Multi-indicator inference scheme for fuzzy assessment of power system transient stability

Liu, Tingjian; Liu, Youbo; Yang, Yue; Taylor, Gareth; Huang, Zhengwen

doi:10.17775/cseejpes.2016.00029

Cited by 16 publications

(12 citation statements)

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“…A power system is usually subjected to deviations from the nominal operating conditions due to load variations and the occurrence of faults. A multi-indicator inference scheme is proposed for fast prediction of transient stability levels of a power system [43]. Transient stability is assessed by a set of transient indicators such as the integral generator speed index (IGSA) [44], wide-area severity induces or the accelerating rate of the generator under the COI reference.…”

Section: Transient Stability Assessment Using Generatorbased Thresholdmentioning

confidence: 99%

Generator‐based threshold for transient stability assessment

et al. 2019

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Power systems are frequently subjected to variations of the operating conditions due to the occurrence of contingencies such as faults, load or generator tripping. These disturbances might enforce the system to enter the instability region and thus deteriorate its overall performance. This paper proposes a systematic approach for transient stability assessment using generator-based threshold. An individual stability limit is designed for each generator instead of using only one pre-defined threshold for all generators. The considered contingencies are characterized by a fault type, location, loading condition and critical clearing time to consider wide range of operating conditions. Two fault locations are only considered to reduce the search space for stability limits; the generator terminals and mid of transmission lines. The critical clearing time is used to find the maximum relative rotor angle (MRRA) of individual generators attained during the entire time domain simulation. The MRRA of each generator represents the maximum angle which can be reached without losing synchronism at a given contingency. Conservative stability limits are drawn around the minimum stable drift of each generator which is chosen from the set of MRRAs of all contingencies. The effectiveness of the proposed approach is tested using the modified New-England, 39bus system.

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Section: Transient Stability Assessment Using Generatorbased Thresholdmentioning

confidence: 99%

Generator‐based threshold for transient stability assessment

et al. 2019

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“…Post-fault responses of power angles, rotor speeds and accelerating powers of generators, and voltage magnitudes of buses contain key information relating to transient stability. Numerous severity indicators have been proposed for stability assessment [18,[23][24][25][26]. In this paper, 14 kinds of indicators are proposed to form the multidimensional space of input features.…”

Section: Response-based Severity Indicatorsmentioning

confidence: 99%

Non‐parametric statistics‐based predictor enabling online transient stability assessment

Liu

et al. 2018

IET Generation, Transmission & Distribution

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Online transient stability assessment (TSA) is of great necessity for fast awareness of transient instability caused by fault contingencies. In this paper, a non-parametric statistics based scheme is proposed for response-based online TSA. A critical clearing time-based stability margin index is defined as the predictive output and 14 kinds of severity indicators are proposed as input features for the TSA predictor. With no prior knowledge of the correlation structure, the non-parametric additive model is used as the basis of the predictor. To screen out the weakly correlated indicators and reduce the dimensionality of the input space, two-stage feature selection is fulfilled by non-parametric independence screening and group Lasso penalised regression successively. The predictor is then learnt by least-squares regression in the reduced multi-feature space. With phasor measurement unit measurements at generator buses, severity indicators can be computed in the real-time and fast evaluation of post-fault stability margin can be made by the offline-trained predictor. The effectiveness of the proposed non-parametric statistics based scheme is demonstrated in a modified New England 39-bus system and a practical 756-bus transmission system in China.

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“…µ + and µ -were selected as 1.32 × 10 -4 m 2 /(V·s) and 1.65 × 10 -4 m 2 /(V·s). 22 The diagrammatic sketch of the conductors with sag in the calculation model from the front view is shown in Fig.11. The change trend of the conductors at the axial direction was approximately parabolic in the corona cage.…”

Section: Corona-loss Analysis Of Bundle-sag Conductors In the Ultmentioning

confidence: 99%

A helical charge simulation based 3-D calculation model for corona loss of AC stranded conductors in the corona cage

et al. 2018

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Corona loss generated from conductors is an indispensable factor in the design of alternating current(AC) transmission lines. Based on a helical charge simulation method, a 3-D calculation model for corona loss considering the outer strands of the conductor in the corona cage was established. Compared to the 2-D corona-loss calculation method, the calculation model proposed in this study incorporates the heterogeneity of the electric fields strength along the axial direction of the stranded conductor. Furthermore, it was also able to calculate the corona loss when the conductor had sagged. The calculation results of the LGJ-300/40 and LGJ-400/35 in the small corona cage show close agreement with the measurement results indicating that the corona-loss calculation model was accurate. In addition, the corona loss of the bundle conductors 4 × LGJ720 with sag in the UHV corona cage was also analyzed.

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Multi-indicator inference scheme for fuzzy assessment of power system transient stability

Cited by 16 publications

References 0 publications

Generator‐based threshold for transient stability assessment

Generator‐based threshold for transient stability assessment

Non‐parametric statistics‐based predictor enabling online transient stability assessment

A helical charge simulation based 3-D calculation model for corona loss of AC stranded conductors in the corona cage

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