Post-Mortems vs. Predictions: Why Wait Until It's Too Late?

Is the quality of your power system good or bad? Is it getting better or worse? These questions haven't been easy to answer, until now. What if you could predict power quality problems so that you could avoid them? You could do this manually by using overlays of the CBEMA curve and plotting events captured by power monitors. It's now possible with new power monitors and predictive maintenance software.Before,

Is the quality of your power system good or bad? Is it getting better or worse? These questions haven't been easy to answer, until now.

What if you could predict power quality problems so that you could avoid them? You could do this manually by using overlays of the CBEMA curve and plotting events captured by power monitors. It's now possible with new power monitors and predictive maintenance software.

Before, you used power monitors only to determine existing conditions on an electrical system or for post-mortem evaluations. Now, newer monitors can help you predict and avoid power quality problems that lead to equipment malfunction, overheating of circuits, and system failure.

Power quality index. This technique converts accumulated power monitoring data into a single-number index called the power quality index, which you can track over time. By trending this index, you can get advance warning of a deteriorating situation that could lead to system failure.

Basically, you give each event an index number that's calculated by determining its relationship to a power quality tolerance curve (PQTC). You can use any curves, but the standard electronic equipment references are the CBEMA (Computer and Business Equipment Manufacturers Association) curve and the ITIC (Information Technology Industry Council) curve, which is proposed as being the "new" CBEMA curve.

You then define the nominal voltage as having an index of zero, as shown Fig. 1 (original article) and the index of an event landing on the PQTC as 100. You give other events an index number based on the ratio of the event's distance from nominal voltage to the same distance from the limits of the PQTC, multiplied by 100. For example, if an event is halfway between nominal voltage and the PQTC, it gets an index of 50. If it's twice the distance from nominal as it is from the PQTC, its index is 200.

You then calculate the mean index at regular intervals, and plot it over time. These plots will show when power quality is deteriorating (index goes up), improving (index goes down), or fluctuating.

Fig. 2 (in the original text), shows the mean value of power quality index plots for power monitors installed at four locations at a facility. In our example, the index for the monitor installed in a network closet shows the most stable power situation, while the index for the lobby monitor is going up at the fastest rate and fluctuating to a greater extent than the other monitors.

If the lobby in Fig. 2 were a critical site, the data indicate it's the worst of the four locations and is worthy of investigation first. If you allow the index to rise, this location will inevitably suffer undesirable power consequences.

By taking action at a particular location, the index plot will confirm your action was appropriate: It will go down and then stabilize. Previously, this involved a time-consuming analysis of large computer files or paper tapes, studying event by event. Indexing quickly and intuitively confirms the power quality is improving (or getting worse).

Reshaping the curve. There's an important advantage in linking the index to a power quality tolerance curve: If the curve doesn't precisely describe the sensitivity of a critical load, you can reshape the PQTC to match this sensitivity. As such, the index plot will provide better prediction.

For example, suppose you plot an event against a CBEMA curve. The index of this event is less than 100 leads you to believe it's safe when in reality it causes disruption. The event could actually be greater than 100, according to real-world performance. In predictive maintenance programs, you want to know when events have an index greater than 100, and you want to track the index to make sure it always stays below 100. By using a modified, more accurate curve, you'll be better informed and alerted to events that could potentially cause failure or disruption.

Developing your own curve. Suppose you don't know the exact shape of the PQTC describing a critical load's operational limits. And, suppose the standard CBEMA curve doesn't quite fit. Furthermore, the equipment supplier can't supply a curve. Now, what can you do? Develop your own specific PQTC for predictive maintenance.

Let's say your equipment is more sensitive to impulses or high-speed transients. Just adjust the PQTC to be more sensitive in the microsecond region, giving the correct indices to events in the impulse (microsecond) region. Start with the standard CBEMA curve and note which events cause actual equipment problems. Then, modify the curve so it correctly describes your equipment sensitivity.

In full-disclosure power monitors, you can automatically plot events against any PQTC. The curve is just an overlay; it has no effect on how you capture the events. Software tools are available for a PC to modify any PQTC at any time, even after you've collected data. Because this data resides in a database, you can recalculate the power quality index at any time simply by changing the curve.

Highlighting trends. Another method of evaluating what's happening to your system uses color to distinguish events according to age. The software in full-disclosure monitors shades oldest events the darkest and recent events the brightest. Color shading gives you an easier way of spotting trends and migration effects that show how conditions are becoming worse (or better) over time.

With the tools described, it's easier to prevent a deteriorating situation from getting worse. With threshold-type monitors you wouldn't be aware of a problem until disturbances eventually grow to the point where they exceed the threshold limits before they are captured. At this point, damage to sensitive equipment may have already occurred.

Comparing historical data. Basically, you can set up a predictive maintenance program by installing monitors at critical locations, with each monitor making a survey for a "business period," such as a week or month. With the survey finished, you download the data into a database and reset the monitors to make another survey. Then, you archive survey databases and compare them periodically.

Multiple databases collected over time give you a power history of your operation and infrastructure activity. Increases in event activity, event amplitude, or the emergence of new types of events are indications of potential problems.

Full-disclosure technology. Power monitoring instruments capable of providing "full-disclosure" information are a breakthrough development that has made predictive maintenance possible for electrical distribution systems. These instruments use digital signal processing and high-speed sampling to capture and store all aspects of power (i.e. power disturbances, harmonics, flicker, and power consumption) in a database on an internal hard drive. You can then download the data for analysis and reporting. These monitors do not require the user to program triggers, thresholds, or set points to isolate power disturbances.

Instruments that use thresholds, etc., to capture events "by exception" are suitable for performing post-mortems, but you cannot use them in a proactive, predictive maintenance program. Setting thresholds eliminates vast areas of the power tolerance curve, creating a "dead zone," as shown in Fig. 3 (original article). Thus, these instruments are blind to conditions that are "bubbling under" the threshold limits. The key advantage of full-disclosure monitors is that they record not only the severe events but also the underlying quiescent data that indicates incipient problems.

The initial survey must establish the true baseline conditions to provide the basis for comparing initial survey data to subsequent data. It's important each subsequent database is a true record of all of the conditions. Full-disclosure instruments consistently capture and analyze data the same way every time. You can only perform predictive maintenance analysis by comparing data from instruments with identical data capture techniques, using the same analysismethods, and with full information of conditions at the monitoring site. You cannot perform predictive maintenance analysis by comparing data between instruments with different threshold settings, using different analysis methods, and with partial information of conditions at the monitoring site.

Portable monitors can perform predictive maintenance. However, portable troubleshooting instruments can also be used in predictive maintenance programs providing they are the full-disclosure types. For example, you can install a portable monitor at a location where power quality is of concern, such as at an adjustable speed drive, or at a computer, or at the output of an UPS. You monitor the power to the equipment for a reasonable period (24 hrs or 48 hrs), and archive the monitoring data. You can then move the monitor on to other locations to perform more surveys, archiving their data. At some later date, return to the first location and perform a survey again.

Repeat the above procedure over weeks or months. Import the archived data into the predictive maintenance software, and the index is plotted for each location. There will be gaps in the plots since monitoring was not continuous, but the index will still indicate whether the power is improving or deteriorating. If equipment problems develop, consulting the index would lead you to include (or eliminate) power quality as a cause before investigating other causes.

By turning the data into useful information, you can better understand the power quality situation of your electrical system. By being armed with predictive knowledge of your electrical system, you can take corrective action before a problem shuts your system down.

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