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Modulated Model Predictive Control and Fault Diagnosis for the Cascaded H-Bridge (CHB) Inverters

Multilevel inverters (MLI) have been widely applied in medium and high voltage applications for their advantages in high quality of output waveforms. Among various multilevel topologies, cascaded H-bridge (CHB) inverters have attracted more attentions for its modular structure, which simplifies the design and implementation. In addition, the modularity of CHB also expands diverse power ratings without many changes in the hardware setup. In a CHB inverter, the AC output voltage can be produced at different voltage levels depending on the number power cells that are cascaded at the output.
To produce the AC output voltage, different modulation schemes and control algorithms have been studied and applied to the CHB inverter. Model predictive control (MPC) has been widely employed among all control algorithms in multilevel topologies due to their advantages such as good dynamic performance, multiple control targets, inclusion of nonlinearity, and flexibility to add more performance objectives. However, one disadvantage of the MPC is that the switching frequency is variable compared with other modulation schemes. Therefore, a new MPC method called modulated model predictive control (M2PC) has been researched to obtain a fixed switching frequency, which improves the harmonic spectrum of load currents and simplifies the filter design.
In the modulated model predictive control, the mathematical model is obtained by electrical model of the system. It means that the operation of the M2PC algorithm relies on the accuracy of the given parameters and model. If there is an error in parameters and model, the performance of the control will be affected negatively. To solve this problem, modulated model-free predictive control (M2FPC) algorithm has been introduced. With this method, the mathematical model is established with measured values instead of given values and model.
Reliability is one of the most important issues in the design of power converters. However, the failure of power switches will lead to the distortion of load currents and voltage waveforms. Also, the distortion in load currents and voltage waveforms causes power imbalance between faulty and healthy phases. To reduce the negative effects of IGBT failure in power converters, the faulty power cells should be found and isolated. Therefore, fault detection and localization algorithm (FDL) should be introduced to detect the fault in power converters and localize the faulty power switches.
FDL algorithm based on the given M2PC scheme is proposed in this thesis for the CHB inverter to make the system more reliable. The FDL algorithm utilizes the phase voltages and load currents to detect the open fault in the CHB inverter and localize the single and multiple open switches by measuring the expected and actual phase voltages. With the faulty information, the faulty power cell can be isolated, and the fault-tolerant control can be applied to make the system work normally even though there is an open fault.
In this thesis, without losing the generality, a seven-level CHB inverter is considered where there are three power cells in each phase. The M2PC algorithm was introduced to obtain the fixed switching frequency with the design of possible voltage vector set and carrier phase-shifting modulation. Based on the proposed M2PC algorithm, the FDL algorithm is designed to detect and localize the open switches to improve the system reliability.
The theoretical analysis and simulation results validate the feasibility of the proposed M2PC algorithms and open fault diagnosis scheme. All possible open-circuit scenarios in power cells are discussed and the M2PC-based FDL algorithm has been verified.
Experimental results verify the feasibility of the proposed M2PC. The experimental result of M2PC algorithm is presented to verify its operation. Also, diverse open scenarios can be diagnosed in the experiments. / Thesis / Master of Applied Science (MASc)

Identiferoai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/28805
Date January 2023
CreatorsPan, Yue
ContributorsNarimani, Mehdi, Electrical and Computer Engineering
Source SetsMcMaster University
LanguageEnglish
Detected LanguageEnglish
TypeThesis

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