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

Author:

Zaki Dina A.1ORCID,Hasanien Hany M.23ORCID,Alharbi Mohammed4ORCID,Ullah Zia5ORCID,Sameh Mariam A.3ORCID

Affiliation:

1. The Higher Institute for Engineering and Technology Fifth Settlement, Cairo 11823, Egypt

2. Electrical Power & Machines Department, Faculty of Engineering, Ain Shams University, Cairo 11517, Egypt

3. Faculty of Engineering & Technology, Future University in Egypt, Cairo 11835, Egypt

4. Electrical Engineering Department, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia

5. School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China

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.

Publisher

MDPI AG

Subject

Energy (miscellaneous),Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment,Electrical and Electronic Engineering,Control and Optimization,Engineering (miscellaneous),Building and Construction

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