Early warning system for spatiotemporal prediction of fault events in a power transmission system

Sun, Chenhao; Wang, Xin; Zheng, Yongjun; Chen, Shaoyi; Yue, Yishi

doi:10.1049/iet-gtd.2018.6389

Cited by 21 publications

(6 citation statements)

References 40 publications

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“…With the continuous progress in the research techniques both at home and abroad, it allows the application of the multiobjective gap evolutionary algorithm in the process of conversion of power-sensitive data. Compared with the traditional recognition methods of the named entity, it is not necessary to carry out manual design or feature comparison, and hidden data features can be extracted from the initial text input process [13][14][15].…”

Section: Power-sensitive Early Warning Data Conversion Platformmentioning

confidence: 99%

Power-Sensitive Early Warning Data Conversion Method Combined with Multiobjective Differential Evolution Algorithm

Gao

Liang

et al. 2022

Mathematical Problems in Engineering

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Aiming at the problems of economy, security, and reliability of power system operation, the multiobjective gap evolutionary algorithm is introduced into power-sensitive early warning data conversion. A power-sensitive early warning data conversion (PSEWDC) method based on multiobjective gap evolutionary algorithm (MOGEA) is proposed. According to the type of equipment with problems in the operation of the power system and the rapid diagnosis of defects and problems, the method quickly locates the problems and analyzes the causes, replaces and rearranges the original data by using the substitution data method to eliminate the nonlinear autocorrelation, and then, according to the convergence of the long-range correlation index, identifies the abnormal values that have no impact on the overall fluctuation of the original sequence, judges that the current sequence reaches the critical point of extreme events, and provides a reference threshold for real-time risk early warning. Finally, the example analysis shows that this method can finally determine the risk early warning threshold of power peak and valley load by analyzing the convergence of long-range correlation index, so as to avoid the blindness of subjective experience, provide a theoretical basis for power load risk early warning, effectively solve the problems of subjectivity, lack of dynamics, and lack of theoretical basis for setting risk threshold in traditional power risk early warning research, and have an intelligent insight into the security situation of power big data.

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Section: Power-sensitive Early Warning Data Conversion Platformmentioning

confidence: 99%

Power-Sensitive Early Warning Data Conversion Method Combined with Multiobjective Differential Evolution Algorithm

Gao

Liang

et al. 2022

Mathematical Problems in Engineering

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show abstract

“…l over (30) where N n represents node number. Then use the min-max normalisation to get the final weight, and the weight of each node or each line is dynamical and timevarying.…”

Section: Temporal Sensitivity Weight Analysismentioning

confidence: 99%

“…The current risk early warning research studies of power system mainly focus on contingencies like fault events and widespread blackouts [30,31]. However, even if there exists no equipment faults, the power quality, line power and line loss in DN may also face time-varying challenges owing to the intermittent output power of DG and fluctuations of the load.…”

Section: Introductionmentioning

confidence: 99%

Distribution network operational risk assessment and early warning considering multi‐risk factors

Chen

Liu

Yan³

et al. 2020

IET Generation, Transmission & Distribution

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“…A large number of research studies are done on power quality and static or dynamic stability [8–10], further incurring more research on the source and the degree of influence caused by the abnormal operation state. The vulnerability evaluation is carried out through the detection and location of the abnormal source to identify weak points [11, 12]. Finally, through the elimination of stability control and sources, the power grid can adapt to and quickly recover from a disturbance.…”

Section: Introductionmentioning

confidence: 99%

Application of random matrix model in multiple abnormal sources detection and location based on PMU monitoring data in distribution network

Yan

Liu

Fang

et al. 2020

IET gener. transm. distrib.

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With the conversion of the global power economy and energy structure, access to a large amount of renewable energy has led to a decrease in power system inertia. The slight abnormal disturbance in the distribution network may have a significant impact on social and economic development. Aim at enhancing power stability and system resiliency; this study focuses on the detection and location of multiple abnormal sources in the distribution network. Most traditional methods use models relying on precise line parameters, subject to poor adaptability to the distribution network with a large number of nodes, and rapidly changing topology. Therefore, this study proposes a novel random matrix model, driven by monitoring data from phasor measurement units distributed on the overhead transmission lines. In this model, linear shrinkage (LS) theory, and Marchenko–Pastur law are combined for noise reduction to ensure the dynamic character and anti‐noise ability. Moreover, data dimensions and sample points may be at the same level in an extensive scale network. The LS and standard condition number rule (SCN) are used for estimating the number of abnormal sources. Finally, the effectiveness of this paper's model is verified in PSCAD. The results indicate that the method has specific dynamic performance and anti‐noise ability.

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Early warning system for spatiotemporal prediction of fault events in a power transmission system

Cited by 21 publications

References 40 publications

Power-Sensitive Early Warning Data Conversion Method Combined with Multiobjective Differential Evolution Algorithm

Power-Sensitive Early Warning Data Conversion Method Combined with Multiobjective Differential Evolution Algorithm

Distribution network operational risk assessment and early warning considering multi‐risk factors

Application of random matrix model in multiple abnormal sources detection and location based on PMU monitoring data in distribution network

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