Abstract
This paper presents the integration of advanced machine learning techniques in the medium voltage distributed monitoring system QuEEN. This system is aimed to monitor voltage dips in the Italian distribution network mainly for survey and research purposes. For each recorded event it is able to automatically evaluate its residual voltage and duration from the corresponding voltage rms values and provide its “validity” (invalidating any false events caused by voltage transformers saturation) and its “origin”(upstream or downstream from the measurement point) by proper procedures and algorithms (current techniques). On the other hand, in the last years new solutions have been proposed by RSE to improve the assessment of the validity and origin of the event: the DELFI classifier (DEep Learning for False voltage dips Identification) and the FExWaveS + SVM classifier (Features Extraction from Waveform Segmentation + Support Vector Machine classifier). These advanced functionalities have been recently integrated in the monitoring system thanks to the automated software tool called QuEEN PyService. In this work, intensive use of these advanced techniques has been carried out for the first time on a significant number of monitored sites (150) starting from the data recorded from 2018 to 2021. Besides, the comparison between the results of the innovative technique (validity and origin of severe voltage dips) with respect to the current ones has been performed at the macro-regional level too. The new techniques are shown to have a not negligible impact on the severe voltage dips number and confirm a non-homogenous condition among the Italian macro-regional areas.
Highlights
Nowadays, the new paradigm of the smart grid requires innovative features and capabilities for power systems engineers
The system was developed in 2006, and it consists of 400 Measurement Units installed in some primary substations and connected to the low voltage (LV) side of voltage transformers (VT), whose primary windings are connected between the medium voltage (MV) phases and the ground
Metrics have to dipsinseverity is determined by considering their duration and residual voltage while the second one corresponds to the second overcurrent threshold (~800 A) whose assess is the evaluation of the voltage dip impact on the users as prescribed by EN 50,160 standard [2] in which is proposed as a classification table that is tripping time is typically set to 250 ms plus the opening time of the circuit breakers
Summary
The new paradigm of the smart grid requires innovative features and capabilities for power systems engineers. Concerning PQ, several surveys can be found in the literature [27,28,29,30,31] which collect different original contributions aimed at the detection and classification of power quality events In this framework, RSE has turned its attention to these promising approaches to improve the capability in the PQ data analysis, especially to enhance the QuEEN functionalities and overcome the limitations given by the methods currently adopted. By considering instead the origin assessment, a standalone application based on Machine Learning (ML) called FExWaveS (Features Extraction from Waveform Segmentation) has been developed [33] It basically performs a VDs waveforms segmentation to extract meaningful features which are used as the input of a Support Vector Machine (SVM) classifier.
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