  It is speculated that higher penetration of inverter-based distributed
photo-voltaic (PV) power generators can increase the risk of tripping events
due to voltage fluctuations. To quantify this risk utilities need to solve the
interactive equations of tripping events for networked PVs in real-time.
However, these equations are non-differentiable, nonlinear, and exponentially
complex, and thus, cannot be used as a tractable basis for solar curtailment
prediction and mitigation. Furthermore, load/PV power values might not be
available in real-time due to limited grid observability, which further
complicates tripping event prediction. To address these challenges, we have
employed Chebyshev's inequality to obtain an alternative probabilistic model
for quantifying the risk of tripping for networked PVs. The proposed model
enables operators to estimate the probability of interdependent inverter
tripping events using only PV/load statistics and in a scalable manner.
Furthermore, by integrating this probabilistic model into an optimization
framework, countermeasures are designed to mitigate massive interdependent
tripping events. Since the proposed model is parameterized using only the
statistical characteristics of nodal active/reactive powers, it is especially
beneficial in practical systems, which have limited real-time observability.
Numerical experiments have been performed employing real data and feeder models
to verify the performance of the proposed technique.
