  The installation of wireless technologies in power substations requires
characterizing the impulsive noise produced by the high-voltage equipment.
Substation impulsive noise might interfere with classic wireless communications
and none of the existing models can reliably represent this noise in wide band.
Previous studies have shown that impulsive noise is characterized by series of
damped oscillations with the amplitude, the duration and the occurrence times
of the impulses that are random. All these characteristics make this noise
time-correlated and the partitioned Markov chain remains an efficient model
that can ensure the correlation between the samples. In this study, we propose
to design a partitioned Markov chain to generate an impulsive noise that is
similar to the noise measured in existing substations, in time and frequency
domains. We configure our Markov chain to produce the impulses with the damped
oscillation effect, then, we determine the probability transition matrix and
the distribution of each state of the Markov chain. Finally, we generate noise
samples and we study the distribution of the impulsive noise characteristics.
Our Markov chain model can replicate the correlation between the measured noise
samples; also the distributions of the noise characteristics are similar in the
simulations and the measurements.
