An improved submodule capacitor voltage measuring algorithm for MMC with reduced sensors

IEEE Sensors Journal

Published On 2021/7/8

The nearest level modulation (NLM) based submodule capacitor voltage measuring technology for a modular multilevel converter (MMC) with reduced sensors can effectively reduce the costs of the data acquisition system of MMC and simplify the operation system. One of the technical challenges of this submodule capacitor voltage measuring technology is to reduce the measurement errors of submodule capacitor voltage. This paper proposes an improved submodule capacitor voltage measuring algorithm to overcome this challenge. In the proposed algorithm, by keeping the operation states of submodules unchanged during the continuous control period, the probability of obtaining the actual capacitor voltage is increased, and then the number of corrections of observed capacitor voltage is significantly increased. Thus, the proposed algorithm can effectively reduce the measuring errors. More significantly, the …

Journal

IEEE Sensors Journal

Published On

2021/7/8

Volume

21

Issue

18

Page

20475-20492

Authors

Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

Position

Professor at

H-Index(all)

104

H-Index(since 2020)

72

I-10 Index(all)

0

I-10 Index(since 2020)

0

Citation(all)

0

Citation(since 2020)

0

Cited By

0

Research Interests

Power Electronics

Smart Batteries

AI

University Profile Page

Qi Zhang

Qi Zhang

Aalborg Universitet

Position

H-Index(all)

10

H-Index(since 2020)

9

I-10 Index(all)

0

I-10 Index(since 2020)

0

Citation(all)

0

Citation(since 2020)

0

Cited By

0

Research Interests

Power electronics

HVDC

Power system protection

University Profile Page

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IEEE Sensors Journal

A self-adaptive planar velocity vector sensor based on vortex-induced torsional swing motions

Flow sensing facilitates the development of fisheries and marine security. However, previously reported flow sensors can typically measure the average flow velocity within a large-scale geographical area. Herein, a self-adaptive sensor for measuring fixed-position, small area, real-time, and all-day planarly velocity vector is proposed. Based on the vortex-induced tortional swing motion mode, the arc-shaped device with a hall sensor or a piezoelectric sensor enables to recognize the 2-D magnitude and direction of the flow velocity. The working mechanism is first elaborated through the boundary layer principle of fluid mechanics. A vibration mechanical model is proposed and solved by Galerkin method to numerically investigate the relation between swing amplitude and torsional frequency for flow speed and direction sensing simultaneously. The model is subsequently validated by experiments. The proposed …

Ying Shen

Ying Shen

Virginia Polytechnic Institute and State University

IEEE Sensors Journal

Low-frequency environmental magnetic noise elimination based on a neural network algorithm for TMR sensor arrays

Tunneling magnetoresistance (TMR) sensors have shown the capability of operating in weak magnetic fields. However, the environmental magnetic noise limits their applications in open field detection. This paper proposes a novel background noise cancellation method based on a backpropagation (BP) neural network for TMR sensor arrays. According to simulation results, the BP based noise reduction method can eliminate background noise more effectively than the traditional coherence coefficient method. The signal-to-noise ratio (SNR) of the sensor can thus be improved by over 20 dB, especially when detecting extremely low SNR signals. This algorithm is demonstrated using a TMR sensor array, which shows a capability of greatly enhancing the sensor array’s limit of detection in open field testing.

Andre Eugenio Lazzaretti

Andre Eugenio Lazzaretti

Universidade Tecnológica Federal do Paraná

IEEE Sensors Journal

ST-NILM: A Wavelet Scattering-Based Architecture for Feature Extraction and Multi-Label Classification in NILM Signals

Non-Intrusive Load Monitoring (NILM) is a relevant tool for improving energy consumption habits, contributing to energy conservation and distribution system planning. In recent years, high-frequency strategies using Deep Learning have been presented in the literature, achieving the state-of-the-art results for detection, feature extraction, and classification of aggregated electrical loads, particularly with the architecture defined as Deep Neural Network Model for Detection, Feature Extraction, and Multi-Label Classification (DeepDFML). DeepDFML used a deep Convolutional Network (DCN) whose trained weights were shared for different output fully connected networks. The performance of DeepDFML depended on the availability of data and data augmentation (DA) strategies. Given this scenario, we propose the ST-NILM, a new integrated architecture based on the Scattering Transform. ST-NILM has a DCN with …

Weiliang (Peter) Xu

Weiliang (Peter) Xu

University of Auckland

IEEE Sensors Journal

Simultaneous Vision and Surface Electromyography Measurements to Evaluate Masticatory Robot TMJ Reaction Forces During Mastication

The advancement in biomechanics motivated the development of biomimetic robots. In mastication, there are limited studies on a masticatory robot capable of reproducing the human mandibular trajectory. Available studies have suggested a reliable simulation of the developed masticatory robot based on human mandibular motion. However, there are limited literatures evaluating the robot’s performance and comparing its results to that of a human subject. Benchmarking and evaluating robotic performance with human subject data establishes an important concept which is essential for further development. Therefore, this paper presents a methodology to evaluate masticatory performance of the developed masticatory robot and human subject during mastication. This methodology utilizes surface electromyography and Hill’s muscle model to estimate masticatory forces in a human subject during occlusion in …

Kai Zhang

Kai Zhang

École Centrale de Lyon

IEEE Sensors Journal

Analysis of the Influence of Temperature on Stress Waves of Cascode GaN HEMT

The third-generation power semiconductor device gallium nitride high electron mobility transistor (GaN HEMT) is widely used in fast charging, mobile communication, and other fields due to its excellent characteristics of voltage and temperature resistance. As the core device in power electronic equipment, GaN HEMT often works under harsh conditions of high temperature and high voltage, so its status monitoring can effectively improve system reliability. Acoustic emission (AE) is a passive, fast, and real-time nondestructive state detection method. Studies have shown that GaN HEMT device will generate stress waves at the moment of switching, which can be collected by AE sensors. In this article, experiments are designed, and repeated tests are carried out under different temperatures and drain–source voltage conditions. A high-temperature-resistant AE sensor with a differential structure is used to collect stress …

Lipo Wang

Lipo Wang

Nanyang Technological University

IEEE Sensors Journal

Feature Entropy Adaptive Network for Weak Magnetic Signal Classification

Magnetic anomaly signals are composed of anomaly signal and the geomagnetic field. Due to the similarity in magnitude between these two types of signals and the difficulty in acquiring magnetic field data, distinguishing between them is challenging, and the available dataset is small. This article aims to address the classification of weak magnetic signals with limited samples obtained from actual measurements, and a novel neural network-based approach for magnetic anomaly classification is proposed. First, the feature selection is performed on the fused magnetic field signal features. The measured magnetic signals are decomposed using the standard orthogonal basis functions (OBFs), and the coefficients of the basis functions are utilized as magnetic moment features. The wavelet transform is employed to calculate the coefficients as the time–frequency features of the magnetic field data. Statistical features …

2023/10/26

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