Neural Network Loadflow (NNL) computation method is invented involving input vector composed of net
nodal injection of real and reactive powers and
diagonal elements of conductance and
susceptance matrices multiplied by the squared
voltage magnitude components of the flat-start normally used as initial solution estimate guess for loadflow solution by conventional NRL or SSDL methods. Training, and testing / validating input-output data sets are generated by applying uniform and non-uniform scaling factors applied to base case loads at PQ-nodes, resistance and
reactance of
transmission line branches. These scale factors are increased until loadflow solution by conventional methods such as Newton-Raphson Loadflow and Super Super Decoupled Loadflow methods diverge.
Divergence of loadflow methods are due to node
voltage, node angle, and numerical instabilities.
Voltage magnitude and
phase angle values in the solution before
divergence are respective stability limits and
voltage magnitude and
phase angle values in loadflow solution provide
direct measure to the respective stability margins. Also Suresh's diakoptiks based
feature selection technique is presented for ANNs calculating one node variable with one
neuron each in their output
layers.