Intelligent system based on orthogonal decomposition technique and fuzzy inference for high impedance location fault in distribution systems with distributed generation

Author(s)

Oureste Elias Batista

• Document type: Doctoral Thesis
• Press: São Carlos.
• Institution: Universidade de São Paulo (USP). Escola de Engenharia de São Carlos (EESC/SBD)
• Defense date: 2016-03-28

Examining board members

Rogério Andrade Flauzino; Carlos Alberto de Castro Junior; Anna Diva Plasencia Lotufo; José Carlos de Melo Vieira Júnior; Keiji Yamanaka

• Advisor: Rogério Andrade Flauzino

Abstract

Modern electric power systems present numerous challenges in its operation. Fault location is a major challenge in Power Distribution Systems due to its large branching, presence of single-phase laterals and the dynamic loads. The influence of the fault impedance is one of the largest, significantly affecting the use of traditional methods for its location, since the magnitude of the fault currents is similar to the load current. In this sense, this thesis aimed to develop an intelligent system for location of high impedance faults, which was based on the application of the decomposition technique of orthogonal components in the pre-processing variables and fuzzy inference to interpret the nonlinearities of Power Distribution Systems with the presence of Distributed Generation. The data for training the intelligent system were obtained from computer simulations of an actual feeder, considering a non-linear modeling of the high impedance fault. The resulting fuzzy system was able to estimate distances to fault with an average absolute error of less than 500 m and a maximum absolute error of 1.5 km order, on a feeder about 18 km long. These results are equivalent to a degree of accuracy for the most occurrences within the ± 10% range. (AU)

• FAPESP's process: 12/21816-6 - Intelligent system based on orthogonal decomposition technique and fuzzy inference for high impedance location fault in distribution systems with distributed generation
• Grantee: Oureste Elias Batista
• Support Opportunities: Scholarships in Brazil - Doctorate