Failure Prevention and Malfunction Localization in Underground Medium Voltage Cables

Igor Aizenberg,Stefano Manetti,Riccardo Belardi,Francesco Grasso,Maria Cristina Piccirilli,Antonio Luchetta,Marco Bindi

doi:10.3390/en14010085

Igor Aizenberg, Stefano Manetti + Show 5 more

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https://doi.org/10.3390/en14010085

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Journal: Energies	Publication Date: Dec 25, 2020
Citations: 10	License type: CC BY 4.0

Affiliation: University of Florence, Manhattan College

Abstract

A smart monitoring system capable of detecting and classifying the health conditions of MV (Medium Voltage) underground cables is presented in this work. Using the analysis technique proposed here, it is possible to prevent the occurrence of catastrophic failures in medium voltage underground lines, for which it is generally difficult to realize maintenance operations and carry out punctual inspections. This prognostic method is based on Frequency Response Analysis (FRA) and can be used online during normal network operation, resulting in a minimally invasive tool. In order to obtain the good results shown in the simulation section, it is necessary to develop a lamped equivalent circuit of the network branch under consideration. The standard π-model is used in this paper to analyse sections of a medium voltage cable and the parameter variations with temperature are used to classify the state of health of the line. In fact, the variation of the electrical parameters produces a corresponding variation in the frequency response. The proposed system is based on the use of a complex neural network with feedforward architecture. It processes the frequency response, allowing the classification of the cable conditions with an accuracy higher than 90%.

Highlights

Monitoring electrical infrastructures represents a fundamental activity for ensuring the continuity of operation of any industrial, commercial, or domestic activity
Once the cable branch with length of about 1 km has been divided into a certain number of sections, the corresponding π-models are calculated, and the complex neural network is trained to classify the state of health of each section
Since there are three fault classes, the performance of the neural network is evaluated by referring to 3 Ns possible combinations, where Ns represents the number of cable sections taken into consideration

Summary

Introduction

Monitoring electrical infrastructures represents a fundamental activity for ensuring the continuity of operation of any industrial, commercial, or domestic activity. Concepts such as quality and stability of the electricity service have become central themes for the study and development of smart grids, opening new perspectives in the field of scientific research [1,2]. This work focuses on the issue of the electricity service continuity, which depends on several factors such as, for example, the degradation level of the most stressed components In this sense, the prognostic analysis of measurements carried out on the electrical network allows us to plane maintenance operations, preventing malfunctions evolving into catastrophic failures [3]. The relevant frequency response depends on the electrical characteristics of the cable, which can undergo variations when a specific failure mechanism arises [4,5]

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