scholarly journals Performance Comparison of PD Data Acquisition Techniques for Condition Monitoring of Medium Voltage Cables

Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4272 ◽  
Author(s):  
Muhammad Shafiq ◽  
Ivar Kiitam ◽  
Kimmo Kauhaniemi ◽  
Paul Taklaja ◽  
Lauri Kütt ◽  
...  
Keyword(s):  
Data Acquisition ◽  
Condition Monitoring ◽  
Partial Discharge ◽  
Performance Comparison ◽  
Practical Implementation ◽  
Monitoring Methods ◽  
Medium Voltage ◽  
Cable Networks ◽  
Improved Performance ◽  
Diagnostic Capabilities

Already installed cables are aging and the cable network is growing rapidly. Improved condition monitoring methods are required for greater visibility of insulation defects in the cable networks. One of the critical challenges for continuous monitoring is the large amount of partial discharge (PD) data that poses constraints on the diagnostic capabilities. This paper presents the performance comparison of two data acquisition techniques based on phase resolved partial discharge (PRPD) and pulse acquisition (PA). The major contribution of this work is to provide an in-depth understanding of these techniques considering the perspective of randomness of the PD mechanism and improvements in the reliability of diagnostics. Experimental study is performed on the medium voltage (MV) cables in the laboratory environment. It has been observed that PRPD based acquisition not only requires a significantly larger amount of data but is also susceptible to losing the important information especially when multiple PD sources are being investigated. On the other hand, the PA technique presents improved performance for PD diagnosis. Furthermore, the use of the PA technique enables the efficient practical implementation of the continuous PD monitoring by reducing the amount of data that is acquired by extracting useful signals and discarding the silent data intervals.

Key Technology in Wind Turbine Transmission System Condition Monitoring: A Review

2013 ◽  
Vol 442 ◽  
pp. 304-310
Author(s):  
Hai Jiang Dong ◽  
Chun Hua Zhao ◽  
Shi Qing Wan ◽  
Xian You Zhong ◽  
Wei Wang
Keyword(s):  
Signal Processing ◽  
Data Acquisition ◽  
Wind Turbine ◽  
Programming Languages ◽  
Condition Monitoring ◽  
Transmission System ◽  
Monitoring Methods ◽  
State Recognition ◽  
Key Technology ◽  
Signal Processing Methods

Key technology for the transmission system of wind turbine condition monitoring is researched in this paper. It gives a summary overview, including monitoring methods, signal processing methods and state recognition technology, data acquisition and transmission technology, programming languages. Understanding of the progress of the field is to provide technical reference and research inspiration for insiders.

Influence of Ring Main Units and Substations on Online Partial-Discharge Detection and Location in Medium-Voltage Cable Networks

2011 ◽  
Vol 26 (2) ◽  
pp. 1064-1071 ◽  
Author(s):  
Paul Wagenaars ◽  
Peter A. A. F. Wouters ◽  
Peter C. J. M. van der Wielen ◽  
E. Fred Steennis
Keyword(s):  
Partial Discharge ◽  
Medium Voltage ◽  
Cable Networks ◽  
Discharge Detection

Direct ammonia fueled solid oxide fuel cells: A comprehensive review on challenges, opportunities and future outlooks

2020 ◽  
pp. 70-91 ◽  
Author(s):  
Molla Asmare ◽  
Mustafa Ilbas
Keyword(s):  
Electric Power ◽  
Energy Demand ◽  
Renewable Energy Sources ◽  
Clean Energy ◽  
Performance Comparison ◽  
Optimum Operating ◽  
Practical Implementation ◽  
Human Society ◽  
Energy Access ◽  
Green Fuel

Nowadays, the most decisive challenges we are fronting are perfectly clean energy making for equitable and sustainable modern energy access, and battling the emerging alteration of the climate. This is because, carbon-rich fuels are the fundamental supply of utilized energy for strengthening human society, and it will be sustained in the near future. In connection with this, electrochemical technologies are an emerging and domineering tool for efficiently transforming the existing scarce fossil fuels and renewable energy sources into electric power with a trivial environmental impact. Compared with conventional power generation technologies, SOFC that operate at high temperature is emerging as a frontrunner to convert the fuels chemical energy into electric power and permits the deployment of varieties of fuels with negligible ecological destructions. According to this critical review, direct ammonia is obtained as a primary possible choice and price-effective green fuel for T-SOFCs. This is because T-SOFCs have higher volumetric power density, mechanically stable, and high thermal shocking resistance. Also, there is no sealing issue problem which is the chronic issues of the planar one. As a result, the toxicity of ammonia to use as a fuel is minimized if there may be a leakage during operation. It is portable and manageable that can be work everywhere when there is energy demand. Besides, manufacturing, onboard hydrogen deposition, and transportation infrastructure connected snags of hydrogen will be solved using ammonia. Ammonia is a low-priced carbon-neutral source of energy and has more stored volumetric energy compared with hydrogen. Yet, to utilize direct NH3 as a means of hydrogen carrier and an alternative green fuel in T-SOFCs practically determining the optimum operating temperatures, reactant flow rates, electrode porosities, pressure, the position of the anode, thickness and diameters of the tube are still requiring further improvement. Therefore, mathematical modeling ought to be developed to determine these parameters before planning for experimental work. Also, a performance comparison of AS, ES, and CS- T-SOFC powered with direct NH3 will be investigated and best-performed support will be carefully chosen for practical implementation and an experimental study will be conducted for verification based on optimum parameter values obtained from numerical modeling.

scholarly journals A Citizen Science Unmanned Aerial System Data Acquisition Protocol and Deep Learning Techniques for the Automatic Detection and Mapping of Marine Litter Concentrations in the Coastal Zone

Drones ◽  
2021 ◽  
Vol 5 (1) ◽  
pp. 6
Author(s):  
Apostolos Papakonstantinou ◽  
Marios Batsaris ◽  
Spyros Spondylidis ◽  
Konstantinos Topouzelis
Keyword(s):  
Deep Learning ◽  
Data Acquisition ◽  
Citizen Science ◽  
Coastal Zone ◽  
Automatic Detection ◽  
Monitoring Methods ◽  
Monitoring Method ◽  
Marine Litter ◽  
Density Maps ◽  
Learning Techniques

Marine litter (ML) accumulation in the coastal zone has been recognized as a major problem in our time, as it can dramatically affect the environment, marine ecosystems, and coastal communities. Existing monitoring methods fail to respond to the spatiotemporal changes and dynamics of ML concentrations. Recent works showed that unmanned aerial systems (UAS), along with computer vision methods, provide a feasible alternative for ML monitoring. In this context, we proposed a citizen science UAS data acquisition and annotation protocol combined with deep learning techniques for the automatic detection and mapping of ML concentrations in the coastal zone. Five convolutional neural networks (CNNs) were trained to classify UAS image tiles into two classes: (a) litter and (b) no litter. Testing the CCNs’ generalization ability to an unseen dataset, we found that the VVG19 CNN returned an overall accuracy of 77.6% and an f-score of 77.42%. ML density maps were created using the automated classification results. They were compared with those produced by a manual screening classification proving our approach’s geographical transferability to new and unknown beaches. Although ML recognition is still a challenging task, this study provides evidence about the feasibility of using a citizen science UAS-based monitoring method in combination with deep learning techniques for the quantification of the ML load in the coastal zone using density maps.

Sign in / Sign up

Export Citation Format

Share Document