This paper surveys the conceptual aspects, as well as recent developments in fault detection, isolation, and service restoration (FDIR) following an outage in an electric distribution system. This paper starts with a discussion of the rationale for FDIR, and then investigates different areas of the FDIR problem. Recently reported approaches are compared and related to discussions on current practices. This paper then addresses some of the often-cited associated technical, environmental, and economic challenges of implementing self-healing for the distribution grid. The review concludes by pointing toward the need and directions for future research.

Fault Detection, Isolation, and Service Restoration in Distribution Systems: State-of-the-Art and Future Trends

This paper focuses on the main features of the smart distribution grid (grid management using agents). The fault management is consisting of three stages including faulted section identification, faulted section isolation, and restoration. The probable control strategies utilized in the smart grid are either centralized, decentralized, or autonomous control. Multi-Agent System (MAS) based model uses both centralized and decentralized strategies. The fault management process is usually carried out based on MAS. A literature survey on fault management control schemes is investigated. Furthermore, the reliability is estimated for both centralized, decentralized, autonomous, and modified centralized control strategies. This paper presents reliability modeling and analysis of all considered control strategies. In this paper, comprehensive detailed reliability models of considered control strategies are developed. Using this equivalent reliability model, various reliability indices are calculated.

/pdf/a-review-smart-distribution-grid-management-using-agents-3jx264si4n.pdf

A review: Smart Distribution Grid Management using Agents

The response time of service restoration for distribution systems (DSs) experiencing a major outage is subject to the progress of fault location. Aimed at expediting fault location and reducing the outage duration under wind disasters, a systematic model to identify the faulted sections for DSs is proposed. To cope with the limited network observability and frequently received incorrect data, information from fault indicators (FIs), protective devices and component fragility analysis are jointly considered. The Bayes’ theorem is applied to evaluate the degree of hypothetical fault scenarios supported by observed data and identify the scenarios with high probabilities. First, logical relations correlating fault scenarios to expected FI overcurrent notifications and protective relay tripping signals are formulated. To fully exploit the information from protective devices, the action sequence of relays with different tripping characteristics are analyzed and unobservable devices such as fuses are considered. Next, the probability to obtain the observed data from the expected ones is derived considering overall uncertainties caused by abnormalities in FI detection and communication, and relay operation. The posterior probability maximization is transformed into a mixed-integer linear programming (MILP) problem. Case studies validate the model effect on improving fault diagnosis accuracy.

A Generic Bayesian Method for Faulted Section Identification in Distribution Systems against Wind-induced Extreme Events

There is a growing need for installation of equipment along the depth of power distribution network in order to quickly detect failures and to obtain information about the state of the network itself. This paper describes the technical implementation of the GPRS communication solution for connecting Faulted Circuit Indicators (FCI), which are installed in MV network, to the supervisory control and data acquisition (SCADA) system at Elektra Zagreb (HEP DSO). FCI devices are used in electric power distribution networks as a means of automatically detecting and identifying faults to reduce outage time

Detecting faults in mv network using gprs

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Detecting faults in mv network using gprs