Modern optimization algorithms for fault location estimation in power systems

This paper presents a fault location estimation approach in two terminal transmission lines using Teaching Learning Based Optimization (TLBO) technique, and Harmony Search (HS) technique. Also, previous methods were discussed such as Genetic Algorithm (GA), Artificial Bee Colony (ABC), Artificial neural networks (ANN) and Cause & effect (C&E) with discussing advantages and disadvantages of all methods. Initial data for proposed techniques are post-fault measured voltages and currents from both ends, along with line parameters as initial inputs as well. This paper deals with several types of faults, L-L-L, L-L-L-G, L-L-G and L-G. Simulation of the model was performed on SIMULINK by extracting initial inputs from SIMULINK to MATLAB, where the objective function specifies the fault location with a very high accuracy, precision and within a very short time. Future works are discussed showing the benefit behind using the Differential Learning TLBO (DLTLBO) was discussed as well.