  Increasing the penetration of variable generation has a substantial effect on
the operational reliability of power systems. The higher level of uncertainty
that stems from this variability makes it more difficult to determine whether a
given operating condition will be secure or insecure. Data-driven techniques
provide a promising way to identify security rules that can be embedded in
economic dispatch model to keep power system operating states secure. This
paper proposes using a sparse weighted oblique decision tree to learn accurate,
understandable, and embeddable security rules that are linear and can be
extracted as sparse matrices using a recursive algorithm. These matrices can
then be easily embedded as security constraints in power system economic
dispatch calculations using the Big-M method. Tests on several large datasets
with high renewable energy penetration demonstrate the effectiveness of the
proposed method. In particular, the sparse weighted oblique decision tree
outperforms the state-of-art weighted oblique decision tree while keeping the
security rules simple. When embedded in the economic dispatch, these rules
significantly increase the percentage of secure states and reduce the average
solution time.
