PSO Optimized Nonlinear Control of DC Microgrid with Intelligent Energy Management System

dc.authorscopusid 59239965200
dc.authorscopusid 55780618800
dc.contributor.author Yousaf,H.
dc.contributor.author Kivanc,O.C.
dc.date.accessioned 2024-09-11T07:42:52Z
dc.date.available 2024-09-11T07:42:52Z
dc.date.issued 2023
dc.department Okan University en_US
dc.department-temp Yousaf H., Istanbul Okan University, Department of Electrical and Electronics Engineering, Istanbul, Turkey; Kivanc O.C., Istanbul Okan University, Department of Electrical and Electronics Engineering, Istanbul, Turkey en_US
dc.description.abstract To meet the United Nations climate stabilization goals by 2050, Clean Energy is playing a vital role to minimize global warming. This study proposes a Clean Energy DC Microgrid with a 2-level control structure. A high level controller consisting of Artificial Neural Network (ANN) based Energy Management System (EMS) with maximum power point tracking (MPPT) schemes and a local level Quick Reaching Law based Global Terminal Sliding Mode Controller (QRL-GTSMC). The Microgrid consists of a Fuel Cell (FC), Electrolyzer, Hydrogen Tank, Battery Energy Storage System (BESS), Photovoltaic (PV) array and a Wind Energy Conversion System (WECS). The inclusion of multiple Distributed Energy Resources (DER) increases the complexity of the system but also provides more flexibility. The proposed configuration provides a Hydrogen cycle with production, storage and utilization. The ANN EMS is trained by data accumulated through a State Logic Controller (SLC). The local level control laws are synthesized using QRL-GTSMC, and the resultant parametric variables are tuned by Particle Swarm Optimization (PSO) algorithm using Integral of Absolute Error (IAE) as the minimization performance index. The system's overall stability, response and performance is tested by introducing uncertainties in the form of changing weather conditions. The results are compared with conventional control technique without a reaching law. The proposed system has been evaluated using the MATLAB/Simulink environment. © 2023 IEEE. en_US
dc.identifier.citationcount 0
dc.identifier.doi 10.1109/EICEEAI60672.2023.10590371
dc.identifier.isbn 979-835037336-3
dc.identifier.scopus 2-s2.0-85199990604
dc.identifier.uri https://doi.org/10.1109/EICEEAI60672.2023.10590371
dc.identifier.uri https://hdl.handle.net/20.500.14517/6267
dc.language.iso en
dc.publisher Institute of Electrical and Electronics Engineers Inc. en_US
dc.relation.ispartof 2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023 -- 2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023 -- 27 December 2023 through 28 December 2023 -- Zarqa -- 201143 en_US
dc.relation.publicationcategory Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.scopus.citedbyCount 0
dc.subject Artificial intelligence en_US
dc.subject Clean Energy en_US
dc.subject Hydrogen Cycle en_US
dc.subject Nonlinear Control en_US
dc.subject PSO algorithm en_US
dc.title PSO Optimized Nonlinear Control of DC Microgrid with Intelligent Energy Management System en_US
dc.type Conference Object en_US

Files