Intelligent Systems for Improved Reliability and Failure Diagnosis in Distribution Systems

Certain smart grid technologies can reduce the number of customers affected by prolonged outages, and thereby increase reliability through automated switching to restore service. Such technologies are useful, but reactive in nature, performing their function only after a fault occurs and an outage has been detected. They must presume that nonfaulted feeder sections and alternative feeders are healthy and capable of carrying increased power flow. Research at Texas A&M University has demonstrated that sophisticated, automated real-time analysis of feeder electrical waveforms can be used to predict failures and assess the health of distribution lines and line apparatus. Reliability can be substantially improved by detecting, locating, and repairing incipient failures before catastrophic failure, often before an outage occurs. Requirements for data and computation are substantially greater than for devices like digital relays and power-quality meters, but feasible with modern electronics. This paper provides selected examples of failures that have been predicted by intelligent distribution fault anticipation (DFA) algorithms. The data requirements and processing analysis to detect these failures are discussed. The problems related to full-scale deployment of the proposed system in a utility-wide application are presented. The authors use experience gained from their long-term research to propose concepts for overcoming these impediments.