In this work a Maximum Power Point Tracker (MPPT) for photovoltaic modules is developed using fuzzy logic. As it is well known, the output of the photovoltaic module is a non-linear curve which has a unique point of m...In this work a Maximum Power Point Tracker (MPPT) for photovoltaic modules is developed using fuzzy logic. As it is well known, the output of the photovoltaic module is a non-linear curve which has a unique point of maximum power (MPP) for a given condition of radiation and temperature. When a load is connected to the module, only in very specifics cases, the operation point will coincide with the MPP, for any other conditions the system will not operate with maximum power. Thus MPPT circuits must guarantee that photovoltaic modules operate with its maximum power at most of the time, independently to the radiation and temperature conditions. In order to achieve this objective, in this paper the input variables of the controller are transformed into linguistic variables, which associate with a set of rules results the displacement of the operation point so as to transfer the maximum power from the photovoltaic module to the load.展开更多
Artificial Neural Network (ANN) equalizers have been successfully applied to mitigate Inter symbolic Interference (ISI) due to distortions introduced by linear or nonlinear communication channels. The ANN architecture...Artificial Neural Network (ANN) equalizers have been successfully applied to mitigate Inter symbolic Interference (ISI) due to distortions introduced by linear or nonlinear communication channels. The ANN architecture is chosen according to the type of ISI produced by fixed, fast or slow fading channels. In this work, we propose a combination of two techniques in order to minimize ISI yield by fast fading channels, i.e., pulse shape filtering and ANN equalizer. Levenberg-Marquardt algorithm is used to update the synaptic weights of an ANN comprise only by two recurrent perceptrons. The proposed system outperformed more complex structures such as those based on Kalman filtering approach.展开更多
文摘In this work a Maximum Power Point Tracker (MPPT) for photovoltaic modules is developed using fuzzy logic. As it is well known, the output of the photovoltaic module is a non-linear curve which has a unique point of maximum power (MPP) for a given condition of radiation and temperature. When a load is connected to the module, only in very specifics cases, the operation point will coincide with the MPP, for any other conditions the system will not operate with maximum power. Thus MPPT circuits must guarantee that photovoltaic modules operate with its maximum power at most of the time, independently to the radiation and temperature conditions. In order to achieve this objective, in this paper the input variables of the controller are transformed into linguistic variables, which associate with a set of rules results the displacement of the operation point so as to transfer the maximum power from the photovoltaic module to the load.
文摘Artificial Neural Network (ANN) equalizers have been successfully applied to mitigate Inter symbolic Interference (ISI) due to distortions introduced by linear or nonlinear communication channels. The ANN architecture is chosen according to the type of ISI produced by fixed, fast or slow fading channels. In this work, we propose a combination of two techniques in order to minimize ISI yield by fast fading channels, i.e., pulse shape filtering and ANN equalizer. Levenberg-Marquardt algorithm is used to update the synaptic weights of an ANN comprise only by two recurrent perceptrons. The proposed system outperformed more complex structures such as those based on Kalman filtering approach.
