Abstract
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.
Highlights
Transient stability refers to the ability of power systems to maintain synchronism when subjected to severe disturbances such as faults [1]
As for data-mining-based transient stability assessment (TSA) in this paper, a stability margin index based on critical clearing time (CCT) is defined as the predictive response, while 14 kinds of response-based severity indicators are proposed as the inputs of the predictor
non-parametric independence screening (NIS) is performed to determine the structure of the addictive model and identify the severity indicators that are weakly correlated to the stability margin
Summary
Transient stability refers to the ability of power systems to maintain synchronism when subjected to severe disturbances such as faults [1]. The pair-wise relative energy function is proposed in [6] for fast identification of the critical generators and the single machine equivalents (SIME) and the equal-area criterion are employed to qualify the transient stability Both the above-mentioned hybrid methods rely on the prediction of the SIME's unstable equilibrium point since they make use of the TEF concepts. To reduce the input dimension without compromising the predictor's accuracy, a novel DT-based TSA scheme is proposed by introducing the characteristic ellipsoid (CELL) theory to extract the key features from limited PMU measurements in [13] Another DT-based framework is proposed in [14] to predict the unstable generator grouping pattern in power systems with renewable generation.
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