By Mark Hatfield and Hahn Tram
Improving and differentiating customer service and reducing operational costs are important strategies for utilities wishing to stay competitive. Outage management systems (OMS) have improved customer services by helping utilities reduce outage duration and by facilitating communication internally within utility operations departments and externally with customers and the public. Distribution management systems (DMS) are helping utilities reduce operational costs by coordinating automated network and manual crew operations and facilitating control and operations centers communication.
While the primary business drivers for implementing DMS/OMS vary among utilities, the benefits generally include a reduction in operating costs by providing decision support for dispatchers and system operators and improving repair crew utilization. DMS/OMS also help improve customer service by automatically providing customers with online information on such things as outage status and service restoration confirmation. The systems also support a variety of customer services such as proactively communicating restoration time changes and follow-up work status, wire warranty programs, etc. In addition, the systems help utilities meet regulatory requirements by providing outage information to the public and government, timely outage status updates during storms and information that can be used during customer litigation. Another benefit to DMS/OMS is that it supports asset management and planning by providing accurate data for reliability trending analysis, reliability improvement planning, reliability-centered maintenance and valued-based planning.
DMS/OMS Data Requirements and Impact on Functionality
To receive the full benefits associated with DMS/OMS, it is important for utilities to ensure that correct data is provided to the systems. Basically, the input data requirements include these four major data categories:
- As-built and as-operated electric distribution network connectivity and facility model.
- The landbase for displaying geographic background information and for locating network equipment and customer services based on street addresses or street intersections.
- Crew data used for maintaining investigation and repair crew, vehicle types, as well as for tracking crew availability and field activities
- Customer information, including customer name, premises address, phone number, electrical location (meter or transformer location), priority indication, etc.
Differences in Modeling
The first two categories represent the biggest data issues. Developing and properly maintaining this data will make or break a utility’s DMS/OMS deployment. Therefore, focus on the network and associated landbase data is imperative.
Differences in electric network modeling can have major impacts on DMS/OMS functionality. Some of the most important differentiation factors are summarized below. Table 1 lists the typical data sources used to develop each model.
Graphical representation. From the network operation standpoint, a graphical user interface is essential, whether the graphical representation is schematic or geographic. A schematic model may provide the dispatcher or operator with better clarity when making switching decisions. However, particularly if landbase data is available, the geographic model presents significant additional benefits. It helps the dispatcher associate non-customer calls (police or fire department) with customer outage calls and helps him direct crews to network equipment locations. If landbase data is available, developing a geographic model should be one of the highest priorities.
Secondary network model. For utilities with extensive secondary networks modeling, the secondary allows operators to isolate problems on the secondary and allows DMS/OMS to track outage statistics in a finer resolution. Secondary network modeling is a low priority for most North American systems and medium for most European systems.
Three-phase connectivity model. The three-phase connectivity model allows utilities to calculate the number of customers affected by phase; for example, the number of customers affected when a single-phase tap fuse blows. The three-phase details are especially important to North American utilities that have extensive single-phase and two-phase circuits. Developing a three-phase connectivity model should be a high priority for most utilities.
Model of spans in addition to device connectivity. Modeling the connectivity of only switching and interruptible devices is adequate for basic OMS if the utility does not perform temporary operations like line cuts and jumpers. On the other hand, including spans in the model will not only support occasional line cut and jumper operations, but also pave the way for OMS to track storm damage assessments and for DMS to perform loading and voltage analyses as part of the switching and control function. This should also be a high priority.
Facility attribution. Facility attribution is not strictly needed for a good OMS model. However, adding key facility attributes to the connectivity model has benefits. The dispatcher will be able to tell the crew what type and size of equipment may need replacement. The attribution in the facility model will support asset management, planning and engineering functions as well. Facility attribution falls into the medium priority category.
Device internals. Device internals are useful in modeling complex network objects like substation equipment and automated transfer switches. They are also useful in providing a clearer graphical user interface for switching these objects in a geographic representation of the primary network. Device internals modeling should be a high priority.
Landbase information. Landbase information helps dispatchers locate non-customer calls and associate them with customer outage calls. When coupled with a geographic referenced network model, landbase information helps dispatchers direct crews to network equipment locations. More frequently, government authorities are requesting utility reliability reports by geographic and political areas, not by substations and circuits. Landbase information will help such reporting. Landbase modeling should be a utility’s highest priority.
Preparing for Conversion
Once the data requirements for meeting the desired DMS/OMS functionality are identified, the next step is to begin the conversion process of loading the network connectivity model into the DMS/OMS system. The quality of the conversion process will ultimately determine the DMS/OMS predictive algorithms and outage area calculation accuracy. Therefore, a rigorous conversion methodology is essential to achieve the required DMS/OMS functionality.
Certain conversion steps should be followed for populating DMS/OMS. First, conversion specifications must be developed. A conversion specification documents all conversion rules necessary to interpret the network sources. The source and conversion rules for each network object and attribute must be identified.
The second step requires data scrub procedure development. Data scrub procedures are required to clarify, correct and consolidate all source materials before they are delivered to the conversion team.
The next step is to conduct a prototype. A small prototype conversion (one or two feeders) provides a minimum conversion specifications test to ensure a larger conversion team can interpret the rules documented by a few experienced individuals. The prototype, which will demonstrate where changes, additions, and clarifications must be made, should be reviewed and conversion specifications modified if necessary. It is important at this step to take time to make the changes.
Once the prototype is complete, full conversion can begin. As each sub-area district or feeder is prepared for conversion, the source records must be formally frozen. As the records are being converted, as-built and as-operated changes will be made in the area. Therefore it will be necessary to identify what has changed after the records were frozen.
Next, a data scrub should be performed. All data cleanup and consolidation tasks must be included in the scrub procedures. Once this is complete, the sources can be delivered to the conversion team, which must document everything that is received.
No matter how good the sources and conversion specifications, the conversion team will still have questions throughout conversion, therefore it is necessary to implement a problem resolution process. It is important to track all problem resolution reports and address them in a timely manner. In addition to the problem resolution process, quality assurance (QA) procedures must also be implemented. This will help ensure that the conversion team is implementing a QA process, including both manual and automated checks, at key steps in the conversion process. Once the data conversion team makes a delivery, it is essential to have an independent QA team conduct an assessment. Finally, once the data is accepted, all changes that occurred after the records were frozen must be posted. As operating districts are completed, DMS/OMS can be deployed.
There are several factors that can impact the cost and duration of a conversion project. One factor to be considered is whether an internal team or an external contractor will perform the conversion process. An internal team may appear to be cheaper, however, an external contractor is often faster and more efficient. Therefore benefits can be realized sooner, thus decreasing the total project cost.
Data quality can also play an important role in determining conversion costs. It is safe to assume the network data will not be as good as expected. So, additional time and money will most likely need to be included for cleaning up the data. Chances are that a utility with a functioning GIS network model has data that is suitable for migration. Utilities with an automated mapping system, may or may not have data that is suitable for migration.
It is essential for a utility to establish a customer-transformer-network link for successful DMS/OMS implementation. Availability and quality of this link will also impact cost and duration.
Considering these factors, Table 2 identifies the general cost ranges for different model representations.
Hahn Tram is an executive consultant, performance team manager and subject manager of DMS and OMS with Convergent Group. Mark Hatfield is a senior technical consultant and subject manager for mobile workforce with Convergent Group.