By Terry Nielsen and Parag Parikh, SPL WorldGroup
The Belfast city fathers were a foresighted group. In the late 1800s, they inaugurated an electrical system that has been growing for more than 100 years.
In central Belfast, the system is largely underground. But, exactly where are those cables? Records of their original locations have, over time, fragmented or disappeared entirely. And, even if Belfast had the original locations down to the foot, the records would today be little more than historical artifacts. World War II intervened. The Belfast Blitz of 1941 shredded the electricity network, along with half the city’s housing. In their subsequent haste to get fellow citizens sheltered and back to a semblance of normal life, those restoring the system can hardly be faulted for skimping on paperwork.
Half a century later, those missing cable locations came back to haunt electricity distributors.
A 1998 storm brought hurricane-force winds to Northern Ireland and left more than 160,000 customers without power. A day later, 30,000 were still in the dark; full restoration took almost a week. The extent of the damage put new impetus behind the local utility’s plan to implement a full outage management system (OMS) that could speed restoration work.
Effective outage management means you need to know the exact location of a break. The most straightforward approach to that issue is to map the grid within a GIS. The OMS then can use the GIS-supplied coordinates as the basis for OMS algorithms.
That made sense for most of the Northern Irish distribution network, which is largely above ground. But what about those underground Belfast cables? The utility couldn’t feed data it didn’t have into the GIS.
Enter an alternative solution: postal codes. As part of the United Kingdom, Belfast converted to the Royal Mail’s postcode system in the late 1960s. Those six- to eight-place alphanumeric designations narrow locations to a street or part of a street-generally no more than 80 properties, and frequently far fewer. By using the postal code, it was relatively easy to determine the proximity of customer trouble calls and group them together to accurately predict outage locations. Even though GIS did not provide the exact location of distribution transformers or the coordinates of the underground cables, the utility was able to detect the outage location and speed toward system recovery.
OMS can be GIS-Independent
A number of vendor-supplied OMS applications today are built directly onto a GIS. They’re particularly common among vendors focused on the GIS business, and it’s tough to impossible to wean them away from GIS dependence. But, GIS independence does exist among purpose-built OMS products. When GIS data isn’t available, these systems can obtain the required topology data from such non-GIS sources as tables in a database, engineering system data, spreadsheet data or legacy systems.
Of course, not just any old data will do. To be effective, an OMS must be able to identify a relatively small geographic area in which a break may be located.
In the United States, for instance, an urban area might well be able to mimic Belfast’s success by using ZIP+4, a nine-digit U.S. Postal Service code that narrows locations down to a city block or less. In more sparsely populated areas, however, ZIP+4 may designate a geographically sprawling “rural route.” For GIS-free OMS implementations in these cases, a utility might use the less widely known POSTNET system, which adds two digits onto the ZIP+4 code, providing a unique number (generally represented as a barcode-like series of long and short lines) for each delivery address in the country.
Another locational data alternative is geocoding. Geocoding assigns spatial references to street addresses that otherwise lack them. For example, an address could be geocoded with coordinates that accurately pinpoint its location on a map. A proximity algorithm uses the coordinates to associate the customer’s service address with a particular transformer. Additional verification could be applied using the transformer’s phase and secondary voltage. All this helps OMS to narrow the grid to predict the outage location.
The ability to implement OMS without GIS means a utility can deploy outage management across its entire enterprise even though it has not completed the process of gathering GIS data for all service areas. One Canadian utility, for instance, went live with an OMS using GIS data for only three of its five regions. While it filled in the GIS data gaps for the other two regions, it used the postal code method.
A mid-Atlantic U.S. utility used GIS-independence as a transition tool. Operating in a heavily urban area with frequent storms, the utility had devised its own GIS-like system before standardized products were widely available. Two decades later, it wrote code to move this data into its OMS while it implemented a separate, vendor-supplied GIS.
Bermuda Electric Light Co. offers a third example. BELCO needed an OMS to manage frequent hurricane-related outages, but GIS data was not initially available. BELCO deployed its OMS using the network data available in CAD files and a relational database. A few years later, once the GIS system was selected and deployed, it was relatively easy to transition from the non-GIS to the GIS data source for the OMS.
In short, utilities can use off-the-shelf third-party street data with their own relational databases, raster images of paper maps and substation detail, CAD drawings, or mainframe-based network data as sources for the OMS.
The expense of obtaining GIS data may not make sense for some sparsely populated areas. To align costs and benefits, OMS vendors and utilities have devised a variety of alternative ways to obtain locational data.
One vendor, for instance, offers a Visio-based tool that contains only the data needed for effective OMS operation, not full GIS designations. A multi-state U.S. utility uses it for a rural region of the upper Midwest while serving its other territories with the standard OMS/GIS combination.
Another vendor offers a hybrid that puts a drawing tool onto a relational database back end. The relational database provides network connectivity information but not necessarily the spatial references for the electrical facilities.
A Canadian utility with similar financial constraints found that its GIS did not have all the details needed for optimal OMS implementation; it assigned customers to pseudo-transformers-a solution that might make technical purists cringe but that, given the cost-benefit specifics of the case, turned out to be good enough.
Cash-strapped utilities can implement OMS by using the relational database, engineering analysis planning database, or mainframe-based electrical network data as the source for OMS data. They can make further modifications using intermediate spatial data editing tools-a sort of “poor man’s CAD.” The service points can be modeled using the geocoding of service addresses and the proximity algorithm.
Multiple GIS Systems
When it comes to GIS, the popular belief that utilities are not early adopters of technology is simply not true. Many utilities started using GIS before the technology was mature. Due to heavy investment in these early systems, some were late in updating their GIS software. Others acquired more recent GIS technology but-due to requirements to alter business processes or free staff for extensive training-have not fully implemented them. And, almost all utilities have found that, while it is easy to select a GIS vendor and deploy GIS software, data conversion and field inspection are expensive and time consuming.
The result, for many utilities, has been multiple GIS systems, which almost always result in incompatible data formats and disparate data sources.
This is where the GIS-independent OMS shines. It can take those multiple systems and use their data as if it came from a single source. That same characteristic also facilitates seamless outage management when two utilities merge or when two or more small utilities want to cut costs by implementing a single OMS with a consolidated control center. GIS independence also lets a utility change from one GIS to another without disrupting its outage management services.
When an OMS uses non-GIS data as a source, it doesn’t provide the outage location prediction accuracy that can be obtained using GIS data. “OMS without GIS” is not the optimal choice. But its availability frees utilities from an unpleasant all-or-nothing scenario. And the same GIS-independence that facilitates these lower-cost total outage solutions also enables cost-effective hybrid outage approaches as well as smooth transitions when utilities merge or move from one GIS to another.
Terry Nielsen is senior director, product management, for SPL WorldGroup. He is responsible for the product content of the SPL OMS and DMS. He was vice president of product strategy and development for CES International and was one of the founders of Configured Energy Systems. He has a B.S. EE from Iowa State University and is a senior member of the IEEE Power Engineering Society.
Parag Parikh is an OMS/DMS Product Consultant of SPL WorldGroup Inc. Parag joined SPL WorldGroup in 2002 and responsible for product strategy and product definition for OMS and DMS products. He is a 1995 graduate of New Mexico State University where he completed his Master of Science in Electrical Engineering degree.
What to Look for in a GIS-independent OMS
1.) Transaction Processing
A GIS-independent OMS generally uses a relational database optimized to support the short transactions typical of the outage process. (Conversely, the database of a GIS-based OMS is designed to support long transactions of geographical data, such as work management and design check-ins and check-outs.)
2.) Graphics Rendering
A GIS-independent OMS typically provides much faster refresh of the electrical network status. (In contrast, a GIS-dependent OMS is adversely affected by the sluggish nature of most GIS graphics rendering engines, which require considerable time to read and interpret the spatial data stored in a database.)
3.) Automatic vs. Manual or Polled Refresh
A GIS-independent OMS typically updates the map whenever anything changes. That reflects the importance of accurate, real-time data to crew safety and system efficiency.
(A GIS-based OMS, in contrast, typically refreshes either when directed by the operator or when the system polls the server. Unfortunately, polling does not scale well. As a consequence, such systems tend toward sluggishness or failure during the heavy loading expected during storms. While these systems can sustain a polling rate of every 10 or 15 seconds during light use, this rate may have to be turned down to several minutes in a production system when there are a large number of users and storm conditions.)
4.) Topology Processing
A GIS-independent OMS generally has topology processing designed specifically for three-phase electrical models. It executes quickly and automatically detects and highlights conditions such as feeder loops and parallels. (In contrast, GIS-based topology processing is designed to handle more general scenarios such as road networks, pipelines, telecommunication networks and gas distribution.)
A GIS-independent OMS frequently supports a continuously updated warm standby disaster recovery site. It should also feature the near-100 percent availability needed for complete outage management. (GIS-based OMS systems are not generally designed for high availability. Many must go down for a few hours to support backups and maintenance activities. Some undergo multi-day downtime for upgrades. Most do not support a warm standby disaster recovery site; instead, disaster recovery sites may use data that is days or even a week old.)
You may be able to obtain special features in a GIS-independent OMS that the GIS-based model doesn’t have. For instance, GIS-independence may give you the ability to add elbows automatically to pad-mounted transformers. You may also be able to model bypass switches around regulators, isolation and bypass switches around breakers, and even the insertion of taps and fuses at certain types of junctions.
Compare systems also for navigational speed between tabular lists and geographic maps, the option to manage outages from either tabular lists or maps, and a symbology that makes sense to the control center operator. And check whether power flow is integrated or bolted on; it will make a difference in the speed and efficiency with which you can work.
GIS-based and GIS-independent systems may also differ in their abilities to support partial restoration, to handle secondary (or LV) outages when the secondary isn’t modelled, to configure alternative prediction rules for different circumstances (rural vs. urban, different types of storms) and to have different rules and rule sets active in different regions.