Sparse Oblique Decision Tree for Power System Security Rules Extraction and Embedding
This work addresses reliability challenges for power system operators by providing embeddable security rules, though it is incremental as it builds on existing weighted oblique decision trees.
The paper tackled the problem of identifying secure operating conditions in power systems with high renewable energy variability by proposing a sparse weighted oblique decision tree to extract linear security rules. The method outperformed state-of-the-art approaches, increasing secure states and reducing solution time when embedded in economic dispatch.
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.