Abstract
With the rapid growth of power market reform and power demand, the power transmission capacity of a power grid is approaching its limit and the secure and stable operation of power systems becomes increasingly important. In particular, in modern power grids, the proportion of dynamic loads with fast recovery characteristics such as air conditioners, refrigerators, and industrial motors is increasing. As well as there is an increasing proportion of different forms of renewable energy in power systems. Therefore, the short-term voltage stability (STVS) of power systems cannot be ignored. This article comprehensively sorts out the STVS problems of power systems from the perspective of data-driven methods and discusses existing challenges.
Highlights
Short-term voltage stability assessment (STVSA) is the linchpin for ensuring the secure and stable operation of a power system [1]
Aiming at spatial and temporal correlations inherent in the complex transient process of smart grids, reference [45] proposed an intelligent machine learning method, which took the corrected system dynamic trajectory as input and used an long short-term memory (LSTM)-based algorithm to learn short-term voltage stability (STVS) features of sequence data, and a highly reliable and robust classification model could be obtained for online STVSA
By collecting and summarizing the references in scientific publications, the latest developments on the data-driven STVSA methods are provided. e research carried out strongly shows that datadriven methods have great potential, especially when the operating conditions of power systems become increasingly complex
Summary
Short-term voltage stability assessment (STVSA) is the linchpin for ensuring the secure and stable operation of a power system [1]. The proportion of dynamic loads with fast recovery characteristics such as air conditioners, refrigerators, and industrial motors is increasing, and short-term voltage stability (STVS) problems of power systems are becoming growingly prominent. An STVSA model can be built by leveraging measurement data and machine learning to ensure the secure and stable operation of power systems.
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