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Hassan Bevrani

Hassan Bevrani

Academic rank: Professor
ORCID:
Education: PhD.
ScopusId: 55913436700
Faculty: Faculty of Engineering
Address: Dept. Of Electrical and Computer Eng, University of Kurdistan, Allameh Hamdi Blvd, Sanandaj PO Box 416, P. C: 66177-15175, Kurdistan, Iran
Phone: +98-87-33624001

Research

Title
Toward intelligent transient stability enhancement in inverter-based microgrids
Type
JournalPaper
Keywords
Microgrids, Inverter-interfaced distributed generations (IIDGs), Transient stability enhancement , Transient current control loop (TCCL), Fuzzy logic (FL) , Artificial neural network (ANN)
Year
2018
Journal NEURAL COMPUTING & APPLICATIONS
DOI
Researchers Hassan Bevrani ، Shoresh shokoohi ، Sajjad Golshannavaz ، Rahmat Khezri

Abstract

Nowadays, the concept of multiple inverter-interfaced distributed generations (IIDGs)-based MG is recognized as a renowned notion. Encountering unexpected transient situations, the fast inflexible response of IIDG may contribute in serious concerns over its successful operation. Contemplating the transient stability paradigm, first swing stability of the investigated system is the mostly pinpointed matter. In the state-of-the-art indices in transient analysis of IIDG-based technologies, the current index is referred as the frequently deployed one. However, this index is capped within the switches’ twice rated current to afford the inverter’s physical constraints. To tackle this requirement, the ongoing study aims at devising an efficient transient current control loop (TCCL) embedded as a part of main control procedure. In this practice, the well-known simple proportional–integral (PI) controller, as the most persuasive industrial choice, is regarded as the supplementary TCCL key unit. The main functionality of the founded TCCL is deemed as a talented transient current limiter in IIDGs during the versatile possible short-circuit situations. In spite of this, the conventional fixed tuning of gains in PI controller would depreciate its safe and reliable operation encountering different contingencies. To rehabilitate this matter, fuzzy logic and artificial neural network concepts are deployed for realizing an adaptive PI controller capable of handling both the connected and autonomous modes of operation. Precise numerical studies are carried out to interrogate the performance of the proposed approach. Results are analyzed in depth.