A new approach for the diagnosis of different types of faults in dc–dc power converters based on inversion method

2020 ◽  
Vol 180 ◽  
pp. 106103 ◽  
Author(s):  
Alexandre Miguel Silveira ◽  
Rui Esteves Araújo
2004 ◽  
Vol 13 (04) ◽  
pp. 813-827 ◽  
Author(s):  
NARENDRA BAWANE ◽  
A. G. KOTHARI

This paper explores the possibility of using neural network to identify faults that may occur in a HVDC converter system. Based on the ability of these networks to distinguish reliably between different types of faults, the feature can be suitably integrated with ANN based controller to improve the dynamic response of AC–DC power system. In this paper, different neural network architectures to distinguish different faults in HVDC converter are proposed, and comparison between them is made under different system perturbations and faults. The method is independent of the operating mode of the converter.


Proposed scheme presents intelligent technique in protection of microgrid. This paper gives new approach in feature extraction of faulted current signal using Discrete Wavelet Transform. Furthermore different parameters like TMS(Time Measurment setting),PSM (Plug setting Multiple ) and CTD (coordination time Duration) are computed from featured faulty current. This course of action used to build genetic differential algorithm for deciding best suitable pair of relay with concept of “survival of fittest”. IEEE 9 bus system is considered for studding different types of faults for utilityconnected and islanded mode. Initially primary pair of relay is activated and secondary protection operates on failure of primary. This study gives effective solution for fast operation of pair of relay in optimized time.


2020 ◽  
Vol 53 (2) ◽  
pp. 13410-13417
Author(s):  
Kevin E. Lucas ◽  
Daniel J. Pagano ◽  
Douglas A. Plaza ◽  
David Alejandro Vaca—Benavides ◽  
Sara J. Ríos

Membranes ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 204
Author(s):  
Ievgen Pylypchuk ◽  
Roman Selyanchyn ◽  
Tetyana Budnyak ◽  
Yadong Zhao ◽  
Mikael Lindström ◽  
...  

Nanocellulose membranes based on tunicate-derived cellulose nanofibers, starch, and ~5% wood-derived lignin were investigated using three different types of lignin. The addition of lignin into cellulose membranes increased the specific surface area (from 5 to ~50 m2/g), however the fine porous geometry of the nanocellulose with characteristic pores below 10 nm in diameter remained similar for all membranes. The permeation of H2, CO2, N2, and O2 through the membranes was investigated and a characteristic Knudsen diffusion through the membranes was observed at a rate proportional to the inverse of their molecular sizes. Permeability values, however, varied significantly between samples containing different lignins, ranging from several to thousands of barrers (10−10 cm3 (STP) cm cm−2 s−1 cmHg−1cm), and were related to the observed morphology and lignin distribution inside the membranes. Additionally, the addition of ~5% lignin resulted in a significant increase in tensile strength from 3 GPa to ~6–7 GPa, but did not change thermal properties (glass transition or thermal stability). Overall, the combination of plant-derived lignin as a filler or binder in cellulose–starch composites with a sea-animal derived nanocellulose presents an interesting new approach for the fabrication of membranes from abundant bio-derived materials. Future studies should focus on the optimization of these types of membranes for the selective and fast transport of gases needed for a variety of industrial separation processes.


2021 ◽  
Vol 13 (11) ◽  
pp. 6194
Author(s):  
Selma Tchoketch_Kebir ◽  
Nawal Cheggaga ◽  
Adrian Ilinca ◽  
Sabri Boulouma

This paper presents an efficient neural network-based method for fault diagnosis in photovoltaic arrays. The proposed method was elaborated on three main steps: the data-feeding step, the fault-modeling step, and the decision step. The first step consists of feeding the real meteorological and electrical data to the neural networks, namely solar irradiance, panel temperature, photovoltaic-current, and photovoltaic-voltage. The second step consists of modeling a healthy mode of operation and five additional faulty operational modes; the modeling process is carried out using two networks of artificial neural networks. From this step, six classes are obtained, where each class corresponds to a predefined model, namely, the faultless scenario and five faulty scenarios. The third step involves the diagnosis decision about the system’s state. Based on the results from the above step, two probabilistic neural networks will classify each generated data according to the six classes. The obtained results show that the developed method can effectively detect different types of faults and classify them. Besides, this method still achieves high performances even in the presence of noises. It provides a diagnosis even in the presence of data injected at reduced real-time, which proves its robustness.


Author(s):  
Emilio Tanowe Maddalena ◽  
Martin W. F. Specq ◽  
Viviane Louzada Wisniewski ◽  
Colin Neil Jones

2012 ◽  
Vol 18 (3) ◽  
pp. 243-260 ◽  
Author(s):  
Phurich Ngamkong ◽  
Pijit Kochcha ◽  
Kongpan Areerak ◽  
Sarawut Sujitjorn ◽  
Kongpol Areerak

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