Efficient Methodology for Detection and Classification of Short-Circuit Faults in Distribution Systems with Distributed Generation

• Main Author: Santos, Andréia da Silva [UNESP]
• Publication Date: 2022
• Other Authors: Faria, Lucas Teles [UNESP], Lopes, Mara Lúcia M. [UNESP], Lotufo, Anna Diva P. [UNESP], Minussi, Carlos R. [UNESP]
• Format: Article
• Language: eng
• Source: Repositório Institucional da UNESP
• Download full: http://dx.doi.org/10.3390/s22239418; http://hdl.handle.net/11449/246458
• topic: distributed generation; distribution systems; fuzzy logic inference; multi-resolution analysis; short-circuit fault classification; short-circuit fault detection; wavelet transform

Fault detection and classification are crucial procedures for electric power distribution systems because they can minimize the occurrence of faults. The methods for fault detection and classification have become more problematic because of the significant expansion of distributed energy resources in distribution systems and the change in their currents due to the action of short-circuiting. In this context, to fill this gap, this study presents a robust methodology for short-circuit fault detection and classification with the insertion of distributed generation units. The proposal methodology progresses in two stages: in the former stage, the detection is based on the continuous analysis of three-phase currents, whose characteristics are extracted through maximal overlap discrete wavelet transform. In the latter stage, the classification is based on three fuzzy inference systems to identify the phases with disturbance. The short-circuit type is identified by counting the shorted phases. The algorithm for short-circuit fault detection and classification is developed in MATLAB programming environment. The methodology is implemented in a modified IEEE 34-bus test system and modeled in ATPDraw with three scenarios with and without distributed generation units and considering the following parameters: fault type (single-phase, two-phase, and three-phase), angle of incidence, fault resistance (high impedance fault and low impedance fault), fault location bus, and distributed generation units (synchronous generators and photovoltaic panels). The accuracy is greater than 94.9% for the detection and classification of short-circuit faults for more than 20,000 simulated cases.