A model-based controller for a single-phase n-level CHB multilevel converter

Fernández, A.; Escobar, G.; Campos-Delgado, Daniel U.; Mtepele, Kassim O.; Martinez‐Rodriguez, Panfilo R.

doi:10.1016/j.ijepes.2020.106454

Cited by 8 publications

(8 citation statements)

References 18 publications

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“…The proposed energy function based MPC method is also compared with five control methods presented in [27] and [35]- [38]. The comparison is based on the type of controller, weighting factor necessity, number of weighting factors, number of required sensors, additional controller necessity for controlling dc capacitor voltages, and reference filter current generation method as shown in Table 3.…”

Section: B Comparison With Classical Mpc Methodsmentioning

confidence: 99%

“…The proposed energy function based MPC method possesses weighting factor free structure, which offers simplicity in the design. Opposed to [27] and [35]- [37], the proposed method in this study and [38] does not need an additional controller for regulating dc capacitor voltages. However, the control method in [38] needs weighting factor and requires one extra sensor compared to the proposed method here.…”

Section: B Comparison With Classical Mpc Methodsmentioning

confidence: 99%

“…However, the control method in [38] needs weighting factor and requires one extra sensor compared to the proposed method here. On the other hand, contrary to the filter current reference generation in the proposed method, the methods in [27] and [35]- [37] require many computations, additional transformations, low pass filter and high pass filter.…”

Section: B Comparison With Classical Mpc Methodsmentioning

confidence: 99%

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An Effective Model Predictive Control Method With Self-Balanced Capacitor Voltages for Single-Phase Three-Level Shunt Active Filters

et al. 2021

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This paper presents an effective model predictive control (MPC) method for single-phase threelevel T-type inverter-based shunt active power filters (SAPFs). The SAPF using T-type inverter topology has not been reported in the literature yet. Contrary to most of the existing MPC methods, the proposed MPC method eliminates the need for using weighting factor and additional constraints required for balancing dc capacitor voltages in the cost function. The design of cost function is based on the energy function. Since the factor used in the formulation of the energy function does not have any adverse influence on the performance of the system, the cost function becomes weighting factor free. The weighting factor free based MPC brings simplicity in the practical implementation. The effectiveness of the proposed MPC method has been investigated in steady-state as well as dynamic transients caused by load changes. The theoretical considerations are verified through experimental studies performed on a 3 kVA system.

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Section: B Comparison With Classical Mpc Methodsmentioning

confidence: 99%

Section: B Comparison With Classical Mpc Methodsmentioning

confidence: 99%

Section: B Comparison With Classical Mpc Methodsmentioning

confidence: 99%

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An Effective Model Predictive Control Method With Self-Balanced Capacitor Voltages for Single-Phase Three-Level Shunt Active Filters

et al. 2021

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“…Powerful three-dimensional visualization and animation functions make 3ds Max widely used in educational projects, such as mechanical design and manufacturing, semiconductor device simulation demonstrations, semi-finished product processing, etc. To build a realistic virtual environment, we must first determine what needs to be created and establish an accurate model [9] . 3ds Max can be used to accurately create 3D models of various electronic circuits.…”

Section: Virtual Visual Simulationmentioning

confidence: 99%

Remote laboratory implementation analysis based on virtual visual simulation

Zhang,

Xin

2024

Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023)

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With the rise of network informatization in assisting teaching, students in schools use various informatization methods for teaching. Students do not need to go to school to attend classes and participate in experimental learning activities through the Human-Computer Interaction Learning Center and "Remote Electronic Laboratory". The rapid development of online teaching should require the design of simulation tools, software and facilities for course content and experimental environments, but these key factors have become one of the bottlenecks affecting the quality of online teaching. This paper uses the neural network algorithm in machine learning and artificial intelligence to conduct intensive training and learning on the output and reception signal samples of semiconductor devices to obtain virtual visualization simulation results. Fit the simple data of the received signal to a system of two-dimensional linear equations to obtain the different magnitudes of the signals and obtain accurate coordinate positions. With the help of machine learning visualization processing, model changes can be compared, and the output effects are reasonable and very accurate. Compared with traditional semiconductor devices that do not have visual interface operations, virtual simulation in remote laboratories further expands the scope of use. The GUI program of the virtual visual simulation model created in this article makes the simulation of electronic circuit models simpler and more convenient, and has extensive promotion and reference effects.

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“…Te authors demonstrated that supplementary PI controllers in voltage magnitude and phase angle loops can provide extra damping to the system, leading to very good responses even under severe three-phase fault conditions. In [13], a method for controlling a 3-phase cascaded H-bridge (3ph-CHB) converter with n-levels was proposed to compensate for reactive power and eliminate the harmonic currents generated by nonlinear loads. Te authors introduced a generalized model of the CHB-nL converter that takes into account the efect of the line impedance.…”

Section: Literature Reviewmentioning

confidence: 99%

Intelligent Integrated Approach for Voltage Balancing Using Particle Swarm Optimization and Predictive Models

Ghaeb,

Al-Naimi,

Alkayyali

2023

Journal of Electrical and Computer Engineering

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In this paper, an intelligent integrated approach is proposed to control the reactive power and restore the voltage balance in a three-phase power system using particle swarm optimization (PSO), Gaussian process regression (GPR), and support vector machine (SVM). The PSO algorithm is used in offline mode to determine the optimal set of firing angles for the thyristor-controlled-reactor (TCR) compensator according to the smallest fitness value required for voltage balancing. The optimum firing angles are then used to train the GPR and SVM regression models. The GPR and SVM models are finally used as a real-time controller to retrieve the voltage balance in online mode. A simulation model and experimental setup of the electrical power system are built. The modeled system consists of a 500 km long transmission line. The line is divided into three-pi sections to guarantee a real system response. Several simulation and practical case studies have been conducted to test and validate the capability of the proposed integrated approach in solving the voltage unbalance problem. The results have revealed the supreme ability of the proposed integrated approach to restore the voltage balance quickly (within 20 ms) and for a wide range of voltage unbalance factors (VUFs) (3.90–8.42%).

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A model-based controller for a single-phase n-level CHB multilevel converter

Cited by 8 publications

References 18 publications

An Effective Model Predictive Control Method With Self-Balanced Capacitor Voltages for Single-Phase Three-Level Shunt Active Filters

An Effective Model Predictive Control Method With Self-Balanced Capacitor Voltages for Single-Phase Three-Level Shunt Active Filters

Remote laboratory implementation analysis based on virtual visual simulation

Intelligent Integrated Approach for Voltage Balancing Using Particle Swarm Optimization and Predictive Models

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