CONTEXT AWARE TRAFFIC SCHEDULING ALGORITHM FOR COMMUNICATION SYSTEM IN POWER DISTRIBUTION NETWORK

Abstract

Smart grid is an evolution from conventional power grid in which power communication system has migrated from one-way to bi-directional communication network. This enables smart grid to communicate with each of its devices and applications in monitoring and controlling the components remotely. Thus, an enormous amount of data will be collected from substations which consist of various types of traffic, and send to master station through utility network. Different kinds of traffic have their own pre-defined priority. However, the priority of data may change depending on the context or situation at a particular time. Hence, the priority of data needs to be redefined according to its context to ensure reliability and timeliness of critical data. Lost or delayed data result in incorrect control actions and risk the stability of smart grid. In order to support diversity of smart grid traffics, a contextual aware based Quality of Service (QoS) management system is needed to be introduced across the entire grid so that the system is aware of the changing context for each traffic. This thesis proposes a contextual aware traffic scheduling algorithm to manage the traffics according to their level of priorities by having the ability to adapt to the preferred requirements. The designated algorithm is tested with Multi-Protocol Label Switching (MPLS) protocol using Network Simulator 3 (NS-3) as a simulation platform. NS-3 is chosen as it is a discrete-event network simulator for research and development purposes. The simulation results from NS-3 is validated with the results obtained from MATLAB. It proves that the contextual aware ranking of each traffic are correctly matched to one another. In order to verify the advantages of the proposed algorithm, it is compared with simulation results without contextual aware algorithm. The quantitative results obtained demonstrated improvements in terms of delay and jitter with an average of 68.49% and 44.87% respectively. Based on these, it is proven that the algorithm with contextual aware performs better for delay and jitter while maintaining the throughput as compared to the algorithm without contextual aware.