Optimization Techniques for Low-Level Control of DC–AC Converters in Renewable-Integrated Microgrids: A Brief Review
The optimization of low-level control for DC–AC power converters is crucial for enhancing efficiency, stability, and adaptability in modern power systems. With the increasing penetration of renewable energy sources and the shift toward decentralized grid architectures, advanced control strategies ar...
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| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/18/6/1429 |
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| author | Guilherme Vieira Hollweg Gajendra Singh Chawda Shivam Chaturvedi Van-Hai Bui Wencong Su |
| author_facet | Guilherme Vieira Hollweg Gajendra Singh Chawda Shivam Chaturvedi Van-Hai Bui Wencong Su |
| author_sort | Guilherme Vieira Hollweg |
| collection | DOAJ |
| description | The optimization of low-level control for DC–AC power converters is crucial for enhancing efficiency, stability, and adaptability in modern power systems. With the increasing penetration of renewable energy sources and the shift toward decentralized grid architectures, advanced control strategies are needed to address challenges such as reduced system inertia and dynamic operating conditions. This paper provides a concise review of key optimization techniques for low-level control, highlighting their advantages, limitations, and applicability. Additionally, emerging trends, such as artificial intelligence (AI)-based real-time control algorithms and hybrid optimization approaches, are explored as potential enablers for the next generation of power conversion systems. Notably, no single optimized control technique universally outperforms others, as each involves trade-offs in mathematical complexity, robustness, computational burden, and implementation feasibility. Therefore, selecting the most appropriate control strategy requires a thorough understanding of the specific application and system constraints. |
| format | Article |
| id | doaj-art-ff0ed08ed21c42378ee7c48622d1fa23 |
| institution | OA Journals |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-ff0ed08ed21c42378ee7c48622d1fa232025-08-20T02:11:16ZengMDPI AGEnergies1996-10732025-03-01186142910.3390/en18061429Optimization Techniques for Low-Level Control of DC–AC Converters in Renewable-Integrated Microgrids: A Brief ReviewGuilherme Vieira Hollweg0Gajendra Singh Chawda1Shivam Chaturvedi2Van-Hai Bui3Wencong Su4Department of Electrical and Computer Engineering (ECE), University of Michigan-Dearborn, Dearborn, MI 48128, USADepartment of Electrical and Computer Engineering (ECE), University of Michigan-Dearborn, Dearborn, MI 48128, USADepartment of Electrical and Computer Engineering (ECE), University of Michigan-Dearborn, Dearborn, MI 48128, USADepartment of Electrical and Computer Engineering (ECE), University of Michigan-Dearborn, Dearborn, MI 48128, USADepartment of Electrical and Computer Engineering (ECE), University of Michigan-Dearborn, Dearborn, MI 48128, USAThe optimization of low-level control for DC–AC power converters is crucial for enhancing efficiency, stability, and adaptability in modern power systems. With the increasing penetration of renewable energy sources and the shift toward decentralized grid architectures, advanced control strategies are needed to address challenges such as reduced system inertia and dynamic operating conditions. This paper provides a concise review of key optimization techniques for low-level control, highlighting their advantages, limitations, and applicability. Additionally, emerging trends, such as artificial intelligence (AI)-based real-time control algorithms and hybrid optimization approaches, are explored as potential enablers for the next generation of power conversion systems. Notably, no single optimized control technique universally outperforms others, as each involves trade-offs in mathematical complexity, robustness, computational burden, and implementation feasibility. Therefore, selecting the most appropriate control strategy requires a thorough understanding of the specific application and system constraints.https://www.mdpi.com/1996-1073/18/6/1429applications of controlgrid-forming convertersgrid-following convertersLMIsadaptive controlmodel predictive control |
| spellingShingle | Guilherme Vieira Hollweg Gajendra Singh Chawda Shivam Chaturvedi Van-Hai Bui Wencong Su Optimization Techniques for Low-Level Control of DC–AC Converters in Renewable-Integrated Microgrids: A Brief Review Energies applications of control grid-forming converters grid-following converters LMIs adaptive control model predictive control |
| title | Optimization Techniques for Low-Level Control of DC–AC Converters in Renewable-Integrated Microgrids: A Brief Review |
| title_full | Optimization Techniques for Low-Level Control of DC–AC Converters in Renewable-Integrated Microgrids: A Brief Review |
| title_fullStr | Optimization Techniques for Low-Level Control of DC–AC Converters in Renewable-Integrated Microgrids: A Brief Review |
| title_full_unstemmed | Optimization Techniques for Low-Level Control of DC–AC Converters in Renewable-Integrated Microgrids: A Brief Review |
| title_short | Optimization Techniques for Low-Level Control of DC–AC Converters in Renewable-Integrated Microgrids: A Brief Review |
| title_sort | optimization techniques for low level control of dc ac converters in renewable integrated microgrids a brief review |
| topic | applications of control grid-forming converters grid-following converters LMIs adaptive control model predictive control |
| url | https://www.mdpi.com/1996-1073/18/6/1429 |
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