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VOLTAGE STABILITY On-line assessment Future power system operation • Power transfers are increased • Environmental reasons restrict the expansion of networks • Better service is required for a reduced cost Data pre-processing Line flows DATA Voltage stability problem • A power system has a limited capability to transmit power especially reactive power to the loads • Voltage instability point is needed to assess in order to quarantee secure operation in normal operation point and after disturbances Neural network approach Voltages Outage list Network topology Unit commitment NN PostMargin Model creation Data calculation and analysis Input feature selection/extraction Estimation of model parameters Generalization capability testing • • • • Neural network generalization test Target value = x NN output=o V o l t a g e most critical contingency normal operation point maximum loading point pre-margin post-margin Parameter 1.2 1.15 1.1 0 10 20 30 40 50 60 Case number 70 80 90 100 0 10 20 30 40 50 60 Case number 70 80 90 100 Output error 0.02 0.01 0 −0.01 −0.02 15 Histogram Voltage stability assessment • The most critical voltage stability margin describes the available transfer capacity – Number of contingencies is studied – Collapse points are calculated Margin 1.25 10 5 0 −0.02 −0.01 0 0.01 0.02 0.03 Output error Test results and conclusions • Accurate voltage stability margin approximation with neural network (maximum output error is 2%) • Model creation time is reduced significantly by input feature selection and extraction www.elteknik.chalmers.se Visiting researcher Sami Repo Tampere University of Technology, Finland Research project is funded by Graduate School in Electrical Engineering and FINGRID Plc.