| Issue |
E3S Web Conf.
Volume 472, 2024
International Conference on Renewable Energy, Green Computing and Sustainable Development (ICREGCSD 2023)
|
|
|---|---|---|
| Article Number | 01005 | |
| Number of page(s) | 13 | |
| Section | Smart and Energy Efficient Systems | |
| DOI | https://doi.org/10.1051/e3sconf/202447201005 | |
| Published online | 05 January 2024 | |
Utilizing Artificial Neural Network For the Regulation Of Electric Springs In Renewable Systems
1 Research scholar, School of Electrical and Electronics Engineering, Sathyabama Institute of Science & Technology, Chennai, 600119, India
2 Associate Professor, School of Electrical and Electronics Engineering, Sathyabama Institute of Science & Technology, Chennai, 600119, India
3 Professor, Department of Electrical and Electronics Engineering, Annamacharya Institute of Technology and Science, Tirupati, 517501, India
4 Assistant Professor, Department of Electrical and Electronics Engineering, JB Institute of Engineering and Technology, Moinabad, 500075, India
* Corresponding author: naag198222@gmail.com
Now a days the research work is going on to improve the performance of electric springs (ESs) by integrating a current source inverter (CSI) and a neural network controller. A new ES configuration that includes CSIs is introduced to enhance ES functionality. This configuration is accompanied by a control strategy that involves direct current control and mitigation of harmonic distortion, like the methods used in active power filters (APFs). By transitioning from voltage source inverters (VSIs) to CSIs and integrating a neural network controller, a significant reduction in total harmonic distortion (THD) can be achieved. The paper presents two distinct control loops, each equipped with proportional-integral (PI) controllers. One loop is focused on regulating the Critical load voltage (CL voltage), while the other is specifically designed for reactive power compensation.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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