Self-similarity and multi-fractal have attracted much attention in the network traffic modeling field. And besides these two characteristics

traffic in the power distribution communication network also shows a kind of significant deterministic behavior. An efficient traffic model should be able to capture the most important features of network traffic flows. And for this reason

an analytical study of the traffic in power distribution communication network is presented and a new feature matching model proposed. This model divides the traffic into deterministic and chaotic flows based on a maximum entropy spectrum analysis. And for chaotic flow

according to its time scale behaviors

the L-system model of small time scale is applied in the fractal Gaussian noise (FGN) model of large time scale. It is shown by simulation results that the proposed model can not only capture the distribution probability faithfully but also depict the self-similarity and multi-fractal characteristics of the traffic. This work is supported by National High Technology Research and Development Program of China (863 Program) (No. 2014AA01A701)

Beijing Natural Science Foundation (No. 4142049)

National Natural Science Foundation of China (No. 51507063)

and Fundamental Research Funds for the Central Universities (No. 2015XS07).