Analysis for Power Quality Monitoring

We are immersed in the so-called digital energy network, continuously introducing new technological advances for a better way of life. Numerous emerging words are in the spotlight, namely: Internet of Things (IoT), Big Data, Smart Cities, Smart Grid, Industry 4.0, etc. To achieve this formidable goa...

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Year of Publication:2020
Language:English
Physical Description:1 electronic resource (210 p.)
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100 1 |a González de la Rosa, Juan-José  |4 auth 
245 1 0 |a Analysis for Power Quality Monitoring 
260 |b MDPI - Multidisciplinary Digital Publishing Institute  |c 2020 
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520 |a We are immersed in the so-called digital energy network, continuously introducing new technological advances for a better way of life. Numerous emerging words are in the spotlight, namely: Internet of Things (IoT), Big Data, Smart Cities, Smart Grid, Industry 4.0, etc. To achieve this formidable goal, systems should work more efficiently, and this fact inevitably leads to power quality (PQ) assurance. Apart from its economic losses, a bad PQ implies serious risks for machines, and consequently for people. Many researchers are endeavoring to develop new analysis techniques, instruments, measurement methods, and new indices and norms that match and fulfil the requirements regarding the current operation of the electrical network. This book offers a compilation of the some recent advances in this field. The chapters range from computing issues to technological implementations, going through event detection strategies and new indices and measurement methods that contribute significantly to the advancement of PQ analysis. Experiments have been developed within the frames of research units and projects, and deal with real data from industry and public buildings. Human beings have an unavoidable commitment with sustainability, which implies adapting PQ monitoring techniques to our dynamic world, defining a digital and smart concept of quality for electricity. 
546 |a English 
653 |a modulation 
653 |a FPGA 
653 |a flicker 
653 |a DC power quality indices 
653 |a limited resources hardware 
653 |a low cost monitor 
653 |a dynamic phasor estimation 
653 |a harmonics 
653 |a RMS voltage estimation 
653 |a islanding operation 
653 |a embedded system 
653 |a signal waveform compression 
653 |a big data 
653 |a digital signal processing 
653 |a data scalability 
653 |a data compression 
653 |a wind-grid distribution 
653 |a voltage fluctuations 
653 |a smart grid (SG) applications 
653 |a power event detection 
653 |a modelling 
653 |a voltage ripple 
653 |a power quality monitoring 
653 |a higher-order statistics (HOS) 
653 |a power distribution systems 
653 |a power quality disturbances 
653 |a reconfigurable computing 
653 |a operation analysis 
653 |a sensor node 
653 |a power quality (PQ) 
653 |a distribution networks 
653 |a wireless sensor network 
653 |a reliability 
653 |a power system measurements 
653 |a embedded microcontroller 
653 |a phasor measurement units 
653 |a IoT 
653 |a soft computing 
653 |a smart grids 
653 |a power quality monitor 
653 |a induction machines 
653 |a sensors and instruments for PQ 
653 |a Kalman filters 
653 |a low-voltage DC networks 
653 |a convolution neural network 
653 |a spectral kurtosis 
653 |a municipal distribution network 
653 |a statistical signal processing 
653 |a detection 
653 |a power quality disturbance 
653 |a smart grid 
653 |a energizing warning 
653 |a dense-mesh topology 
653 |a low computational cost 
653 |a fourth-order statistics 
653 |a PQ indices and thresholds 
653 |a machine learning 
653 |a voltage sags 
653 |a power quality 
653 |a computational solutions for advanced metering infrastructure (AMI) 
653 |a constant amplitude trend 
653 |a phasor measurement 
653 |a improved principal component analysis 
653 |a long-term 
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