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Voltage Stability Constrained Operation Optimization: An Ensemble Sparse Oblique Regression Tree Method
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DOI：10.1109/TPWRS.2023.3236164.png)
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Abstract
En 中文
Voltage stability remains a main challenge for high renewable penetrated power systems. In future power systems, the dispatch needs to consider the voltage stability margin. However, considering static and transient voltage stability is challenging because a complex relationship exists between voltage stability and operation state variables. This paper proposes an ensemble sparse oblique regression tree method for voltage stability constrained operation optimization. First, we train multiple oblique regression trees on voltage stability simulation datasets. The trained trees are then grouped into ensemble using a boosting method to extract accurate and understandable voltage stability rules. Finally, the rules are embedded as mixed-integer linear programming constraints in a linear optimal power flow model. Validation on the IEEE-14 case shows the interpretability and efficiency of the proposed algorithm. Case studies on the Qinghai power grid and IEEE-118 further show that the proposed strategy can effectively improve the voltage stability margin of optimized operation states.
Keywords:
Power system stability
Stability criteria
Numerical stability
Regression tree analysis
Thermal stability
Indexes
Transient analysis
High renewable energy penetration
oblique regression tree
static voltage stability
stability-constrained optimal power flow
stability rule extraction
transient voltage stability
Journal
IF:
7.2
Papers:
1.1W
Citations:
5.0W