensorless brushless DC motor based on sliding mode observer exhibits multiple zero crossing in back el...This paper describes an adaptive gain sliding mode observer for brushless DC motor for large variations in speed. Sensorless brushless DC motor based on sliding mode observer exhibits multiple zero crossing in back electromotive force （EMF） which leads to commutation problems at low speed. In this paper, a modified sliding mode observer incorporating a speed component in the estimation of back EMF is proposed. It is found that after incorporating the speed component in the back EMF observer gain, multiple zero crossings at low speeds and phase shift at higher speeds are eliminated. The trapezoidal back EMF observer is implemented experimentally on a digital signal processor （DSP） board. The effectiveness of the proposed method is demonstrated through simulations and experiments.展开更多
文摘Three-phase pulse width modulation converters using insulated gate bipolar transistors(IGBTs)have been widely used in industrial application.However,faults in IGBTs can severely affect the operation and safety of the power electronics equipment and loads.For ensuring system reliability,it is necessary to accurately detect IGBT faults accurately as soon as their occurrences.This paper proposes a diagnosis method based on data-driven theory.A novel randomized learning technology,namely extreme learning machine(ELM)is adopted into historical data learning.Ensemble classifier structure is used to improve diagnostic accuracy.Finally,time window is defined to illustrate the relevance between diagnostic accuracy and data sampling time.By this mean,an appropriate time window is achieved to guarantee a high accuracy with relatively short decision time.Compared to other traditional methods,ELM has a better classification performance.Simulation tests validate the proposed ELM ensemble diagnostic performance.
文摘This paper describes an adaptive gain sliding mode observer for brushless DC motor for large variations in speed. Sensorless brushless DC motor based on sliding mode observer exhibits multiple zero crossing in back electromotive force （EMF） which leads to commutation problems at low speed. In this paper, a modified sliding mode observer incorporating a speed component in the estimation of back EMF is proposed. It is found that after incorporating the speed component in the back EMF observer gain, multiple zero crossings at low speeds and phase shift at higher speeds are eliminated. The trapezoidal back EMF observer is implemented experimentally on a digital signal processor （DSP） board. The effectiveness of the proposed method is demonstrated through simulations and experiments.
