Abstract
The reliability of systems and components is a fundamental need for the efficient development of a smart distribution grid. In fact, the presence of a fault in one component of the grid could potentially lead to a service interruption and loss of profit. Since faults cannot be avoided, the introduction of a diagnostic scheme could predict the fault of a component in order to carry out predictive maintenance. In this framework, this paper proposes a novel Fault Detection and Isolation (FDI) scheme for AC/DC converters in MV/LV substations. In order to improve the reliability of the FDI procedure, the system architecture includes also an Instrument Fault Detection and Isolation section for identifying faults that could occur on the instruments and sensors involved in the monitoring process of the AC/DC converter. The proposed architecture is scalable, easily upgradable, and uses cost-effective sensors. Tests, carried out on a real test site, have demonstrated the efficacy of the proposal showing very good IFDI diagnostic performance for the 12 types of faults tested. Furthermore, as the FDI diagnostic performance regards, it shows a detection rate close to 100%.
Highlights
The development of intelligent electricity grids (Smart Grids) brings new challenges to reliability and safety; the presence of faults in various components in Smart Grids is a critical issue
If on one hand, predictive maintenance is very important to guarantee the quality of the services and to allow optimized and low-cost maintenance schemes, on the other hand, the application of Fault Detection and Isolation (FDI) and Instrument Fault Detection and Isolation (IFDI) solutions is typically based on multisource sensors that can generate large amounts of data
It has been customized in order to supervise up to eight measurement systems and, per each of them, it can show if a fault is occurring: sensing system failure, AC/DC converter failure, or if it is evolving to failure
Summary
The development of intelligent electricity grids (Smart Grids) brings new challenges to reliability and safety; the presence of faults in various components in Smart Grids is a critical issue. If on one hand, predictive maintenance is very important to guarantee the quality of the services and to allow optimized and low-cost maintenance schemes, on the other hand, the application of FDI and IFDI solutions is typically based on multisource sensors that can generate large amounts of data. This aspect, we are in the era of industry 4.0 and Big Data, can become a problem, as these huge amounts of data, necessary for diagnosis, can overload the communication network and the system (communication storage and processing).
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