A neural-network-based model predictive control of three-phase inverter with an output LC Filter

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

Neural Network based Model Predictive Controllers for Modular Multilevel Converters

Modular multilevel converter (MMC) has attracted much attention for years due to its good performance in harmonics reduction and efficiency improvement. Model predictive control (MPC) based controllers are widely adopted for MMC because the control design is straightforward and different control objectives can be simply implemented in a cost function. However, the computational burden of MPC imposes limitations in the control implementation of MMC because of many possible switching states. To solve this, we design machine learning (ML) based controllers for MMC based on the data collection from the MPC algorithm. The ML models are trained to emulate the MPC controllers which can effectively reduce the computation burden of real-time control since the trained models are built with simple math functions that are not correlated with the complexity of the MPC algorithm. The ML method applied in this study is a neural network (NN) and there are two types of establishing ML controllers: NN regression and NN pattern recognition. Both are trained using the sampled data and tested in a real-time MMC system. A comparison of experimental results shows that NN regression has a much better control performance and lower computation burden than the NN pattern recognition

Machine-Learning Based Model Predictive Control for a Three-phase Inverter

In recent years, the study of three-phase inverter controls has become important with the rising use of renewable energy sources (RES) in the form of distribution generation (DG). Many control types have been developed for DG inverters and others were traditional controls for the generation of the main grid power that were adapted for a system with less inertia. Among these controls is the model predictive control (MPC) which allows for a fast transient response and good reference tracking. One disadvantage of the MPC is that it does this prediction and optimization online which can limit the applications due to computational loading. Although there are some solutions to this problem in the form of a finite control-set MPC (FCS-MPC) which takes advantage of the only two states of a switch mode converter to reduce complexity, this still takes the form of a nonlinear online optimization problem. However, compared with the continuous control set (CCS) MPC, using the FCSMPC may result in poor performance due to the degradation of the switching frequency. The high computation of CCS-MPC prevents it from being implemented in the resources limited digital signal processor (DSP). To reduce the computational burden, machine learning (ML) methods such as artificial neural networks (ANN) are used for learning the input and output of the MPC. This thesis compares the ANN-MPC, and support vector machine (SVM) based MPC in a three-phase inverter. A comparison of total harmonic distortion (THD), and reference tracking during different scenarios will be provided

Inverse application of artificial intelligence for the control of power converters

This paper proposes a novel application method, Inverse Application of Artificial Intelligence (IAAI) for the control of power electronic converter systems. The proposed method can give the desired control coefficients/references in a simple way because, compared to conventional methods, IAAI only relies on a data-driven process with no need for an optimization process or substantial derivations. Noting that the IAAI approach uses artificial intelligence to provide feasible coefficients/references for the power converter control, rather than building a new controller. After illustrating the IAAI concept, a conventional application method of Artificial Neural Network (ANN) is discussed, an optimization-based design. Then, a two-source-converter microgrid case is studied to choose the best droop coefficients via the optimization-based approach. After that, the proposed IAAI method is employed for the same microgrid case to quickly find good droop coefficients. Furthermore, the IAAI method is applied to a modular multi-level converter (MMC) case, extending the MMC operation region under unbalanced grid faults. In the MMC case, both simulation and experimental online tests validate the operation, feasibility and practicality of IAAI