A Credit Based Abnormal Traffic Filtering Method for Time Sensitive Network in Substations

Time sensitive networking (TSN) has the advantage of deterministic latency, which is highly compatible with the strong real-time and highly reliable communication requirements of substation automation. To address the problem of filtering abnormal traffic in substation networks, a credit based abnormal traffic filtering method for time sensitive network in substations is proposed. At the import of the switch, an improved Per Stream Filtering and Policing (PSFP) structure is proposed, which gradually filters out abnormal traffic in the input stream with frame length exceeding the threshold, not reaching at the specified time, and exceeding the bandwidth preset value through stream filters, flow gates, and credit based flow limiters. On this basis, a credit based flow limiting algorithm is proposed, which configures parameters such as idle slope, send slope, and maximum credit based on the bandwidth and maximum burst size of the business flow, and filters out abnormal network traffic through credit value. Finally, a multi bay substation communication network model was built and simulation experiments were carried out. The experimental results show that the proposed method restores the heavily occupied bandwidth resources of the switch, reduces the delay and jitter of each business flow, and ensures the reliable transmission of each flow in substation during network storm.