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https://www.um.edu.mt/library/oar/handle/123456789/144354| Title: | Data analytics for photovoltaics reliability and operations |
| Authors: | Bartolo, Brian (2025) |
| Keywords: | Photovoltaic power systems -- Malta Cloud computing -- Malta |
| Issue Date: | 2025 |
| Citation: | Bartolo, B. (2025). Data analytics for photovoltaics reliability and operations (Master's dissertation). |
| Abstract: | The rapid growth of photovoltaic (PV) systems has increased the importance of reliable and standardised monitoring frameworks to ensure sustained performance and operational reliability, particularly in challenging environments such as small island states. This research was conducted within the framework of the Horizon Europe PROMISE project, which brought together an interdisciplinary consortium of scientists and engineers to investigate innovative approaches to PV system reliability, digitalisation, and sustainable operation through the use of Living Laboratories and Test Sites. The primary aim of this MSc by Research study was to design, implement, and validate a PV monitoring and anomaly detection framework aligned with internationally recognised standards and open-data practices. A comprehensive review of IEC 61724 and related literature was first undertaken to establish best practices in PV performance monitoring, data quality assurance, and anomaly detection. This review informed the system architecture and provided a reference framework for both the present work and future research activities. The experimental implementation involved the deployment of electrical and meteorological monitoring hardware across ten PV Living Laboratories in Malta. Sensors were selected and justified based on standard compliance and site-specific constraints and were integrated using local controllers and standardised communication protocols. Data acquisition was performed locally using Raspberry Pi devices, while a scalable cloud-based architecture was developed using Microsoft Azure to support data ingestion, storage, processing, and real-time visualisation through Power BI. Anomaly detection was implemented in two stages, addressing both low-level operational faults and higher-level performance deviations. The framework was validated using real operational data from the Malta PV Living Laboratories, demonstrating its ability to improve system observability and support early fault identification. The outcomes of this study highlight the suitability of Living Laboratories for applied PV research and position Malta as a practical testbed for the development and evaluation of digitalised PV monitoring solutions relevant to wider European contexts. |
| Description: | M.Sc.(Melit.) |
| URI: | https://www.um.edu.mt/library/oar/handle/123456789/144354 |
| Appears in Collections: | Dissertations - FacEng - 2025 |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 2519ENRENR502005081752_1.PDF Restricted Access | 14.98 MB | Adobe PDF | View/Open Request a copy |
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