Flexible AC transmission systems (FACTS) devices are power electronics based shunt or series devices that are connected to the power network and can improve the system performance and the quality of supply. Applicatio...
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Flexible AC transmission systems (FACTS) devices are power electronics based shunt or series devices that are connected to the power network and can improve the system performance and the quality of supply. Application of three types of FACTS devices, a STATCOM, a SCRC and a UPFC, is studied in a 45 bus multimachine power system, as part of the Brazilian power system. The system is exposed to various transient and dynamic disturbances, and the performance of the system reinforced with the FACTS devices is compared with the original system. Simulation results are provided that indicate the effectiveness of these FACTS devices in the overall system behaviour
The recent advances in learning control theory are reviewed. First insight is provided into the concept and principle associated with learning control under a repeatable control environment. Then a briefing is given t...
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The recent advances in learning control theory are reviewed. First insight is provided into the concept and principle associated with learning control under a repeatable control environment. Then a briefing is given to the two main streams of learning control-iterative learning control and repetitive control. Subsequently we survey several hot topics in the field of learning control. New trends in learning control using Lyapunov methods are also sketched, followed by remarks on future directions.
A complete Fault Tolerant control solution calls for a nonlinear adaptive controller with universal approximation capability and guaranteed stability. To fulfill this requirement we propose the use of Neural Networks ...
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This work proposes a gain scheduling adaptive control scheme based on fuzzy systems, neural networks and genetic algorithms for nonlinear plants. A fuzzy PI controller is developed, which is a discrete time version of...
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A complete fault tolerant control solution calls for a nonlinear adaptive controller with universal approximation capability and guaranteed stability. To fulfill this requirement we propose the use of neural networks ...
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A complete fault tolerant control solution calls for a nonlinear adaptive controller with universal approximation capability and guaranteed stability. To fulfill this requirement we propose the use of neural networks trained online under a globalized dual heuristic programming architecture supervised by a decision logic capable of identifying controller malfunctions in early stages and providing new avenues with greater probability of convergence using information from a dynamic model bank. The classification and distinction of controller malfunctions and of the faults in the system is achieved through three independent quality indexes. Proof-of-the-concept simulations of nonlinear plants demonstrate the approach legitimacy
The growth of use of graph-structured databases modeled on n-partite graphs has increased the ability to generate more flexible databases. However, the calculation of certain features in these databases may be highly ...
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The growth of use of graph-structured databases modeled on n-partite graphs has increased the ability to generate more flexible databases. However, the calculation of certain features in these databases may be highly resource-consuming. This work proposes a method for approximating these features by sampling. A discussion of the difficulty of sampling in n-partite graphs is made and an evolutionary algorithm-based method is presented that uses the information from a smaller subset of the graph to infer the amount of sampling needed for the rest of the graph. Experimental results are shown on a publications database on Anthrax for finding the most important authors.
This work proposes a gain scheduling adaptive control scheme based on fuzzy systems, neural networks and genetic algorithms for nonlinear plants. A fuzzy PI controller is developed, which is a discrete time version of...
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This work proposes a gain scheduling adaptive control scheme based on fuzzy systems, neural networks and genetic algorithms for nonlinear plants. A fuzzy PI controller is developed, which is a discrete time version of a conventional one. Its data base as well as the constant PI control gains are optimally designed by using a genetic algorithm for simultaneously satisfying the following specifications: overshoot and settling time minimizations and output response smoothing. Hence, the optimization problem is a multiobjective one, from which results an optimal fuzzy Pl controller. A neural gain scheduler is designed, by the backpropagation algorithm, to tune the optimal parameters of the fuzzy PI controller at some operating points. Simulation results are shown to demonstrate the efficiency of the proposed structure for a DC servomotor adaptive speed control system used as an actuator of robotic manipulators.
In this paper, a new type of output feedback control, called a receding horizon finite memory control (RHFMC), is proposed for stochastic discrete-time state space systems. Constraints such as linearity and finite mem...
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ISBN:
(纸本)0780387309
In this paper, a new type of output feedback control, called a receding horizon finite memory control (RHFMC), is proposed for stochastic discrete-time state space systems. Constraints such as linearity and finite memory structure with respect to an input and an output, and unbiasedness from the optimal state feedback control are required in advance. The proposed RHFMC is chosen to minimize an optimal criterion with these constraints. The RHFMC is obtained in an explicit closed form using the output and input information on the recent time interval. It is shown that the RHFMC consists of a receding horizon control and an FIR filter. The stability of the RHFMC is investigated for stochastic systems.
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.
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