Comparison of measurement features used as inputs in a learning-based fault location method for power distribution systems

Abstract

This paper presents a comparative study of the measurement features used as inputs of a fault locator based on Support Vector Machines, which is aimed to analyze single-phase faults. Studies have shown that a huge database is required to obtain high performance, but a problem is associated with the excessive computing time required to evaluate such databases. This study examines properly these inputs to determine which are the most significant ones in terms of performance. Tests are performed on a 75 bus 34.5 kV distribution system, with 75000 shunt faults, implemented in ATP. According to the results, 12 features related to magnitude variations of phase voltage and current between fault and pre-fault steady states were relevant to achieve a performance of 96.3%, with a computational time of training and cross-validation of approximately six minutes.

Keywords

• fault location
• measurement features
• support vector machines
• attributes
• distribution systems