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Traditional connection of a generating station to the transmission grid typically requires either a new transmission line to an existing substation or building a new substation to segment an existing transmission line. Both of these options are capital intensive which often hurts economic viability especially small- or medium-size nonconventional generating stations. The direct connection to an existing transmission line, without the switching station, is the most economical solution to interconnect the generating station but it desensitizes the line protection and poses a risk to the ability of the protection to detect faults, particularly ground faults involving fault resistances. Multifunction relays, combined with communication-assisted schemes, now offer innovative solutions to overcome protection desensitization concerns. This paper describes these solutions which are successfully implemented by a large Canadian electric utility over the last two decades to tap connect numerous generators to the transmission grid. [ABSTRACT FROM PUBLISHER]

Today’s anywhere and anytime broadband connection and audio/video capture have boosted the deployment of crowdsourced livecast services (or crowdcast). Bridging a massive amount of geo-distributed broadcasters and their fellow viewers, such representatives as Twitch.tv, Youtube Gaming, and Inke.tv, have greatly changed the generation and distribution landscape of streaming content. They also enable rich online interactions among the crowd, and strive to offer personalized Quality-of-Experience (QoE) for individual viewers. Given the ultra-large scale and the dynamics of the crowd, personalizing QoE however is much more challenging than in early generation streaming services. The rich interactions among the broadcasters, viewers, and the network system, on the other hand, also offer invaluable data that could be utilized towards informed management. This paper presents DeepCast, an edge-assisted crowdcast framework that explores the sheer amount of viewing data towards intelligent decisions for personalized QoE demands. DeepCast seamlessly integrates cloud, CDN, and edge servers for crowdcast content distribution, and advocates a data-driven design that extracts the hidden information from the complex interactions among the system components. Through deep reinforcement learning (DRL), it automatically identifies the most suitable strategies for viewer assignment and transcoding at edges. We collect multiple real-world datasets and evaluate the performance of DeepCast with trace-driven experiments. The results demonstrate its flexibility and effectiveness towards better personalized QoE and lower cost for crowdcast systems. [ABSTRACT FROM AUTHOR]