Wide‐area measurement system‐based model‐free approach of post‐fault rotor angle trajectory prediction for on‐line transient instability detection

Huang, Dan; Yang, Xiuyuan; Chen, Shuyong; Meng, Tao

doi:10.1049/iet-gtd.2017.1523

Cited by 26 publications

(6 citation statements)

References 30 publications

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“…The method is tested on a well-known test system to simplify analysis and focus on the key interpretability theme. However, accurate estimation of transient stability status using larger systems has been proven in [38], [39]. For the proposed method to be implemented in larger systems, the additional computational burden primarily relates to the increased number of RMS-TDS required to construct ML models at each busbar and thus capture the entire stability boundary of a system.…”

Section: B Computational Speedmentioning

confidence: 99%

Using SHAP Values and Machine Learning to Understand Trends in the Transient Stability Limit

Hamilton

Papadopoulos

2024

IEEE Trans. Power Syst.

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Section: B Computational Speedmentioning

confidence: 99%

Using SHAP Values and Machine Learning to Understand Trends in the Transient Stability Limit

Hamilton

Papadopoulos

2024

IEEE Trans. Power Syst.

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“…) where 𝑁 𝐷 is the number of samples, the output of the model for input 𝒳 𝑖 is 𝒴 ̂𝑖 (the inference result), the class of input 𝒳 𝑖 is 𝒴 𝑖 (the class label), W is the weight vector, and λ is the regularization coefficient to reduce overfitting. The optimizer to minimize the loss function (12), the stochastic gradient descent with momentum (SGDM) [39], is 𝑾 𝑗+1 = 𝑾 𝑗 − 𝛼𝛁𝐸 𝐿 (𝑾 𝑗 ) + 𝛾(𝑾 𝑗 − 𝑾 𝑗−1 ) (13) where j is the iteration number, α is the learning rate, W is the weight vector, 𝛁𝐸 𝐿 (𝑾 𝑗 ) is the gradient of the loss function (12) with respect to 𝑾 at iteration j, and γ is the momentum which determines the contribution of the previous gradient step to the current iteration. In the training dataset (8), the feature map converted from the DSI (10) and the transient stability (2) for each case are assigned to the input 𝒳 𝑖 and label 𝒴 𝑖 , respectively.…”

Section: B Fine-tuning For Transient Stability Assessmentmentioning

confidence: 99%

“…As a result, machine learning technology began to be used for transient stability assessment (TSA) in earnest. The stability classifications through the neural network [10], support vector machine (SVM) [11], curve fitting [12], and decision tree [13] were proposed. SVMs have been widely used for classification problems with their excellent learning speed and accuracy.…”

Section: Introductionmentioning

confidence: 99%

Transient Stability Assessment Using Deep Transfer Learning

Kim,

Lee,

Kim

et al. 2023

IEEE Access

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This study proposes a deep transfer learning method using a deep convolutional neural network pre-trained with ImageNet for transient stability assessment. The procedure of deep transfer learning, incorporating the role and considerations of the transient stability assessment system, is suggested. The transient assessment system learns the relationship between the severity of disturbances and transient stability in the power system through the proposed training method. The severity of a disturbance based on the physical causal relationship of the angle stability is proposed to train and implement the transient stability assessment model. The power system state variables obtained from the phasor measurement unit are converted into the feature map described by the severity of a disturbance, enabling the training of transient stability characteristics of the power system to the deep convolutional neural network. The training dataset is constructed using the time-domain simulation on IEEE 39 and IEEE 118-bus benchmark power system models configured in MATLAB/Simulink. As a result of deep transfer learning, which involves training with freezing some of the convolutional layers of the pre-trained deep network, the most suitable model for transient stability assessment is selected among the pre-trained deep networks. The effectiveness of the proposed method is compared with other approaches using the confusion matrix, and the robustness against noise interference is also investigated.

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“…, N , i = j}. It should be noted that the rotor angle would not swing obviously after clearing fault becasue the rotor shaft of generator has a big inertia [31], thus t 1 is the moment of several cycles after clearing fault.…”

Section: Online Coherent Groups Identification Schemementioning

confidence: 99%

An WAMS-Based Online Generators Coherency Identification Approach for Controlled Islanding

et al. 2020

Self Cite

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In this paper, an online model-free approach based on the largest Lyapunov exponents (LLE) and angular velocity deviation is provided for coherent groups identification (CGI). Firstly, by using a model-free LLE calculation method, the generator coherency identification (GCI) criterions are proposed by establishing the mathematical relationship between the LLE and angular velocity difference. Secondly, an online CGI scheme for power systems is designed. Compared with the existing measurement-based methods, the breakthrough work of this paper is that the proposed GCI criterions combines the stability theory of nonlinear dynamic trajectory based on LLE with the physical mechanism of the out-of-step between generators, such that it can improve both the speed and the accuracy of the GCI. Besides, the proposed approach needs not to find the optimal observation window size and shorten the required observation window size greatly. Moreover, it can be applied to online CGI with small computation requirement. Extensive test results on New England 39-Bus System and practical East China (EC) power grid, as well as the comparisons with existing CGI methods verify high accuracy and efficiency of the proposed approach. INDEX TERMS Coherency identification, largest Lyapunov exponents (LLE), dynamic features, post-fault phase trajectory, wide-area measurement system (WAMS).

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Wide‐area measurement system‐based model‐free approach of post‐fault rotor angle trajectory prediction for on‐line transient instability detection

Cited by 26 publications

References 30 publications

Using SHAP Values and Machine Learning to Understand Trends in the Transient Stability Limit

Using SHAP Values and Machine Learning to Understand Trends in the Transient Stability Limit

Transient Stability Assessment Using Deep Transfer Learning

An WAMS-Based Online Generators Coherency Identification Approach for Controlled Islanding

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