This paper presents a novel mathematical model for fast fault section estimation in a Distribution Control Center (DCC).
Then, to determine fault section and fault location, the arrival times of the first peak waves generated by the fault is utilized.
This paper presents an artificial bee colony algorithm to enhance the fault section estimation performance in power systems.
Results show that the proposed methodology is capable of performing fault section estimation in a very fast and reliable manner.
In this paper, a new method based on the Gabor transform (GT) for fault section determination and fault location calculation in multi-terminal lines is presented.
Four different ANN networks are designed for detection of the fault, fault section identification, classification of fault and location of fault in time domain.
According to the genesis of marginal fault, section pattern, and plane combination, three types of structural slope breaks are distinguished in different tectonic units of Qikou Sag.
Through this proposed approach, it benefits the engineers to find the accurate fault section among voluminous alarms, and reduces the possibility of inaccurate diagnosis.
This paper proposes a new fault diagnosis approach based on combined wavelet transform and adaptive neuro-fuzzy inference system for fault section identification, classification and location in a series compensated transmission line.
The fault section can be located mainly by comparing the power direction of positive sequence fault components among feeders, as well as comparing the output current of PV inverter with the current through its connecting switchgear.
The proposed relaying technique is tested on a single-junction and a multi-junction hybrid transmission lines under different fault conditions and is found fast and accurate independent of fault type, fault section, fault resistance, fault inception angle (FIA) and load angle.
