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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
Fuzzy logic fine-tuning approach for robust load frequency control in a multi-area power system
Type
JournalPaper
Keywords
load frequency control, multi-area power system, robust fuzzy logic fine-tuning approach
Year
2016
Journal ELECTRIC POWER COMPONENTS AND SYSTEMS
DOI
Researchers Hassan Bevrani ، Shoresh shokoohi ، Sajjad Golshannavaz ، Rahmat Khezri

Abstract

This article addresses the design procedure and numerical validation of a robust fuzzy logic-based fine-tuning approach devised to enhance load frequency control capabilities in multi-area power systems. The founded robust fuzzy logic-based fine-tuning approach is intended for judicial parameter tuning of a proportional-integral controller encountering fault occurrences or severe changes in system loading conditions. Unlike conventional proportional-integral controllers that are generally designed for fixed operating conditions, the outlined approach demonstrates robust performance with power system uncertainties and disturbances. In this context, the projected robustness is principally emanated due to a fuzzy logic extensibility feature, furnishing the established framework to have a proliferated observability on control space. The design procedure of a robust fuzzy logic-based fine-tuning controller is substantially nourished by expert knowledge regarding the overall system performance. Apt fuzzified and defuzzified rule-based mechanisms are wisely included as the key building blocks of the fuzzy inference engine. Extensive numerical studies are launched in a thorough investigation of the proposed approach. As well, the robust fuzzy logic-based fine-tuning performance is compared with that of two extra robust methods, namely the H∞-iterative linear matrix inequality as well as the hybrid genetic algorithm and linear matrix inequality. The obtained results are deeply analyzed.