AC microgrids are becoming increasingly important for providing reliable and sustainable power to communities. However, the evolution of distribution systems into microgrids has changed the
Abstract Protecting DC microgrids (DCMGs) from faults is critical due to the rapid current changes that occur in milliseconds. The application of FSSTH in the context of fault diagnosis is particularly
The fault diagnosis methods for microgrid can be divided into three types:model-driven method, knowledge rule-driven method and data-driven method . The model-based method needs a deep understanding of the system model and lays a high requirement based on mathematics.
A cloud–edge framework-based intelligent fault diagnosis method for the microgrid is presented in this paper. An intelligent fault diagnosis platform is constructed based on the CloudPSS.Theoretical analyses and test results show the effectiveness of the proposed method. Besides, the proposed method is economical and reliable.
By includingheterogeneous sensors throughout the micro-grid, many fault detection and isolation methods can be developed to provide early indication of faults in the micro-grid infrastructure. For example, vibration or strain sensors could be installed along the transmission lines to monitor if unhealthy loads are passing through the lines.
However, to ensure the power supply reliability of microgrid,diagnosing the faults in a microgrid is also important in the operation and maintenance of microgrid, which is rarely studied in the literature. In the fault diagnosis techniques, the features of the operational data of fault equipment are extracted firstly.
Micro-grids are usually monitored usingsmart meters and non-invasive sensing devices for diagnosing faults to maintain stability and performance in island mode.
Technical literature abounds with several fault diagnosis methods applied to the different components of a micro-grid. These methods primarily fall under two categories:(i) model-based and (ii) data-driven approaches. Model-based approaches for fault diagnosis require a detailed understanding of how the component functions.
