TY  - JOUR
TI  - Supervised Learning-Aided Control of a DC-DC Power Converter in Wind Energy Conversion Systems
AB  - Over the last decades, to adopt high penetration of renewable energy sources (RESs) in electrical energy systems, distributed energy resources (DERs) have become prominent. Due to easy attainability status of small wind turbines (WTs), wind energy conversion systems (WECSs) are feasible applications for small customers, especially in windy areas. The next decade is likely to witness a considerable rise in DERs. In this context, WECSs are preferred broadly, thus harvesting wind energy into electrical energy effectively is a substantial issue. WTs can be got involved in the grid-connected or autonomous mode with a variety of topologies. In this paper, we examine to control of DC-DC boost converter of a WECS with the help of artificial intelligence (AI)-aided PI controller based on supervised learning method. Regarding the proposed method, artificial neural networks (ANNs) as a subset of AI are utilized. To test and ensure the applicability of the proposed control method, a small WECS with a permanent magnet synchronous generator (PMSG) connected a DC bus was implemented in MATLAB/Simulink environment. The proposed ANN scheme has reached a high accuracy rate with an overall mean squared error (MSE) equal to 7.4e-08. The results present that dynamic response and less complexity with a high accuracy rate have been obtained under study. The main target of this study is to reduce the number of sensors in the control layer. Thus, a cost-effective and more reliable structure is obtained with fewer sensor requirements.
AU  - Dursun, Erkan
AU  - AKPOLAT, Alper Nabi
AU  - Kuzucuoglu, Ahmet Emin
DO  - 10.7240/jeps.897076
PY  - 2021
JO  - International journal of advances in engineering and pure sciences (Online)
VL  - 33
IS  - 0
SN  - 2636-8277
SP  - 47
EP  - 56
DB  - TRDizin
UR  - http://search/yayin/detay/514997
ER  -