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

Fatima Louisa Yousfi, Djaffar Ould Abdeslam, Tahar Bouthiba, Ngac-Ky Nguyen, Jean Merckle
2012 IEEE Transactions on Power Delivery  
This paper presents a new method for online symmetrical components and phase angles extraction from High Voltage (HV) transmission line faults. This method is based on the Adaline neural networks and the instantaneous power theory (IPT), 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 αβ frame are separated by using four Adaline neural networks. The
more » ... 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 angles estimation between direct and inverse current components. These phase angles permit to classify 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.
doi:10.1109/tpwrd.2012.2196526 fatcat:z7liky2f75f6hnvawiva5p6a6e