INTELLIGANT CONDITION MONITORING SYSTEM FOR GRID INTERCONNECTED / POWER TRANSFORMERS

摘要

The purpose of present invention is to introduce methodology to know the status of the grid interconnected/power transformer commissioned in transmission or distribution substation. The algorithm developed and tested is based on application of Artificial Neural Network (ANN). The existing measurable parameters like oil temperature, winding temperature, low oil level, Moisture Content and Hydrogen Content by hydran meters, and efficiency deviation data patters applied as input parameters for the design of Intelligent Condition Monitoring System (ICMS). This ICMS avoids the unnecessary outages, prevent breakdowns thereby increases the availability of the equipment and cause considerable savings in the economy of utility. The main advantage of the model is that it can be applied for online monitoring to grid interconnected /power transformer of any ratings. Following invention is described in detail with the help of Fig. 1 showing the online algorithm developed for ICMS, Fig. 2 showing sub-routine algorithm for selection of relevant ANN structure when any five inputs parameters are available. Fig. 3 showing sub-routine algorithm for selection of relevant ANN structure when any four inputs parameters are available, Fig. 4 showing Module A or ANN structures for all the six input parameters, Fig. 5 showing sub-modules or ANN structures in respect of Module B for any five input parameters, Fig. 6 showing sub-modules or ANN structures in respect of Module C for any four input parameters, Fig. 7 showing oil temperature sensor/transducer circuitry, Fig. 8 showing winding temperature sensor/transducer circuitry, Fig. 9 showing low oil level sensor/transducer circuitry, Fig. 10 showing hydran meter (H2O and H2) PPM sensor/transducer circuitry and Fig. 11 showing energy meter (HV and LV side) connection circuitry for determining efficiency deviation.