Protection Mechanism in Reliability Evaluation Approach to Multistate System with Common Cause Failure

Qin, Jinlei; Liu, Zheng; Zhu, Youchan

doi:10.1155/2021/6691362

Cited by 3 publications

(2 citation statements)

References 35 publications

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“…(2) the protection and response mechanism to a specific event is similar [30]. For example, for GT events, the inertial and governor responses help to stabilize the frequency, leading to similar behaviors of frequency for the first 3 curves in Fig.…”

Section: A Validation Of the Transfer Positivitymentioning

confidence: 94%

A Transfer Learning Framework for Power System Event Identification

Weng

2022

IEEE Trans. Power Syst.

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The increasing uncertain components of power systems foster the wide applications of Machine Learning (ML) techniques. While traditional ML models demand a large set of data, data-scarce dilemmas exist for new meters, devices, and new grids. Further, for rich historical measurements, valuable data may still be limited, especially for targets like identifying system events that rarely occur in the power system. To enhance the event type differentiation and localization for a datalimited grid, we propose a Transfer Learning (TL) framework to transfer knowledge from a data-rich grid (source grid) to the target grid, using measurements from Phasor Measurement Units (PMUs). The transferring process is challenging because of (1) high-volume data with redundant information, (2) different measurement dimensionalities, (3) dissimilar data distributions, and (4) disjoint event-location-label spaces for two grids. To handle the challenges of (1) to (3), we propose a joint optimization to reduce dimensionality and maximize common knowledge in a shared low-dimensional feature space, where the commonality lies in the same dimensions and close data distributions. Such an optimization-based procedure is verified via rigid mathematical theorems given the same label space, i.e., event-type-label space. However, for event localization, challenge (4) obstructs the optimization. Therefore, we design a label space alignment method to relabel the event location by the event zone location and build an event zone estimation problem. Then, the framework is generalized to both tasks. Finally, comprehensive experiments demonstrate the advantages of the proposed methods over stateof-the-art transfer learning models.

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Section: A Validation Of the Transfer Positivitymentioning

confidence: 94%

A Transfer Learning Framework for Power System Event Identification

Weng

2022

IEEE Trans. Power Syst.

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“…Therefore, it is necessary to effectively identify fault characteristics [1][2][3] . The research shows that the power supply reliability of power system directly affects social production and people's daily life, and in the state of rapid increase in power supply demand [4][5][6] , people have higher and higher requirements for power reliability. When power equipment fails, its signal waveform will change accordingly.…”

Section: Introductionmentioning

confidence: 99%

Design of automatic identification algorithm for double-feature fault signal waveform of power equipment

Tang,

Li,

Lei

et al. 2024

International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023)

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The conventional automatic identification algorithm of double-feature fault signal waveform of power equipment mainly uses ART (Adaptive Resonnance Theory) network for classification and discrimination, which is easily influenced by the identification mapping relationship, resulting in low correct identification rate of fault signal waveform. Therefore, it is necessary to design a brand-new automatic identification algorithm of double-feature fault signal waveform of power equipment. That is to say, the waveform characteristics of dual-feature fault signal of power equipment are extracted, and the optimization algorithm for automatic identification of dual-feature fault signal waveform of power equipment is generated, so that the automatic identification of fault signal waveform is realized. The experimental results show that the designed double-feature fault signal waveform automatic identification algorithm for power equipment has a high correct fault identification rate, which proves that the designed double-feature fault signal waveform automatic identification algorithm for power equipment has good identification effect, reliability and certain application value, and has made certain contributions to improving the operation safety of power equipment.

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Survival signature for reliability evaluation of a multi-state system with multi-state components

Qin

Coolen

2022

Reliability Engineering & System Safety

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Protection Mechanism in Reliability Evaluation Approach to Multistate System with Common Cause Failure

Cited by 3 publications

References 35 publications

A Transfer Learning Framework for Power System Event Identification

A Transfer Learning Framework for Power System Event Identification

Design of automatic identification algorithm for double-feature fault signal waveform of power equipment

Survival signature for reliability evaluation of a multi-state system with multi-state components

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