Teaching-Learning Optimization Based Adaptive Fuzzy Logic Controller for Frequency Control in an Autonomous Microgrid

Anil Kumar, Srikanth N V

Abstract


This paper addresses teaching learning optimization based adaptive fuzzy logic controller (AFLC) for frequency control in an autonomous AC microgrid. This autonomous microgrid consists of various renewable energy sources like PV and Wind power with high degrees of nonlinearities and also with variable load disturbances which can significantly, influences the system frequency. By considering all these uncertainties, the microgrid frequency control problem faces new challenges. Controlling of microgrid in autonomous mode is becoming a difficult task than in grid connected mode. In this paper novel AFLC is proposed for secondary frequency control. In proposed controller fuzzy input and output membership functions (MFs) scaling factors are tuned in online according to operating conditions using teaching learning based optimization technique (TLBO). In present work diesel engine generator is responsible for generation-load balance (secondary frequency control) in microgrid (MG). The robustness of the proposed controller is compared with conventional PI controller, fuzzy PI controller and PSO tuned fuzzy PI controller.


Keywords


Microgrid; adaptive fuzzy logic controller; teaching learning based optimization; frequency control.

Full Text:

PDF

References


IEEE Standard 1547.4-2011, IEEE Guide for Design, Operation, and Integration of Distributed Resource Island Systems with Electric Power Systems, The Institute of Electrical and Electronics Engineers,2011.

N. Hatziargyriou, H. Asano, R. Iravani, and C. Marnay,"Microgrids", IEEE power and energy magazine , vol.5,pp.78-94,2007.

K. Naidu, H. Mokhlis, and A.H.Bakar, "Multi-objective optimization using weighted sum artificial bee colony algorithm for load frequency control", International journal of electric power and energy systems, Elsevier, vol.55,pp.657–67,2014.

M.R. Sathya, and M.M.T. Ansari, "Load frequency control using Bat-inspired algorithm based dual mode gain scheduling of PI controllers for interconnected power system", International journal of electric power and energy systems, Elsevier, vol.64, pp.365–74,2014.

A.S.Waleed, W.L.Stefan, H. Daryoush, and Octavin,"Power quality enhancement in autonomous microgrid operation using particle swarm optimization", International journal of electric power and energy systems, Elsevier, vol. 42,pp.139-149,2012.

T. Masuta, and A. Yokoyame, "Supplementary load frequency control by use of a number of a both electrical vehicles and heat pump water heaters", IEEE Transactions on smart grid, vol.3, pp.1253-62,2015.

L. Xio, X. Shiwe, and C. KA-wing,"A simple decentralize charging control scheme of plug-in electrical vehicles for alleviating wind farm intermittency", Energy procedia, Elsevier ,

vol .61,pp.1789-92,2014.

L. Tao, J.H. David, and Z. Congchong,"Non disruptive load-side control for frequency regulation in power systems", IEEE transactions on smart grid , vol.7, pp.2142-2153,2016.

K. Mojtaba, and M. Hameed. "Frequency control of microgrids in autonomous mode by novel control scheme based on droop characteristics", Electric power components & systems, Taylor & Francis , vol.41,pp.16-30,2013.

S.Debbarma, and A.Dutta, ''Utilizing electric vehicles for LFC in restructured power systems using fractional ordercontroller", IEEE transactions on smart grid, issue 99,pp.1-11,2016

A. Ghafouri, and J. Milmonfared, "Coordinated control of distributed energy resources and conventional power plants for frequency control of power systems", IEEE transactions on smart grid, vol .6,pp.104-114,2015.

L.I. Xiangjun, H. Dong, and L. XIcokang, "Battery energy storage station (BESS)- based smoothing control of photovoltaic (PV) and wind power generation fluctuations", IEEE transactions on smart grid, vol.4, pp.464-473, 2013.

D. Erkan, and K. Osman, "Comparative evaluation of different power management strategies of a standalone PV/Wind/PEMFC hybrid power systems", International journal of electric power and energy systems, Elsevier, vol .34,pp.81-89, 2012.

D. Ipsakis, S. Voutetakis, P. Seferlis, F. Stergiopoulos, and C. Elmasides, "Power management strategies for a stand-alone power system using renewable energy sources and hydrogen storage", International journal of hydrogen energy, Elsevier, vol. 34,pp.7081–95,2009.

J.L. Soon, and H.K. Jun, "Coordinated control algorithm for distributed battery energy storage system for mitigating voltage and frequency deviations", IEEE transactions on smart grid, vol.7, pp.1713-1722,2016.

H. Bevarani, R.F. Mohammad, and A. Sirwan, "Robust frequency control in an islanded microgrid: H∞ and µ synthesis approaches", IEEE transactions on smart grid , vol .7, pp.706-716, 2016.

K.P. Shasi, and R. Soumya, "Frequency regulation in hybrid power systems particle swarm optimization and linear matrix inequalities based robust controller design", International journal of electric power and energy systems, Elsevier, vol. 63, pp.887-900,2014.

H. Bevarani, and F. Habibi, "Intelligent frequency control in an AC microgrid: online PSO based fuzzy tuning approach", IEEE transactions on smart grid , vol.3, pp.1935-1944,2012

S. Praksah, and K.S. Sunil, "Load frequency control of multi area power systems using neuro-fuzzy hybrid intelligent controllersâ€, Electric power components and systems, Taylor & Francis, vol.61, pp.526-532,2015

K.A. El-metwally ,"An adaptive fuzzy logic controller for two area load frequency control problem", 12th international middle- east power system conference, Aswan, Egypt, pp.300-306,2008.

R. Rao,â€Review of applications of TLBO algorithm and a tutorial for beginners to solve the unconstrained and constrained optimization problemsâ€, Decision science letters , Vol.5,pp.1-30,2016

R.K. Mudi, and N.R. Pal ,â€A robust self-tuning scheme for PI- and PD-type fuzzy controllersâ€, IEEE transactions on fuzzy , vol.7, pp.2-16,1999.




DOI (PDF): https://doi.org/10.20508/ijrer.v7i4.6337.g7238

Refbacks

  • There are currently no refbacks.


Online ISSN: 1309-0127

Publisher: Gazi University

IJRER is cited in SCOPUS, EBSCO, WEB of SCIENCE (Clarivate Analytics);

IJRER has been cited in Emerging Sources Citation Index from 2016 in web of science.

WEB of SCIENCE between 2020-2022; 

h=30,

Average citation per item=5.73

Impact Factor=(1638+1731+1808)/(189+170+221)=9.24

Category Quartile:Q4