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This paper describes a new approach to estimate harmonics/interharmonics of power system voltages and currents based on a hybrid artificial neural network (ANN) and extended Kalman filter (EKF) structure. A very low sampling rate is used to implement the proposed ANN-EKF structure with the modest hardware demands. The ANN structure is used for spectral estimation, consumes a very low processing power,...
This paper discusses short-horizon prediction of wind speed and power using wind turbine data collected at 10 s intervals. A time-series model approach to examine wind behavior is studied. Both exponential smoothing and data-driven models are developed for wind prediction. Power prediction models are established, which are based on the most effective wind prediction model. Comparative analysis of...
This research presents a new approach to analyze harmonics in electrical power distribution network through statistical estimation technique of neural networks. The typical power system in current times is encountered by numerous power quality (PQ) problems. This is due to the increased usage of electronic circuitry that involves continuous power switching. The advanced switching and non linear nature...
The main objective involved with this paper consists of presenting the results obtained from the application of artificial neural networks and statistical tools in the automatic identification and classification process of faults in electric power distribution systems. The developed techniques to treat the proposed problem have used, in an integrated way, several approaches that can contribute to...
Efficiency and certainty of payback have not yet attained desired level for solar photovoltaic energy systems. Despite huge development in prediction of solar radiation data, a clear disconnect in extraction and effective engineering utilization of pertinent information from such data is acting as a major roadblock towards penetration of this emerging technology. It is crucial to identify and optimize...
A new hybrid technique using support vector machines (SVM) and artificial neural networks (ANN) to forecast the next dasia24psila hours load is proposed in this paper. The forecasted load for the next dasia24psila hours is obtained by using four modules consisting of the basic SVM, Peak and Valley ANN, averager and forecaster and adaptive combiner. These modules try to extract the various components...
The conventional Generation Scheduling (GS) is a day ahead activity, aiming at minimizing operating cost subject to system as well as device constraints. The anticipated load, generation and network availability are inputs to GS problem. The new attribute added to conventional GS problem is frequency. Under Availability Based Tariff (ABT), frequency plays important role in deciding the real time generation...
A new hybrid technique using support vector machines (SVM) to forecast the next `24' hours load is proposed in this paper. Four modules consisting of the basic SVM, peak and valley SVM, averager and forecaster and adaptive combiner form the integrated method for load forecasting. The proposed architecture can forecast the next `24' hours load. The basic SVM uses the historical data of load and temperature...
Despite huge development in prediction of solar radiation data, there is a clear disconnect in extraction and effective utilization of pertinent information from such data. Use of quality function deployment (QFD) can smartly identify the most significant statistics representing insolation availability for a solar PV installation. A MATLAB program has been used to build the annual frequency distribution...
The main objective involved with this paper consists of presenting the results obtained from the application of artificial neural networks and statistical tools in the automatic identification and classification process of faults in electric power distribution systems. The developed techniques to treat the proposed problem have used, in an integrated way, several approaches that can contribute to...
The objective of the present paper is to present a multi-parametric approach based on artificial neural networks for identification and classification purposes of high-impedance faults in distribution systems. More specifically, the proposed methodology uses artificial neural networks integrated with other several statistical techniques that have also been used these problem types. Besides providing...
The main objective involved with this paper consists of presenting the results obtained from the application of artificial neural networks and statistical tools in the automatic identification and classification process of faults in electric power distribution systems. The developed techniques to treat the proposed problem have used, in an integrated way, several approaches that can contribute to...
The research of alternative forms of energy production became more important nowadays in a context where the natural resources are scarce. In this sense, thermosiphon systems have been developed as an alternative way of energy economy for the water heating process using a renewable energy source: the sun. A thermosiphon system is greatly influenced by several parameters: the ambient temperature, the...
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