Non-parametric statistics-based predictor enabling online transient stability assessment

Online transient stability assessment (TSA) is of great necessity for fast awareness of transient instability caused by fault contingencies. In this paper, a non-parametric statistics based scheme is proposed for response-based online TSA. A critical clearing time-based stability margin index is defined as the predictive output and 14 kinds of severity indicators are proposed as input features for the TSA predictor. With no prior knowledge of the correlation structure, the non-parametric additive model is used as the basis of the predictor. To screen out the weakly correlated indicators and reduce the dimensionality of the input space, two-stage feature selection is fulfilled by non-parametric independence screening and group Lasso penalised regression successively. The predictor is then learnt by least-squares regression in the reduced multi-feature space. With phasor measurement unit measurements at generator buses, severity indicators can be computed in the real-time and fast evaluation of post-fault stability margin can be made by the offline-trained predictor. The effectiveness of the proposed non-parametric statistics based scheme is demonstrated in a modified New England 39-bus system and a practical 756-bus transmission system in China.