Classification and Characterization of Non-Technical Losses on Smart Grid Scenarios
Smart Meters; Smart Grid; Non-technical losses; Ensemble; ITQ.
Today, grid resilience as a feature has become non-negotiable, significantly when power interruptions can impact the economy. Smart Grids (SGs) widespread popularity enables an immense amount of fine-grained electricity consumption data to be collected. However, risks can still exist in the Smart Grid (SG), as valuable data are exchanged among SG systems; theft or alteration of this data could violate consumer privacy. A substantial amount of electrical energy is lost throughout the distribution system, and these losses are divided into two types: technical and non-technical. Non-technical losses (NTL) are any electrical energy consumed and not invoiced. They may occur due to illegal connections, issues with energy meters such as delay in the installation or reading errors, contaminated, defective, or non-adapted measuring equipment, very low valid consumption estimates, faulty connections, and disregarded customers. Non-technical losses are the primary cause of revenue loss in the SG. According to a recent study, electrical utilities lose $89.3 Billion per year due to non-technical losses. This thesis proposes two methods of detection of NTL; classification and characterization. For the classification, we created an ensemble predictor-based time series classifier for NTL detection. The proposed predictor ministers the user's energy consumption as a data input for classification, from splitting the data to executing the classifier. It assumes the temporal aspects of energy consumption data during preprocessing, training, testing, and validation stages. The suggested predictor is time-series oriented, from data splitting to the classifier's performance. The method has the advantage of classifying heterogeneous features in data. For the characterization, we used a study based on Information Theory Quantifiers (ITQ) to mitigate this challenge. First, we convert the user's energy consumption time series into a Bandt-Pompe (BP) probability distribution function using a sliding window. The second step is to extract the ITQ used by the technology. We then apply each metric to the Probability Density Function (PDF) and map the layers to characterize their behavior. This method has the advantage when we have big data.