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LOSS ESTIMATION IN LV CIRCUITS USING INTELLIGENT TECHNIQUES - THE RGE EXPERIENCE. Carlos César Barioni de Oliveira Enerq/USP André Méffe Enerq/ USP Hernán Prieto Schmidt Enerq/ USP Mauro Augusto da Rosa RGE. Objectives.
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LOSS ESTIMATION IN LV CIRCUITS USING INTELLIGENT TECHNIQUES - THE RGE EXPERIENCE Carlos César Barioni de Oliveira Enerq/USP André Méffe Enerq/ USP Hernán Prieto Schmidt Enerq/ USP Mauro Augusto da Rosa RGE
Objectives • Determining types of LV circuits representing all of the utility´s LV circuits by means of: • Attributes: (1) distribution transformer rated power (kVA), (2) transformer type, (3) rated voltage (kV) and (4) type of circuit location (urban or rural). • Typical daily load curves for each consumer class and consumption range • Developing techniques and tools to classify a specific network for one of the pre-set types of LV circuits. • Estimating losses in LV circuits whose topological data is unknown.
Classification Process • Out of 50.000 networks, 187 representative networks were selected. • Besides the data available from Data Bases, the network topological information was obtained. • 50.000 LV circuits: • Type of transformer • Type ofarea • Rated power • Rated voltage • 187networks: • Loss coefficient • Network length
Classification techniques and tools • Classification techniques (Hierarchical and Self Organizing Map-SOM), allowed grouping the sampled networks into categories having similar attributes. • Upon comparison of both techniques, the Hierarchical Classificationtechnique proved to be more appropriate for the objective of this study
LV Network Modelling • The LV network analysis is based on the losses coefficient (LC) and average length (L) of the corresponding representative network. • The losses daily curve is then obtained from the transformer daily load curve:
Conclusions • New methodology for estimating demand and energy losses in LV circuits - reduced set of circuits and the extrapolation of some parameters (loss coefficient and circuit length) to the whole LV circuit population • It overcomes the lack of information normally associated with such circuits.