A Multi-Decomposition Approach for Accelerated Time-Domain Simulation of Transient Stability Problems

Zadkhast, Sajjad; Jatskevich, Juri; Vaahedi, E.

doi:10.1109/tpwrs.2014.2361529

Cited by 75 publications

(26 citation statements)

References 34 publications

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“…XGBoost introduces model complexity to measure the computational efficiency of the algorithm. The objective function of XGBoost is defined as shown in equation (2).…”

Section: Principles Of Xgboost and Fm Model A Principles Of Xgbomentioning

confidence: 99%

“…Time-domain simulation rely on solving nonlinear differential algebraic equations that model power systems. But they are computationally intensive and require accurate system data [2]. By contrast, the direct methods, such as transient energy function method [3] and extended equal-area criterion [4], can compute quickly and provide the stability margin, but the assessment results are conservative.…”

Section: Introductionmentioning

confidence: 99%

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Transient Stability Assessment of Power System Based on XGBoost and Factorization Machine

Gao

2020

IEEE Access

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As the deployment of wide-area measurement systems (WAMS) expands, data-driven methods are playing an increasingly important role in transient stability assessment (TSA). However, if the measured data is disturbed by noise or the topological structure of the power system changes, the performance of the model based on data-mining would decline so that it could not meet the needs of real-world scenarios. In this paper, we develop a TSA methodology based on extreme gradient boosting (XGBoost) and factorization machine (FM). Utilizing XGBoost to complete automatically the feature construction and transform the power flow into a sparse matrix. The influence of noise can be reduce due to the sparsity of the new feature set. On this basis, FM algorithm, which has advantage in processing the large sparse matrix, is introduced into the model to complete fault classification. Furthermore, the feature crossing function of FM further mines the interactive information of the spatio-temporal features. For changed topology scenarios, we propose a extended-training set scheme. Adding some pivotal data of topology changes to the training set improves the robustness of the model. Compared with existing studies, the proposed assessment model based on XGBoost-FM not only has the better generalization performance in the case of noise interference or changed topology, but also has the least time consumption and complexity.

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“…XGBoost introduces model complexity to measure the computational efficiency of the algorithm. The objective function of XGBoost is defined as shown in equation (2).…”

Section: Principles Of Xgboost and Fm Model A Principles Of Xgbomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Transient Stability Assessment of Power System Based on XGBoost and Factorization Machine

Gao

2020

IEEE Access

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“…The time domain simulation (TDS) [3] is regarded as the most accurate method of transient stability analysis, and is often used as a standard for verifying other transient stability analysis methods. The method calculates the change of the The associate editor coordinating the review of this manuscript and approving it for publication was Canbing Li.…”

Section: Introductionmentioning

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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“…Presently available methods for real-time transient rotor angle stability assessment after a fault are mostly classified into two categories: model-based methods [6,7] and model-free methods [8,9]. Model-based methods that depend on a power system model are not particularly appropriate for online stability assessment implementation due to computational complexity and high computation cost associated with the size of power system [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Wide-Area Measurement—Based Model-Free Approach for Online Power System Transient Stability Assessment

Huang

Chen

et al. 2018

Energies

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Online assessment of transient stability of power systems is critical for avoiding blackout. The increasing installation of phasor measurement units in power systems and the advancement of the wide-area measurement systems make it possible to develop methods for online assessment of power systems by using a large amount of real-time synchronous data. In this paper, we propose an approach based on the largest Lyapunov exponent (LLE) for online transient rotor angle stability assessment, using only wide-area measurement systems data. Through establishing a mathematical model that accounts for the LLE value and the rotor speed, as well as analyzing the post-fault phase-plane trajectory, we present a fast stability criterion to determine whether transient instability occurs. The proposed approach is model-free and does not need long-time LLE data to identify the final sign of the LLE. Furthermore, to reduce computational cost and realize online assessment for large-scale power systems, the critical generator pair (CGP) is recognized as the observation for the rotor angle stability assessment, and an effective algorithm for identifying the CGP is presented. Several case studies on the IEEE-39 bus test system and East China power grid are reported to demonstrate the accuracy and effectiveness of the proposed approach.Keywords: power system stability; online stability assessment; the largest Lyapunov exponent (LLE); rotor angle speed; wide-area measurement system (WAMS)

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A Multi-Decomposition Approach for Accelerated Time-Domain Simulation of Transient Stability Problems

Cited by 75 publications

References 34 publications

Transient Stability Assessment of Power System Based on XGBoost and Factorization Machine

Transient Stability Assessment of Power System Based on XGBoost and Factorization Machine

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

Wide-Area Measurement—Based Model-Free Approach for Online Power System Transient Stability Assessment

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