Computing Institute, A. C. Simões Campus, Federal University of Alagoas-UFAL, Maceió, AL 57072-970, Brazil.
Center of Agrarian Sciences, Engineering and Agricultural Sciences Campus, Federal University of Alagoas-UFAL, Rio Largo, AL 57100-000, Brazil.
Sensors (Basel). 2021 May 10;21(9):3293. doi: 10.3390/s21093293.
Monitoring and data acquisition are essential to recognize the renewable resources available on-site, evaluate electrical conversion efficiency, detect failures, and optimize electrical production. Commercial monitoring systems for the photovoltaic system are generally expensive and closed for modifications. This work proposes a low-cost real-time internet of things system for micro and mini photovoltaic generation systems that can monitor continuous voltage, continuous current, alternating power, and seven meteorological variables. The proposed system measures all relevant meteorological variables and directly acquires photovoltaic generation data from the plant (not from the inverter). The system is implemented using open software, connects to the internet without cables, stores data locally and in the cloud, and uses the network time protocol to synchronize the devices' clocks. To the best of our knowledge, no work reported in the literature presents these features altogether. Furthermore, experiments carried out with the proposed system showed good effectiveness and reliability. This system enables fog and cloud computing in a photovoltaic system, creating a time series measurements data set, enabling the future use of machine learning to create smart photovoltaic systems.
监测和数据采集对于识别现场可用的可再生资源、评估电能转换效率、检测故障以及优化电能生产至关重要。商业用途的光伏系统监测系统通常价格昂贵且无法进行修改。本工作提出了一种用于微小型光伏发电系统的低成本实时物联网系统,可以监测连续电压、连续电流、交流功率和七种气象变量。所提出的系统测量所有相关的气象变量,并直接从电站(而非从逆变器)获取光伏发电数据。该系统使用开放软件实现,无需电缆即可连接到互联网,在本地和云端存储数据,并使用网络时间协议来同步设备时钟。据我们所知,文献中没有工作能够同时具备这些特点。此外,使用所提出的系统进行的实验表明其具有良好的有效性和可靠性。该系统可在光伏系统中实现雾计算和云计算,创建时间序列测量数据集,从而能够在未来使用机器学习来创建智能光伏系统。