8
Carlo Gavazzi LTD
RSWT
RSWT DS ENG
17/05/2018
Mode of operation
Starting Method
The RSWT series of soft starters is based on a current ramp starting methodology to limit the motor starting current and, at
the same time minimise water hammering effects. Additionally, the RSWT is equipped with an intelligent and self-learning
algorithm to adjust the starting torque automatically at every start. The algorithm makes use of the internal current and
voltage measurement circuits to detect when the motor starts to rotate. During every start, the RSWT adjusts the starting
parameters to achieve a ramp-up time as close as possible to the one set by the user. This function is done automatically
by the RSWT and within 5 starts (typically) the proper starting parameters will be found (assuming the load is the same).
During ramp-down, the RSWT will use the “self-learned” parameters to adjust the ramp-down time in such a way to respect
the setting done by the user on the ramp-down knob.
Important: Due to the self-learning algorithm present on the RSWT series, when the RSWT is first tested on a small motor,
the starting parameters will be optimised for that motor size. If the same RSWT is then installed/ tested on a larger motor,
the starting parameters will be optimised during the first start and the RSWT might trigger an alarm. If this happens, fol
-
lowing the alarm recovery period, the RSWT will update the(self-learned) start parameters and perform another start. This
process will then continue during successive starts such that the optimal starting parameters are found.
User ramp-up seng
Default starng current
Self-adjustment (1) starng current
∆
Time (sec)
Cu
rrent
(A
rm
s
)
∆
Self-adjustment (n) starng current
Actual start me
(measured by RSWT)
Cu
rrent
(A
rm
s
)
Cu
rrent
(A
rm
s
)
Time (sec)
Time (sec)
User ramp-up seng
User ramp-up seng
Start 1:
RSWT starts from default starting voltage/torque (internal) value.
The pump start time can be shorter/longer than user ramp-up setting.
Start 2:
During the 2nd start, the RSWT will start with the “self-learned”
start parameters (from the previous start) to reduce the difference be-
tween the user-set ramp-time vs the actual ramp-time measured by the
RSWT (
Δ
time).
Start n:
For the nth start, the RSWT algorithm will keep the ramp-time as
close as possible to the user set-ramp time. The self-adjusting algorithm
will remain active at every start to make sure that the RSWT adapts the
starting parameters to any load changes.
Note: During motor starting, the RSWT will limit the current to a maximum of 3.5xFLC setting.
User ramp-up seng
Default starng current
Self-adjustment (1) starng current
∆
Time (sec)
Cu
rrent
(A
rm
s
)
∆
Self-adjustment (n) starng current
Actual start me
(measured by RSWT)
Cu
rrent
(A
rm
s
)
Cu
rrent
(A
rm
s
)
Time (sec)
Time (sec)
User ramp-up seng
User ramp-up seng
User ramp-up seng
Default starng current
Self-adjustment (1) starng current
∆
Time (sec)
Cu
rrent
(A
rm
s
)
∆
Self-adjustment (n) starng current
Actual start me
(measured by RSWT)
Cu
rrent
(A
rm
s
)
Cu
rrent
(A
rm
s
)
Time (sec)
Time (sec)
User ramp-up seng
User ramp-up seng
