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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...
In this work, an efficient fuzzy logic controller is used to improve the controllability of a SVC (static var compensator). The SVC device is a member of the FACTS (flexible alternating current transmission system) family employed in power transmission systems. The new fuzzy logic controller replaces the existing PI regulator and permits better disturbance compensation and voltage regulation. The...
In this paper, two Adaline-based schemes for identifying the harmonic currents generated by nonlinear loads in power distribution systems are presented. Each neural method is based on formalized expressions of the active and reactive instantaneous powers. In both methods, the expressions of the instantaneous powers are learned on-line by Adalines. The amplitude of each harmonic term is thus individually...
This paper presents a new harmonic currents identification method called neural synchronous method and based on artificial neural networks. Its theoretical aspect relies on a new decomposition of the load current signals. Adaline neural networks are used in order to learn this decomposition on-line; the fundamental currents can therefore be estimated at each sampling time. The fundamental currents...
In this paper, two efficient and reliable neural approaches to control an inverter are developed. The objective is to improve the compensation performance of a conventional active power filter (APF) with a homogeneous neural structure allowing an efficient hardware implementation. The first control approach is based on a neural PI regulator. This technique uses an Adaline to determine the PI parameters...
A new neural approach for identifying and compensating for harmonic distortions is proposed in this paper. Based on a new signal decomposition of the voltage and the current, this method identifies the direct, inverse and homopolar components of both the voltages and the currents of the supply network. For one signal, the component extraction requires four independant Adaline networks. The components...
This work presents theoretical studies and practical results obtained with voltage component extraction. The paper is centered on a new method for estimating the direct, inverse and homopolar voltage components from unbalanced and disturbed power systems. We introduce and develop a new decomposition of the voltages in the DQ-space that results in linear expressions explicitly separating AC from DC...
This paper introduces a new neural method for harmonic identification and compensation. Based on Adaline networks, the proposed method is called the diphase currents method. The architecture and the learning are formulated based on an original decomposition of the disturbed currents. These currents are converted in the alphabeta- or DQ-spaces to separate each harmonic component in a linear expression...
In this paper, we consider the problem of estimating the frequency of a sinusoidal signal whose amplitude and frequency could be either constant and time-varying. We present an artificial neural network approach for the on-line estimation of the signal frequency. The neural network architecture and learning is formulated based on an original decomposition of the signal to estimate. We show that the...
This work describes an improved Adaline neural networks method for online extracting the direct, inverse and homopolar voltage components from a composite voltage. A new voltage decomposition is thus proposed and developed. These skills are transferred to four Adalines by fixing their inputs. Adaline neural networks are used with a LMS learning process to compute the weights biases and thus to find...
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