DTE Energy Improves Power Outage Management With Smart Grid Sensors

DTE Energy demonstrates use of smart grid sensors in power restoration following a substation fire.
Published: Thu 24 Sep 2015

Almost mid-way into an initiative to modernize DTE Energy’s electric grid with Tollgrade’s Lighthouse smart grid sensors, and with a full 12 months of sensor data from DTE Energy and other utilities, some key insights can be drawn.

Line disturbances predominate

In the third edition of the Predictive Grid Quarterly Report, which is aimed at benchmarking the use of the technology for outage prevention, it is found that 84% of the events monitored were line disturbances, compared to 12% momentaries and 4% power outages. This is similar to earlier findings for the first 6 months of data and indicates an opportunity to uncover underlying issues before they become power interruptions. [Engerati-Smart Grid Sensors - Helping Utilities To Tackle Outages More Effectively]

Likewise as also identified previously, the line disturbances and outages follow a similar pattern. Most events occur in the summer months then drop off dramatically in October, with a slight uptick in the winter during the months of November and December. The data also shows a large spike in events of all types in March due to several ice storms that impacted a large section of North America. As the spring season progressed, line disturbances and outages increased. This indicates how closely line disturbances are related to outages, and the importance of viewing line disturbances as an early indicator to outages.

Editor's Note: If your hungry for more info on the Intelligent Grid & Automation including; Self Healing Networks and intelligent approacheds to Asset and Outage Management; check out our Intelligent Grid In-Focus Track

Increased situational awareness with smart grid sensors

On July 23 shortly after 8.00 am a fire broke out at a DTE Energy substation caused by an equipment failure and creating a significant power outage to an area affecting about 10,000 customers including a number of municipal facilities.

As many customers as possible – somewhat less than two-thirds – were transferred to alternative power lines while in parallel temporary substations and generators were brought in to begin the process of safely restoring power to the remaining customers. However, the company was facing a loss of situational awareness of the grid in the area as well as the prospect of an upcoming heat wave.

To overcome these issues, smart grid sensors were installed within a period of hours in identified key areas that required load monitoring. With near real-time data, engineers and grid operators were able to make informed decisions that helped prevent mistakes or cause additional power outages. In addition, the sensors were able to monitor the system continuously and provide alarms if any abnormalities were identified, allowing the crew to focus on repairing and restoring the equipment at the substation.

“Information and data are everything these days. But having the right data, in real-time, to take the actions required in an emergency situation like we recently experienced is the true value behind these smart grid sensors,” said Russel Pogats, DTE’s Director Electrical Engineering. “Without the sensors sending us the data we needed to monitor the condition of our assets in real-time, this outage could have been much more significant, costing us hundreds of thousands of dollars and leaving our customers in the dark during a heat storm for many days.”

Lessons learned

Findings also show that to use the LightHouse data more efficiently, it is preferable to add smart grid sensors on the line near existing switching locations so that if a fault occurs, the sensors will pinpoint the actual section location. Utility linemen can then go directly to a nearby switch, open it and isolate the problem area while almost immediately restoring power to the remaining customers. This methodology will allow DTE Energy to have a significant impact on power restoration time for customers.

Waveform mapping also has been carried out for underground cable failures, wire contact and pole top transformer outages.

To date, Detroit-based DTE Energy has successfully integrated Tollgrade’s sensor data into its Historian and Distribution Management Systems (DMS). This is the first step in a process that will ultimately give field personnel remote access to information on the status of sensors, its physical location, and details about line disturbances, momentaries and outages that are occurring in near real-time. Additionally, the sensor data is now integrated with DTE Energy’s SCADA system.

Further reading

Tollgrade and DTE Energy: Predictive Grid Quarterly Report. Volume 3, September 2015