Hybrid Driving Training and Particle Swarm Optimization Algorithm-Based Optimal Control for Performance Improvement of Microgrids

Dina A. Zaki, Hany M. Hasanien (), Mohammed Alharbi, Zia Ullah and Mariam A. Sameh
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Dina A. Zaki: The Higher Institute for Engineering and Technology Fifth Settlement, Cairo 11823, Egypt
Hany M. Hasanien: Electrical Power & Machines Department, Faculty of Engineering, Ain Shams University, Cairo 11517, Egypt
Mohammed Alharbi: Electrical Engineering Department, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia
Zia Ullah: School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Mariam A. Sameh: Faculty of Engineering & Technology, Future University in Egypt, Cairo 11835, Egypt

Energies, 2023, vol. 16, issue 11, 1-18

Abstract: This paper discusses the importance of microgrids in power systems and introduces a new method for enhancing their performance by improving the transient voltage response in the face of disturbances. The method involves using a hybrid optimization approach that combines driving training-based and particle swarm optimization techniques (HDTPS). This hybrid approach is used to fine-tune the system’s cascaded control scheme parameters, based on proportional–integral–accelerator (PIA) and proportional–integral controllers. The optimization problem is formulated using a central composite response surface methodology (CCRSM) to create an objective function. To validate the suggested control methodology, PSCAD/EMTDC software is used to carry out the simulations. The simulations explore various scenarios wherein the microgrid is transformed into an islanded system and is subjected to various types of faults and load changes. A comparison was made between the two proposed optimized controllers. The simulation results demonstrate the effectiveness of using a PIA-optimized controller; it improved the microgrid performance and greatly enhanced the voltage profile. In addition, the two controllers’ gains were optimized using only PSO to ensure that the outcomes of the HDTPS model demonstrated the same results. Finally, a comparison was made between the two optimization techniques (HDTPS and PSO); the results show a better impact when using the HDTPS model for controller optimization.

Keywords: central composite response surface methodology (CCRSM); islanded microgrids; optimization algorithms; renewable energy resources (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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