Research On Quantitative Analysis And Optimization Control Of Traffic Flow In Smart Substation

A smart substation, an important part of Smart Grid, has developed rapidly in the lastfew years and been realized to be a breakthrough of smart grid developing, which has thefundamental idea of information sharing, and has the network as the implementation ofinformation transmission and has the traffic flow as the basic carrier of information. However,the analysis method remains on the stage of qualitative, which mainly shows up in the factthat the planning and design mainly rely on traffic flow estimation or the experience ofdesigners and the operation and maintance rely on network analysis equipment, and thenetwork reliability relies on the redundant without restriction. The fundamental reason lies inthe lack of the quantitative analysis and optimal control method of the secondary system. Inthis paper, the traffic flow of the smart substation is studied, as well as the quantitativeanalysis method and optimization control of traffic flow.Aiming at the problem of lack of quantitative analysis method for steady-state trafficflow in smart substation, this paper presents an information flow model for process layernetwork of smart substation for the first time. Firstly the physical network model includingthe topology information of the process layer is built, and with combination of the packetsbroadcast domain divided in switches, the logical network model is established to reflect thelogical connection relationship. This paper then, through analyzing the sending packetsbehavior of each information sources, builds the node model. Furthermore, the informationflow model is given, which is divided into steady-state traffic, limit traffic, specific scenariostraffic, and network anomaly traffic to meet different need of the analysis of differentoperation modes. Finally, it gives two typical examples, and uses network analyzer to verifythe validity of the models and methods in this paper.A method of dynamic simulation of the traffic flow in a smart substation is developedbased on the Optimized Network Engineering Tools (OPNET) modeler for dynamic-statequantitative analysis. The modeling need of IEC61850, Intelligent Electronic Devices (IED),switches and the simulation scenes is analyzed, and the models are constructed using theOPNET model library and custom modeling. A typical substation is taken as an example to study the impact of queuing policy, transmission path, traffic management, etc. on thedynamic performance of traffic flow, which verifies that the proposed method is an effectivetool to do research on the performance of traffic flow.Aiming at the problem of traffic flow uncertain switching, the concept of determinedswitching delay, determined transmission path and determined traffic load is proposed for thefirst time in this paper. Uncertainties of the traffic flow in the network are also analyzed. Andthe four core technologies, including the identification and marking of traffic flow based onthe analysis of the Substation Configuration Description (SCD) documents, the measurementof switching delay based on the time stamp, the chosen of the transmission path based on theApplication Identification (APPID) and the traffic management based on the hierarchicallyscheduling, are studied. Based on these technologies, an intelligent switch withbusiness-aware is developed cooperated with the PTSwtich Power technology co., LTD inGuangzhou, which can meet the performance requirement of traffic flow.Based on the results above, a prioritization scheme with the characteristics includingdetermined switching, network sampling and tripping, all traffic flow in one network,synchronization without external clock and business-aware is proposed for the first time.Finally, a typical220kV substation is taken as an example to analyze the static and dynamicnetwork performance of the prioritization scheme, which shows the superiority and feasibilityof the prioritization scheme.This research is supported by the National Nature Foundation of China (51377026), theGuangdong Nature Foundation of China (S2013010012885), Guangdong Province Scienceand technology program project (2010B01090028) and the science and technology project ofChina Southern Power Grid (K-GD2011-505). Part of the research achievement has beenapplied in operation, which verifies the effectiveness of the work in this paper.