32 research outputs found
Reliability Monitoring Based on Higher-Order Statistics: A Scalable Proposal for the Smart Grid
The increasing development of the smart grid demands reliable monitoring of the power
quality at different levels, introducing more and more measurement points. In this framework,
the advanced metering infrastructure must deal with this large amount of data, storage capabilities,
improving visualization, and introducing customer-oriented interfaces. This work proposes a method
that optimizes the smart grid data, monitoring the real voltage supplied based on higher order
statistics. The method proposes monitoring the network from a scalable point of view and offers
a two-fold perspective based on the duality utility-prosumer as a function of the measurement
time. A global PQ index and 2D graphs are introduced in order to compress the time domain
information and quantify the deviations of the waveform shape by means of three parameters.
Time-scalability allows two extra features: long-term supply reliability and power quality in the
short term. As a case study, the work illustrates a real-life monitoring in a building connection point,
offering 2D diagrams, which show time and space compression capabilities, as well
Intelligent Methods for Characterization of Electrical Power Quality Signals using Higher Order Statistical Features
This paper considers a few important techniques classification for to identify several power quality disturbances. For this purpose, a process
based in HOS has been realized to extract features that help in classification. In this stage the geometrical pattern established via higher-order
statistical measurements is obtained, and this pattern is function of the amplitudes and frequencies of the power quality disturbances associated to the
50-Hz power-line. Once the features are managed will be segmented to form training and test sets and them will be applied in the statistical methods
used to perform automatic classification of PQ disturbances. The best technique of those compared is selected according to correlation and mistake
rates
Forecasting PM10 in the Bay of Algeciras Based on Regression Models
Different forecasting methodologies, classified into parametric and nonparametric, were
studied in order to predict the average concentration of PM10 over the course of 24 h. The comparison
of the forecasting models was based on four quality indexes (Pearson’s correlation coefficient,
the index of agreement, the mean absolute error, and the root mean squared error). The proposed
experimental procedure was put into practice in three urban centers belonging to the Bay of Algeciras
(Andalusia, Spain). The prediction results obtained with the proposed models exceed those obtained
with the reference models through the introduction of low-quality measurements as exogenous
information. This proves that it is possible to improve performance by using additional information
from the existing nonlinear relationships between the concentration of the pollutants and the
meteorological variables
Power quality events detection using fourth-order spectra
This paper introduces the use of a fourth-order
frequency-domain statistical estimator, the spectral kurtosis
(SK), in the field of power-quality analysis. The research has
been organized in the frame of a research national project
and points towards the implementation of these techniques
into an automatic platform to perform PQ analysis in power
plants and power inverters. Higher-order statistics in the
frequency domain manage to distinguish 3 types of electrical
anomalies (sags, swells and transients), with an accuracy of
83%
Exogenous Measurements from Basic Meteorological Stations forWind Speed Forecasting
This research presents a comparative analysis of wind speed forecasting methods
applied to perform 1 h-ahead forecasting. The main significant development has been
the introduction of low-quality measurements as exogenous information to improve these
predictions. Eight prediction models have been assessed; three of these models [persistence,
autoregressive integrated moving average (ARIMA) and multiple linear regression] are used
as references, and the remaining five, based on neural networks, are evaluated on the basis
of two procedures. Firstly, four quality indices are assessed (the Pearson’s correlation
coefficient, the index of agreement, the mean absolute error and the mean squared error).
Secondly, an analysis of variance test and multiple comparison procedure are conducted.
The findings indicate that a backpropagation network with five neurons in the hidden layer is
the best model obtained with respect to the reference models. The pair of improvements
(mean absolute-mean squared error) obtained are 29.10%–56.54%, 28.15%–53.99% and
4.93%–14.38%, for the persistence, ARIMA and multiple linear regression models,
respectively. The experimental results reported in this paper show that traditional agricultural
measurements enhance the predictions
Design and Test of a High-Performance Wireless Sensor Network for Irradiance Monitoring
Cloud-induced photovoltaic variability can affect grid stability and power quality, especially
in electricity systems with high penetration levels. The availability of irradiance field forecasts in the
scale of seconds and meters is fundamental for an adequate control of photovoltaic systems in order
to minimize their impact on distribution networks. Irradiance sensor networks have proved to be
efficient tools for supporting these forecasts, but the costs of monitoring systems with the required
specifications are economically justified only for large plants and research purposes. This study deals
with the design and test of a wireless irradiance sensor network as an adaptable operational solution
for photovoltaic systems capable of meeting the measurement specifications necessary for capturing
the clouds passage. The network was based on WiFi, comprised 16 pyranometers, and proved to be
stable at sampling periods up to 25 ms, providing detailed spatial representations of the irradiance
field and its evolution. As a result, the developed network was capable of achieving comparable
specifications to research wired irradiance monitoring network with the advantages in costs and
flexibility of the wireless technology, thus constituting a valuable tool for supporting nowcasting
systems for photovoltaic management and control
Online System for Power Quality Operational Data Management in Frequency Monitoring Using Python and Grafana
This article proposes a measurement solution designed to monitor the instantaneous
frequency in power systems. It uses a data acquisition module and a GPS receiver for time stamping
and traceability. A Python-based module receives data, computes the frequency, and finally transfers
the measurement results to a database. The frequency is calculated with two different methods,
which are compared in the article. The stored data is visualized using the Grafana platform, thus
demonstrating its potential for comparing scientific data. The system as a whole constitutes an
efficient, low-cost solution as a data acquisition system.This research is funded by the Spanish Ministry of Science and Education through the project PID2019-108953RB-C21; has been co-financed by the European Union under the 2014-2020 ERDF Operational Program. Additionally, funding for frequency monitoring comes from the Andalusian-FEDER project FEDER-UCA18-108516 (Intelligent Techniques for visualization and data compression of PQ data in the smart grid)
Application of Spectral Kurtosis to Characterize Amplitude Variability in Power Systems’ Harmonics
The highly-changing concept of Power Quality (PQ) needs to be continuously reformulated
due to the new schemas of the power grid or Smart Grid (SG). In general, the spectral content is
characterized by their averaged or extreme values. However, new PQ events may consist of large
variations in amplitude that occur in a short time or small variations in amplitude that take place
continuously. Thus, the former second-order techniques are not suitable to monitor the dynamics
of the power spectrum. In this work, a strategy based on Spectral Kurtosis (SK) is introduced to
detect frequency components with a constant amplitude trend, which accounts for amplitude values’
dispersion related to the mean value of that spectral component. SK has been proven to measure
frequency components that follow a constant amplitude trend. Two practical real-life cases have
been considered: electric current time-series from an arc furnace and the power grid voltage supply.
Both cases confirm that the more concentrated the amplitude values are around the mean value,
the lower the SK values are. All this confirms SK as an effective tool for evaluating frequency
components with a constant amplitude trend, being able to provide information beyond maximum
variation around the mean value and giving a progressive index of value dispersion around the mean
amplitude value, for each frequency component
Reconfigurable Web-Interface Remote Lab for Instrumentation and Electronic Learning
Lab sessions in Engineering education are designed to reinforce theoretical concepts. However, there is usually not enough time to reinforce all of them. Remote and virtual labs give students more time to reinforce those concepts. In particular, with remote labs, this can be done interacting with real lab instruments and specific configurations. This work proposes a flexible configuration for Remote Lab Sessions, based on some of 2019 most popular programming languages (Python and JavaScript). This configuration needs minimal network privileges, it is easy to scale and reconfigure. Its structure is based on a unique Reception-Server (which hosts User database, and Time Shift Manager, it is accessible from The Internet, and connects Users with Instruments-Servers) and some Instrument-Servers (which manage hardware connection and host experiences). Users always connect to the Reception-Server, and book a shift for an experience. During the time range associate to that shift, User is internally forwarded to Instrument-Server associated with the selected experience, so User is still connected to the Reception-Serer. In this way, Reception-Server acts as a firewall, protecting Instrument-Servers, which never are open to The Internet. A triple evaluation system is implemented, User session logging with auto-evaluation (objectives accomplished), a knowledge test and an interaction survey. An example experience is implemented, controlling a DC source using Standard Commands for Programmable Instruments