2–10 EPM 9450/9650 ADVANCED POWER QUALITY METERING SYSTEM – INSTRUCTION MANUAL
DEMAND INTEGRATORS CHAPTER 2: METER OVERVIEW
Example: With settings of 3 five-minute subintervals, subinterval averages are computed
every 5 minutes (12:00, 12:05, 12:15, etc.) for power readings over the
previous five-minute interval (11:55-12:00, 12:00-12:05, 12:05-12:10, 12:10-12:15, etc.). In
addition, every 5 minutes the subinterval averages are averaged in groups of 3 (12:00.
12:05, 12:10, 12:15. etc.) to produce a fifteen (5x3) minute average every 5 minutes (rolling
(sliding) every 5 minutes) (11:55-12:10, 12:00-12:15, etc.).
Thermal Demand:
Traditional analog Watt-hour (Wh) meters use heat-sensitive elements to measure
temperature rises produced by an increase in current flowing through the meter. A pointer
moves in proportion to the temperature change, providing a record of demand. The
pointer remains at peak level until a subsequent increase in demand moves it again, or
until it is manually reset. The EPM 9450/9650 mimics traditional meters to provide Thermal
Demand readings.
Each second, as a new power level is computed, a recurrence relation formula is applied.
This formula recomputes the thermal demand by averaging a small portion of the new
power value with a large portion of the previous thermal demand value. The proportioning
of new to previous is programmable, set by an averaging interval. The averaging interval
represents a 90% change in thermal demand to a step change in power.
Predictive Window Demand:
Predictive Window Demand enables the user to forecast average demand for future time
intervals. The EPM 9450/9650 meter uses the delta rate of change of a Rolling Window
Demand interval to predict average demand for an approaching time period. The user can
set a relay or alarm to signal when the Predictive Window reaches a specific level, thereby
avoiding unacceptable demand levels. The EPM 9450/9650 calculates Predictive Window
Demand using the following formula:
Example
: Using the previous settings of 3 five-minute intervals and a new setting of 120%
prediction factor, the working of the Predictive Window Demand could be described as
follows:
At 12:10, we have the average of the subintervals from 11:55-12:00, 12:00-12:05 and
12:05-12:10. In five minutes (12:15), we will have an average of the subintervals 12:00-
12:05 and 12:05-12:10 (which we know) and 12:10-12:15 (which we do not yet know). As a
guess, we will use the last subinterval (12:05-12:10) as an approximation for the next
subinterval (12:10-12:15). As a further refinement, we will assume that the next subinterval
might have a higher average (120%) than the last subinterval. As we progress into the
subinterval, (for example, up to 12:11), the Predictive Window Demand will be the average
of the first two subintervals (12:00-12:05, 12:05-12:10), the actual values of the current
subinterval (12:10-12:11) and the prediction for the remainder of the subinterval, 4/5 of the
120% of the 12:05-12:10 subinterval.
# of Subintervals = n
Subinterval Length = Len
Partial Subinterval Length = Cnt
Prediction Factor = Pct
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