AN EXPERIENCE OF LOCATING HIGH-IMPEDANCE FAULTS THROUGH SMART METERS' ALARMS IN POWER DISTRIBUTION NETWORKS

2021 ◽  
Author(s):  
D. S. Pereira ◽  
C. F. M. Almeida ◽  
L. H. L. Rosa ◽  
N. Kagan ◽  
A. Dominice
Keyword(s):  
Power Distribution ◽  
Distribution Networks ◽  
Smart Meters ◽  
Power Distribution Networks ◽  
High Impedance ◽  
High Impedance Faults

scholarly journals High-Impedance Fault Diagnosis: A Review

Energies ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 6447
Author(s):  
Abdulaziz Aljohani ◽  
Ibrahim Habiballah
Keyword(s):  
Fault Diagnosis ◽  
Power Distribution ◽  
Distribution Networks ◽  
Fault Current ◽  
Power Distribution Networks ◽  
High Impedance ◽  
Look Ahead ◽  
High Impedance Fault ◽  
Overcurrent Relays ◽  
High Impedance Faults

High-impedance faults (HIFs) represent one of the biggest challenges in power distribution networks. An HIF occurs when an electrical conductor unintentionally comes into contact with a highly resistive medium, resulting in a fault current lower than 75 amperes in medium-voltage circuits. Under such condition, the fault current is relatively close in value to the normal drawn ampere from the load, resulting in a condition of blindness towards HIFs by conventional overcurrent relays. This paper intends to review the literature related to the HIF phenomenon including models and characteristics. In this work, detection, classification, and location methodologies are reviewed. In addition, diagnosis techniques are categorized, evaluated, and compared with one another. Finally, disadvantages of current approaches and a look ahead to the future of fault diagnosis are discussed.

scholarly journals MACHINE LEARNING AND IOT FOR SMART GRID

2020 ◽  
Vol XLIV-4/W3-2020 ◽  
pp. 233-240
Author(s):  
M. Fouad ◽  
R. Mali ◽  
A. Lmouatassime ◽  
M. Bousmah
Keyword(s):  
Machine Learning ◽  
Energy Consumption ◽  
Smart Grid ◽  
Power Distribution ◽  
Short Term Memory ◽  
Electrical Energy ◽  
Distribution Networks ◽  
Smart Meters ◽  
Power Distribution Networks ◽  
Electricity Grid

Abstract. The current electricity grid is no longer an efficient solution due to increasing user demand for electricity, old infrastructure and reliability issues requires a transformation to a better grid which is called Smart Grid (SG). Also, sensor networks and Internet of Things (IoT) have facilitated the evolution of traditional electric power distribution networks to new SG, these networks are a modern electricity grid infrastructure with increased efficiency and reliability with automated control, high power converters, modern communication infrastructure, sensing and measurement technologies and modern energy management techniques based on optimization of demand, energy and availability network. With all these elements, harnessing the science of Artificial Intelligence (AI) and Machine Learning (ML) methods become better used than before for prediction of energy consumption. In this work we present the SG with their architecture, the IoT with the component architecture and the Smart Meters (SM) which play a relevant role for the collection of information of electrical energy in real time, then we treat the most widely used ML methods for predicting electrical energy in buildings. Then we clarify the relationship and interaction between the different SG, IoT and ML elements through the design of a simple to understand model, composed of layers that are grouped into entities interacting with links. In this article we calculate a case of prediction of the electrical energy consumption of a real Dataset with the two methods Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM), given their precision performances.

High-impedance fault modeling and classification in power distribution networks

2021 ◽  
pp. 107676
Author(s):  
José Genilson Sousa Carvalho ◽  
Aryfrance Rocha Almeida ◽  
Danton Diego Ferreira ◽  
Bartolomeu Ferreira dos Santos ◽  
Luis Henrique Pereira Vasconcelos ◽  
...  
Keyword(s):  
Power Distribution ◽  
Distribution Networks ◽  
Fault Modeling ◽  
Power Distribution Networks ◽  
High Impedance ◽  
High Impedance Fault
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