Detection and identification of energy theft in advanced metering infrastructures

•A novel method for detection and identification of energy theft is proposed for advanced metering infrastructures.•The energy theft detection is made by making comparisons of estimated loads, using a three phase static state estimation, with the consumer loads at the transformers level, considering the estimated daily load curve.•Self-Organizing Map and Multilayer Perceptron Artificial Neural Networks are used for identification step.•A comparative analysis with existing methodologies in the literature is presented.•The proposed methodology achieved best results. This paper presents a novel approach for detection and identification of energy theft in distribution systems considering advanced metering infrastructure. For the energy theft detection stage, a three phase state estimator based on phasor measurement units is used to detect the transformers which have evidence of energy theft. The next step is to identify consumers which are stealing energy. A Self-Organizing Map (SOM) was trained for clustering consumers according to similar consumption patterns. For each class defined by the SOM, a Multilayer Perceptron Artificial Neural Network (MP-ANN) for classification of consumers into two classes, either honest or fraudulent, was created. The main contribution of the energy theft detection step is the reduction of the number of transformers which have suspect consumers without the need to install measurement units on all transformers. The use of ANN allows to identify the fraudulent users considering either cyber or physical attacks. Tests were conducted for energy theft detection step on the IEEE 70 busbar test system. Real data from 5000 consumers were used for identification of fraudulent users. The results show the effectiveness and robustness of the proposed technique, presenting a detection rate close to 93% with a false positive rate less than 2%.