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Io: intelligent sensor for applications in the electrical sector

Grant number: 01/13296-8
Support type:Research Grants - Innovative Research in Small Business - PIPE
Duration: July 01, 2003 - February 29, 2004
Field of knowledge:Engineering - Electrical Engineering - Industrial Electronics, Electronic Systems and Controls
Principal Investigator:Bruno Abrantes Basseto
Grantee:Bruno Abrantes Basseto
Company:ADTS Soluções em Automação e Engenharia Ltda
City: São Paulo

Abstract

The expansion of the Brazilian electrical sector has suffered a number of restrictions which bring it close to its installed limit, operating under critical conditions. Faced with this, more effective automatic monitoring systems have shown themselves to be essential, with the support of advanced information systems, capable of assisting operators in generation, transmission and distribution. With the precise information supplied by such sets of resources, the planning of investments or the programming of repairs can be carried out in a more efficient and effective manner. The proposal to develop intelligent, socall Io, sensors, dedicated to the monitoring of power equipment, offers a very promising alternative to several of the problems currently facing the electricity sector. The use of small, low cost data acquisition devices, installed in close proximity to the transducers of the signals to be monitored, can appreciably simplify the systems for conditioning signals and reduce the cabling required, contributing greatly to the overall cost reduction. These sensors, since they have the capacity for the local storage of data and since they are interconnected in an expandable system on a fiber optic network, which offers great immunity to electromagnetic noise, represent a flexible and efficient solution. Since such devices carry microprocessors, they offer local processing capacity, enabling the volume of information exchanged between the various components of the diagnostic system to be enormously reduced, among other advantages. The introduction of such intelligent sensors to the predictive diagnostic system should permit a significant reduction in the costs related to the data acquisition systems in the field and also an appreciable improvement in the performance of the system as a whole, with the increase in the local processing capacity of the intelligent data acquisition units. (AU)