Wide Area Measurement-based Transient Stability Prediction using Long Short-Term Memory Networks

Saner, Can Berk; Kesici, Mert; Mahdi, Mohammed; Yaslan, Yusuf; Genç, V. M. İstemihan

doi:10.1109/sgcf.2019.8782391

Cited by 6 publications

(4 citation statements)

References 11 publications

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“…In addition, obtaining a state matrix for large-scale power systems can be complicated and computationally expensive as it requires detailed mathematical models for all components and their control circuits [21]. On the other hand, power system operators can benefit from the availability of various measurements that are provided by WAMS across the entire network [22]. The collected data carries useful and non-linearized information concerning the real-time operation of a system and hence, dynamic signatures which can be deployed to construct an accurate mapping using machine learning techniques.…”

Section: Hybrid Cnn and Lstm Deep Learning Models Formentioning

confidence: 99%

“…It can be set up to forecast 1D multivariate time series. By default, the Conv-LSTM2D class anticipates that input data in (22) will be in the following shape: [samples, time steps, rows, columns, channels]. The definition of each data at any time step is an image of (rows × columns) data points.…”

Section: Conv-lstm Sssp Model: Data Preparation and Implementationmentioning

confidence: 99%

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Deep Learning-Based Models for Predicting Poorly Damped Low-Frequency Modes of Oscillations

Muhammed

Isbeih

Moursi

et al. 2024

IEEE Trans. Power Syst.

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This work proposes a real-time deep learning-based model for predicting the small-signal stability of an electrical network. The trained models equip power system operators with an accurate and fast monitoring tool which can be used during online operation. To achieve this objective, three different model architectures are employed in this research; stacked long short-term memory (LSTM), convolutional neural network (CNN)-LSTM and Convectional LSTM (Conv-LSTM). These models are trained using datasets which contain the oscillatory parameters (frequency and damping ratio) of both local and inter-area modes of oscillations. In addition, the voltage phasors at different buses are taken as the model input where the output comprises the real-time oscillatory patterns of the modes. Furthermore, the overall performance of proposed models is shown for the New-England 10-machine, 39-bus, IEEE 16-machine, 68-bus, 5-area, and IEEE 50-machine, 145-bus benchmark test cases. The main findings show that training CNN-LSTM and Conv-LSTM models provide better performance compared with the stacked-LSTM model. The former models have less number of parameters and thus shorter training time. In addition, CNN_LSTM and Conv-LSTM models are less prone to overfitting problems in the network and have a better ability in capturing spatial and temporal features inherent in input data.

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Section: Hybrid Cnn and Lstm Deep Learning Models Formentioning

confidence: 99%

Section: Conv-lstm Sssp Model: Data Preparation and Implementationmentioning

confidence: 99%

Deep Learning-Based Models for Predicting Poorly Damped Low-Frequency Modes of Oscillations

Muhammed

Isbeih

Moursi

et al. 2024

IEEE Trans. Power Syst.

View full text Add to dashboard Cite

show abstract

“…Recently, Recurrent Neural Networks (RNNs) were also proposed, because of their ability to consider temporal correlations, either with Long Short-Term Memory (LSTM) units [37], [81], [82] or Gated Recurrent Units (GRU) [83], [84].…”

Section: Learning a Modelmentioning

confidence: 99%

Recent Developments in Machine Learning for Energy Systems Reliability Management

2020

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This paper reviews recent works applying machine learning techniques in the context of energy systems reliability assessment and control. We showcase both the progress achieved to date as well as the important future directions for further research, while providing an adequate background in the fields of reliability management and of machine learning. The objective is to foster the synergy between these two fields and speed up the practical adoption of machine learning techniques for energy systems reliability management. We focus on bulk electric power systems and use them as an example, but we argue that the methods, tools, etc. can be extended to other similar systems, such as distribution systems, micro-grids, and multi-energy systems.

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“…To similar effect, a gated recurrent unit model was developed for TSA assessment but featured a much more comprehensive input signal space [20]. Furthermore, two separate classifiers based on LSTM networks are presented to predict the stability status when a power system is subjected to a disturbance [18]. These classifiers deploy voltage measurements and rate-of-change-of-frequency (RoCoF) during the first five cycles of the post-fault period.…”

mentioning

confidence: 99%

A Unified Online Deep Learning Prediction Model for Small Signal and Transient Stability

Azman

Isbeih

Moursi

et al. 2020

IEEE Trans. Power Syst.

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This paper proposes a novel unified prediction approach for both small-signal and transient rotor angle stability as opposed to other studies that have only addressed transient rotor angle stability. Deep learning techniques are employed in this paper to train an online prediction model for rotor angle stability (RAS) using the voltage phasor measurements which are collected across the entire system. As a result, the trained model provides a fast yet accurate prediction of the transient stability status when a power system is subjected to a disturbance. Also, if the system is transiently stable, the prediction model updates the power system operator concerning the damping of low-frequency local and inter-area modes of oscillations. Therefore, the presented approach provides information concerning the transient stability and oscillatory dynamic response of the system such that proper control actions are taken. To achieve these objectives, advanced deep learning techniques are employed to train the online prediction model using a dataset which is generated through extensive time domain simulations for wide range of operating conditions. A convolutional neural network (CNN) transient stability classifier is trained to operate on the transient response of the phasor voltages across the entire system and provide a binary stability label. In tandem, a long-short term memory (LSTM) network is trained to learn the oscillatory response of a predicted stable system to capture the step-by-step dynamic evolution of the critical poorly damped low-frequency oscillations. The superior performance of the proposed model is tested using the New-England 10-machine, 39-bus, IEEE 16-machine, 68-bus, 5-area and IEEE 50-machine, 145-bus test systems and is verified with time domain simulation.

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Wide Area Measurement-based Transient Stability Prediction using Long Short-Term Memory Networks

Cited by 6 publications

References 11 publications

Deep Learning-Based Models for Predicting Poorly Damped Low-Frequency Modes of Oscillations

Deep Learning-Based Models for Predicting Poorly Damped Low-Frequency Modes of Oscillations

Recent Developments in Machine Learning for Energy Systems Reliability Management

A Unified Online Deep Learning Prediction Model for Small Signal and Transient Stability

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