光伏系统公共数据存储库的开发和实施:马耳他生活实验室案例研究

Brian Bartolo , Brian Azzopardi , Kenneth Scerri
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引用次数: 0

摘要

向可再生能源,特别是光伏系统(pv)的过渡,是实现碳中和目标的关键。然而,通过数据共享进行有效的绩效监测仍然具有挑战性,特别是在具有独特环境条件的地区。本研究通过引入马耳他在PROMISE项目下的第一个pv公共数据存储库来解决这些差距。该计划集成了符合IEC标准的高精度实验室级传感器,以确保强大的数据采集和处理。该方法以国际最佳做法为基础,采用先进的监测框架,以高时间分辨率捕获气象和系统参数。该系统使用了马耳他十个光伏生活实验室中的三个,反映了地中海岛屿地区气候和马耳他环境的典型安装。数据收集和处理遵循欧洲标准和现有公共数据存储库的既定实践,确保兼容性和互操作性。通过利用开源平台,存储库支持实时可视化和结构化历史数据集的提供。数据中心性能比率等性能指标被进一步用于强调数据共享和协作在优化系统性能和推进预测性维护策略方面的价值。这项工作为当地光伏监测和数据共享建立了一个可扩展的框架,推进可再生能源的研究和创新,并促进马耳他参与外国项目。未来的扩展目标是包括额外的实验室,自动数据上传,并集成人工智能(AI)驱动的故障检测工具,提高系统的实用性和可靠性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Development and implementation of a public data repository for photovoltaic systems: Case study malta's living laboratories
The transition to renewable energy, particularly Photovoltaic systems (PVs), is pivotal in achieving carbon neutrality goals. However, effective performance monitoring through data sharing remains challenging, especially in regions with unique environmental conditions. This study addresses these gaps by introducing Malta's first public data repository for PVs under the PROMISE project. The initiative integrates high-precision, laboratory-grade sensors compliant with IEC standards to ensure robust data acquisition and processing. The methodology builds on international best practices, employing advanced monitoring frameworks to capture meteorological and system parameters with high temporal resolution. Using three of the ten PV Living Laboratories in Malta, the system reflects typical installations in a Mediterranean Island Territory climate and the Maltese context. Data collection and processing adhere to European standards and established practices from existing public data repositories, ensuring compatibility and interoperability. By leveraging open-source platforms, the repository enables real-time visualization and the provision of structured historical datasets. Performance metrics, such as the DC performance ratio, are further employed to highlight the value of data sharing and collaboration in optimizing system performance and advancing predictive maintenance strategies. This work establishes a scalable framework for local PV monitoring and data sharing, advancing research and innovation in renewable energy and promoting Malta's participation in foreign programs. Future expansions aim to include additional laboratories, automate data uploads, and integrate Artificial intelligence (AI) driven fault detection tools, enhancing the system's utility and reliability.
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