<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In this paper, we preprocess the equipment data using by exponential smoothing method, fit the fault information by using the distribution model, and evaluate the fitting degree of the function with the help of the K-S test to derive the equipment fault information function. In accordance with the correlation mapping between the distribution network health index and the existence of faults, the health index of the distribution network equipment in the region before and after the self-healing of faults is comprehensively calculated, and the state assessment of the equipment is carried out. Before and after the fault self-healing of the IEEE33 node system, the system loss is reduced by 58.89%, as evidenced by the results. Before and after the fault self-healing, Condition 1: the line health index ranges from 1.317 to 3.777, and the transformer health index ranges from 2.011 to 4.646. Condition 2: the line health index ranges from 2.284 to 4.059, and the transformer health index ranges from 1.653 to 4.047, respectively. And Condition 3: the health indices all increase by varying from 0.711 to 2.067. Localized equipment degradation has a significant impact on the overall health of the network, with lines affecting the system to a greater extent than transformers. To summarize, the proposed health index state assessment method can effectively determine the health state of distribution networks before and after fault self-healing.</p>
</abstract>

In this paper, we preprocess the equipment data using by exponential smoothing method, fit the fault information by using the distribution model, and evaluate the fitting degree of the function with the help of the K-S test to derive the equipment fault information function. In accordance with the correlation mapping between the distribution network health index and the existence of faults, the health index of the distribution network equipment in the region before and after the self-healing of faults is comprehensively calculated, and the state assessment of the equipment is carried out. Before and after the fault self-healing of the IEEE33 node system, the system loss is reduced by 58.89%, as evidenced by the results. Before and after the fault self-healing, Condition 1: the line health index ranges from 1.317 to 3.777, and the transformer health index ranges from 2.011 to 4.646. Condition 2: the line health index ranges from 2.284 to 4.059, and the transformer health index ranges from 1.653 to 4.047, respectively. And Condition 3: the health indices all increase by varying from 0.711 to 2.067. Localized equipment degradation has a significant impact on the overall health of the network, with lines affecting the system to a greater extent than transformers. To summarize, the proposed health index state assessment method can effectively determine the health state of distribution networks before and after fault self-healing.

Analysis of health state before and after fault self-healing based on a high percentage of distributed distribution grid systems

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

In this paper, we preprocess the equipment data using by exponential smoothing method, fit the fault information by using the distribution model, and evaluate...

Analysis of health state before and after fault self-healing based on a high percentage of distributed distribution grid systems

Online veröffentlicht: 07. Juni 2024

Seitenbereich: -

Eingereicht: 09. Feb. 2024

Akzeptiert: 18. Apr. 2024

DOI: https://doi.org/10.2478/amns-2024-1397

Schlüsselwörter
Exponential smoothing method, K-S test, Health state assessment, Fault self-healing, Distribution modeling

© 2024 Zhongqiang Zhou et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Analysis of health state before and after fault self-healing based on a high percentage of distributed distribution grid systems

Online veröffentlicht: 07. Juni 2024

Seitenbereich: -

Eingereicht: 09. Feb. 2024

Akzeptiert: 18. Apr. 2024

DOI: https://doi.org/10.2478/amns-2024-1397

SchlüsselwörterExponential smoothing method, K-S test, Health state assessment, Fault self-healing, Distribution modeling

© 2024 Zhongqiang Zhou et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
Exponential smoothing method, K-S test, Health state assessment, Fault self-healing, Distribution modeling