Banca de DEFESA: LUCAS DE LIMA BASTOS

Uma banca de DEFESA de DOUTORADO foi cadastrada pelo programa.
DISCENTE: LUCAS DE LIMA BASTOS
DATA: 28/03/2024
HORA: 14:30
LOCAL: A definir
TÍTULO:

CLASSIFICATION AND CHARACTERIZATION METHODS OF NON-TECHNICAL LOSSES ON SMART GRID SCENARIOS

PALAVRAS-CHAVES:

Smart Meters; Smart Grid; Non-technical Losses; Ensemble Learning; Information Theory Quantifiers.

PÁGINAS: 73
GRANDE ÁREA: Engenharias
ÁREA: Engenharia da Computação
RESUMO:

Nowadays, grid resilience as a feature has become non-negotiable, significantly when power interruptions can impact the economy and the society. 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), since SG systems exchange valuable data, and distribution system loses a substantial amount of electrical energy. We divide this loss into two categories: technical and non-technical loss. 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, fraudulent activities, 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. Annually, electrical utilities incur billions in losses due to non-technical reasons. This thesis presents two methods of detection of NTL, namely classification and characterization. For the classification, we create an ensemble predictor-based time series classifier for NTL detection. This predictor uses the user’s energy consumption as a data input for classification, from splitting the data to executing the classifier. Also, it assumes the temporal aspects of energy consumption data during pre-processing, training, testing, and validation stages. The classification method has the advantage of classifying heterogeneous features in data. The characterization method proposes a study based on Information Theory Quantifiers (ITQ) to mitigate this challenge. First, we use a sliding window to convert the user’s energy consumption time series into a Bandt-Pompe (BP) probability distribution function. Then, we extract the used ITQ. Finally, we then apply each metric to the Probability Density Function (PDF) and map the layers to characterize their behavior. The characterization method is advantageous to be used when we have big data.

MEMBROS DA BANCA:
Presidente - 1700540 - EDUARDO COELHO CERQUEIRA
Interno - 2244893 - DENIS LIMA DO ROSARIO
Externo à Instituição - ALLAN DOUGLAS BENTO DA COSTA
Externo à Instituição - IAGO LINS DE MEDEIROS
Externo à Instituição - THAIS LIRA TAVARES DOS SANTOS