iWSN-9603-160-ME-IP33 iWSN Wireless 3-phase 2-loop intelligent power meter with 6 off 100A Current CT


Key Features

  • 2-loop 3-phase or 4-loop single-phase AC circuits measurement
  • Provides 6 100A split current transformers (CT)
  • 3P4W, 3P3W, 3P3W 2CT, 1P3W or 1P2W wiring types
  • Bi-direction kWh measurement
  • True RMS calculations for voltage and current signals
  • Powers by the measured voltage cable; no extra power wiring is needed
  • Built-in 2A Fuse to keep the main circuit safe
  • Timestamp for each measured data by the built-in RTC
  • 3-minute history data cache for supplement
  • Uses 433MHz radio frequency and provides max. 64 wireless sub-network.
  • IP33 protection to prevent from circuit short by fire sprinkler system
  • Connector cover to avoid the exposed connector and electric shock


The ICP DAS iWSN-9603 series module is a 3-phase AC power meter, which provides one 3-phase voltage input and two 3-phase 100A current CT inputs, and suits measuring the power information of different equipment powered by the same AC source. By means of wireless communication and powering from the measured voltage cable, it can greatly reduce the cost and duration of installation, and satisfy to the demand of distributed deployment and quick setup.

Moreover, the features of Sub-GHz radio frequency and data supplement mechanism effectively improve the reliability of the wireless communication especially in the crowed or seriously shielded space of the factory environment. Consider the maintenance and installation issues, the iWSN-9603 series module is configured by the DIP switches, and uses special housing with IP33 protection to avoid the circuit short while the fire sprinkler system is activated if the module is located out of the panel box.

Through the design of RTC and data timestamp, the ICP DAS iWSN-9603 series can fit the power saving applications, such as machine and process power efficiency improvement, power information administration, calculation of carbon emission, predictive maintenance, and big data analysis of power consumption.