AC network insulation monitor IDAX series
medium-voltage

AC network insulation monitor - IDAX series - MEGGER - medium-voltage
AC network insulation monitor - IDAX series - MEGGER - medium-voltage
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AC network insulation monitor - IDAX series - MEGGER - medium-voltage - image - 3
AC network insulation monitor - IDAX series - MEGGER - medium-voltage - image - 4
AC network insulation monitor - IDAX series - MEGGER - medium-voltage - image - 5
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AC network insulation monitor - IDAX series - MEGGER - medium-voltage - image - 7
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for AC networks, medium-voltage

Description

Measure moisture content, tan delta/power factor and oil conductivity using DFR in the field DFR (Dielectric Frequency Response) measurements for insulation diagnostics The IDAX is an insulation diagnostic analyser based on DFR (Dielectric Frequency Response), also known as FDS (Frequency Domain Spectroscopy). DFR technology is an established test procedure in laboratories that, in an innovative effort by Megger, has been adapted for field use in the IDAX range of instruments. DFR is the measurement of capacitance and losses (tan delta or power factor) over multiple frequencies. The measured DFR curve is dependent on insulation geometry, moisture, oil conductivity, and temperature. By advanced curve fitting to the reference material model, the IDAX calculates moisture content in solid insulation, the oil’s conductivity at 25 °C reference temperature, and tan delta/power factor at 20 °C reference temperature. In these calculations, ITC (Individual Temperature Correction), another important Megger innovation, is used to translate test data from the test object temperature to the reference temperatures. The IDAX software incorporates an ITC-corrected frequency sweep designed explicitly to assess instrument transformers and bushings. Thanks to a novel approach to the combination of time and frequency domain data, the IDAX provides the shortest measurement time in the marketplace for a full DFR measurement from 1 kHz to 10 μHz. Separate reference models are fitted to each data set (time or frequency) prior to transformation and combination, which eliminates the risk of artefacts introduced by approximations or transformation of incomplete data sets.

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