Ozarks Electric Blends AMR and OMS Functionality

As electric utilities seek to cost-justify automatic meter reading (AMR) system deployments, it’s important that they look for value beyond the remote read. One Arkansas-based co-op found that AMR could function as a valuable component of outage management.

When Ozarks Electric Cooperative in Fayetteville, Ark., installed its outage management and prediction system (OMS), automated call processing helped them take many more customer outage reports than ever before. With more outage data and the automated system to analyze it, the co-op was better able to quickly and accurately predict the source of each outage, determine if it was isolated or growing, and direct repair crew activities in the field.

After years of experience with their OMS, Ozarks decided to install a TWACS AMR system (from Distribution Control Systems Inc.). The TWACS system was collecting data on power outages and failures, and the Ozarks operations department wanted to consolidate that data. Systematic analysis of AMR outage and failure reports should, in theory, be able to uncover points on the distribution grid that were likely sources problems, personnel in the operations department reasoned. Results then could be used to better organize preventative maintenance activities.

The methodology they wished to use was straightforward. Outages were a clear indication of a device failure; blinks on the other hand might be an indication of the potential for future trouble. They knew there must be a way to efficiently use the AMR data to uncover operational problems, such as those related to right of way and insulators or to distinguish between problems on the primary side vs. the secondary side of a transformer. But, blocking them from reaching their goal of predictive analysis was the difficulty in wading through the volumes of historical data their AMR system recorded. The time required to organize, review, analyze and interpret the compiled data was too great to make even a one-time project feasible.

Ozarks called its outage management and prediction system vendor, Allen, Texas-based dataVoice International, to help solve the problem. Since the automated OMS already tracked the location of each customer outage call on the utility’s connectivity grid, why couldn’t it do the same with AMR reports?

The OMS schematic representation of the distribution grid used by the operations center was designed to turn a line section yellow when an outage was reported on that section or to turn it red when the outage was confirmed. The same thing should be possible with AMR data; the only real difference would be the source of the report. dataVoice engineers worked with Ozarks to take the analysis a step beyond their expectations. Data from both the AMR and OMS systems would be correlated and report times and locations compared.

Ozarks Electric Cooperative tracks both power outages and “blinks” on a dynamic map of its system. Blue indicates blinks reported by AMR units.

A “side-by-side” comparison of AMR and OMS data would make analysis easier and much more conclusive. AMR reports that were concurrent with OMS-confirmed outages clearly were valid. There had been a confirmed, recorded failure and the device had been pinpointed. When AMR devices reported a blink or outage and the OMS didn’t, a potential problem might be lurking. If a blink or two appeared on a line section that had no outages confirmed by the OMS (or the operations center), there probably wasn’t a problem. But, where frequent blinks on line sections or under a device did not coincide with an OMS-confirmed outage, those were likely sources of future problems–failures just waiting to happen.

Ozarks Electric Cooperative tracks both power outages and “blinks” on a dynamic map of its system. Blue indicates blinks reported by AMR units.
Click here to enlarge image

Ozarks began the analysis process by selecting the start and end times for the data they wanted to study. Data from their AMR system was networked to the OMS system. Visual displays of the AMR reports and OMS outage data on system connectivity schematics made failure patterns very clear. From these patterns, Ozarks easily pinpointed locations of devices, lines and meters that had the greatest potential to fail. A revised preventative maintenance program began immediately.

Because the analysis process is now easy and requires minimal personnel time, regular AMR/OMS data analysis and correlation is a standard part of operating procedure. Results are as expected; identifying potential failures and prioritizing maintenance has paid off by reducing revenue losses due to outages. In retrospect, the process wasn’t that difficult; it was primarily a matter of finding a new way to use the information they already had.

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