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This paper presents an integration of dual intelligent algorithms: artificial neural network (ANN) based fundamental component extraction algorithm and fuzzy logic based DC-link voltage self-charging algorithm (fuzzy self-charging algorithm), in a three-phase three-wire shunt active power filter (SAPF). The ANN and fuzzy logic are trained using the modified Widrow-Hoff (W-H) weight updating algorithm...
This paper presents new intelligent-based technique namely Quantum-Inspired Evolutionary Programming-Artificial Neural Network (QIEP-ANN) to predict the amount of load to be shed in a distribution systems during undervoltage load shedding. The proposed technique is applied to two hidden layers feedforward neural network with back propagation. The inputs to the ANN are the load buses and the minimum...
Flash floods are a dangerous natural disaster as they have killed more people than any other natural disaster and caused millions of ringgit in property damage. This paper presents a new approach for modeling rainfall forecasting using the artificial neural network technique (ANN). Daily actual data from the years 2007 to 2010, collected from 3 main stations in Selangor, were used to develop the ANN...
Load forecasting is very essential to the operation of electric utility. It is a pre-requisite to economic dispatch of electrical power and enhances the efficiency besides ensuring reliable operation of a power system. Electrical energy demand is highly dependent on various independent variables such as the weather, temperature, holidays, and days in a week. The accuracy of the forecast is important...
This paper presents the evolutionary neural network (ENN) model for the prediction of output from a grid-connected photovoltaic system installed at Malaysian Energy Centre (PTM), Bangi, Malaysia. The ENN model had been developed using evolutionary programming (EP) through the optimization of the number of nodes in the hidden layer, the learning rate and the momentum rate. The ENN model employs solar...
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