Reprint
Analysis for Power Quality Monitoring
Edited by
May 2020
210 pages
- ISBN978-3-03928-110-7 (Paperback)
- ISBN978-3-03928-111-4 (PDF)
This is a Reprint of the Special Issue Analysis for Power Quality Monitoring that was published in
Chemistry & Materials Science
Engineering
Environmental & Earth Sciences
Physical Sciences
Summary
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.
Format
- Paperback
License and Copyright
© 2020 by the authors; CC BY-NC-ND license
Keywords
power system measurements; dynamic phasor estimation; Kalman filters; phasor measurement; power quality; signal waveform compression; higher-order statistics (HOS); power quality (PQ); computational solutions for advanced metering infrastructure (AMI); smart grid (SG) applications; harmonics; constant amplitude trend; fourth-order statistics; detection; spectral kurtosis; low-voltage DC networks; power quality disturbances; power quality monitoring; DC power quality indices; voltage ripple; reconfigurable computing; FPGA; power quality; spectral kurtosis; digital signal processing; embedded system; power quality disturbance; convolution neural network; improved principal component analysis; wind-grid distribution; power quality (PQ); embedded microcontroller; low cost monitor; sensor node; wireless sensor network; IoT; RMS voltage estimation; low computational cost; limited resources hardware; power event detection; energizing warning; power quality; voltage sags; islanding operation; induction machines; modelling; distribution networks; power quality; phasor measurement units; voltage fluctuations; flicker; modulation; power distribution systems; smart grids; dense-mesh topology; municipal distribution network; smart grid; power quality monitor; long-term; operation analysis; power quality (PQ); PQ indices and thresholds; reliability; sensors and instruments for PQ; big data; machine learning; soft computing; statistical signal processing; data scalability; data compression