Transient Stability Detection Using CNN-LSTM Considering Time Frame of Observation

Azhar, Izzuddin Fathin; Putranto, Lesnanto Multa; Irnawan, Roni

doi:10.1109/ict-pep53949.2021.9601021

Cited by 3 publications

(4 citation statements)

References 18 publications

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“…Passing through convolution layers and pooling layers, the data are organized into vector features related to transient stability. The features are aggregated through the fully connected layer, and transiently stable and unstable states are determined through a softmax classifier [16].…”

Section: Preprocessing Of Dynamic Datamentioning

confidence: 99%

“…Using these data, various research studies have proposed artificial intelligent models, such as support vector machines (SVMs), known for their superior classification performance [10], long short-term memory (LSTM) [11], neural networks (NNs) [12], and convolutional neural networks (CNNs) [6,13], etc. Moreover, there are studies that have combined artificial intelligence techniques with conventional mathematical methods [14] or integrated two or more artificial intelligence techniques [15,16]. In recent research trends, a significant number of studies have been focusing on using CNN models that transform data into images and use them as inputs for artificial intelligence models.…”

Section: Introductionmentioning

confidence: 99%

“…It is a method that greatly increases image analysis performance and is used in computer vision, object recognition, and object detection. It has been used as a popular image analysis method in image-related fields, such as [14][15][16][17]. Recently, studies using saliency maps have been published in the field of power system load prediction [18,19].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Power System Transient Stability Assessment Using Convolutional Neural Network and Saliency Map

Lee,

Kim,

Park

et al. 2023

Energies

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This study proposes a model for transient stability assessment, which is a convolutional neural network model combined with a saliency map (S–CNN model). The convolutional neural network model is trained on dynamic data acquired through the data measurement devices of a power system. Applying the saliency map to the acquired dynamic data visually highlights the critical aspects of transient stability assessment. This reduces data training time by eliminating unnecessary aspects during the convolutional neural network model training, thus improving training efficiency. As a result, the proposed model can achieve high performance in transient stability assessment. The dynamic data are acquired by configuring benchmark models, IEEE 39 and 118 bus systems, through MATLAB/Simulink and performing time-domain simulations. Based on the acquired dynamic data, the performance of the proposed model is verified through a confusion matrix. Furthermore, an analysis of the effects of noise interference on the performance is conducted.

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Section: Preprocessing Of Dynamic Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Power System Transient Stability Assessment Using Convolutional Neural Network and Saliency Map

Lee,

Kim,

Park

et al. 2023

Energies

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“…In 2021, [12] introduced a two-stage method for the power system TSA using a snapshot ensemble LSTM network. In [13], the application of CNN-LSTM for transient stability prediction based on PMU data is investigated, considering stability conditions resulting from network changes. In addition, the stacked autoencoders and ensemble learning are proposed by [14].…”

Section: Introductionmentioning

confidence: 99%

Refined Deep Transfer Learning with CNN-LSTM and SDAE for Adaptive Assessment of Power System Transient Stability with Time Series Data

Song

Yang

Rashed

et al. 2023

Preprint

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Transient stability assessment (TSA) plays a critical role in ensuring the reliable operation of power systems. However, existing approaches for TSA often encounter challenges such as data imbalances, limited sample sizes, and the need for adaptability in the face of system changes, necessitating the exploration of more advanced techniques. This paper proposes a novel deep transfer learning (DTL) framework to address these limitations that incorporates CNN-LSTM and stacked denoising auto-encoder (SDAE) techniques, aiming to significantly improve the speed and accuracy of power system TSA, especially in online applications and adaptability to system changes. First, the utilization of SDAE enables effective feature extraction, while the implementation of class weight balancing and cross-entropy loss function techniques effectively addresses data imbalances. Second, a CNN-LSTM classifier is constructed using transfer progressive learning. This approach allows for the effective analysis of spatial and temporal dynamic measurements by leveraging unsupervised pre-training (auto-encoder) and additional CNN-LSTM layers. Third, we propose the DTL, which leverages knowledge transfer from the CNN-LSTM model and incorporates fine-tuning techniques. This innovative approach ensures adaptability under in four scenarios, which is a prevalent challenge in power systems for continuous prediction. As compared with other techniques, the results demonstrate that our proposed approach achieves TSA accuracy of up to 99.68% on the IEEE 39-bus system and 99.80% on the South Carolina 500-bus system. Furthermore, to compare the performance of continuous prediction with other methods, our proposed method exhibits a significant improvement of 2% even with a limited sample size.

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Transient Stability Assessment Considering Number and Location of PMUs Using CNN-LSTM

Azhar,

Putranto,

Irnawan

2023

2023 IEEE 11th International Conference on Smart Energy Grid Engineering (SEGE)

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Transient Stability Detection Using CNN-LSTM Considering Time Frame of Observation

Cited by 3 publications

References 18 publications

Power System Transient Stability Assessment Using Convolutional Neural Network and Saliency Map

Power System Transient Stability Assessment Using Convolutional Neural Network and Saliency Map

Refined Deep Transfer Learning with CNN-LSTM and SDAE for Adaptive Assessment of Power System Transient Stability with Time Series Data

Transient Stability Assessment Considering Number and Location of PMUs Using CNN-LSTM

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