Intelligent Fault Detection System for Microgrids

Cristian Cepeda, Cesar Orozco-Henao, Winston Percybrooks, Juan Diego Pulgarín-Rivera, Oscar Danilo Montoya, Walter Gil-González and Juan Carlos Vélez
Additional contact information
Cristian Cepeda: Department of Electrical and Electronic Engineering, Universidad del Norte, Barranquilla 080001, Colombia
Cesar Orozco-Henao: Department of Electrical and Electronic Engineering, Universidad del Norte, Barranquilla 080001, Colombia
Winston Percybrooks: Department of Electrical and Electronic Engineering, Universidad del Norte, Barranquilla 080001, Colombia
Juan Diego Pulgarín-Rivera: Department of Electrical and Electronic Engineering, Universidad del Norte, Barranquilla 080001, Colombia
Oscar Danilo Montoya: Faculty of Engineering, Universidad Distrital Francisco José de Caldas, Bogotá D.C. 11021, Colombia
Walter Gil-González: Smart Energy Laboratory, Universidad Tecnológica de Bolívar, Cartagena 131001, Colombia
Juan Carlos Vélez: Department of Electrical and Electronic Engineering, Universidad del Norte, Barranquilla 080001, Colombia

Energies, 2020, vol. 13, issue 5, 1-21

Abstract: The dynamic features of microgrid operation, such as on-grid/off-grid operation mode, the intermittency of distributed generators, and its dynamic topology due to its ability to reconfigure itself, cause misfiring of conventional protection schemes. To solve this issue, adaptive protection schemes that use robust communication systems have been proposed for the protection of microgrids. However, the cost of this solution is significantly high. This paper presented an intelligent fault detection (FD) system for microgrids on the basis of local measurements and machine learning (ML) techniques. This proposed FD system provided a smart level to intelligent electronic devices (IED) installed on the microgrid through the integration of ML models. This allowed each IED to autonomously determine if a fault occurred on the microgrid, eliminating the requirement of robust communication infrastructure between IEDs for microgrid protection. Additionally, the proposed system presented a methodology composed of four stages, which allowed its implementation in any microgrid. In addition, each stage provided important recommendations for the proper use of ML techniques on the protection problem. The proposed FD system was validated on the modified IEEE 13-nodes test feeder. This took into consideration typical features of microgrids such as the load imbalance, reconfiguration, and off-grid/on-grid operation modes. The results demonstrated the flexibility and simplicity of the FD system in determining the best accuracy performance among several ML models. The ease of design’s implementation, formulation of parameters, and promising test results indicated the potential for real-life applications.

Keywords: fault detector; microgrid; machine learning-based techniques (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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