Extended Kalman Filtering for Full-State Estimation and Sensor Reduction in Modular Multilevel Converters

Pizarro, Germán; Poblete, Pablo; Droguett, Gabriel; Pereda, Javier; Nüñez, Felipe

doi:10.1109/tie.2022.3165286

Cited by 18 publications

(6 citation statements)

References 17 publications

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“…In conclusion, model-based methods such as SMO or Kalman Filters are (i) easy to implement, (ii) can provide accurate and (iii) robust estimation. However, for large-scale systems with high dimensionality or severe nonlinear behavior, the real process may not be correctly represented and the estimation may differ from the real values even under normal conditions due to high computational load requirement [84]. For MMCCs, model-based state estimation methods are widely used and they differ mainly in robustness and estimation speed due to linear or nonlinear modeling and/or the consideration of noise/disturbances.…”

Section: B Model-based Fdd Methodsmentioning

confidence: 99%

Faults in Modular Multilevel Cascade Converters–Part II: Fault Tolerance, Fault Detection and Diagnosis, and System Reconfiguration

Rojas

Jerez

Hackl

et al. 2022

IEEE Open J. Ind. Electron. Soc.

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Modular Multilevel Cascade Converters (MMCCs) are considered a promising power electronics topology in industry. Their scalability allows to reach (ultra/very) high voltage levels with low harmonic content and high efficiency and makes MMCCs an ideal solution for high-power applications; such as electrical drives, solid-state transformers and high-voltage direct-current (HVDC) transmission systems. However, the high levels of thermal, electrical and mechanical stress on the power electronics devices and the large number of components (e.g. capacitors or semiconductors) make MMCCs prone to faults. Fault detection and diagnosis (FDD) in combination with fault isolation and system reconfiguration techniques, based on cell redundancy, can increase the reliability, availability and safety of MMCCs, which is crucial for their utilization in critical energy applications. This second part of the paper comprehensively surveys: (i) fault tolerance and fault detection & diagnosis (FDD; e.g. expert system, model-or hardware and data-based FDD methods) and (ii) system reconfiguration strategies (e.g. cold-or hot-redundant) for MMCCs. Finally, the state-of-the-art, challenges and future research trends and opportunities towards reliable MMCC-based systems are revealed.

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Section: B Model-based Fdd Methodsmentioning

confidence: 99%

Faults in Modular Multilevel Cascade Converters–Part II: Fault Tolerance, Fault Detection and Diagnosis, and System Reconfiguration

Rojas

Jerez

Hackl

et al. 2022

IEEE Open J. Ind. Electron. Soc.

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show abstract

“…With the system descriptions of the DSBC-MMCC in (3) or (11), the observer can be designed. As the observability of such a switched and control-affine system is cumbersome, a thorough Lyapunov-based observer design and proof are provided which implicitely will show observerability as well.…”

Section: Proposed Lyapunov-based Observermentioning

confidence: 99%

“…As the observability of such a switched and control-affine system is cumbersome, a thorough Lyapunov-based observer design and proof are provided which implicitely will show observerability as well. For the switching system in (3) or the averaged system in (11) with the state vectors…”

Section: Proposed Lyapunov-based Observermentioning

confidence: 99%

A generic Lyapunov-based Observer for Double-Star-Chopper-Cell/Bridge-Cell Modular-Multilevel-Cascade-Converters

Testa¹,

Kalmbach²,

Hackl³

2023

Preprint

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<p>Modular Multilevel Cascade Converter (MMCC) have achieved a great importance in high voltage converter applications. As they require a high amount of capacitors whose voltages must be known for the control of the MMCC, many voltage sensors are needed. This paper presents a nonlinear Lyapunov-based observer for cell voltage estimation. The proposed observer does solely rely on the knowledge of the phase currents i.e. no voltage sensors are required and the convergence of the estimator is shown with a stability proof by Lyapunov. The performance of the observer is validated simulatively using <em>MATLAB/Simulink</em>.</p>

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“…In the literature, several observer topologies for MMCCs were proposed, which can be divided into four classes: Sliding mode observers (SMOs) [7,8], Neuronal Networks based observers (NNOs) [9], Kalman filters (KFs) [10,11], and adaptive observers (AOs) [12,13].…”

Section: Introductionmentioning

confidence: 99%

“…The classical Kalman filter in [10] is based on a linear timeinvariant converter model. The observer in [11] goes one step further. An Extended Kalman Filter (EKF) is used, which is an extension of the KF for nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

A generic Lyapunov-based Observer for Double-Star-Chopper-Cell/Bridge-Cell Modular-Multilevel-Cascade-Converters

Testa¹,

Kalmbach²,

Hackl³

2023

Preprint

View full text Add to dashboard Cite

show abstract

Extended Kalman Filtering for Full-State Estimation and Sensor Reduction in Modular Multilevel Converters

Cited by 18 publications

References 17 publications

Faults in Modular Multilevel Cascade Converters–Part II: Fault Tolerance, Fault Detection and Diagnosis, and System Reconfiguration

Faults in Modular Multilevel Cascade Converters–Part II: Fault Tolerance, Fault Detection and Diagnosis, and System Reconfiguration

A generic Lyapunov-based Observer for Double-Star-Chopper-Cell/Bridge-Cell Modular-Multilevel-Cascade-Converters

A generic Lyapunov-based Observer for Double-Star-Chopper-Cell/Bridge-Cell Modular-Multilevel-Cascade-Converters

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