A comprehensive assessment to propose an improved line stability index

Yari, Siavash; Khoshkhoo, Hamid

doi:10.1002/etep.2806

Cited by 19 publications

(16 citation statements)

References 89 publications

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“…To assess the ability of the proposed algorithm, its performance will be compared with SVSI [28], FVSI [44,45] and coupled single-port circuit (CSPC)method [46] indices whose critical thresholds are unity (in CSPC method, it is assumed that only locally measured data are available and the method cannot detect contingency occurrence in the network by WAMS).…”

Section: Simulation Resultsmentioning

confidence: 99%

Voltage stability assessment algorithm to predict power system loadability margin

Aghdam

Khoshkhoo

2020

IET Generation, Transmission & Distribution

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Section: Simulation Resultsmentioning

confidence: 99%

Voltage stability assessment algorithm to predict power system loadability margin

Aghdam

Khoshkhoo

2020

IET Generation, Transmission & Distribution

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“…are used to form other indices. 92 Researchers have utilized an existing index or proposed new ones to develop new integrity protection schemes. These indices are discussed below sections.…”

Section: Index-based Methodsmentioning

confidence: 99%

“…Apart from these two, other methods based on P‐V and P‐Q curves, sensitivity analysis, minimum singular value of Jacobian, artificial intelligence tools, and so forth. are used to form other indices 92 . Researchers have utilized an existing index or proposed new ones to develop new integrity protection schemes.…”

Section: Methods Used In Sips Implementationmentioning

confidence: 99%

Recent trends in integrity protection of power system: A literature review

Rajalwal

Ghosh

2020

Int Trans Electr Energ Syst

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Background: An increase in the load demand has been forcing the electrical components to work near their allowable limits. The transformation of the power system in terms of technology and geographical stretch has imposed the necessity of employing advanced protection schemes in the system. The system integrity protection scheme (SIPS) has evolved as an effective measure with the gradual sprawl of the conventional grid with local measurements to a smart grid with wide-area measurement system. Aims: SIPS is a rapidly growing field in the power system and thus the study of its performance analysis, implementation methods, and applications is necessary. This paper provides a comprehensive overview of SIPS technology. Materials & Methods: In this paper, SIPS definition and classification are briefly discussed. SIPS normalization schemes that include load shedding, controlled islanding, generation rejection, ou-of-step prediction, relay settings are elaborated. To implement these normalization schemes, various methods such as index-based, optimization-based, WAMS based, EAC based, and ML-based are discussed and compared. Finally, a comparative assessment is framed based on SIPS applied for different power system issues such as transient instability, voltage instability, overloading, line contingency, protection security, etc. Results: It is observed that most of the proposed SIPS are implemented for improving transient stability. The preferred solution for implementing the SIPS by the researchers is load shedding. It is also observed that there is a lack of implementation of SIPS with renewable energy systems.

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“…For instance, these could be related to a minimum level of loadability of the system in its post-disturbance state. The loadability limit could then be computed by, for instance, parameterized continuation methods such as the continuation power flow (CPF) method [26], or by certain line indicators [27]. Other stability criteria could include the capability of the system in its post-disturbance state to handle yet another disturbance.…”

Section: A Generation Of Training Datamentioning

confidence: 99%

Voltage Instability Prediction Using a Deep Recurrent Neural Network

Hagmar

Tong

Eriksson

et al. 2021

IEEE Trans. Power Syst.

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This paper develops a new method for voltage instability prediction using a recurrent neural network with long short-term memory. The method is aimed to be used as a supplementary warning system for system operators, capable of assessing whether the current state will cause voltage instability issues several minutes into the future. The proposed method uses a long sequence-based network, where both real-time and historic data are used to enhance the classification accuracy. The network is trained and tested on the Nordic32 test system, where combinations of different operating conditions and contingency scenarios are generated using time-domain simulations. The method shows that almost all N-1 contingency test cases were predicted correctly, and N-1-1 contingency test cases were predicted with over 95 % accuracy only seconds after a disturbance. Further, the impact of sequence length is examined, showing that the proposed long sequenced-based method provides significantly better classification accuracy than both a feedforward neural network and a network using a shorter sequence.

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A comprehensive assessment to propose an improved line stability index

Cited by 19 publications

References 89 publications

Voltage stability assessment algorithm to predict power system loadability margin

Voltage stability assessment algorithm to predict power system loadability margin

Recent trends in integrity protection of power system: A literature review

Voltage Instability Prediction Using a Deep Recurrent Neural Network

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