Abstract
Model predictive control (MPC) has become one of the well-established modern control methods for three-phase inverters with an output $LC$ filter, where a high-quality voltage with low total harmonic distortion (THD) is needed. Although it is an intuitive controller, easy to understand and implement, it has the significant disadvantage of requiring a large number of online calculations for solving the optimization problem. On the other hand, the application of model-free approaches such as those based on artificial neural networks approaches is currently growing rapidly in the area of power electronics and drives. This paper presents a new control scheme for a two-level converter based on combining MPC and feed-forward ANN, with the aim of getting lower THD and improving the steady and dynamic performance of the system for different types of loads. First, MPC is used, as an expert, in the training phase to generate data required for training the proposed neural network. Then, once the neural network is fine-tuned, it can be successfully used online for voltage tracking purpose, without the need of using MPC. The proposed ANN-based control strategy is validated through simulation, using MATLAB/Simulink tools, taking into account different loads conditions. Moreover, the performance of the ANN-based controller is evaluated, on several samples of linear and non-linear loads under various operating conditions, and compared to that of MPC, demonstrating the excellent steady-state and dynamic performance of the proposed ANN-based control strategy.
Original language | English |
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Article number | 8819887 |
Pages (from-to) | 124737-124749 |
Number of pages | 13 |
Journal | IEEE Access |
Volume | 7 |
DOIs | |
Publication status | Published - 2019 |
Funding
This work was supported by Nazarbayev University through the NU-ORAU Program, under Grant 06/06.17.24.
Keywords
- Artificial neural network
- Model predictive control
- Three-phase inverter
- UPS systems
ASJC Scopus subject areas
- General Computer Science
- General Materials Science
- General Engineering
- Electrical and Electronic Engineering