Detecção e identificação de perdas comerciais de energia elétrica: uma abordagem para smart grids

Abstract

This work aims to present a methodology for the detection of energy theft in distribution systems as well as the identification of the fraudulent users considering Advanced Metering Infrastructure, widely used in Smart Grids. For the stage of energy theft detection, a Static Three Phase State Estimator based on Synchronised Phasor Measurement Units (PMUs) is used. This step aims to detect the transformers in the distribution network, which have evidence of energy theft, by observing the estimated load curve. The next step is to identify consumers, connected to suspected transformers, which are stealing energy. For this, a Kohonen Artificial Neural Network (ANN) was trained to clustering honest consumers according to similar patterns of consumption, with the consumption data from the Smart Meters at every customer. For each class defined by the Kohonen ANN a MultiLayer Perceptron (MLP) ANN for classification of consumers into two classes, honest or fraudulent was created. The two major contributions of this work are the use of energy theft detection technique to reduce the number of false positives (FP) from the classification methods, without the need for individual measurements on the transformers, andtheuseofANNfromdistributionsystemmeasurementsmakethetechiniquerobust, in which there is no need for measurements without comercial loss of energy for all consumers. The way that the measurement data is treated allows maintaining the privacy of the consumers which is a debated question on the world. Tests were conducted for energy theft detection step with IEEE 33 Buses and IEEE 70 Buses systems. For the fraudulent user identification step, actual consumption data were used over 5000 consumers from their SmartMetersreleasedbytheElectricityAuthorityandSustainableEnergyofIreland. The tests showed good results making the proposed methodology applicable in the detection and identification of energy theft in Smart Grids.