Implementation of a New Method for an Improved Voltage Dips Evaluation by the Italian Power Quality Monitoring System in Presence of VT Saturation Effects

Abstract

The paper deals with a recent measurement improvement introduced into the Italian power quality monitoring system, concerning voltage dips detection and their proper evaluation in the presence of voltage transformer VT saturation phenomena. Saturation effects, essentially on network operated with isolated neutral, can give origin to "false" voltage dips, which can affect the on-line voltage monitoring during a few specific events (i.e. single line-to-ground faults). The "false dip" problem, on the other hand, can be considered negligible in compensated neutral networks. A new criterion has been developed in cooperation with the Polytechnic of Turin, based on the detection of a second harmonic component in the measured voltages, which, once provided with the proper setting parameters, can succeed in identifying "false" dips. The criterion effectiveness has been evaluated on field. The major advantage of the filter implementation in the monitoring system is that, from now on, network operated with isolated neutral could be considered quite similar to those operated with neutral compensation from the point of view of voltage dip performance.

Automated Tool Based on Deep Learning to Assess Voltage Dips Validity: Integration in the QuEEN MV network Monitoring System

“…• QuEEN validity criterion 1 outcome: a field that identifies the outcome of the algorithm based on the 2 nd harmonic evaluation, implemented in the MUs firmware [6], [7],…”

“…In order to handle this problem a criterion, based on the detection of the 2 nd harmonic component has been implemented in QuEEN. The criterion, developed by RSE in cooperation with the Politecnico di Torino [6], [7] is able to identify "false" voltage dips with a global performance of 87%. Indeed, the 2 nd harmonic criterion has not a Boolean answer, as in some undefined cases as very short false voltage dips or multistage events, it is not able to identify the event type (true or false).…”

Advanced Machine Learning Functionalities in the Medium Voltage Distributed Monitoring System QuEEN: A Macro-Regional Voltage Dips Severity Analysis

“…To properly identify those events and remove them from statistics, a criterion has been implemented in each MU. The algorithm, developed in cooperation with the Politecnico di Torino [25], is based on the detection of a second harmonic component in the measured voltages: the measurement is performed over 20 ms and refreshed every 10 ms. Then the following conditions are independently checked on all three phases:  the occurrence of a threshold overcoming for the 2nd harmonic level (fixed to a given percent of the rms value)  the succession of a minimum number of consecutive exceedances of the 2nd harmonic threshold. Energies 2021, 14, x FOR PEER REVIEW 5 of 26 employed measuring system in the PSs is characterized by the installation of phase-toground inductive VTs.…”

“…To properly identify those events and remove them from statistics, a criterion has been implemented in each MU. The algorithm, developed in cooperation with the Politecnico di Torino [25], is based on the detection of a second harmonic component in the measured voltages: the measurement is performed over 20 ms and refreshed every 10 ms. Then the following conditions are independently checked on all three phases:…”

“…To assess the validity of VDs, a criterion based on the detection of the 2nd harmonic component has been implemented. The algorithm has been developed by RSE in cooperation with the Politecnico di Torino [25]. Its global performance achieves an accuracy equal to 87% as it is not always able to give a Boolean answer: in fact, in some cases, named undefined, it is not able to identify the event type (true or false).…”