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Information overload is a problem for all of us today, with data on everything from today’s weather forecast to next week’s airfares both easier to access and less expensive to store. Industrial power-system managers face a similar challenge in their efforts to monitor and improve the quality of electricity delivered to their equipment: today’s data-gathering devices are providing a such wealth of new data points, that making sense of it all can sometimes seem overwhelming.
New equipment means new kinds of planning
Just five to 10 years ago, power-system managers often relied on specialized power-quality analyzers, fault recorders and sequence-of-event recorders to track overall system performance. This equipment was expensive, and so it was typically specified for a handful of select points within the system in order to capture a snapshot of the electrical activity at the time of any disturbance. Today’s devices, however, can capture and report a tremendous amount of detailed data about the health of the power system, often using architecture similar to that of modern business information systems (see Figure 1).
As a result of this similarity, power pros – and the consulting engineers working with them – might want to take a lesson from a process managers of business information systems have developed in the face of their own data-overload challenges. Initiating such a system requires managers to take a step back from system-design details to understand how any decisions will fit into broader business goals. Outlined in four steps, this planning model can be described as follows:
- Articulate your top business goals.
- Translate those goals into a small number of key performance indicators (KPIs).
- Focus on measuring those KPIs with data.
- Communicate the results broadly, so that data can become actionable intelligence.
So, an example top business goal might be to protect ongoing manufacturing processes from interruption and
equipment damage by tracking the reliability of connected power systems and the impact that this reliability has on the IT equipment controlling its processes. To develop the corresponding KPIs for this effort, power-system designers can reference a number of standards developed by the Institute of Electrical and Electronics Engineers (IEEE) and other such groups that define the tolerance specific types of equipment have to variations in delivered power – those tolerance limits could help define the performance requirements your facility needs to measure. (For further direction, the Schneider Electric white paper, “The Seven Types of Power Problems” discusses the most common types of power disturbances, using IEEE standards for defining power-quality problems.)
With KPIs identified, specifiers can go on to begin selecting measurement devices, and here, an understanding of how facility personnel will actually use resulting data can be critical. For example, how selected devices display information they retrieve can make a difference in how useful the data becomes in day-to-day operations – after all, managers will need to be able to easily understand captured information if they’re going to put that information to practical use. Data displays typically fall into two main categories:
- High-level overviews of a KPI that provide a general indication of power-system reliability.
- Detailed drill-down views of specific KPI data to help engineering staff understand where system vulnerabilities might exist, along with those vulnerabilities’ root causes.
Additionally, data organization can be equally critical in staff efforts to translate that information into actionable intelligence. Such options can include:
- Displaying data in tables, charts and time-series trends, for easier understanding of high-level metrics and for comparison to reference metrics and changes over time.
- Organizing data by key attributes, such as physical location, circuit and load type.
- Organizing data by time range, such as the ability break down monthly totals into totals by day of week or different shifts in a day.
How to learn more
Obviously, this blog post only scratches the surface in its description of the ways to rethink power-monitoring plans based on today’s connected, microprocessor-based monitoring equipment. The value of such advanced systems lies not in the quantity of data they can collect, but rather in the quality of insight they can deliver. You can learn more about incorporating key performance indicators into your next project in an in-depth Schneider Electric white paper, “Using Key Performance Indicators to Manage Power System Reliability,”