Doing more with less: Leveraging your spatial asset data

Doing more with less: Leveraging your spatial asset data

Larry Kuhl
VP – Partnerships & Business Development
Coherent Networks, Inc. (An Osmose Company)
1 Adler Drive
East Syracuse, New York 13057

Abstract
Utility industry re-regulation, merger activity, more sophisticated competitors, and more stringent regulatory demands are driving companies to “do more with less.” Having to do more with less requires innovative approaches to leveraging existing resources—data assets and employees. Leveraging data is key to achieving the benefits associated with corporate mergers and acquisitions. The integration of different operation support systems and the migration of data from one platform to another are necessary steps to leverage the data. All utilities strive to implement operational enhancements that involve the integration of existing systems. When systems are integrated, the data that drives these systems must be migrated and integrated, too. But the required data integration or migration is not always a success. In fact, 75 percent of respondents in recent IT surveys report significant problems as a result of defective or poorly integrated data. Before the applications and processes that draw on this data can be trusted and counted on, the data itself must be verified as accurate. This paper will examine issues associated with spatial data accuracy, data sources, target applications, typical errors, and solutions to address these issues. It will explore techniques for capturing, verifying and validating data to ensure that it is accurate and complete.

Introduction
The electric utility industry is experiencing re-regulation, the introduction of new more sophisticated business entities, more stringent regulatory demands, and customers with expanding menus of energy choices. These are just a few of the factors that have resulted in increased merger and acquisition activity and that drive utilities to “do more with less” such as looking for new solutions to improve operational efficiency and meet constituency expectations. Corporate mergers often result in having different operating systems that address the same basic need. In many cases, to achieve the anticipated benefits of the merger, consolidation or integration of information systems is required. Business expectations drive the need for operational enhancements, improved reliability, increased customer satisfaction, and cost reduction. In some cases, these are conflicting goals. The common denominator in all cases, however, is the pressing need to deal effectively with the data used to drive these systems and to maximize the use and value of that data.

The merging of different business entities typically requires the merging of different business processes, inevitably leading to the integration and replacement of existing systems. When systems are integrated, the data that drives these systems must be migrated and integrated, as well. But the required data integration is not always a success. In fact, according to PriceWaterhouseCoopers, 75 percent of respondents in recent IT surveys report significant problems as a result of defective or poorly integrated data. Before the processes and corresponding applications that draw on data can be trusted and counted on, the data itself must be accurate.

This paper briefly explores the issues of data accuracy and consistency, the impact of quality data on Operations Support Systems, and the corresponding need for operational applications targeted for the electrical utility industry. Techniques for verifying and validating data to ensure that it is accurate and complete are reviewed. Methods of integrating applications using messaging bus and point-to-point solutions are discussed. An example of how the combination of technologies can be leveraged to benefit utilities is also presented.

Data - Sources, Target applications, errors & error correction

Sources & Targets
There are many sources of data needed by end-use Operations Support System (OSS) applications. Many of these systems rely on a geospatial representation of network assets. Accurate meter reading and other customer load related information, real-time network element status, network connectivity, crew compliment and location, customer status, construction order content and status, cost, and other basic information drive realtime business decisions. Additionally, accurate and up-to-date detailed physical asset attribute (size, rating, etc.) and configuration, customer-to-facility relationships, phasing, electrical connectivity, construction configuration or type, and structural integrity data are essential to maximizing the performance of many electric-based OSS applications. Typical source systems include CAD, GIS, CIS, AMR, SCADA, Distribution Automation/SCADA, Asset Management Systems, and Graphical Design Estimation.

The data from these systems, in turn, drives Outage Management (OMS), Work Management (WMS), Distribution Management (DMS), Energy Management (EMS), and Engineering Analysis packages. The conjunction of data from such a wide range of sources opens the door to data omissions, conflicts, gaps, and errors.

Errors
What types of data errors can impact an OSS and where do the errors come from? Data errors can result from data migration and data integration efforts if these projects are not properly monitored and controlled. Here, incompatible data formats, incomplete data, and duplicate data from two or more sources can offer stiff project challenges above and beyond what may have been inaccurate to start with. However, many errors result from a lack of proper data-entry business processes and data ownership. Typically, these errors arise due to limited error checking at various stages in the data management process. Another contributing factor is the inability of personnel who rely on the data to easily make corrections. Other error sources are often beyond the reasonable control of the utility. For example, when performing restoration efforts after a major storm (ice, hurricane, etc.), the primary focus is to get power back to customers, not to accurately report how the network may have been altered. Data-capture problems during these emergencies are compounded by the use of “foreign” crews who are not familiar with the local utility’s data-capture procedures.

The following represents the situation encountered after performing an initial assessment on a typical sample data set:

Anchor Text Outside of File (39 of 7,992)
Linear Objects (Primaries, Secondaries, Services) with 1 vertex (1,582 of 26,682)
Construction Spans w/o phasing (132 of 3,174)
- 30 missing phasing
- 102 missing phasing, node ids, and circuit ids
Fuses/Switches/Reclosers w/o conductors (348 of 348)
- 102 not aligned w/ conductor or Open/Close status unset
Primary Conductors node id unset (6 of 20,513)
Network connectivity issues (132 of 3,193)
- 75 logical connectivity (node id attribute) problem
- 57 graphical connectivity problems

On a percentage basis, these problems may not seem extreme, but their impact on key operations support system applications can be very significant.

The connectivity and phasing problems discovered would have a severe impact on the operations of an OMS or an engineering analysis package. Inaccurate data can create a multitude of problems—wrong assumptions about which device has operated to clear a fault, thus resulting in sending crews to the wrong locations, over- or under-designed facilities, and false facility loading predictions that lead to shortened facility life and potentially dangerous situations.

Other errors are associated with switching updates, loads, construction types, conductor sizes, consistency in device information like regulator settings, and customer-to-facility (usually the transformer) linkage. These data errors can also pose problems for a variety of applications. Fuses won’t clear faults properly and cause damage to expensive utility and customer assets. Upstream isolation devices may operate before downstream devices have a chance to isolate a fault. This leads to interrupting more customers than necessary, negative impact on performance metrics, an increase in customer complaints, and possibly to performance rate penalties. So, the question that remains is how do we detect and fix these errors?

Error Correction
With any approach to correcting errors, if procedures are not put in place to keep the data correct, then the effort will be in vain as the data will quickly become incorrect and thus limit the expected return on investment for the data correction effort. One approach involves performing a complete field audit. This approach is the most effective way to gain an accurate representation of network assets as they exist in the field. Though laborintensive, it can also be the most cost-effective approach.

Another approach is to detect errors in the target system and go back to the respective sources in an iterative manner and make corrections. This approach is very labor intensive and does not always produce the best results.

Another approach involves the implementation of advanced data gateways to limit the amount of field verification required. Advanced data gateways detect errors, automatically correct many of the errors, provide reports on what was fixed, and provide the tools for highlighting and fixing the remaining errors that cannot be automatically corrected. This approach allows the utility to target the areas that need to be fieldsurveyed, thus reducing the overall expense.

Probably the best approach is a combination of the field audit and the advanced data gateway. This approach delivers the most accurate data possible.

Data/application integration strategies
Dashed lines indicate communications that may or may not be necessary depending on data integration requirements.
Three strategies for integrating applications come immediately to mind: Point-to-point, bus, and hub-and-spoke architectures. Point-to-point interfaces are common throughout the industry. They can be set up in either a batch or a transaction-based mode. They can be implemented as custom interfaces, gateways, by embedding components using COM technology, or by a combination of these technologies. There are a variety of information-bus technologies on the market from TIBCO, Vitria, IBM and others. They offer an excellent means to transport data from one application to another if you assume the data is accurate to start with. Bus architectures hide the messaging. Technology from SISCO takes the bus architecture for utilities one step further by utilizing a Common Information Model (CIM) that has been defined for transmission and is well on its way for distribution asset information. However, data validation for the target systems cannot be completely addressed by this approach. Many utilities are finding their source system data is not accurate enough to support their operations support systems and have turned to advanced gateway technology to maximize the performance of their applications.

Each approach has advantages and disadvantages. I will not debate the merits of these architectures. Instead, I will focus on the need for accurate data regardless of the transport mechanism or integration architecture. For each architecture alternative, an interface or adapter is required. This interface or adapter can be either a simple translation mechanism or an advanced data gateway.

A gateway, configured to do more than simply transport data, is a unique technology that offers several advantages over an application interface. Advanced gateways focus on the data content needed by target applications. They provide not only a mechanism to fix data ambiguities and deliver the correct data in the exact format needed by the target application, but also the ability to return corrected information back to the data source or sources. Gateways for operations support system applications can also have an intuitive geospatial data viewing and editing environment, advanced data modeling to handle any target application and standard representations like the CIM, and pre-configured data exports including a direct link to message bus data transport technologies. They are compatible with virtually any geospatial source as well as non-geospatial sources.

Sophisticated gateways provide a means to integrate data from a variety of source systems for delivery to specific target systems. Source systems contain data needed by your target system. However, typically, the data is not in the form or of the high quality required by the target system to operate at its full potential. Gateways provide automated routines (mini-applications) that not only indicate where the problems are, but also correct many of the problems automatically. This technology also automatically integrates, constructs, and validates the data attributes and relationships needed by specific target systems. In addition to the automated features, a high-performance graphical user interface for data viewing and editing is included to ensure fast and easy correction of any problems requiring operator attention. Making the process “fast and easy” requires an extensive set of flexible tools. For example, when a “verify” routine has been executed, a list of discrepancies is displayed. When the user selects one of the discrepancies, the user interface automatically navigates to the highlighted object in question, provides a description of what is wrong and opens the tool needed to fix the problem. Once the simple operation to correct the situation is performed, a re-verify can be executed to make sure the problem has been corrected.

For the specific integration task at hand, the gateway provides:

access to any geospatial data source
linkage between GIS Model and Network Element Tags (monitoring points)
validation and verification of network data against model requirements
identification and correction of data discrepancies

Gateway Technology
Example of Benefits Associated with the Combination of Integration Technologies
By combining several technologies (gateway functionality, COM/ActiveX, and PI capabilities) along with direct access to real-time data sources, the utility can leverage existing assets to increase operational efficiencies and, ultimately, improve customer satisfaction.

Taking advantage of the information already in a CAD or GIS allows the utility to eliminate redundant graphics and data associated with data-entry activities. Combining historical network status and network configuration information provides the ability to replay specific events to more accurately analyze root causes of network disturbances. With the more accurate data, studies to place capacitors, regulators, and sectionalizers/reclosers will generate more accurate results. Real-time displays can be made available to anyone in the utility.

Operations personnel can take advantage of having detailed asset information along with real-time data to operate the network more closely to design limits. This information will also facilitate dynamic feeder profiling, verification of load reduction in reaction to timeof- use (TOU) rate signals, and enhanced load balancing. Using dynamic network representation capabilities, network operations personnel can quickly take action so that overload situations can be avoided which, ultimately, extends the life of existing facilities and prevents certain types of customer outages.

With access to up-to-date network information and real-time data from Operations, system planners can enhance protection coordination, develop solutions to lower line losses, and defer the construction of new facilities.

And that’s just scratching the surface. The real power of combining these technologies to integrate this data has yet to be realized. The network engineers, planners, and operations personnel will determine how their current capabilities will be enhanced, which will drive new and improved business processes and associated new Operations Support System applications.

The key to achieving new levels of operational efficiency is consistent, accurate, and upto- date data. Using the right mix of information technologies and data maintenance procedures will ensure this key to success can be achieved.