In the context of smart electrical grids, continuous asset monitoring is crucial to ensure the efficiency, safety, and reliability of the system. Predictive management allows for anticipating failures and planning maintenance optimally, reducing costs and making the grid more sustainable and resilient.
To meet this challenge, ENGING has developed PreditTransf, an innovative solution that collects only electrical variables to make an accurate diagnosis of transformers, one of the most important pieces of equipment in the network.
This technology identifies signs of wear or failures in the normal operation of these assets days, weeks, or even months in advance.
The benefits of PreditTransf include a significant reduction in operational costs, the avoidance of unplanned downtimes, decreased inactivity time, and prevention of energy waste.
Figure 1 – Electrical panel with the hardware for data collection and transmission; Figure 2 – Installation of measurement sensors on the transformer
As part of the ATE Agenda, the Vale d’Este Electric Cooperative (CEVE) is testing this solution in a pilot project. ENGING installed sensors to measure medium and low voltage currents in distribution transformers. These sensors were placed on the cables and near the equipment, with the collected data sent to an ENGING electrical panel installed on-site.
This panel also receives information from the PAS (Programmable Acquisition System), a compact and versatile device that collects, processes, and sends data to ENGING’s servers. The PAS can read different types of electrical signals, with various intensities, and allows for real-time monitoring of the transformers’ status, both by CEVE and by ENGING’s technical team.
Figure 3 – PAS System: detail of the main power and programming card
The data is then analyzed and presented on the online platform ePreditMntc, where it´s possible to consult the current status of each transformer, operational trends, and signs of potential problems.
Based on the collected data, ENGING has drawn significant conclusions about the operational behavior of the equipment, highlighting the robustness and effectiveness of the solution in a real environment.
Figure 4 – Initial dashboard of the ePreditMntc platform with the 4 transformers under monitoring of CEVE
(credits: ENGING)
