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An original three-phase neural approach for phase and symmetrical components estimation is proposed in this paper. This neural structure can be used for power quality control, in an active power filtering scheme for example. The approach is composed of a neural symmetrical voltage components extraction and a neural phase detection technique. These functional tasks are decomposed and approximated by...
This paper presents a new method for predicting distribution transformerpsilas total current and total voltage harmonic distortion with artificial neural network. The method is based on the backpropagation learning technique. This paper shows the proposed method is promising in total harmonic distortion prediction. For better system planning it is necessary to analyze and predict the behavior of harmonics...
Intelligent techniques of harmonic detection or estimation are nowadays of a great interest in power system applications, their ability to deal with high non-linearities attract researchers to investigate the performance of these methods mainly based on artificial intelligence namely using artificial neural networks (ANNs). In the literature many harmonic detection or estimation methods were presented,...
This paper compares different variants of the least mean squares (LMS) algorithm. The objective consists in finding the best compromise between on-line learning and computational costs. Indeed, an algorithm with low computational complexity for updating Adalines weights is required for a real-time implementation of a modular neural Active Power Filter (APF). This filtering scheme is inserted in an...
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