Reliability of IEC 61850 based substation communication network architecture considering quality of repairs and common cause failures

Mission-critical IEC 61850 system architectures are designed to tolerate hardware failures to achieve the highest reliability performance.Hence,multi-channel systems are used in such systems within industrial facilities to isolate machinery when there are process abnormalities.Inevitably,multi-channel systems introduce Common Cause Failure(CCF)since the subsystems can rarely be independent.This paper integrates CCF into the Markov reliability model to enhance the model flexibility to investigate synchronous generator intra-bay SCN architecture reliability performance considering the quality of repairs and CCF.The Markov process enables integration of the impact of CCF factors on system performance.The case study results indicate that CCF,coupled with imperfect repairs,significantly reduce system reliability performance.High sensitivity is observed at low levels of CCF,whereas the highest level of impact occurs when the system diagnostic coverage is 99%based on ISO 13849-1,and reduces as the diagnostic coverage level reduces.Therefore,it is concluded that the severity of CCF depends more on system diagnostic coverage level than the repair efficiency,although both factors impact the system overall performance.Hence,CCF should be con-sidered in determining the reliability performance of mission-critical communication networks in power distribution centres.

WPD-ResNeSt:Substation Station Level Network Anomaly Traffic Detection Based on Deep Transfer Learning

With the advancement of new infrastructures,the digitalization of the substation communication network has rapidly increased,and its information security risks have become increasingly prominent.Accurate and reliable substation communication network flow models and flow anomaly detection methods have become an important means to prevent network security problems and identify network anomalies.The existing substation network analyzers and flow anomaly detection algorithms are usually based on threshold determination,which cannot reflect the inherent characteristics of substation automation flow based on IEC 61850 and have low detection accuracy.To effectively detect abnormal traffic,this paper fully explores the substation network traffic rules,extracts the frequency domain features of the station level network,and designs an abnormal traffic identification model based on the ResNeSt convolutional neural network.Transfer learning is used to solve the problem of insufficient abnormal traffic labeled samples in the substation.Finally,a new method of abnormal traffic detection in smart substation station level communication networks based on deep transfer learning is proposed.The T1-1 substation communication network is constructed on OPNET for abnormal simulations,and the actual network traffic in a 110kV substation is fused with CIC DDoS2019 and KDD99 data sets for the algorithm performance test,respectively.The accuracy reached is 98.73%and 98.95%,indicating that the detection model proposed in this paper has higher detection accuracy than existing algorithms.