Impact of load variations on arcing fault detection in LV distribution networks

2010 ◽  
Author(s):  
T.A. Kawady ◽  
A.-M.I. Taalab ◽  
M. El-Geziry
Keyword(s):  
Fault Detection ◽  
Distribution Networks ◽  
Load Variations ◽  
Arcing Fault

Arcing fault detection in underground distribution networks-feasibility study

2000 ◽  
Vol 36 (6) ◽  
pp. 1756-1761 ◽  
Author(s):  
Theodore Maffetone ◽  
J. Cultrera ◽  
Mo-Shing Chen ◽  
Wei-Jen Lee ◽  
W. Charytoniuk
Keyword(s):  
Fault Detection ◽  
Feasibility Study ◽  
Distribution Networks ◽  
Arcing Fault

A Searching Based Method for Locating High Impedance Arcing Fault in Distribution Networks

2019 ◽  
Vol 34 (2) ◽  
pp. 438-447 ◽  
Author(s):  
S. Hamid Mortazavi ◽  
Zahra Moravej ◽  
S. Mohammad Shahrtash
Keyword(s):  
Distribution Networks ◽  
High Impedance ◽  
Arcing Fault

scholarly journals Application of Solid-State Transformers in a Novel Architecture of Hybrid AC/DC House Power Systems

Energies ◽  
2020 ◽  
Vol 13 (13) ◽  
pp. 3432 ◽  
Author(s):  
Fabio Bignucolo ◽  
Manuele Bertoluzzo
Keyword(s):  
Solid State ◽  
Power Systems ◽  
Distribution Networks ◽  
Heat Pumps ◽  
Operating Conditions ◽  
Dynamic Simulations ◽  
Load Variations ◽  
Solid State Transformers ◽  
Local Resources ◽  
Induction Cooking

The ongoing diffusion of solid-state DC/DC converters makes possible a partial migration of electric power systems from the present AC paradigm to a future DC scenario. In addition, the power demand in the domestic environment is expected to grow considerably, for example, due to the progressive diffusion of electric vehicles, induction cooking and heat pumps. To face this evolution, the paper introduces a novel electric topology for a hybrid AC/DC smart house, based on the solid-state transformer technology. The electric scheme, voltage levels and converters types are thoroughly discussed to better integrate the spread of electric appliances, which are frequently based on internal DC buses, within the present AC distribution networks. Voltage levels are determined to guarantee high safety zones with negligible electric risk in the most exposed areas of the house. At the same time, the developed control schemes assure high power quality (voltage stability in the case of both load variations and network perturbations), manage power flows and local resources according to ancillary services requirements and increase the domestic network overall efficiency. Dynamic simulations are performed, making use of DIgSILENT PowerFactory software, to demonstrate the feasibility of the proposed distribution scheme for next-generation smart houses under different operating conditions.

scholarly journals Mitigating Faults and Revenue Losses Using Fault Detectors at Trans Amadi Industrial Layout, Port Harcourt Rivers State

2021 ◽  
Vol 9 (12) ◽  
pp. 883-890
Author(s):  
Nwoke G. O.
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fault Detection ◽  
Transmission Line ◽  
Transmission Lines ◽  
Distribution Networks ◽  
Back Propagation Algorithm ◽  
Artificial Neural ◽  
Industrial Layout

Abstract: Transmission line fault detection is an important aspect of monitoring the health of a power plant since it indicates when suspected faults could lead to catastrophic equipment failure. This research looks at how to detect generator and transmission line failures early and investigates fault detection methods using Artificial Neural Network approaches. Monitoring generator voltages and currents, as well as transmission line performance metrics, is a key monitoring criterion in big power systems. Failures result in system downtime, equipment damage, and a high danger to the power system's integrity, as well as a negative impact on the network's operability and dependability. As a result, from a simulation standpoint, this study looks at fault detection on the Trans Amadi Industrial Layout lines. In the proposed approach, one end's three phase currents and voltages are used as inputs. For the examination of each of the three stages involved in the process, a feed forward neural network with a back propagation algorithm has been used for defect detection and classification. To validate the neural network selection, a detailed analysis with varied numbers of hidden layers was carried out. Between transmission lines and power customers, electrical breakdowns have always been a source of contention. This dissertation discusses the use of Artificial Neural Networks to detect defects in transmission lines. The ANN is used to model and anticipate the occurrence of transmission line faults, as well as classify them based on their transient characteristics. The results revealed that, with proper issue setup and training, the ANN can properly discover and classify defects. The method's adaptability is tested by simulating various defects with various parameters. The proposed method can be applied to the power system's transmission and distribution networks. The MATLAB environment is used for numerous simulations and signal analysis. The study's main contribution is the use of artificial neural networks to detect transmission line faults. Keywords: Faults and Revenue Losses

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