Online System for Power Quality Operational Data Management in Frequency Monitoring Using Python and Grafana

Abstract

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.

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

“…In addition to the frequency, other power quality parameters were calculated: variance, kurtosis, skewness, VRMS, total harmonic distortion (THD) and a power quality index (PQI) developed in [13]. These calculations were done using three different types of analysis (see Table 1), depending on which computation window was used: Frequency measurements were carried out according to two different procedures: a standardized method, used in accordance with the norm [10], storing frequency data every 10 s; and a specific method, in which the frequency and its associated uncertainty were calculated every second, using a method based on Allan's variance.…”

“…These calculations were done using three different types of analysis (see Table 1), depending on which computation window was used: Frequency measurements were carried out according to two different procedures: a standardized method, used in accordance with the norm [10], storing frequency data every 10 s; and a specific method, in which the frequency and its associated uncertainty were calculated every second, using a method based on Allan's variance. This method, and its benefits over the traditional one, are explained in [13]. In other works-for example, in [4]-a third method was used, based on zero crossings; but as the authors noted, this method was not as reliable, since interharmonics or phase disturbances led to errors in calculations.…”

“…In addition to the frequency, other power quality parameters were calculated: variance, kurtosis, skewness, V RMS , total harmonic distortion (THD) and a power quality index (PQI) developed in [13]. These calculations were done using three different types of analysis (see Table 1), depending on which computation window was used: Analysis Type 1 measured waveform time features cycle-by-cycle.…”