Review of NILM applications in smart grids: power quality assessment and assisted independent living

2020 ◽  
Author(s):  
Patricio G. Donato ◽  
Alvaro Hernandez ◽  
Marcos A. Funes ◽  
Ignacio Carugati ◽  
Ruben Nieto ◽  
...  
Keyword(s):  
Quality Assessment ◽  
Power Quality ◽  
Smart Grids ◽  
Independent Living

scholarly journals Modelling of HVDC Link for Power Quality Assessment

2020 ◽  
Vol 1529 ◽  
pp. 052061
Author(s):  
S H N Yusof ◽  
S A Jumaat ◽  
B C Kok
Keyword(s):  
Quality Assessment ◽  
Power Quality

A metropolitan municipality power quality assessment as per IEEE 1459-2010

2017 ◽  
Author(s):  
S. Kasemuana ◽  
A. S. De Beer ◽  
J. H. C. Pretorius
Keyword(s):  
Quality Assessment ◽  
Power Quality

High Frequency Modeling of Smart Sensor for Precise Power Quality Assessment Using Fuzzy Logic

2016 ◽  
Vol 25 (06) ◽  
pp. 1650056 ◽  
Author(s):  
G. Sudha ◽  
K. R. Valluvan
Keyword(s):  
Fuzzy Logic ◽  
Quality Assessment ◽  
Power Quality ◽  
High Frequency ◽  
Wide Band ◽  
Electrical Equipment ◽  
Distribution Networks ◽  
Accurate Method ◽  
Energy Difference ◽  
Critical Issue

Power Quality Assessment (PQA) is a critical issue both in transmission and distribution networks. Therefore, it is necessary to precisely classify the disturbances in shortest possible time to prevent the malfunction or increase of losses in the electrical equipment through appropriate remedial techniques. This paper proposes a highly accurate method of PQA through data acquisition using smart sensors, the Rogowski coils (RCs). RCs with wide band width and linear characteristics allow faithful reproduction of high-frequency (HF) signals. In the proposed method, simulated disturbance signals are applied to RC. The output signals are subjected to multilevel wavelet decomposition and then computation of the energy difference in the detailed components between the disturbance signal and the pure sinusoidal waveform is performed to design a fuzzy logic Power Quality Classifier. The classifier is tested by varying the magnitude, frequency and duration of the disturbance and found to be accurate to 98.38%. The classification accuracy depends mainly on the performance of sensors at HFs. Thus, with RCs as sensors instead of conventional instrument transformers, it is found that the precision of power quality classification is greatly improved.

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