  Under frequency load shedding (UFLS) constitutes the very last resort for
preventing total blackouts and cascading events. Fluctuating operating
conditions and weak resilience of the future grid require UFLS strategies adapt
to various operating conditions and non-envisioned faults. This paper develops
a novel data-enabled predictive control algorithm KLS to achieve the optimal
one-shot load shedding for power system frequency safety. The algorithm
utilizes a latent extractor network to track parameter variations in the system
dynamic model, enabling a coordinate transformation from the delay embedded
space to a new space where the dynamics can be linearly represented. To address
approximation inaccuracies and the discrete nature of load shedding, a safety
margin tuning scheme is integrated into the KLS framework, ensuring that the
system frequency trajectory remains within the safety range. Simulation results
show the adaptability, prediction capability and control effect of the proposed
UFLS strategy.
