Time domain simulation based transient stability assessment and control

Chan, Ka Wing; Cheung, C.H.; Su, H.T.

doi:10.1109/icpst.2002.1067798

Cited by 12 publications

(3 citation statements)

References 7 publications

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“…extended equal area criterion is proposed to transform the multimachine system to an equivalent single machine infinite bus system and provide a quantitative criterion to determine transient stability [112]. EEAC can be integrated into time domain simulations and calculate the transient stability margin , [113].…”

Section: Transient Stability Constrained Unit Commitmentmentioning

confidence: 99%

Stability-Constrained Power System Scheduling: A Review

Luo

Teng

2020

IEEE Access

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Section: Transient Stability Constrained Unit Commitmentmentioning

confidence: 99%

Stability-Constrained Power System Scheduling: A Review

Luo

Teng

2020

IEEE Access

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“…AGGREGATION METHOD To reduce analysis time, aggregation is introduced to simplification. [5] When we transform a multi-machine system into an EOMIB, we classify each of the machines into a group of critical machines and noncritical machines by using numerical simulation data. Conventionally in the SIME method, the aggregation is conducted by the behavior of machine angles in just the simple unstable-domain.…”

Section: A Swing Equationmentioning

confidence: 99%

A new aggregation approach for the single machine equivalent to calculate the decelerating energy of the stable-domain

Tabuchi

Iwamoto

Hosogoe³

et al. 2010

2010 Conference Proceedings IPEC

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This paper proposes a new aggregation approach for the single machine equivalent (SIME) method. To assess the transient stability of the power systems rapidly, observers sometimes need to aggregate a multi-machine system into a simplified system. However, there is more than one approach for aggregation and thus observers cannot know which way is the most appropriate. This paper first explains the basic theory of the single machine equivalent (SIME) method. Next, a new aggregation approach is proposed which can be applied not only to the unstable-domain, but also to the stable-domain, and simulations are carried out to verify the validity of the proposed scheme.

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“…Scala et al (1998) introduced a time-domain simulation method that solves the state-space differential equations to obtain the detailed dynamic behaviors of the power systems. Chan et al (2002) presented a new development in online transient stability assessment and control using the time-domain simulation method. From the assessment results shown in the aforementioned study, the proposed approaches have a good stability accuracy rate, but it is also easy to find that the calculation results highly depend on the system model and parameters which means the computational effort is at a high level.…”

Section: Introductionmentioning

confidence: 99%

Sparse Dictionary Learning for Transient Stability Assessment

Wang

Pang

Cheng³

2022

Front. Energy Res.

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Transient stability assessment (TSA) has always been a fundamental and challenging problem for ensuring the security and operation of power systems. With more power electronic interface resources integrated into the grid and large renewable energies, the stability of the power system is jeopardized. Therefore, TSA of the power system should be considered in advance to keep the system running stable. In recent years, with the development of artificial intelligence (AI) technologies such as artificial neural network (ANN), support vector machine (SVM), and Markov decision process, TSA has improved dramatically. In this study, a sparse dictionary learning approach is proposed to improve the precision of the classification accuracy of transient stability assessment in power systems. Case studies of TSA using multi-layer support vector machine (ML-SVM) and long short-term memory network–based recurrent neural network (LSTM-RNN) are discussed as benchmarks to validate the proposed method. The stable and unstable dictionary learnings are designed based on datasets obtained by simulating thousands of different time-domain simulation (TDS) scenarios performed on the New-England 39-bus system in the PSAT (power system analysis toolbox) toolbox. Stable and unstable dictionaries are developed based on the K-SVD approach. The testing dataset contains both stable and unstable samples which steps into the sparse coding process to obtain the indexes. Compared with the indexes, the system’s final TSA is targeted. The proposed method exhibits satisfactory classification accuracy in transient stability prediction and provides the ability to reduce false alarms both in positives and negatives of the power system.

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Time domain simulation based transient stability assessment and control

Cited by 12 publications

References 7 publications

Stability-Constrained Power System Scheduling: A Review

Stability-Constrained Power System Scheduling: A Review

A new aggregation approach for the single machine equivalent to calculate the decelerating energy of the stable-domain

Sparse Dictionary Learning for Transient Stability Assessment

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