The indirect vector controlled IM （induction motor） drive involves decoupling of the stator current into torque and flux producing components. This paper proposes the implementation of a fuzzy logic control scheme a...The indirect vector controlled IM （induction motor） drive involves decoupling of the stator current into torque and flux producing components. This paper proposes the implementation of a fuzzy logic control scheme applied to a two d-q current components model of an induction motor. An intelligent based on fuzzy logic controller is developed with the help of knowledge rule base for efficient control. The performance of fuzzy logic controller is compared with that of the proportional integral controller in terms of the settling time and dynamic response to sudden load changes. The harmonic pattern of the output current is evaluated for both fixed gain proportional integral controller and the fuzzy logic based controller. The performance of the IM drive has been analyzed under steady state and transient conditions. Simulation results of both the controllers are presented for comparison.展开更多
The Brazilian electric sector reform established that the remuneration of distribution utilities must be through the management of their systems. This fact increased the necessity of control and management of load flo...The Brazilian electric sector reform established that the remuneration of distribution utilities must be through the management of their systems. This fact increased the necessity of control and management of load flows through the connection points between the distribution systems and the basic grid as a function of the contracted amounts. The objective of this control is to avoid that these flows exceed some thresholds along the contracted values, avoiding monetary penalties to the utility or unnecessary amounts of contracted flows that overrates the costumers. This question highlights the necessity of forecast the flows in these connection points in sufficient time to permit the operator to take decisions to avoid flows beyond the contracted ones. In this context, this work presents the development of a neural network based load flow forecaster, being tested two time-series neural models： support vector machines and Bayesian inference applied to multilayered perceptron. The models are applied to real data from a Brazilian distribution utility.展开更多
文摘The indirect vector controlled IM （induction motor） drive involves decoupling of the stator current into torque and flux producing components. This paper proposes the implementation of a fuzzy logic control scheme applied to a two d-q current components model of an induction motor. An intelligent based on fuzzy logic controller is developed with the help of knowledge rule base for efficient control. The performance of fuzzy logic controller is compared with that of the proportional integral controller in terms of the settling time and dynamic response to sudden load changes. The harmonic pattern of the output current is evaluated for both fixed gain proportional integral controller and the fuzzy logic based controller. The performance of the IM drive has been analyzed under steady state and transient conditions. Simulation results of both the controllers are presented for comparison.
文摘The Brazilian electric sector reform established that the remuneration of distribution utilities must be through the management of their systems. This fact increased the necessity of control and management of load flows through the connection points between the distribution systems and the basic grid as a function of the contracted amounts. The objective of this control is to avoid that these flows exceed some thresholds along the contracted values, avoiding monetary penalties to the utility or unnecessary amounts of contracted flows that overrates the costumers. This question highlights the necessity of forecast the flows in these connection points in sufficient time to permit the operator to take decisions to avoid flows beyond the contracted ones. In this context, this work presents the development of a neural network based load flow forecaster, being tested two time-series neural models： support vector machines and Bayesian inference applied to multilayered perceptron. The models are applied to real data from a Brazilian distribution utility.
