  Power systems must maintain the frequency within acceptable limits when
subjected to a disturbance. To ensure this, the most significant credible
disturbance in the system is normally used as a benchmark to allocate the
Primary Frequency Response (PFR) resources. However, the overall reduction of
system inertia due to increased integration of Converter Interfaced Generation
(CIG) implies that systems with high penetration of CIG require more frequency
control services, which are either costly or unavailable. In extreme cases of
cost and scarcity, regulating the most significant disturbance magnitude can
offer an efficient solution to this problem. This paper proposes a Machine
Learning (ML) based technique to regulate the disturbance magnitude of the
power system to comply with the frequency stability requirements i.e., Rate of
Change of Frequency (RoCoF) and frequency nadir. Unlike traditional approaches
which limit the disturbance magnitude by using the Centre Of Inertia (COI)
because the locational frequency responses of the network are analytically hard
to derive, the proposed method is able to capture such complexities using
data-driven techniques. The method does not rely on the computationally
intensive RMS-Time Domain Simulations (TDS), once trained offline.
Consequently, by considering the locational frequency dynamics of the system,
operators can identify operating conditions (OC) that fulfil frequency
requirements at every monitored bus in the network, without the allocation of
additional frequency control services such as inertia. The effectiveness of the
proposed method is demonstrated on the modified IEEE 39 Bus network.
