<abstract xmlns="http://www.w3.org/1999/xhtml">
<p>In this paper, a decision model for grid fault diagnosis based on multi-source information fusion is established, and the probability of component fault is obtained by using the change of switching quantity characteristic information and electrical quantity characteristic information during grid fault, and the static fault degree obtained by switching quantity information and the voltage and current energy distortion degree and fault degree obtained by electrical quantity information is used as independent evidence bodies, which are fused by improved D-S evidence fusion technique, and the fused The results are decided by improved fuzzy C-mean decision model to finally determine the fault components. Then, the optimal control of the grid is studied, and the corresponding self-healing control model is established according to the different operating conditions of the system so as to propose a self-healing control strategy of the microgrid based on the improved particle swarm algorithm. After analysis and verification, the accuracy of the grid fault diagnosis method proposed in this paper reaches 0.9681, and the diagnosis results are consistent with the pre-defined fault elements compared with FCM. The system network loss decreases from 0.146 kW to 0.031 kW, and the maximum power supply capacity increases from 1.574 to 2.468 after using the improved particle swarm algorithm-based microgrid self-healing control strategy. Therefore, the method in this paper can improve the reliability of grid operation and resist the risk of accidents.</p>
</abstract>

In this paper, a decision model for grid fault diagnosis based on multi-source information fusion is established, and the probability of component fault is obtained by using the change of switching quantity characteristic information and electrical quantity characteristic information during grid fault, and the static fault degree obtained by switching quantity information and the voltage and current energy distortion degree and fault degree obtained by electrical quantity information is used as independent evidence bodies, which are fused by improved D-S evidence fusion technique, and the fused The results are decided by improved fuzzy C-mean decision model to finally determine the fault components. Then, the optimal control of the grid is studied, and the corresponding self-healing control model is established according to the different operating conditions of the system so as to propose a self-healing control strategy of the microgrid based on the improved particle swarm algorithm. After analysis and verification, the accuracy of the grid fault diagnosis method proposed in this paper reaches 0.9681, and the diagnosis results are consistent with the pre-defined fault elements compared with FCM. The system network loss decreases from 0.146 kW to 0.031 kW, and the maximum power supply capacity increases from 1.574 to 2.468 after using the improved particle swarm algorithm-based microgrid self-healing control strategy. Therefore, the method in this paper can improve the reliability of grid operation and resist the risk of accidents.

Research on Hazard Analysis and Control Strategy of Power System Dispatching Operation Based on Information Fusion Technology

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

In this paper, a decision model for grid fault diagnosis based on multi-source information fusion is established, and the probability of component fault is...

Research on Hazard Analysis and Control Strategy of Power System Dispatching Operation Based on Information Fusion Technology

Online veröffentlicht: 30. Sept. 2023

Seitenbereich: -

Eingereicht: 01. Nov. 2022

Akzeptiert: 10. Apr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00410

Schlüsselwörter
Information fusion technology, Power system scheduling, D-S evidence fusion, Fuzzy C-means, Particle swarm algorithm.

© 2023 Xiangzhen He et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Research on Hazard Analysis and Control Strategy of Power System Dispatching Operation Based on Information Fusion Technology

Online veröffentlicht: 30. Sept. 2023

Seitenbereich: -

Eingereicht: 01. Nov. 2022

Akzeptiert: 10. Apr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00410

SchlüsselwörterInformation fusion technology, Power system scheduling, D-S evidence fusion, Fuzzy C-means, Particle swarm algorithm.

© 2023 Xiangzhen He et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Information fusion technology, Power system scheduling, D-S evidence fusion, Fuzzy C-means, Particle swarm algorithm.