Pulse Width Modulation (PWM) strategies are crucial for controlling DC-AC power converters. In particular, transformerless inverters require specific PWM techniques to improve efficiency and to deal with leakage ground current issues. In this paper, three hybrid PWM methods are proposed for a DCM-232 three-phase topology. These methods are based on the concepts of carrier-based PWM and space vector modulation. Calculations of time intervals for active and null vectors are performed in a conventional way, and the resulting waveforms are compared with a carrier signal. The digital signals obtained are processed using Boolean functions, generating ten signals to control the DCM-232 three-phase inverter. The performance of the three proposed PWM methods is evaluated considering the reduction in leakage ground current and efficiency. The proposed modulation techniques have relevant performances complying with international standards, which make them suitable for transformerless three-phase photovoltaic (PV) inverter markets. To validate the proposed hybrid PWM strategies, numerical simulations and experimental tests were performed.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8780749 | PMC |
http://dx.doi.org/10.3390/mi13010036 | DOI Listing |
Sci Rep
November 2024
Department of Electrical and Electronics Engineering, Sri Sai Ram Engineering College, Chennai, India.
In this article, a 12 switch 31 L multi-level inverter (MLI) is proposed with the benefits of least switching devices for electric vehicle applications. In most electric vehicles (EV), conventional inverters are utilized so the lifetime of electric vehicle induction motors is reduced due to the high THD level and high voltage stress. To rectify this, a new inverter topology is proposed with minimum switching devices by increasing the level to 31, and also the THD should be maintained within IEEE standards.
View Article and Find Full Text PDFJ Phys Chem Lett
October 2024
College of Integrated Circuit Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.
The integration of optoelectronic devices with reservoir computing offers a novel and effective approach to in-sensor computing. This work presents a hybrid digital-physical solution that leverages the high-performance poly[(bithiophene)-alternate-(2,5-di(2-octyldodecyl)-3,6-di(thienyl)-pyrrolyl pyrrolidone)] (DPPT-TT) organic polymer-based photodiodes for the hardware implementation of reservoir computing system. The photodiodes demonstrate nonlinear photoelectric responses, fading memory, and cyclical stability, in relation to the temporal information on light stimuli.
View Article and Find Full Text PDFSci Rep
August 2024
Center for Semiconductor Technology, Korea Institute of Science and Technology, Seoul, 02792, South Korea.
Neuromorphic computing research is being actively pursued to address the challenges posed by the need for energy-efficient processing of big data. One of the promising approaches to tackle the challenges is the hardware implementation of spiking neural networks (SNNs) with bio-plausible learning rules. Numerous research works have been done to implement the SNN hardware with different synaptic plasticity rules to emulate human brain operations.
View Article and Find Full Text PDFSci Rep
July 2024
Department of Computer Engineering, University of Peradeniya, Peradeniya, Sri Lanka.
Cardiac monitoring systems in Internet of Things (IoT) healthcare, reliant on limited battery and computational capacity, need efficient local processing and wireless transmission for comprehensive analysis. Due to the power-intensive wireless transmission in IoT devices, ECG signal compression is essential to minimize data transfer. This paper presents a real-time, low-complexity algorithm for compressing electrocardiogram (ECG) signals.
View Article and Find Full Text PDFFront Biosci (Landmark Ed)
May 2024
Hunan Engineering & Technology Research Centre for Agricultural Big Data Analysis & Decision-Making, Hunan Agricultural University, 410128 Changsha, Hunan, China.
Background: Ubiquitination is a crucial post-translational modification of proteins that regulates diverse cellular functions. Accurate identification of ubiquitination sites in proteins is vital for understanding fundamental biological mechanisms, such as cell cycle and DNA repair. Conventional experimental approaches are resource-intensive, whereas machine learning offers a cost-effective means of accurately identifying ubiquitination sites.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!