Assessment and Quantify the Impact of Different Data Flow Control Methods on Digital Substation Communication Network

Due to the randomness of fault events or abnormal data injections (such as storming/avalanche data), the time critical data message (such as GOOSE) can be affected, especially when the data network reaches its data exchange nearly full capacity. To address the impact studies of a large number of GOOSE events (due to faults or emergency operation switches) on time critical message exchanges, the main knowledge contributions in this regard are (i) formulating a Poisson queuing equation to represent GOOSE messages as random message arrivals based on IEC61850 standard, and (ii) modelling of Poisson queuing process as random data arrivals into data buffers for three widely accepted data flow control methods, (iii) formulating a generic data flow control analysis methodology for the study of the impact of different data flow control methods on the P&C time critical messages. Three data flow control models based on the Poisson queuing model are implemented using OPNET. The results are compared and analysed. The discussion has been made to confirm that suitable data flow control method can ensure the network data flow performance of the time critical messages for protection and control functions.

I. Introduction

Power substations are enablers for the electricity network power flow to be controlled and directed safely and securely from generation to demand. To support the UK government target of net zero-carbon emissions by 2050, a step change in how apply digital technologies into substations would be essential to increase the lifetime, efficiency and utilisation of electricity energy infrastructure as well as the flexibility and controllability for increasing integration of renewable generation and low carbon technologies.