Speed-up the computing efficiency of power system simulator for engineering-based power system transient stability simulations

Meng, Ke; Dong, Zhekang; Wong, K.P.; Xu, Yan; Luo, Fj J.

doi:10.1049/iet-gtd.2009.0701

Cited by 63 publications

(35 citation statements)

References 11 publications

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“…When the new instances appear unexperienced by the ELMs, they should be used to enrich and/or update the KB. In order to obtain more online instances, distributed computation technology for time-domain simulation can be adopted in this model [17]. As new instances are on-line generated, the original KB can be continuously enriched and/or updated, which simultaneously update and/or retrain the ELMs.…”

Section: Elm-based Tsa Modelmentioning

confidence: 99%

“…For on-line instance generation, with the hourly load forecasting and unit commitment (wind power generation prediction can also be included), the prospective operating points for the next period can be generated very rapidly, then distributed computation technique can be adopted to speed up the computing efficiency of detailed time-domain simulation [17]; in this way, large number of on-line instances can be produced.…”

Section: Kb Generationmentioning

confidence: 99%

“…Four severe contingencies are applied including three-phase, twophase-to-ground and single-phase-to-ground fault with different clearing times and post-fault topologies. The detailed time-domain simulation is performed using the PSS/E package [17,20].…”

Section: Kb Generationmentioning

confidence: 99%

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Real-time transient stability assessment model using extreme learning machine

Zhang

Meng

et al. 2011

IET Gener. Transm. Distrib.

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135

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In recent years, computational intelligence and machine learning techniques have gained popularity to facilitate very fast dynamic security assessment for earlier detection of the risk of blackouts. However, many of the current state-of-the-art models usually suffer from excessive training time and complex parameters tuning problems, leading to inefficiency for realtime implementation and on-line model updating. In this study, a new transient stability assessment model using the increasingly prevalent extreme learning machine theory is developed. It has significantly improved the learning speed and can enable effective on-line updating. The proposed model is examined on the New England 39-bus test system, and compared with some state-of-the-art methods in terms of computation time and prediction accuracy. The simulation results show that the proposed model possesses significant superior computation speed and competitively high accuracy.

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Section: Elm-based Tsa Modelmentioning

confidence: 99%

Section: Kb Generationmentioning

confidence: 99%

See 1 more Smart Citation

Real-time transient stability assessment model using extreme learning machine

Zhang

Meng

et al. 2011

IET Gener. Transm. Distrib.

Self Cite

135

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“…Some new recent developments in transient stability assessment are reported in [7]- [8]. There are also research efforts in using parallel processing [9]- [10] to speed-up the time-domain simulations.…”

Section: Introductionmentioning

confidence: 99%

A new methodology for the purpose of real-time local control of transient stability

Miah

2014

2014 IEEE PES T&D Conference and Exposition

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A new methodology is introduced solely for use in real-time local control of transient stability. The proposed method is based on localized transient stability of a power system. This is a completely new idea. In this method, the postfault power system is represented by a two-generator localized power system at the site of each individual generator. If each of these localized power systems reaches its stable equilibrium, then the power system reaches its stable equilibrium. Therefore, in terms of real-time local control of transient stability, if each of the localized power systems is driven to its respective stable equilibrium by local control actions with local computations using the locally measured data, then the power system is in stable equilibrium. Thus the method can be easily implemented for real-time local control of transient stability. This paper primarily focuses on the basic concepts of the method and presents some preliminary test results.Index Terms-localized power system, localized stability, realtime local control, transient stability.

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“…In practice, time-domain simulation method is however very time-consuming and is difficult to provide useful information on dynamic security control. To accelerate the speed of time-domain simulation, distributed computing architecture is usually needed [2].…”

mentioning

confidence: 99%

Advanced Pattern Discovery-based Fuzzy Classification Method for Power System Dynamic Security Assessment

Luo

Dong

Chen

et al. 2015

IEEE Trans. Ind. Inf.

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1 Abstract-Dynamic security assessment (DSA) is an important issue in modern power system security analysis. This paper proposes a novel pattern discovery (PD) based fuzzy classification scheme for the DSA. Firstly, the PD algorithm is improved by integrating the proposed centroid deviation analysis technique and the prior knowledge of the training data set. This improvement can enhance the performance when apply it to extract the patterns of data from a training data set. Secondly, based on the results of the improved PD algorithm, a fuzzy logic-based classification method is developed to predict the security index of a given power system operating point. In addition, the proposed scheme is tested on the IEEE 50-machine system and is compared with other state-of-the-art classification techniques. The comparison demonstrates that the proposed model is more effective in the dynamic security assessment of a power system.

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Speed-up the computing efficiency of power system simulator for engineering-based power system transient stability simulations

Cited by 63 publications

References 11 publications

Real-time transient stability assessment model using extreme learning machine

Real-time transient stability assessment model using extreme learning machine

A new methodology for the purpose of real-time local control of transient stability

Advanced Pattern Discovery-based Fuzzy Classification Method for Power System Dynamic Security Assessment

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