  Voltage stability refers to the ability of a power system to maintain
acceptable voltages among all buses under normal operating conditions and after
a disturbance. In this paper, a measurement-based voltage stability assessment
(VSA) framework using online deep learning is developed. Since the topology
changes induced by transmission contingencies may significantly reduce the
voltage stability margin, different network topologies under different
operating conditions are involved in our training dataset. To achieve high
accuracy in the training process, a gradient-based adaptive learning algorithms
is adopted. Numerical results based on the NETS-NYPS 68-bus system demonstrate
the effectiveness of the proposed VSA approach. Moreover, with the proximal
function modified adaptively, the adaptive algorithm with momentum outperforms
traditional nonadaptive algorithms whose learning rate is constant.
