Transient stability evaluation model based on SSDAE with imbalanced correction

Wang, Huaiyuan; Ye, Weitao

doi:10.1049/iet-gtd.2019.1388

Cited by 18 publications

(10 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since all operating conditions of a system cannot be contained by training data, it is difficult to fit an ideal classification boundary to achieve 100% assessment accuracy. 16,18,21,27 It is found in our research that the most misclassifications occur in the cases whose fault clearing times are close to their critical clearing times (CCTs). Therefore, an RTSA framework with parallel structure is proposed to distinguish between critical cases and noncritical cases.…”

Section: Real-time Transient Stability Assessment Frameworkmentioning

confidence: 75%

“…However, the accurate identification of critical state cases is a tricky issue for data‐driven RTSA. Since all operating conditions of a system cannot be contained by training data, it is difficult to fit an ideal classification boundary to achieve 100% assessment accuracy 16,18,21,27 . It is found in our research that the most misclassifications occur in the cases whose fault clearing times are close to their critical clearing times (CCTs).…”

Section: Real‐time Transient Stability Assessment Frameworkmentioning

confidence: 90%

See 1 more Smart Citation

Transient stability assessment model with parallel structure and data augmentation

Chen

Lin

Wang

2021

Int Trans Electr Energ Syst

Self Cite

View full text Add to dashboard Cite

Summary Critical situations are difficult to predict reliably by the machine learning‐based transient stability assessment (TSA) methods. Therefore, the practicality of the data‐driven TSA is limited. A parallel TSA framework constructed by two basic predictors and a comprehensive decider (CD) is proposed to achieve fast and reliable real‐time transient stability assessment (RTSA). A cost‐sensitive method is utilized for stacked sparse auto‐encoders to establish two basic predictors with opposite evaluation biases. Then, the outputs of the two basic predictors are sent to the CD. Finally, the stability of the non‐critical cases can be judged directly, and the critical cases are suggested to be analyzed by other methods. Besides, in order to enhance the reliability of the parallel predictor, a simple data augmentation approach with Gaussian white noise is employed to expand the classification boundaries. A fault severity factor is introduced to filter basic critical samples for data augmentation to improve the performance of the proposed framework. The effect of the proposed strategy is verified on the IEEE‐39 bus system and a realistic regional system.

show abstract

Section: Real-time Transient Stability Assessment Frameworkmentioning

confidence: 75%

Section: Real‐time Transient Stability Assessment Frameworkmentioning

confidence: 90%

Transient stability assessment model with parallel structure and data augmentation

Chen

Lin

Wang

2021

Int Trans Electr Energ Syst

Self Cite

View full text Add to dashboard Cite

show abstract

“…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].…”

Section: Introductionmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

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

Lashgari

Shahrtash

2021

IET Generation Trans & Dist

View full text Add to dashboard Cite

A novel agent-based online prediction method is presented in this paper, to predict the status of both transient and short-term voltage (STV) stability, against fault occurrence. In the proposed method, the trajectories of the relative frequency deviation (ΔF) and the power imbalance (ΔP) are estimated by employing the third-degree polynomial curve fitting method. By tracking the estimated trajectories on ΔF-ΔP plane and checking some simple defined rules, an early prediction of both transient and STV instability is achieved in an organized multi-agent system (MAS). In order to evaluate the performance of the proposed algorithm, the method has been tested on IEEE 39-bus system, IEEE 118-bus system and IEEE Nordic test system. Based on the obtained results, the proposed algorithm has an overall accuracy of 99.5% under symmetrical and asymmetrical faults, PMU measurement error, different operating points and topological changes.

show abstract

“…Consequently, the practicability of the assessment model is gradually being widely concerned in recent years. It is well‐known that the misclassification cost of stable samples and unstable samples is significantly different in large‐scale complex power systems [30–32]. On one hand, if stable samples are predicted as unstable ones, the system can keep stable.…”

Section: Introductionmentioning

confidence: 99%

Transient stability assessment model with improved cost‐sensitive method based on the fault severity

Wang

Chen

2020

IET Generation, Transmission & Distribution

Self Cite

View full text Add to dashboard Cite

Transient stability evaluation model based on SSDAE with imbalanced correction

Cited by 18 publications

References 33 publications

Transient stability assessment model with parallel structure and data augmentation

Transient stability assessment model with parallel structure and data augmentation

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

Transient stability assessment model with improved cost‐sensitive method based on the fault severity

Contact Info

Product

Resources

About