In this paper, a two-stage Bayesian learning-based approach is proposed to enable online dynamic security assessment (DSA) and preventive control in power systems. To develop a reliable data mining-based model for DSA, the Bayesian neural network is learned by Bayes by Backprop to detect the security status in real-time and to inform the system operators with the confidence of the prediction. For dynamic insecurity prevention, the security-constrained optimal power flow model incorporating the Bayesian neural network based security constraints is proposed, which is solved by the derivation-free Bayesian optimization. Case studies on the IEEE 39-bus system show that the Bayesian neural network is able to detect the security status of the power system correctly and reliably and is able to avoid the over-fitting problem compared with the traditional point estimation neural network. Also, Bayesian optimization is able to solve the non-analytical SCOPF model efficiently to mitigate the dynamic insecurity by preventive generation rescheduling.

Total transfer capability (TTC) is an effective indicator to evaluate the transmission limit of the interconnected systems. However, due to the large-scale wind power integration, operation conditions of a power system may change rapidly, yielding time-varying characteristics of TTC. As a result, the traditional time-consuming transient stability constrained TTC model is unable to assess the online transmission margin. In this paper, we propose an online measurement-based TTC estimator using the nonparametric analytics. It consists of three major steps: the probabilistic data generation approach, the composite feature selection method and the group Lasso regression-based training scheme. Specifically, we present a probabilistic data generation approach to take into account the uncertainties of the day-ahead generation scheduling and reduce the number of redundant or infeasible data. Then, the composite feature selection is presented to reduce the dimension of the generated data without losing relevant information. Finally, these selected features are trained by the group Lasso regression to learn the correlation between the TTC and the online measurements. Once real-time measurements are available, the TTC can be assessed immediately through this learned inherent correlation. Extensive numerical results carried out on the modified New England test system demonstrate the feasibility of the proposed TTC estimator for online applications.

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.