On the Modeling and Analysis of Communication Traffic in Intelligent Electric Power Substations

The underlying substation communication network (SCN) is of paramount importance in facilitating the advanced functionalities of substation automation. To design and maintain an efficient SCN to guarantee high transmission availability and information integrity, an accurate traffic model is required and the traffic characteristics need to be understood. This paper exploits the challenge of modeling and analyzing SCN data traffic from the data generating mechanism to the transmission and retransmission mechanisms, and reveals the symbiosis of short-range dependence and self-similarity. Recognizing that there is little research effort available, this paper proposes a viable approach in modeling the SCN traffic and mathematically analyzes the associated parameters aiming to accurately model the self-similarity behavior of SCN traffic, describes the symbiotic characteristics, and effectively predicts the traffic pattern. Further, the proposed modeling approach is validated and assessed through a case study by using a realistic 24-h dataset collected from a 110 kV substation's supervisory-control-and-data-acquisition system. The result clearly demonstrates that the model can accurately describe and forecast the SCN traffic with the autocorrelation function mean square error as small as $\mathrm{MSE}\left(\mathrm{m}\right)=2.077\times {10}^{-3}$. The insights obtained from this work can significantly promote the design and operation of SCNs.