Operation rule extracting based on time series association analysis in transient stability study

A robust operation rule extracting algorithm is proposed based on adaptive time series association analysis here. It is applied to extract association knowledge among the accumulating massive operation data. A data model describing the system state and topological structure before/after fault happening is built up at first. The initial abstract features are independent of the scale of a power system. A recursive principal component analysis method is employed to yield the reduced-size feature space. The extract features are then fed into the associative analysis. For considering temporal constraint, the generated associative rules not only reflect the relationship between power system operating condition and transient stability, but also reveal some valuable information about operating characteristics. Simulation results on on IEEE 39-bus test system demonstrate the feasibility and efficiency of the proposed method.