A Novel Method Simulating Human Eye Recognition for Sector Judgement of SVPWM Algorithm

The conventional space vector pulse width modulation (SVPWM) algorithm is mature and widely used in the control of three-phase inverters. As we know, the position of the voltage vectors can be seen directly by human eyes, which can be used to replace the existing way for sector selection in the conventional SVPWM algorithm. Based on the fact, a novel method simulating human eye recognition for sector selection of SVPWM is proposed in this paper. In the real application, machine can replace human eyes, and it can `see' the sector step by step in which the voltage vector is located, and immediately give the switching time of the two non-zero voltage vectors. Theoretically, it can save the running time and complexity of the SVPWM algorithm reflected in the situation that multiple inverters are connected in parallel with the number of voltage vectors being increased. The feasibility of the proposed SVPWM algorithm has been validated by both simulation and experiments, which offers the possibility of the application in the multiple or multilevel converters

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

Cost-Effective Model Predictive Control Techniques for Modular Multilevel Converters

In this thesis, model predictive control (MPC) techniques are investigated with their applications to modular multilevel converters (MMCs). Since normally a large number of submodule (SM) capacitor voltages and gate signals need to be handled in an MMC, the MPC schemes studied in this thesis are employed for determining only the voltage levels of converter arms, while gate signals are subsequently generated by the conventional sorting method. Emphasis is given to inner-loop current control in terms of phase current and circulating current, aiming at performance enhancement and computation reduction. A variable rounding level control (VRLC) approach is developed in this thesis, which is based on a modification of the conventional nearest level control (NLC) scheme: instead of the conventional nearest integer function, a proper rounding function is selected for each arm of the MMC employing the MPC method. As a result, the simplicity of the NLC is maintained while the current regulating ability is improved. The VRLC technique can also be generalized from an MPC perspective. Different current controllers can be considered to generate the arm voltage references as input of the VRLC block, thus refining the control sets of the MPC. Based on the decoupled current models, the accumulated effect of SM capacitor voltage ripples is investigated, revealing that the VRLC strategy may not achieve a proper performance if the accumulated ripple is nontrivial compared to the voltage per level. Two indexes are also proposed for quantifying the current controllability of the VRLC. Benefiting from this analysis, A SM-grouping solution is put forward to apply such MPC techniques to an MMC with a large number of SMs, leading to an equivalent operation of an MMC with much reduced number of SMs, which significantly increases the current regulating capability with reduced complexity. As an example, the SM-grouping VRLC proposal is analyzed and its system design principles are described. This thesis also develops another MPC technique which directly optimizes the cost function using quadratic programming technique. Both a rigorous and a simplified procedure are provided to solve the optimization problem. Compared with the conventional finite control set (FCS)-MPC method which evaluates all voltage level combinations, the proposed scheme presents apparent advantage in terms of calculation cost while achieving similar performance

Advances in Theoretical and Computational Energy Optimization Processes

The paradigm in the design of all human activity that requires energy for its development must change from the past. We must change the processes of product manufacturing and functional services. This is necessary in order to mitigate the ecological footprint of man on the Earth, which cannot be considered as a resource with infinite capacities. To do this, every single process must be analyzed and modified, with the aim of decarbonising each production sector. This collection of articles has been assembled to provide ideas and new broad-spectrum contributions for these purposes