Three-level T-type converters are necessary interfaces for distributed energy resources to interact with the public grid. Naturally, designing a control strategy, featuring superior dynamics and strong robustness, is a promising solution to guarantee the efficient operation of converters. This article presents an improved finite-time control (IFTC) strategy for three-level T-type converters to enhance the dynamic performance and anti-disturbance capacity. The IFTC strategy integrates a dual-loop structure to regulate the dc-link voltage and grid currents. Specifically, the voltage regulation loop employs a finite-time adaptive controller that can counteract load disturbances without relying on current sensors. In the current tracking loop, finite-time controllers combined with a command filter are constructed to obtain fast and accurate current tracking. In this loop, the command filter is utilized to avoid calculating the derivative of current references. Theoretical analysis and experimental results demonstrate the IFTC strategy's effectiveness.
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http://dx.doi.org/10.1016/j.isatra.2024.04.035 | DOI Listing |
ISA Trans
July 2024
School of Control Science and Engineering, Shandong University, Jinan, 250061, China.
Three-level T-type converters are necessary interfaces for distributed energy resources to interact with the public grid. Naturally, designing a control strategy, featuring superior dynamics and strong robustness, is a promising solution to guarantee the efficient operation of converters. This article presents an improved finite-time control (IFTC) strategy for three-level T-type converters to enhance the dynamic performance and anti-disturbance capacity.
View Article and Find Full Text PDFSensors (Basel)
February 2024
School of Electronic Information Engineering, Changchun University of Science and Technology, Changchun 130022, China.
Compared with traditional two-level inverters, multilevel inverters have many solid-state switches and complex composition methods. Therefore, diagnosing and treating inverter faults is a prerequisite for the reliable and efficient operation of the inverter. Based on the idea of intelligent complementary fusion, this paper combines the genetic algorithm-binary granulation matrix knowledge-reduction method with the extreme learning machine network to propose a fault-diagnosis method for multi-tube open-circuit faults in T-type three-level inverters.
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