Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Mechanical Engineering for Society and Industry
Adhi, Andrew Cahyo
Unknown Affiliation
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Transportation

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
SCADA-driven variable similarity-based model for fault detection and predictive maintenance in photovoltaic systems Widodo, Achmad; Prahasto, Toni; Syamsuddin, Agussalim; Adhi, Andrew Cahyo; Kusumawardhani, Amie
Mechanical Engineering for Society and Industry Vol. 6 No. 1 (2026): Issue in Progress
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/mesi.14236

Abstract

Global efforts to mitigate the rise in average global temperatures have increasingly emphasized the adoption of renewable energy sources. Among these initiatives, the deployment of solar power plants has emerged as a promising solution, utilizing photovoltaic (PV) systems to convert solar energy—an abundant, cost-free, and year-round resource—into electricity. Solar power plants present a viable alternative to fossil fuels traditionally used in thermal power generation. The reliability of PV systems, which directly influences their performance, functionality, safety, and economic viability, is a critical factor in realizing this potential. This article presents the development of a predictive maintenance framework for PV systems, incorporating anomaly detection and fault diagnosis based on supervisory control and data acquisition (SCADA) data. The methodology employs a variable similarity-based model (VBM) to identify anomalies and diagnose faults, while generating predictive alerts to inform operators of potential issues, thereby enabling proactive maintenance scheduling. The proposed framework is validated using real SCADA data collected from an operational solar power plant. The results demonstrate that the method effectively detects anomalies with reasonable accuracy, underscoring its practicality for application in solar power plant operations.
Co-Authors Achmad Widodo Amie Kusumawardhani, Amie Syamsuddin, Agussalim Toni Prahasto