Distributed Generation System: Loss Detection of Grid Events via Pattern Identification

The purpose of this paper is to discuss the different types of pattern identification methods that are commonly used for loss detection of grid events in renewable energy Distributed Generation (DG) sources. The research paper is divided into four parts: Introduction, background, literature review, and conclusion. The introduction provides an overall overview of the topic, identifying reasons why pattern identification methods are important in recognizing islanding events. The second section of the paper highlights detailed analysis of distributed generation systems and the risks that might arise when islanding is not detected. The literature review analyzes three major pattern identification artificial neural networks, decision tree classifier, and adaptive neuro fuzzy inference system. These three systems use machine learning to train the systems through algorithms to identify islanding and non islanding system. The fourth section is a generalized summary of the entire paper.