Machine Learning-Based Classification of Electrical Low Voltage Cable Degradation

Egnonnumi Lorraine Codjo, Bashir Bakhshideh Zad, Jean-François Toubeau, Bruno François and François Vallée
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Egnonnumi Lorraine Codjo: Centrale Lille, Arts Et Metiers Institute of Technology, University of Lille, JUNIA, ULR 2697-L2EP, F-59000 Lille, France
Bashir Bakhshideh Zad: Power Systems & Markets Research Group, Electrical Power Engineering Unit, University of Mons, B-7000 Mons, Belgium
Jean-François Toubeau: Power Systems & Markets Research Group, Electrical Power Engineering Unit, University of Mons, B-7000 Mons, Belgium
Bruno François: Centrale Lille, Arts Et Metiers Institute of Technology, University of Lille, JUNIA, ULR 2697-L2EP, F-59000 Lille, France
François Vallée: Power Systems & Markets Research Group, Electrical Power Engineering Unit, University of Mons, B-7000 Mons, Belgium

Energies, 2021, vol. 14, issue 10, 1-20

Abstract: Low voltage distribution networks have not been traditionally designed to accommodate the large-scale integration of decentralized photovoltaic (PV) generations. The bidirectional power flows in existing networks resulting from the load demand and PV generation changes as well as the influence of ambient temperature led to voltage variations and increased the leakage current through the cable insulation. In this paper, a machine learning-based framework is implemented for the identification of cable degradation by using data from deployed smart meter (SM) measurements. Nodal voltage variations are supposed to be related to cable conditions (reduction of cable insulation thickness due to insulation wear) and to client net demand changes. Various machine learning techniques are applied for classification of nodal voltages according to the cable insulation conditions. Once trained according to the comprehensive generated datasets, the implemented techniques can classify new network operating points into a healthy or degraded cable condition with high accuracy in their predictions. The simulation results reveal that logistic regression and decision tree algorithms lead to a better prediction (with a 97.9% and 99.9% accuracy, respectively) result than the k-nearest neighbors (which reach only 76.7%). The proposed framework offers promising perspectives for the early identification of LV cable conditions by using SM measurements.

Keywords: smart meter; low voltage distribution networks; load flow computation; cable condition degradation; cable insulation wear; machine learning approaches; decision tree; k-nearest neighbors; logistic regression (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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