Transient Stability Assessment Using Individual Machine Equal Area Criterion Part II: Stability Margin

Wang, Songyan; Yu, Jie; Zhang, Wei

doi:10.1109/access.2018.2853637

Cited by 29 publications

(67 citation statements)

References 6 publications

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“…In a confusion matrix, T s represents the number of actual stable samples that are predicted to be stable, F us represents the number of actual stable samples that are predicted to be unstable and so on. Therefore, the related indices are defined as follows [31], [32]:…”

Section: Transient Stability Assessment 1) Evaluation Indicesmentioning

confidence: 99%

Anti-Jitter and Refined Power System Transient Stability Assessment Based on Long-Short Term Memory Network

et al. 2020

IEEE Access

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In order to maintain the stable operation of power systems, quick and accurate transient stability assessment (TSA) after the fault clearance is important. Machine learning methods have been widely used in the transient stability analysis of power systems. However, how to make good use of time series data of PMUs and effectively balance the contradiction between rapidity and accuracy brings new challenges to TSA. To address this problem, we propose an anti-jitter dynamic evaluation method based on long-short term memory (LSTM) network. In this model, the trajectory cluster characteristics of generators power angles after fault clearance are taken as inputs, and an improved LSTM is used to learn the nonlinear mapping relationship between the input characteristics and the transient stability. Meanwhile, by the use of sliding time windows and anti-jitter mechanism, a hierarchical real-time prediction framework is constructed to effectively utilize the time series data of PMUs. The case studies on two systems indicate that the proposed method has superior evaluation accuracy and general performance. In addition, the proposed method can effectively evaluate the stability margin or instability degree of samples, which provides reliable reference information for emergency control. INDEX TERMS Deep learning, long-short term memory, recurrent neural network, transient stability assessment, artificial intelligence.

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Section: Transient Stability Assessment 1) Evaluation Indicesmentioning

confidence: 99%

Anti-Jitter and Refined Power System Transient Stability Assessment Based on Long-Short Term Memory Network

et al. 2020

IEEE Access

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“…where max(δ T ij ) is the maximum value of rotor angle deviation of any two generators in transient process [11], [12].…”

Section: Preliminary Screening Of Transient Stability Characterismentioning

confidence: 99%

“…a) The first feature selection. Mutual information values between each candidate feature and the target class are calculated by formula (12). A dataset (D) for selected features is created and the feature with the highest MI (f i , y) is chosen as the first selected feature.…”

Section: ) Mrmrmentioning

confidence: 99%

A Stratified Method for Large-Scale Power System Transient Stability Assessment Based on Maximum Relevance Minimum Redundancy Arithmetic

Zheng

et al. 2019

IEEE Access

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A stratified method to transient stability assessment in large-scale power systems based on mutual information theory and artificial intelligence algorithm is proposed in this paper. A set of inter-complementary dynamic stability features are picked up one by one through the maximum-relevance minimum-redundancy (MRMR) algorithm. Besides, multiple extreme learning machines (ELMs) are trained based on the generated feature datasets. Because of the high requirement of evaluation speed in practical application, in order to balance the contradiction between assessment speed and accuracy, a hierarchical assessment structure is adopted in the final assessment process. Different ensemble classifiers with different response times are trained to construct different layers. The performance of the proposed technique is tested in the IEEE-39 bus system and a practical 1648 bus system provided by PSS/E. The experimental results indicate that, compared to other traditional methods, the proposed hierarchical method can give a more accurate result in a shorter period of time. As an efficient method, it is suitable for on-line transient stability assessment.

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“…If not, the mismatched control actions might deteriorate the power system and lead to cascading faults or even blackouts [3–5]. The transient stability assessment (TSA) method based on the direct method could quantify the transient stability of the system through the stability margin, which could give effective guidance for emergency control for guaranteeing the power system maintains synchronous stability after fault [6].…”

Section: Introductionmentioning

confidence: 99%

Analytical on‐line method of determining transient stability margin using protection information for asymmetric faults

Wang

Liu

Zhu

et al. 2019

IET Generation, Transmission & Distribution

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Transient Stability Assessment Using Individual Machine Equal Area Criterion Part II: Stability Margin

Cited by 29 publications

References 6 publications

Anti-Jitter and Refined Power System Transient Stability Assessment Based on Long-Short Term Memory Network

Anti-Jitter and Refined Power System Transient Stability Assessment Based on Long-Short Term Memory Network

A Stratified Method for Large-Scale Power System Transient Stability Assessment Based on Maximum Relevance Minimum Redundancy Arithmetic

Analytical on‐line method of determining transient stability margin using protection information for asymmetric faults

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