Fault location prediction on double-circuit transmission lines based on the hierarchical temporal memory
Dikio C. Idoniboyeobu, Biobele Alexander Wokoma, Emmanuel N. Osegi · 2017 IEEE 3rd International Conference on Electro-Technology for National Development (NIGERCON) · 2017
A novel approach to the fault location estimation and prediction analysis in doubly fed power transmission lines is presented. This approach uses an Artificial Intelligence (AI) technique and technology called the Hierarchical Temporal Memory (HTM). HTM is a state-of-the-art online learning neural/machine learning technique and tool based on a suite of biological plausible algorithms coined the Cortical Learning Algorithm (CLA). The results on using this new technique on an artificially generated fault data for fault location prediction are presented as well. The results show that the proposed approach can indeed lead to superior results when compared to a similar conventional machine learning technique called the Online Sequential Extreme Learning Machine (OS-ELM).