Title :
A process level network traffic prediction algorithm based on ARIMA model in smart substation
Author_Institution :
Electr. Power Res. Inst., Hubei Electr. Power Co., Wuhan, China
Abstract :
The introduction of the concept of process level into smart substation makes protection and control of input and output values depend on the network communication. In order to ensure the stability and reliability of the smart substation process layer network, it must forecast the process level network traffic effectively and make early analysis warning. This paper proposed a smart substation process level network traffic prediction algorithm based on ARIMA model from the demand of smart substation process layer network traffic monitoring system. Our proposed algorithm introduces a dynamic error correction factor on the basis of Astrom algorithm based on the linear minimum variance prediction principle. It can solve the problem of stochastic effects in prediction, and the prediction precision is improved. The experimental results show that the algorithm can effectively predict the process level network traffic, reproduce the statistical properties of the real flow measurement, and guarantee the safe operation of power grid.
Keywords :
autoregressive moving average processes; power grids; reliability; safety; statistical analysis; stochastic processes; substation protection; ARIMA model; Astrom algorithm; dynamic error correction factor; flow measurement; input values control; linear minimum variance prediction principle; output values control; power grid; process level; process level network traffic prediction algorithm; safe operation; smart substation process layer network traffic monitoring system; smart substation process reliability; smart substation process stability; smart substation protection; statistical properties; stochastic effects problem; Autoregressive processes; Heuristic algorithms; Mathematical model; Prediction algorithms; Predictive models; Substations; Telecommunication traffic; ARIMA model; dynamic error correction factor; linear minimum variance prediction; network traffic prediction; smart substation;
Conference_Titel :
Signal Processing, Communication and Computing (ICSPCC), 2013 IEEE International Conference on
Conference_Location :
KunMing
DOI :
10.1109/ICSPCC.2013.6663896