Fault Detection System for Smart City Distribution Networks: A Long Short-Term Memory-Based Approach

A. Herrada, C. Orozco-Henao, Juan Diego Pulgarín Rivera (), J. Mora-Flórez and J. Marín-Quintero
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A. Herrada: Department of Electrical and Electronic Engineering, Universidad del Norte, Barranquilla 081007, Colombia
C. Orozco-Henao: School of Electrical, Electronics and Telecommunications Engineering, Universidad Industrial de Santander, Bucaramanga 680002, Colombia
Juan Diego Pulgarín Rivera: Department of Electrical and Electronic Engineering, Universidad del Norte, Barranquilla 081007, Colombia
J. Mora-Flórez: Department of Electrical Engineering, Universidad Tecnológica de Pereira, Pereira 660003, Colombia
J. Marín-Quintero: Energy Department, Universidad de la Costa, Barranquilla 080002, Colombia

Energies, 2025, vol. 18, issue 13, 1-19

Abstract: One of the primary goals of smart cities is to enhance the welfare and comfort of their citizens. In this context, minimizing the time required to detect fault events becomes a crucial factor in improving the reliability of distribution networks. Fault detection presents a notable challenge in the operation of Smart City Distribution Networks (SCDN) due to complex operating conditions, such as changes in the network topology, the connection and disconnection of distributed energy resources (DERs), and varying microgrid operation modes, all of which can impact the reliability of protection systems. To address these challenges, this paper proposes a fault detection system based on Long Short-Term Memory (LSTM), leveraging instantaneous local current measurements. This approach eliminates the need for voltage signals, synchronization processes, and communication systems for fault detection. On the other hand, LSTM methods enable the implicit extraction of features from current signals and classifies normal operation and fault events through a binary classification formulation. The proposed fault detector was validated on several intelligent electronic devices (IED) deployed in the modified IEEE 34-node test system. The obtained results demonstrate that the proposed detector achieves a 90% accuracy in identifying faults using instantaneous current values as short as 1/4 of a cycle. The results obtained and its easy implementation indicate potential for real-life applications.

Keywords: deep learning; smart city distribution networks; fault detection (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: 2025
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