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Increasing the penetration of variable generation has a substantial effect onthe operational reliability of power systems. The higher level of uncertaintythat stems from this variability makes it more difficult to determine whether agiven operating condition will be secure or insecure. Data-driven techniquesprovide a promising way to identify security rules that can be embedded ineconomic dispatch model to keep power system operating states secure. Thispaper proposes using a sparse weighted oblique decision tree to learn accurate,understandable, and embeddable security rules that are linear and can beextracted as sparse matrices using a recursive algorithm. These matrices canthen be easily embedded as security constraints in power system economicdispatch calculations using the Big-M method. Tests on several large datasetswith high renewable energy penetration demonstrate the effectiveness of theproposed method. In particular, the sparse weighted oblique decision treeoutperforms the state-of-art weighted oblique decision tree while keeping thesecurity rules simple. When embedded in the economic dispatch, these rulessignificantly increase the percentage of secure states and reduce the averagesolution time.

Sparse Oblique Decision Tree for Power System Security Rules Extraction and Embedding