  The classification of anomalies or sudden changes in power networks versus
normal abrupt changes or switching actions is essential to take appropriate
maintenance actions that guarantee the quality of power delivery. This issue
has increased in importance and has become more complicated with the
proliferation of volatile resources that introduce variability, uncertainty,
and intermittency in circuit behavior that can be observed as variations in
voltage and current phasors. This makes diagnostics applications more
challenging. This paper proposes using quotient gradient system (QGS) to train
two-stage partially recurrent neural network to improve anomaly classification
rate in power distribution networks using high-fidelity data from micro-phasor
measurement units (PMUs). QGS is a systematic approach to finding solutions of
constraint satisfaction problems. We transform the PMUs data from the power
network into a constraint satisfaction problem and use QGS to train a neural
network by solving the resulting optimization problem. Simulation results show
that the proposed supervised classification method can reliably distinguish
between different anomalies in power distribution networks. Comparison with
other neural network classifiers shows that QGS trained networks provide
significantly better classification. Sensitivity analysis is performed
concerning the number of PMUs, reporting rates, noise level and early versus
late data stream fusion frameworks.
