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

Detection and characterization of amplitude defects using spectral kurtosis

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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

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)

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

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

Site Characterization Index for Continuous Power Quality Monitoring Based on Higher-order Statistics

The high penetration of distributed generation (DG) has set up a challenge for energy management and consequently for the monitoring and assessment of power quality (PQ). Besides, there are new types of disturbances owing to the uncontrolled connections of non-linear loads. The stochastic behaviour triggers the need for new holistic indicators which also deal with big data of PQ in terms of compression and scalability so as to extract the useful information regarding different network states and the prevailing PQ disturbances for future risk assessment and energy management systems. Permanent and continuous monitoring would guarantee the report to claim for damages and to assess the risk of PQ distortions. In this context, we propose a measurement method that postulates the use of two-dimensional (2D) diagrams based on higher-order statistics (HOSs) and a previous voltage quality index that assesses the voltage supply waveform in a continous monitoring campaign. Being suitable for both PQ and reliability applications, the results conclude that the inclusion of HOS measurements in the industrial metrological reports helps characterize the deviations of the voltage supply waveform, extracting the individual customers' pattern fingerprint, and compressing the data from both time and spatial aspects. The method allows a continuous and robust performance needed in the SG framework. Consequently, the method can be used by an average consumer as a probabilistic method to assess the risk of PQ deviations in site characterization.This work was supported by the Spanish Ministry of Science and Innovation (Statal Agency for Research), and the EU (AEI/FEDER/UE) via project PID2019-108953RB-C21 Strategies for Aggregated Generation of Photovoltaic Plants: Energy and Meteorological Operational Data (SAGPVEMOD), and the precedent TEC2016-77632-C3-3-R