A Neural-Network-Based Model Predictive Control of Three-Phase Inverter With an Output $LC$ Filter

Ihab S Mohamed,Ton Duc Do,Stefano Rovetta,Ahmed A Zaki Diab,Tomislav Dragicevic

doi:10.1109/access.2019.2938220

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.

Highlights

The three-phase inverter is an extensively popular device, which is commonly used for transferring energy from a DC voltage source to an AC load
The control of three-phase inverters has received much attention in the last decades both in the scientific literature and in the industry-oriented research [1], [2]. For applications such as uninterruptible power supplies (UPSs), energy-storage systems, variable frequency drives, and distributed generation, the inverters are commonly used with an output LC filter to provide a high-quality sinusoidal output voltage with low total harmonic distortion (THD) for various types of loads, especially for unbalanced or nonlinear loads [3]–[6]
This paper focuses on the control of a three-phase inverter with output LC filter using a feed-forward artificial neural network (ANN)-based Model predictive control (MPC), which has not been reported in the literature, where MPC is only used as a teacher for training the neural network

Summary

INTRODUCTION

The three-phase inverter is an extensively popular device, which is commonly used for transferring energy from a DC voltage source to an AC load. The linear methods, which require carrier-based modulators, have the advantage of constant switching frequency, their dynamic response is weak comparing with HVC, because of the slow response of the modulator Both linear and nonlinear methods are extensively used for generating the switching signals of the inverter because of the simplicity of the controller implementation. The ANN-based controllers have some advantages compared to other control methods such as: (i) their design does not require the mathematical model of the system to be controlled, considering the whole system as a black-box; (ii) they can generally improve the performance of the system when they are properly tuned; (iii) they are usually easier to be tuned as compared to conventional controllers; (iv) they can be designed based on the data acquired from a real system or a plant in the absence of necessary expert knowledge.

SYSTEM DESCRIPTION AND MODELING

PROPOSED NEURAL NETWORK ARCHITECTURE

ANN TRAINING PROCEDURE

SIMULATION IMPLEMENTATION AND RESULTS

CONCLUSION AND FUTURE WORK