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Extracting Full Value from Electric Utility Monitoring Schemes

While a Monitoring and Diagnostics (M&D) scheme has become essential for electric grid operational purposes, the main enterprise value lies in non-operational M&D data uses. Any network plan to support M&D must include operational and non-operational data and ensure that all enterprise parts can take advantage of the data.

Implementing M&D requires an enterprise-wide strategy that relies on Intelligent Electronic Devices (IEDs) and systems as data sources, networks for communications, and M&D analytic applications with response requirements matched to operational and non-operational data extraction from IEDs on transformers and in substations. As operators receive operational data for grid management, enterprise business managers must receive non-operational data to create enterprise value. Without non-operational uses, many utilities are foregoing as much as 80 percent of the potential benefits from the IEDs.

Operational data must reach the control center via a secure network with stringent response requirements. Non-operational data needs a network with higher bandwidth for digitized waveforms and sequence-of-events reports, but this network has lower requirements for security and latency. Intelligence requires constant communication. Investing in transformer M&D and substation automation makes no sense without always-on communications networks.

Designing an information architecture to deliver non-operational data to business managers requires matching their needs to the data sources. A virtual data mart extracts on-demand, non-operational data for processing into actionable business intelligence.

A Holistic Approach

Implementing the communication and presentation of non-operational data to enterprise users is complex and demands thinking outside traditional silos. Third-party facilitation is essential to keep participants motivated, cooperative, and focused on the end result.

Once completed, however, the effort unlocks significant value in resolving business problems, supporting a condition-based maintenance program, and laying the groundwork for future expansion. Specifically, with a good information architecture, new IEDs can be added to the same data-handling arrangement as defined by three types of plans: a data map that includes all IED outputs (data points), an IED template that matches the data points to stakeholders’ needs in the utility, and a matrix showing the attributes (e.g., frequency of sampling) that non-operational data must take for each stakeholder. These plans define how non-operational IED data is extracted, routed, and presented.

Ideally, the substation automation system, such as Supervisory Control and Data Acquisition (SCADA), extracts operational and non-operational data from IEDs on transformers, and protection and control equipment using data concentrators. Operational data then goes to the control center, while non-operational data is routed across the firewall to data repositories for on-demand retrieval by business units.

Data for Business Units

To design a transformer M&D and substation automation scheme that make best use of the data, that data must be delivered to the right people at the right place and time in a useful format. Presentation is integral to the outcome. Applications turn data into information and, after further analyses, formats such as dashboards translate the information into actionable business intelligence. The dashboards must be tailored to each business unit’s needs.

For non-operational data, business unit managers and operations groups are likely to use the data. More than two dozen business units can make use of the non-operational data generated by transformer M&D and substation IEDs. These business units include maintenance, asset management, power quality, planning, and engineering groups.

The initial step in designing an information architecture to deliver non-operational data is to query business managers about who needs the data, the sort and form needed, and the specific time intervals for capture. The resulting document becomes a resource referred to as an “enterprise-wide data requirements matrix.”

Enterprise users must take inventory of available IEDs and corresponding data maps. Users possibly aided by outside consultation must determine what formats best serve their needs. The availability of non-operational data and archived operational data may be new to many people.

Creating an IED Template

After an inventory, managers must determine which points in each data map have value for stakeholders by creating IED templates. This task is complex, partly because IED characteristics vary by model and vendor. Each model may yield different types of data in different ways. Therefore, every device’s characteristics and data outputs must be documented to complete this step.

These characteristics might include a sampling rate that results in a recorded value every two seconds. The available data might represent a mathematical outcome of that sampling or might be event-driven. And, a user might need only the peak or average value for each hour. Perhaps a specific data point is only relevant to a user when the value exceeds a pre-determined threshold. Users must determine whether the range, average, mean, or some other data variant is useful.

The IED templates and data requirements matrix determine the network architecture required to capture and communicate data from the IEDs to enterprise users. The templates and data matrix require accuracy to produce useful results for the enterprise.

Data Marts

To reach end users, non-operational data is extracted from the IEDs, gathered by a data concentrator, and conveyed by networks through the enterprise firewall to a virtual data mart. Once there, the data is accessed via an enterprise network application. The term “virtual” refers to a data server that sits on top of and is logically linked to a utility’s data repositories.

Many utilities rely on several data repositories. This approach adds a federated data server, which manages and routes all enterprise network data. Stored data is transparent to users because the server is logically linked to all data repositories; the server simply finds and delivers requested data.

Enterprise users can request operational data from the enterprise data mart, which is populated from the control center historian (operations SCADA historian). Typically, the internal operations SCADA historian contains the operational data superset for use by operations, and the external SCADA historian contains a subset of operational data for use by stakeholder groups. The internal SCADA historian is within the firewalls of operations, and the external SCADA historian is outside the firewalls of operations. The operations SCADA historians contain a time series of data at a predetermined sampling rate.

To extract maximum value from IED/networking investments, the data management plan must ensure that every potential end user in a utility has desktop access to non-operational and operational data. This fundamental consideration needs to govern the design of the communications networks and all related work.

Converting Data

Data has little value until processing converts it into comprehensible information. Further processing, which can occur at several levels, turns the information into business intelligence.

IEDs or related devices can process data to some extent; other processing can occur in a data concentrator or a substation desktop PC. Processing near the source reduces data amounts that must be communicated upstream for centralized processing.

To produce business intelligence, managers must participate in three activities:

  • Identifying the necessary data through the data requirements matrix exercise.
  • Selecting and learning to use applications that turn that data into actionable intelligence.
  • Deciding how outputs are delivered via visualization so business intelligence is understandable and actionable.

Operations and Asset Management

Though operational data from IEDs alert operators to transformer problems, the aim of the data architecture is to avoid a fix-on-fail approach. Utilities can use non-operational data to become more proactive through condition-based asset management and maintenance to improve transformer diagnostics and extend the useful life of components.

This approach increases grid reliability, safety, and related enterprise value. Patterns may emerge that reveal conditions or operational approaches that hasten a transformer’s demise. In other words, the exploitation of non-operational data is a boon to transformer M&D and substation automation.

Driving Organizational Change

Success in transformer M&D and substation automation not only relies on technical considerations — it has organizational and cultural implications as well. The need for enterprise-wide engagement in a transformer M&D business case presents organizational and cultural challenges to a utility accustomed to tolerating silos and rivalries. These concerns can result in unjustifiable redundant systems. Ultimately, the over-arching goal is to increase reliability and safety, extract the full value of M&D investments, and create customer value.

Utilities cannot afford “islands of automation” created by different operations or business units. Enterprise business unit managers must cooperate with other managers across the enterprise in the data mapping phase and data template creation that help make non-operational data from substations available to all. Territorialism and silos are expensive, inefficient, and unproductive. This is a well-established fact of transformer M&D, substation automation, and data mart setup, confirmed by case studies.

Traditional advice on how to accomplish these outcomes often centers on executive leadership and management buy-ins. Clear direction from utility executives, with an emphasis on staff adaptation and the benefits of working toward the common good, can be effective measures. For a utility staff with an appetite for change, that may be enough — too often it is not. But a utility organization has management tools to ensure that this divide is bridged, perhaps using enterprise-wide metrics in job evaluations and compensation processes. The upside is a stronger business case, better outcomes, and a more nimble organization prepared for even greater changes.

The Grid’s Future

An enterprise-wide, holistic approach to transformer M&D and substation automation leads a utility to select enabling communications networks and mix and match them for the utility’s specific needs.

Achieving greater visibility into transformer health and substation functions has a broad impact on other systems and a utility’s business processes and culture. Strategic planning and implementation provide a classic example of a technology in pursuit of grid modernization and its promise of greater reliability and safety as well as more secure, efficient operations in customer value services. Therefore, the need for a holistic approach, the impact of one implementation on other systems, and the resulting evolution of business processes and utility culture offer a fundamental process applicable to other grid modernization projects.

In fact, improved transformer M&D and substation automation and the changes they propel are just harbingers of changes to come. The full exploitation of operational and non-operational data, relying on enabling communications networks, is only the beginning of the Big Data era for utilities. As grid modernization progresses, new sources of data and new, previously unimagined, uses for the data remain to be discovered and transformed into value for all stakeholders.

By John D. McDonald

P.E., Director of Technical Strategy and Policy Development, GE Energy Management-Digital Energy

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