Algebraic Framework for Outage Information Management in Distribution Networks

Abstract

During faulty conditions, a sustained and heterogeneous stream of information, coming mainly from field signaling devices and customer calls, arrives at the Distribution Management System. This data should be promptly and systematically processed so that the faulted section can be isolated and service restored to customers as soon as possible. This paper presents a matrix-based framework allowing the status of the distribution grid and associated protection, signaling and switching devices to be compactly modeled and tracked. By means of algebraic expressions, the proposed information management system can infer the existence of a blackout (loss of load) and assist the operator in decision making during the identification and isolation of the affected areas. The proposed framework considers the potential presence of embedded distributed generation. With the help of both a tutorial system and a real-life case study, application examples are discussed to illustrate the potential of the proposed methodology.

A Literature Review on the Optimal Placement of Static Synchronous Compensator (STATCOM) in Distribution Networks

“…To check the reliability of the system, there are different reliability indices such as the system average interruption duration index (SAIDI), the system average interruption frequency index (SAIFI), the customer average interruption frequency index (CAIFI), the customer average interruption duration index (CAIDI), energy not supplied (ENS), average energy not supplied (AENS), etc. [76][77][78][79][80]. The minimization of ENS denotes the improvement of system reliability [33,81,82].…”

Integrated Distribution Management System: Architecture, Functions, and Application in China

“…In addition, the scale of DN is much larger than that of the TN, and is mainly maintained in the geographic information system (GIS) and product management system (PMS). The real-time or quasi-real-time measurements are generated from distribution automation devices, DERs, customers meters, and trouble calls, and thus a tremendous amount of information is involved [34]. The frequent updating and switching operations of DN also produce a large number of changes in topology.…”

“…During the fault detection, isolation, and recovery, the geographical information and remote sensing images are essential for operators to make decisions. Meanwhile, the fault isolation and recovery processes cannot be fully automated [34]. Therefore, it is necessary to develop a quicker and wiser OMS with the ability of large-scale processing and visualization of massive data.…”