Abstract
Transient stability assessment (TSA) has always been a fundamental means for ensuring the secure and stable operation of power systems. Due to the integration of new elements such as power electronics, electric vehicles and renewable power generations, dynamic characteristics of power systems are becoming more and more complex, which makes TSA an increasingly urgent task. Since traditional time-domain simulations and direct method cannot meet the actual operation requirements of power systems, data-driven TSA has attracted growing attention from both academia and industry. This paper makes a comprehensive review from the following four aspects: feature extraction and selection, model construction, online learning and rule extraction; and then, summarizes the challenges and prospects for future research; finally, draws the conclusions of this review. This review will be beneficial for relevant researchers to better understand the research status, key technologies, and existing challenges in the field.
Highlights
Transient stability assessment (TSA) is a fundamental means for ensuring the secure and stable operation of power systems
Considering the postfault measurement information provided by phasor measurement units (PMUs), reference [19] proposes a feature selection method based on the improved maximal-relevance and minimal redundancy criterion and support vector machines (SVMs) for transient stability assessment
The results show that the multilayer SVM (MLSVM) is able to reduce the possibility of misclassification of transient stability assessment
Summary
It proposes a temporal self-adaptive scheme, it aims to balance the trade-off between assessment accuracy and response time. It utilizes graph convolution to integrate network topology information and adopts one-dimensional convolution to exploit temporal information It can assess whether the system will be stable or unstable, and predict the instability mode for the unstable status. It can be early predicted based on the measured postfault values of the generator voltages, speeds, or rotor angles It proposes a strategy combining grey region and two SVMs to deal with the problems of false alarms and false dismissals. It uses genetic algorithm for a MLSVM-based TSA model to identify valued feature subsets with varying numbers of features It uses cross-entropy to evaluate the fitting performance of base learners and to set the weight coefficient in the ensembler.
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