Adaline for Online Symmetrical Components and Phase-Angles Identification in Transmission Lines

This paper presents a new method for online symmetrical components and phase-angle extraction from high-voltage transmission-line faults. This method is based on the Adaline neural networks and the instantaneous power theory, also known as the
$p-q$
method. A new current decomposition is proposed in order to derive the direct, inverse, and homopolar current components. The average and oscillating terms of powers in the
$\alpha \beta$
frame are separated by using four Adaline neural networks. The Adalines use a cosine and sine as inputs in order to learn the linear combination of the powers. The resulting symmetrical components are used by three other Adalines for phase-angle estimation between direct and inverse current components. These phase angles permit classifying the fault types. The neural networks use an online learning process-based Widrow–Hoff algorithm and can adapt their weight parameters to the power-supply evolution. Simulation results show the performance and the robustness of this method and provide a perspective for protection relay improvement.