Among ac drives, the permanent magnet synchronous motor has been gaining popularity owing to its high torque to current ratio, large power to weight ratio, high efficiency, high power factor and robustness. Because of...
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Among ac drives, the permanent magnet synchronous motor has been gaining popularity owing to its high torque to current ratio, large power to weight ratio, high efficiency, high power factor and robustness. Because of its technical and economic advantages, Permanent Magnet Synchronous Motor drive technology is a serious contender for replacing the existing technologies. In this paper we report the utilization of a novel controller (BELBlC) based on emotion processing mechanism in brain. Our results show superior control characteristics especially very fast response, simple implementation and robustness with respect to disturbances and manufacturing imperfections. Our proposed method enables the designer to shape the response in accordance with the multiple objectives of his/her choice.
In this paper, we propose an atlas-based method for hippocampus-amygdala complex segmentation. An atlas is registered on all subjects and its transformation is calculated for each subject. This transformation is appli...
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This paper presents an efficient method to obtain the optimal power flow (OPF) problem under constrained emission dispatch by applying reactive tabu search (RTS) algorithm. The RTS is developed as a derivative-free op...
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This paper presents an efficient method to obtain the optimal power flow (OPF) problem under constrained emission dispatch by applying reactive tabu search (RTS) algorithm. The RTS is developed as a derivative-free optimization technique in solving constrained emission OPF problem significantly reduces the computational burden with the strategies that make the search process robust and fast. The effectiveness of the proposed approach has been demonstrated through the IEEE 30-bus, 6-generator, test system. The simulation results reveal that the proposed RTS can yield highly optimal solution and tan reduce computational execution time superior to a standard tabu search. Moreover, the proposed method provides better solution than previous literatures with promising results.
In this paper we report the utilization of a novel controller (BELBIC) based un emotion processing mechanism in brain for power system. Using the BELBIC controller, both transient stability and voltage regulation of p...
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In this paper we report the utilization of a novel controller (BELBIC) based un emotion processing mechanism in brain for power system. Using the BELBIC controller, both transient stability and voltage regulation of power systems are achieved. The special characteristic of this controller that makes it effective is its flexibility its five gain parameters that give good freedom for choosing favorite response. With this degree of freedom choosing of these parameters involves trade-off between overshoot and speed of response. The effectiveness of the proposed BELBIC controller is shown through some computer simulations on a (SMIB) power system.
This paper investigates digital modeling and control strategies applied to the voltage regulation of a microgenerator system, placed in the Electric Energy Generation Laboratory of Federal University of Para. Identifi...
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This paper investigates digital modeling and control strategies applied to the voltage regulation of a microgenerator system, placed in the Electric Energy Generation Laboratory of Federal University of Para. Identification parametric techniques are used in order to obtain a representative model for the controller design. With a suitable model, controllers of proportional integral type are designed, based on the root locus and fuzzy systems strategies, in order to improve the voltage regulation of the microgenerator system.
This paper presents a new method to solve the constrained unit commitment problem by applying ant colony optimization (ACO) based on the diversity control approach. The pheromone updating rule is modified to control t...
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This paper presents a new method to solve the constrained unit commitment problem by applying ant colony optimization (ACO) based on the diversity control approach. The pheromone updating rule is modified to control the diversification by adopting a simple mechanism for random selection in ACO. The proposed method is tested on the 10-unit test system with a scheduling time horizon of 24 hours. The numerical results show an economical saving in the total operating cost when compared to the previous literature results. Moreover, two types of the proposed diversity control technique have the features of easy implementation and a better convergence rate superior to a standard ACO.
Accidents caused by drivers' drowsiness have a high fatality rate because of the marked decline in the drivers' vehicle control abilities. Preventing accidents caused by drowsiness is highly desirable but requ...
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ISBN:
(纸本)0780385675
Accidents caused by drivers' drowsiness have a high fatality rate because of the marked decline in the drivers' vehicle control abilities. Preventing accidents caused by drowsiness is highly desirable but requires techniques for continuously detecting, estimating, and predicting the level of alertness of drivers. This paper proposes a brain-machine interface that combines electroencephalographic power spectrum estimation, principal component analysis, and fuzzy neural networks to estimate/predict drivers' drowsiness level in a virtual-reality-based driving simulator. The driving performance is defined as deviation between the center of the vehicle and the center of the cruising lane. Our results demonstrated that the proposed method is feasible to accurately estimate quantitatively driving performance in a realistic driving simulator.
The growing number of traffic fatalities in recent years has become a serious concern to society. Accidents caused by drivers' drowsiness behind the steering wheel have a high fatality rate because of the marked d...
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ISBN:
(纸本)0780383591
The growing number of traffic fatalities in recent years has become a serious concern to society. Accidents caused by drivers' drowsiness behind the steering wheel have a high fatality rate because of the marked decline in the drivers' abilities of perception, recognition, and vehicle control abilities while sleepy. Preventing accidents caused by drowsiness requires a technique for detecting, estimating, and predicting the level of alertness of a driver and a mechanism for maintaining his/her maximum performance. This work describes a system that combines electroencephalographic (EEG) power spectrum estimation, principal component analysis, and fuzzy neural network model to estimate/predict drivers' drowsiness level in a driving simulator. Our results demonstrated that, for the first time, it is feasible to accurately estimate task performance, accurately estimate quantitatively measured driving performance, expressed as deviation between the center of the vehicle and the center of the cruising lane, in a realistic driving simulation.
This paper addresses the developing of a fault diagnosis system for detection of broken rotor bars, a common mechanical fault in cage induction machines, using efficient feature extraction techniques and a neural netw...
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This paper addresses the developing of a fault diagnosis system for detection of broken rotor bars, a common mechanical fault in cage induction machines, using efficient feature extraction techniques and a neural network classifier. The proposed algorithm uses the stator current and motor speed as inputs. Fast Fourier Transform is utilized to obtain the frequency spectrum of the current signal. An efficient algorithm is then used to extract suitable features out of the frequency spectrum of the signal. The relevance of the features for the purpose of fauIt detection is investigated and verified. A neural network classifier is then developed and applied to distinguish different motor conditions. A series of data collected from experiments on a three phase 3 hp cage induction machine performed in different load and fauIt conditions are used to provide data for training and then testing the classifier. Experimental results confirm the etkiency of the proposed algorithm for detection of broken bar faults.
We propose an adaptive output feedback control design for global asymptotic stabilization of feedforward systems based on our recent results on dynamic high gain scaling based controller design for strict-feedback sys...
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We propose an adaptive output feedback control design for global asymptotic stabilization of feedforward systems based on our recent results on dynamic high gain scaling based controller design for strict-feedback systems. The system is allowed to contain uncertain functions of all the states and the input as long as they satisfy certain bounds. Unknown parameters are allowed in the bounds assumed on uncertain functions. If the uncertain functions involve the input, then the output-dependent functions in the bounds need to be polynomially bounded. It is also shown that if the uncertain functions can be bounded by a function independent of the input, then the polynomial boundedness requirement can be relaxed. The designed controllers have a simple structure being essentially a linear feedback with state-dependent dynamic gains and do not involve any saturations or recursive computations. The observer utilized to estimate unmeasured states is similar to a Luenberger observer with dynamic observer gains. The Lyapunov functions are quadratic in state estimates, observer errors, and parameter estimation error. The stability analysis is based on our recent results on uniform solvability of coupled state-dependent Lyapunov equations. The controller provides strong robustness properties both with respect to uncertain parameters and additive disturbances. This robustness is the key to the output feedback controller design.
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