May 1

Employing EGM’s Meta-AlertTM System for Condition Based Monitoring

One of the common notions in today’s electric market is that in order to make the most of the grid, a utility needs to implement condition based monitoring. The logic behind condition based monitoring is that the maintenance should be adjusted to the actual status of the grid, rather than to a fixed set of periodic intervals. This perception goes hand in hand with the understanding that the growing dependency on the grid makes it more vulnerable which in turn dictates a need for a comprehensive maintenance and troubleshooting strategy.

The advantages of condition based monitoring are clear. To name just a few – it reduces overhead costs, it saves valuable work time of unnecessary maintenance (thus also contributing to a safer work environment), it reduced unexpected downtime, and it helps identifying the location of the problem and it nature thus reducing time of diagnosis and locating the exact place of the damage.

Condition based monitoring thus depends on accurate real time data regarding the status of the grid. To achieve this end, sensors are placed on the grid itself, and detect in real time deviations from the normal values of work. The time frame between the first identification of the grid’s abnormality and the actual malfunction is the time frame in which the utility needs to get the data, analyze it, and carry out the appropriate maintenance in order to avoid reaching the malfunction. This way, a utility can surmount internal problems such as leakages, as well as external factors such as extreme weather events, cyber and security threats etc.

EGM’s Meta-AlertTM system exhibits that ability, and much more. Using EGM’s Meta-AlertTM provides utilities with real time data collection and analysis, resulting in a complete picture about the status of the grid. The Meta-AlertTM system includes a net of sensors spread across the grid, measuring grid parameters such as electrical voltage and current, cable movement, cable temperature and even smoke detection. Through this the system identifies the initiation of a failure mechanism at its incipient stage, at times even months before the problem surfaces.

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