Data-driven approach for spatiotemporal distribution prediction of fault events in power transmission systems

Sun, Chenhao; Wang, Xin; Zheng, Yongjun

doi:10.1016/j.ijepes.2019.06.023

Cited by 20 publications

(9 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The model stability is poor during longterm and large-scale application. The literature [12] deeply analyzed the causes of failures and analyzed the relationship between the causes of failures and environmental attributes, then evaluated the impact of failure prediction on overall performance prediction, finally, established a failure prediction model. According to the literature [13][14][15][16][17][18][19], these methods do not need a thorough examination of the mechanism of equipment fault and fall within the black box concept.…”

Section: Introductionmentioning

confidence: 99%

Fault Critical Point Prediction Method of Nuclear Gate Valve with Small Samples Based on Characteristic Analysis of Operation

Liu

et al. 2022

Materials

View full text Add to dashboard Cite

The number of fault samples for the new nuclear valve is commonly rare; thus, the machine learning algorithm is not suitable for the fault prediction of this kind of equipment. In order to overcome this difficulty, this paper proposes a novel method for the fault critical point prediction of the gate valve based on the characteristic analysis of the operation process variables. The operation process of gate valve switch often contains various fault characteristics and information, and this method first adopts the Shannon entropy to describe the power spectrum of vibration signal relevant to the operation process of gate valve switch, and then employs the mean value of the power spectrum entropy as an indirect process variable and further investigates the differences between the indirect process variable under the healthy state and the fault state with a different fault degree. In addition, the power signal of the gate valve is also employed as the direct process variable and the features of the direct process variable under the healthy state and the fault state with different fault degrees are also investigated. Based on the previous indirect process variable and direct process variable, the prediction approach for the critical point of the gate valve fault is established by analyzing the change in the indirect process variable and direct process variable before and after faults. Finally, the data of a nuclear gate valve experiment are employed to demonstrate the feasibility of the proposed method and the results show that the proposed method can effectively predict the fault critical point of the mentioned nuclear gate valve. If the diagnostic threshold is set properly, the critical point prediction of a nuclear gate valve fault can be realized as 100% or close to 100%. Furthermore, the proposed method can be directly applied to the nuclear gate valve in a nuclear power plant to improve the operation reliability of the valve. At the same time, the method can be applied to the fault diagnosis and prediction of valves in other fields, such as the chemical industry.

show abstract

Section: Introductionmentioning

confidence: 99%

Fault Critical Point Prediction Method of Nuclear Gate Valve with Small Samples Based on Characteristic Analysis of Operation

Liu

et al. 2022

Materials

View full text Add to dashboard Cite

show abstract

“…In ref. [13], Sun et al. realized equipment failure prediction by performing weighted association regularization processing of environmental factors.…”

Section: Introductionmentioning

confidence: 99%

A failure assessment method of island distribution lines based on model‐data hybrid drive

Zheng

Tang

et al. 2022

IET Generation Trans & Dist

View full text Add to dashboard Cite

Open circuit faults of island distribution lines account for more than 70% of the total number of faults in the distribution network. Failure assessment of lines plays a crucial role in the operational safety of the island distribution network. Aiming at the problems of multiple operational failure factors and difficulty in failure assessment, a failure assessment method of island distribution lines based on model-data hybrid drive is proposed in this paper. Specifically, in order to accurately describe the actual operating status and failure mechanism of distribution lines in the typical island environment, model-driven method is used to formulate a comprehensive operational failure index of distribution lines, which combines with the impact of strong wind and corrosion operation scenarios and the operating characteristics of lines. On this basis, to improve the accuracy of failure assessment under the influence of multiple factors in the island environment, an improved light gradient boosting machine model is constructed in a data-driven manner. Finally, based on actual data from distribution network in Zhoushan, China, the case study reveals that the proposed method could effectively assess the operational failure of island distribution lines.

show abstract

“…He selected the most sensitive features as the characteristic variables of the input recurrent neural network, so that a recurrent neural network bearing life prediction model based on health indicators is constructed. Sun [12] extensively studied all environmental attributes including the cause of the fault, discussed the relationship between potential faults and environmental attributes, evaluated the impact of fault-induced fault prediction on the overall prediction performance, and proposed a fault prediction model based on environmental attributes. It can be seen from the above literatures that the data-driven fault prediction model does not need to have a good understanding of the fault mechanism of the system, as long as there is sufficient data, but the accuracy of the fault prediction is not based on the high fault prediction technology of the model, and it is affected by the error of the collected data.…”

Section: Introductionmentioning

confidence: 99%

A Fault Prediction Model of Adaptive Fuzzy Neural Network for Optimal Membership Function

Zhang

et al. 2020

IEEE Access

View full text Add to dashboard Cite

As an essential and challenging technology of fault prediction and health management(PHM), fault prediction technology has been a research focus in the field of fault diagnosis. However, the current model-based fault prediction technology and data-driven fault prediction technology have some limitations, and it is difficult to effectively apply them in practice. Therefore, this paper combines the advantages of two kinds of fault prediction technology, sets the fault distribution function as the membership function of the adaptive fuzzy neural network based on the full analysis of the fault mechanism. The use of the fault distribution function to highly generalize the law of fault occurrence, and the strong self-learning ability of the neural network can effectively tap the potential fault information of the fault data, thereby using the fault distribution function to fit the fault data, and forming a set of membership functions by presetting a variety of membership functions, so as to expand the applicability of the proposed model in fault prediction. The experimental results show that the fault prediction model proposed in this paper has the advantages of high prediction accuracy, fast convergence speed and good applicability. INDEX TERMS Adaptive fuzzy neural network, fault mechanism, fault prediction, membership function.

show abstract

Data-driven approach for spatiotemporal distribution prediction of fault events in power transmission systems

Cited by 20 publications

References 40 publications

Fault Critical Point Prediction Method of Nuclear Gate Valve with Small Samples Based on Characteristic Analysis of Operation

Fault Critical Point Prediction Method of Nuclear Gate Valve with Small Samples Based on Characteristic Analysis of Operation

A failure assessment method of island distribution lines based on model‐data hybrid drive

A Fault Prediction Model of Adaptive Fuzzy Neural Network for Optimal Membership Function

Contact Info

Product

Resources

About