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The modeling of wind power curve is important in turbine performance monitoring and in wind power forecasting. There are several techniques to fit the power curve of a wind turbine, which can be classified into parametric and nonparametric methods. This paper explores the advantages offered by the nonparametric methods for modeling of wind turbine power curve, because the application of nontraditional...
This article presents results from models developed to improve wind speed forecasting based on techniques of echo state networks, multi-layer perceptron (MLP) and neuro-fuzzy system (ANFIS). Wind generation is a direct function of wind speed and, in contrast with conventional generation systems, is not easily dispatchable. Wind forecasting is crucial both for wind farm operators and utility operators...
This paper presents the results of models created for prediction of wind power generation using Echo State Networks (ESN). An echo state network consist of a large, randomly connected neural network, the reservoir, which is driven by an input signal and projects to output units. ESN offer an intuitive methodology for using the temporal processing power of recurrent neural networks without the hassle...
Wind energy - generated from wind power - is plentiful, renewable, clean and available in many places in the world. This energy is generated by wind turbines, in which the wind captured by propellers is connected to a turbine that drives an electrical generator. The use of this source to generate electricity on a commercial scale began in the 1970s, when the international oil crisis escalated. The...
Wind Power forecasting is extremely important to assist in planning and programming studies for the operation of wind power generation. Several studies have shown that the Brazilian wind potential can contribute significantly to the electricity supply, especially in the Northeast Brazil, where winds present an important feature of being complementary in relation to the flows of the San Francisco River...
Wind forecasting is extremely important to assist in planning and programming studies for the operation of wind power generation. Several studies have shown that the Brazilian wind potential can contribute significantly to the electricity supply, especially in the Northeast, where winds present an important feature of being complementary in relation to the flows of the San Francisco River. However,...
This paper provides a comparison between two methods for time series forecasting. The first method is based on traditional recurrent neural networks (RNNs) while the second method is based in Reservoir Computing (RC). Reservoir Computing is a new paradigm that offers an intuitive methodology for using the temporal processing power of RNNs without the inconvenience of training them. So we decided to...
This work deals with the application of Artificial Neural Networks (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) to provide the decentralized daily load short-term forecasting which is based on the average daily temperature. It is not an easy task to forecast the load demand of an electrical regional mainly because of the system reconfiguration either temporary (operational maneuvers) or...
Dissolved gas analysis (DGA) is one of the most useful techniques do detect the incipient faults of power transformer. However, the identification of the faulted location by the traditional method is not always an easy task due to the variability of gas data and operational natures. This work aims to develop an intelligent system of preventive maintenance for automatically detecting incipient fault...
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