Matrix deployment based on microservice architecture, visualization presentation through the integration of GIS and business data; distributed collection, high-speed and accurate data acquisition to ensure the fluency of the system. Discover and track PV system abnormalities at any time to ensure safe operation; customize warning scenarios as required to achieve lean management. Mining effective information based on real data, accurately measuring investment returns and improving project ROI.
Combined with site 3D geographic data model, real-time meteorological data and irradiance data, the PV tracking control algorithm is a new generation of big data-driven closed-loop self-learning tracking system solution, which relies on the topological algorithm of neural network. Using the AI intelligent algorithm for accurate analysis and prediction, the PV tracking control algorithm calculates the best operating angle of each array and sends different control commands to each TCU, so as to achieve the global optimal tracking. In this case, the power generation efficiency of PV power plant is improved by 1~6%.
The development structure is generally based on B/S, distributed microservice structure, the front-end and back-end are separated so that the R&D personnel has their own duties, the back-end development is focused on business implementation, the front-end is focused on representation presentation, compatible with DBMS such as MySQL, NoSQL such as MongoDB, redis, etc. Development tools can be freely chosen according to business needs such as Eclipse, IDEA, etc.
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