Communication-less ensemble classifier-based protection scheme for DC microgrid with adaptiveness to network reconfiguration and weather intermittency. (June 2021)

• Record Type: Journal Article
• Authors: Tiwari, Shankarshan Prasad; Koley, Ebha; Ghosh, Subhojit
• Is Part Of: Sustainable energy, grids and networks. Volume 26(2021)
• Publication Date: 2021-06
• Subjects: DC microgrid -- Protection scheme -- Local information -- Ensemble classifier -- Weather intermittency -- DER outage; Renewable energy sources -- Periodicals; Smart power grids -- Periodicals; Electric power systems -- Periodicals; 333.794
• DOI: 10.1016/j.segan.2021.100460
• Languages: English
• ISSNs: 2352-4677

Abstract

Abstract: DC microgrids have received wide interest in recent times because of the advantages related to improved power transfer capability, high efficiency, compatibility with distributed energy resources (DER) and reduced losses. However, the adoption of DC microgrids has been restricted because of the challenges in designing a reliable protection scheme. The challenge results from the high magnitude of the fault current without zero crossing, coupled with the requirement of high rapidity in fault detection. The scenario is further complicated during the variation in operational dynamics of microgrid either due to weather intermittency or network reconfiguration arising out of DER outage. Non-addressal of the above issues results in reduced grid resilience because of possible relay malfunction during stressed scenarios. In this regard, an ensemble classifier-based protection scheme has been proposed for DC microgrid with adaptability to system reconfiguration and weather intermittency. The adaptiveness has been achieved by online identification of network topology, while immunity to weather variation is attained by stochastic modelling of solar irradiance and wind speed. The use of local information for executing the protection tasks avoids the issues related to data loss and latency in the communication link. The task of operating mode detection, topology identification, fault detection/classification and section identification have been formulated as a set of classification problems and further solved using a random subspace sampling-based ensemble classifier technique. The reliability of the proposed scheme has been extensively validated for a wide range of fault scenarios involving wide variation in network topology, weather condition and fault parameters.