Statistical Dataset and Data Acquisition System for Monitoring the Voltage and Frequency of the Electrical Network in an Environment Based on Python and Grafana

Abstract

This article presents a unique dataset, from a public building, of voltage data, acquired using a hybrid measurement solution that combines PythonTM for acquisition and GrafanaTM for results representation. This study aims to benefit communities, by demonstrating how to achieve more efficient energy management. The study outlines how to obtain a more realistic vision of the quality of the supply, that is oriented to the monitoring of the state of the network; this should allow for better understanding, which should in turn enable the optimization of the operation and maintenance of power systems. Our work focused on frequency and higher order statistical estimators which, combined with exploratory data analysis techniques, improved the characterization of the shape of the stress signal. These techniques and data, together with the acquisition and monitoring system, present a unique combination of low-cost measurement solutions, which have the underlying benefit of contributing to industrial benchmarking. Our study proposes an effective and versatile system, which can do acquisition, statistical analysis, database management and results representation in less than a second. The system offers a wide variety of graphs to present the results of the analysis, so that the user can observe them and identify, with relative ease, any anomalies in the supply which could damage the sensitive equipment of the correspondent installation. It is a system, therefore, that not only provides information about the power quality, but also significantly contributes to the safety and maintenance of the installation. This system can be practically realized, subject to the availability of internet access.

Characterization of Hydropower Inlet Effect on the Electric Network Frequency

“…First, the network signal was acquired at the Algeciras Technical School of Engineering using hardware, which essentially consists of a chassis and a data acquisition card (DAQ) from the manufacturer NI™, which receives on one hand the voltage input (50 Hz, 230 V RMS) of the electricity grid, and on the other hand, the pulsed signal (1 PPS) from the GPS receiver (Symmetricon™) [9].…”

“…In this paper, after performing a data analysis with MATLAB TM , the hourly points of maximum and minimum frequency were detected using two different algorithms. The frequency data was obtained based on the methods described in previous work [9]. Analyzing the production data provided by the official website of the Spanish Electric Network, we searched for a relationship between different energy sources, including Disclaimer/Publisher's Note: The statements, opinions, and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s).…”

Methodology for the Surveillance the Voltage Supply in Public Buildings Using the ITIC Curve and Python Programming

“…In this way, the PPS signal is used to verify that the sampling rate is correct and, therefore, the time of 20 ms between each RMS measurement is real. As explained in [14], where the measurement chain of the system that monitors the electrical network and the different analyses carried out are detailed, the PPS signal is used to corroborate the time base of the acquisition card. The RMS voltage is calculated every single cycle of 50 Hz; therefore, between two PPS (1 s) there must be 50 RMS values.…”

Multivariate Statistical and Correlation Analysis between Acoustic and Geotechnical Variables in Soil Compression Tests Monitored by the Acoustic Emission Technique

“…Among the common techniques used in multivariate statistical analysis, we highlight correlation analysis [42], which is used to assess the relationship between two or more variables, helping to identify which variables are most strongly related and how they influence one another, and regression analysis [43,44], which is used to model the relationship between a dependent variable and one or more independent variables, allowing the value of one variable to be predicted as a function of the other related variables. Today, the use of specialized tools [47] and software [48,49] makes it easier to perform these complex and advanced statistical analyses on the data collected.…”