Enhancing Efficiency and Connectivity in a Complex Infrastructure

This is a large-scale rooftop self-consumption solar project with a total capacity of 42 MW. The entire project comprises nearly thirty rooftops, including 328 inverters and 3280 strings. The electrical infrastructure for the project are highly

complex. The rooftops are categorized into six zones, each with specific configurations; some operate under a “Zero Feed-in” setup, while others utilize partial “Net Metering.”

In terms of communication, navigating the restrictions that prevent the connection of internal networks and the installation of physical fiber optic networks at the end-user level required collaboration with a network service provider. Together, we devised a wireless Access Point (AP) solution, allowing constructors to access comprehensive PV device information on individual rooftops without disrupting on-site operations. Additionally, a Master-Slave architecture was employed to facilitate cross-rooftop “Zero Feed-in” control and PQIV data transmission to the TNB SCADA system. Utilizing thingnario software solutions, we effectively overcame the challenges inherent in on-site construction. This optimization enabled the EPC team to execute the project with heightened efficiency and reduced effort.

AI Smart Diagnosis to Assist Maintenance

Because the project spans across 30 factory rooftops, the on-site environment is challenging. It becomes highly inefficient and difficult for maintenance personnel to individually inspect each device and string. PHOTON provides

intelligent AI-driven analysis through extensive data collection, directly informing users of the root causes of issues, thus reducing on-site inspection time.

In addition, thingnario offers a multi-tiered plant structure. This allows for group-based management of abnormal events, issue categorization, potential loss estimation, and prioritization of maintenance tasks. As a result, maintenance

personnel’s efficiency is significantly enhanced, enabling them to make optimal maintenance decisions through the system.