Work

Enhancing Fraud Detection in Renewable Energy Grids through Behind-the-Meter PV Disaggregation

Year: 2024

Type: conference-paper

Abstract: Detecting non-technical losses (NTL) in electrical grids with high renewable energy penetration presents unique challenges. This study proposes a methodology that combines a two-step behind-the-meter (BTM) photovoltaic (PV) disaggregation algorithm with an unsupervised fraud detection model to improve the detection rate in renewable energy grids. The methodology is validated using real-world data from the Ausgrid dataset, comprising several prosumers with BTM PV systems. The disaggregation algorithm accurately estimates the PV capacity with a Mean Average Percentual Error (MAPE) of 7.85%. Applying the disaggregation model improves fraud detection performance, increasing the Matthews Correlation Coefficient (MCC) from 0.654 to 0.747. These services have been developed and tested within the OMEGA-X European project.

Source: IEEE PES Innovative Smart Grid Technologies Europe

DOI: 10.1109/isgteurope62998.2024.10863072

Authors: Marc Jené-Vinuesa, Abdullah Abdullah, Mònica Aragüés-Peñalba, Andreas Sumper

Institution: Universitat Politècnica de Catalunya

Language: English

Topic: Electricity Theft Detection Techniques

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure, Climate action

Open Access status: green

Funders: European Commission, Institució Catalana de Recerca i Estudis Avançats, HORIZON EUROPE Framework Programme