This paper aims at presenting a new method for the evaluation of technical (demand and energy) losses in electrical power distribution systems. A computational tool was developed and implemented at Eletropaulo, the largest distribution company in Brazil. The methodology divides the distribution system into eight different segments, namely: energy meters, customer connections to the network, low voltage network, distribution transformers, medium voltage network, distribution substations, subtransmission system and other technical losses. The latter segment includes equipment losses in capacitors, voltage regulators, connectors, insulators and so forth. The computational tool comprises two modules. The first one determines technical losses in specific networks in a hierarchical way. From the evaluation of losses in a representative part of the distribution system, per unit loss indices for each segment are readily computed. Such indices are transferred to a second module, which is responsible for the assessment of a global energy balance for the overall distribution system.

This paper aims to present a methodology for the calculation of technical losses per segment of a power distribution system. One of the most important data is the billed energy of each customer. After this calculation, it is achieved the energy supplied by each feeder. This calculated energy is, then, compared with the measured energy. As a result of this comparison, it is possible to correct the technical losses calculated previously, considering the flow of the non-billed energy (theft and fraud) through the network. Consequently, it is achieved a new value for the technical losses and for the non-technical losses. On this paper it is presented the methodology for the calculation of technical losses with the correction through the measurements and the results obtained.

No abstract

The Brazilian Association of Energy Distribution Utilities estimates that non-technical losses represent more than 5.5% of the total energy distributed, most coming from fraud and theft. To try to mitigate those losses, the distribution utilities send field crews for the inspection of possible fraudster clients. However, the procedure is expensive and gives no financial return to the utility if it is not focused on areas with high fraud probability. On those locations, there is a correlation between losses and socioeconomic indices. Thus, this work proposes a model able to select clients with high fraud probability, which should be visited by the field crews. The smart grid structure, energy consumption data, clients' registration data and socio-economic indices from the 2010 Brazilian Census are used by the model.

orientador, pelo apoio e pelas contribuições que foram fundamentais para o desenvolvimento deste trabalho. Aos professores Hector Arango e Nelson Kagan pelas valiosas contribuições na etapa de Qualificação desta Tese. Ao professor Hernan Prieto Schmidt pelas grandes contribuições nos trabalhos envolvendo redes neurais artificiais. Aos meus amigos e colegas de profissão Marcelo Marquesan, Mauro Augusto da Rosa, Luciano Brasil, Renato Guimarães, Edson Akira e Sunny Jonathan pela colaboração no desenvolvimento de diversas etapas deste trabalho. Aos meus amigos e colegas de trabalho Alden, Fábio, Guaraldo, Henrique, Mário, Penin e Tania pelas dicas, paciência, apoio e incentivo. Ao meu irmão Eloy e aos meus familiares que me apoiaram e me incentivaram durante essa etapa da minha vida. À CAPES pelo apoio financeiro durante o programa de doutorado. A todos que direta ou indiretamente contribuíram para o desenvolvimento deste trabalho. À Deus por tudo, sem exceção.