Abstract
The present paper presents the implementation of next-generation centralized Protection, Automation, and Control (PAC) solution for Medium Voltage (MV) power grids, developed in the scope of the SCALE project [1]. The main goals of the project are the development, testing, and field pilot deployment of an innovative, fully digital PAC system for Substation Automation (SAS), centralizing in a single device the functionalities of several bay-level Intelligent Electronic Devices (IED). The envisioned system, comprised of a Centralized Protection and Control (CPC) device and Merging Units (MU)/Process Interface Units (PIU), constitutes a highly flexible, resilient, future-proof solution that relies both on modern IEC 61850 standards and on legacy industrial protocols to guarantee multi-vendor interoperability and continued integration with multi-generation devices inside and outside of the substation. Centralizing SAS functionalities in a single device provides access to a wide range of data and measurements that unlocks technologically advanced substation-centric network automation applications. © The Institution of Engineering and Technology 2023.

Abstract
Modern distribution networks increasingly incorporate intelligent automation schemes to enhance resilience and reduce service interruptions following faults. To support these strategies, this paper investigates the use of machine learning models for fault location, aiming to quickly identify the faulted area and support safe service restoration of non-faulted areas. A comparative study is conducted using three supervised learning methods: Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting (GB), applied to fault location in a distribution test system adapted to include Distributed Energy Resources (DER). Using steady-state current measurements generated from probabilistic fault scenarios based on historical data, each model is evaluated in terms of classification accuracy and computational feasibility. Results indicate that the models demonstrated high classification accuracy and efficient execution time, confirming the viability of machine learning (ML)-based approaches as effective decision-support tools for intelligent fault isolation and service restoration.