An Intelligent Transient Stability Assessment Framework With Continual Learning Ability

Artificial Intelligence Techniques for Power System Transient Stability Assessment

“…Furthermore, the IEEE 68-bus system (16 machines) and IEEE 145-bus system (50 machines) ought to be mentioned here, as these are somewhat larger test case power systems that also serve as benchmarks [17]. However, these are far less popular among researchers, with certain notable exceptions, e.g., [18][19][20][21][22]. Finally, it can be stated that there are several other test case power systems that have been used for TSA and related analyses, but these are often not fully disclosed and almost always lack certain information.…”

“…An imbalanced dataset (which is heavily skewed in favor of the stable class) necessitates a special splitting strategy that will preserve the class imbalance between the training and test sets. Otherwise, the performance of the machine learning models will be influenced detrimentally by the presence of class imbalance [21]. Consequently, the next step in the pipeline is a so-called stratified shuffle split.…”

An agent‐based fast concurrent prediction scheme for transient and short‐term voltage instability

“…Post-fault data of all load buses and the long short-term memory networks (LSTM) based on deep recurrent neural network (RNN) algorithm have been employed in [21] for predicting the STV stability status. In order to face the biased performance of the machine-learning based methods due to the imbalance of training samples, a stacked sparse denoising autoencoder (SSDAE) model has been used in [22] for predicting the transient stability status.…”

“…Indicators in the literature for predicting or detecting transient stability status or STV stability status can be classified into the direct indicators and the indirect indicators. Rotor angle and frequency of synchronous generators are two direct indicators to determine the transient stability status [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28], also the slip of induction motors and voltage magnitudes are two direct indicators to evaluate STV stability status [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29]. The voltage magnitudes are indirect indicators for predicting the transient instability and have shown successful performances, reported by [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28].…”

“…Rotor angle and frequency of synchronous generators are two direct indicators to determine the transient stability status [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28], also the slip of induction motors and voltage magnitudes are two direct indicators to evaluate STV stability status [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29]. The voltage magnitudes are indirect indicators for predicting the transient instability and have shown successful performances, reported by [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28]. The frequency of synchronous generators may increase or decrease by transient instability or STV instability (i.e.…”