A Modular Multilevel Series/Parallel Converter for a Wide Frequency Range Operation
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2019-10-01
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Abstract
When providing ac output, modular multilevel converters (MMCs) experience power fluctuation in the phase arms. The power fluctuation causes voltage ripple on the module capacitors, which grows with the output power and inversely to the output frequency. Thus, low-frequency operations of MMCs, e.g., for motor drives, require injecting common-mode voltages and circulating currents, and strict dc voltage output relative to ground is impossible. To address this problem, this paper introduces a novel module topology that allows parallel module connectivity in addition to the series and bypass states. The parallel state directly transfers power across the modules and arms to cancel the power fluctuations and hence suppresses the capacitor voltage ripple. The proposed series/parallel converter can operate at a wide frequency range down to dc without common-mode voltages or circulating currents; it also allows sensorless operation and full utilization of the components at higher output frequencies. We present detailed simulation and experiment results to characterize the advantages and limitations of the proposed solution.
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Li, Z, F Ricardo Lizana, Z Yu, S Sha, AV Peterchev and SM Goetz (2019). A Modular Multilevel Series/Parallel Converter for a Wide Frequency Range Operation. IEEE Transactions on Power Electronics, 34(10). pp. 9854–9865. 10.1109/TPEL.2019.2891052 Retrieved from https://hdl.handle.net/10161/22567.
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Angel V Peterchev
I direct the Brain Stimulation Engineering Lab (BSEL) which focuses on the development, modeling, and application of devices and paradigms for transcranial brain stimulation. Transcranial brain stimulation involves non-invasive delivery of fields (e.g., electric and magnetic) to the brain that modulate neural activity. It is widely used as a tool for research and a therapeutic intervention in neurology and psychiatry, including several FDA-cleared indications. BSEL develops novel technology such as devices for transcranial magnetic stimulation (TMS) that leverage design techniques from power electronics and computational electromagnetics to enable more flexible stimulus control, focal stimulation, and quiet operation. We also deploy these devices in experimental studies to characterize and optimize the brain response to TMS. Another line of work is multi-scale computational models that couple simulations of the electromagnetic fields, single neuron responses, and neural population modulation induced by electric and magnetic brain stimulation. These models are calibrated and validated with experimental neural recordings through various collaborations. Apart from understanding of mechanisms, we develop modeling, algorithmic, and targeting tools for response estimation, dose individualization, and precise localization of transcranial brain stimulation using advanced techniques such as artificial neural networks and machine learning. Moreover, BSEL is involved in the integration of transcranial brain stimulation with robotics, neuronavigation, intracranial electrophysiology recordings, and imaging modalities such as functional magnetic resonance imaging (fMRI) and electroencephalography (EEG), as well as the evaluation of the safety of device–device interactions, for example between transcranial stimulators and implants. Importantly, we collaborate widely with neuroscientists and clinicians within Duke and at other institutions to translate developments from the lab to research and clinical applications. For over 15 years, BSEL has been continuously supported with multiple NIH grants as well as funding by DARPA, NSF, Brain & Behavior Research Foundation, Coulter Foundation, Duke Institute for Brain Sciences, MEDx, Duke University Energy Initiative, and industry. Further, some of our technology has been commercialized, for example as ElevateTMS cTMS, or incorporated in free software packages, such as SimNIBS.
Stefan M Goetz
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