Implementation of Data Logging and Fault History in the Automatic Transfer Switch System at the Automation Laboratory, Electrical Engineering Department, Politeknik Negeri Manado - Ilshi Tesalonika Lapian; Jesika Diana Tabar; Renaldy Rondonuwu; Fitria Claudya Lahinta; Alfrets Septy Wauran (Journal of Social Research, 2026)
Automatic Transfer Switch (ATS) systems are essential for maintaining power continuity in automation laboratories, where equipment such as PLCs, HMIs, and servo drives requires an uninterrupted power supply. Despite widespread ATS deployment, most laboratory-scale implementations operate without a structured mechanism for recording fault events, leaving operators without historical data for reliability evaluation, root-cause analysis, or maintenance planning.This paper presents the design and implementation of an integrated data logging and fault history system for an ATS at the Automation Laboratory, Electrical Engineering Department, Politeknik Negeri Manado, using a PLC–SCADA architecture over Modbus TCP/IP. Two complementary logging mechanisms were implemented: periodic time-series recording of electrical parameters (voltage, current, and frequency) at one-second intervals, and event-driven fault logging that captured timestamped records upon detection of undervoltage, blackout, and trip conditions. The SCADA platform provided real-time visualization, alarm management, historical trend display, and CSV data export. Fifty simulated fault cycles across three fault categories were conducted to evaluate system performance. All fault events were correctly detected and logged, achieving a 100% detection rate, zero false positives, and timestamp accuracy of ±12 ms. Reliability metrics—Mean Time To Repair (MTTR = 2.31 s), Mean Time Between Failures (MTBF = 6.72 h), and system availability (99.990%)—were computed automatically from the historian database.The system transformed a conventional ATS installation into a data-rich monitoring platform capable of supporting quantitative reliability analysis and evidence-based maintenance decision-making.