Data-Driven Relaying System for Transmission Line Series Compensated with Double Circuit

Abstract

This paper introduces double circuit transmission lines data-based modelling, control, defect identification and categorization of fixed series capacitor compensated. One of the severe problems of double circuit transmission lines is reciprocal connection between two of circuits of compensated transmission line in series for which most of the conventional schemes will not work. One of the solutions for this type of problems is to design a scheme considering the situations that may arise in the system. Different situations can be represented with different data and from data the algorithm should able to predict the outcome. So in this paper in double circuit line Artificial Neural Networks are employed to identify faults and categorize them with fixed series capacitor compensated lines on which mutual coupling between the lines have no effect. The data-driven relaying plan is tested for a variety of malfunction scenarios, including short-circuit defect, cross-country error, fault kind, and malfunction location, defect resistance, and angle of error inception. For every evaluated defect condition, the suggested data-driven relaying technique is proved to be 100% accurate.