Fault Diagnosis of Distributed Energy Distribution Network Based on PSO-BP

Xiaokun Han; Dongming Jia; Xiang Dong; Dongwei Chen

doi:10.4108/ew.7242

Authors

Xiaokun Han State Grid Beijing Electric Power Maintenance Branch
Dongming Jia State Grid Beijing Electric Power Maintenance Branch
Xiang Dong State Grid Beijing Electric Power Maintenance Branch
Dongwei Chen State Grid Beijing Electric Power Maintenance Branch

DOI:

https://doi.org/10.4108/ew.7242

Keywords:

Backpropagation neural network, Particle swarm algorithm, Dynamic coefficients, Acceleration constants, Distribution network, Faults

Abstract

With the increasing scale of distribution network at distribution time, its complexity grows geometrically, and its fault diagnosis becomes more and more difficult. Aiming at the slow convergence and low accuracy of traditional backpropagation neural network in dealing with single-phase ground faults, the study proposes a backpropagation neural network based on improved particle swarm optimization. The model optimizes the weights and acceleration constants of the particle swarm algorithm by introducing dynamic coefficients to enhance its global and local optimization seeking ability. It is also applied in optimizing the parameters of backpropagation neural network and constructing the routing model and ranging model for fault diagnosis about distributed energy distribution network. The simulation results revealed that the maximum absolute error of the improved method is 0.08. While the maximum absolute errors of the traditional backpropagation neural network and the particle swarm optimized backpropagation neural network were 0.65 and 0.10, respectively. The fluctuation of the relative errors of the research method was small under different ranges of measurements. At 8.0 km, the minimum relative error was 0.39% and the maximum relative error was 2.81%. The results show that the improved method proposed in the study significantly improves the accuracy and stability of fault diagnosis and localization in distribution networks and is applicable to complex distribution network environments. The method has high training efficiency and fault detection capability and provides an effective tool for distribution network fault management.

Downloads

Download data is not yet available.

References

[1] G. S. Eldeghady, H. A. Kamal, and M. A. M. Hassan, “Fault diagnosis for PV system using a deep learning optimized via PSO heuristic combination technique,” Electrical Engineering, vol. 105, no. 4, pp. 2287–2301, Mar. 2023. doi: 10.1007/s00202-023-01806-6.

[2] S. Qi, X. Lu, H. Liu, L. Zhu, and F. Wang, “Application of genetic algorithm optimization based BP Neural Network in fault diagnosis of distribution network,” Journal of Electric Power Science and Technology, vol. 38, no. 3, pp. 182–187+196, Mar. 2023. doi: 10.19781/j.issn.1673-9140.2023.03.020.

[3] M. Zhang, J. Fang, H. Wang, F. Hao, X. Lin, and Y. Wang, “Application of graphene gas sensor technological convergence PSO-SVM in distribution transformer insulation condition monitoring and fault diagnosis,” Materials Express, vol. 13, no. 10, pp. 1743–1752, 2023. doi: 10.1166/mex.2023.2517..

[4] Y. Li and Y. Li, “Transformer Fault Diagnosis Method based on PSO-GMNN Model,” Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering), vol. 16, no. 4, pp. 417–425, 2023. doi: 10.2174/2352096516666221222164311.

[5] X. Tan, G. Ao, and W. Li, “RESEARCH ON THE DIAGNOSIS OF DISTRIBUTION NETWORK FAULT DATA USING A FAULT PREDICTION MODEL,” International Journal of Mechatronics and Applied Mechanics, vol. 13, no. 1, pp. 112–118, 2023. doi: 10.1109/ICCOMM.2010.5509068.

[6] C. Ling, T. Li, M. Lu, Y. Wu, X. Zhou, Y. Su, and X. Guo, “Reliability Prediction of the Distribution Network Based on Wavelet Neural Network with Quantum Particle Swarm Optimization Algorithm,” Electric Power Components and Systems, vol. 51, no. 4, pp. 398–408, Feb. 2023. doi: 10.1080/15325008.2023.2173828.

[7] P. Zhang, Z. Cui, Y. Wang, and S. Ding, “Application of BPNN optimized by chaotic adaptive gravity search and particle swarm optimization algorithms for fault diagnosis of electrical machine drive system,” Electrical Engineering, vol. 104, no. 2, pp. 819–831, Jun. 2022. doi: 10.1007/s00202-021-01335-0.

[8] Y. Liu, J. Kang, C. Guo, and Y. Bai, “Diesel engine small-sample transfer learning fault diagnosis algorithm based on STFT time–frequency image and hyperparameter autonomous optimization deep convolutional network improved by PSO–GWO–BPNN surrogate model,” Open Physics, vol. 20, no. 1, pp. 993–1018, Oct. 2022. doi: 10.1515/phys-2022-0197.

[9] P. D. Raval and A. S. Pandya, “A hybrid PSO-ANN-based fault classification system for EHV transmission lines,” IETE Journal of Research, vol. 68, no. 4, pp. 3086–3099, May 2022. doi: 10.1080/03772063.2020.1754299.

[10] M. Shafiullah, M. A. Abido, and A. H. Al-Mohammed, “Intelligent fault diagnosis for distribution grid considering renewable energy intermittency,” Neural Computing and Applications, vol. 34, no. 19, pp. 16473–16492, May 2022. doi: 10.1007/s00521-022-07155-y.

[11] N. S and W. Ying, “Research on PSO-RBF Traction Transformer Fault Diagnosis Based on Adam Optimization,” Journal of Electrical Engineering, vol. 18, no. 4, pp. 209–216, Jan. 2024. doi: 10.11985/2023.04.023.

[12] L. Yi, J. Long, J. Huang, X. Xu, W. Feng, and H. She, “Fault diagnosis of oil-immersed transformer based on MGTO-BSCN,” Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6021–6034, Apr. 2023. doi: 10.3233/JIFS-223443.

[13] Y. Xing, B. Wang, Z. Gong, Z. Hou, F. Xi, G. Mou, Q. Du, F. Gao, and K. Jiao, “Data-driven fault diagnosis for PEM fuel cell system using sensor pre-selection method and artificial neural network model,” IEEE Transactions on Energy Conversion, vol. 37, no. 3, pp. 1589–1599, Sep. 2022. doi: 10.1109/TEC.2022.3143163.

[14] S. Song, S. Zhang, W. Dong, X. Zhang, and W. Ma, “A new hybrid method for bearing fault diagnosis based on CEEMDAN and ACPSO-BP neural network,” Journal of Mechanical Science and Technology, vol. 37, no. 11, pp. 5597–5606, Nov. 2023. doi: 10.1007/s12206-023-1003-7.

[15] L. Ma, G. Wang, P. Zhang, and Y. Huo, “Fault Diagnosis Method of Circuit Breaker Based on CEEMDAN and PSO‐GSA‐SVM,” IEEJ Transactions on Electrical and Electronic Engineering, vol. 17, no. 11, pp. 1598–1605, Jul. 2022. doi: 10.1002/tee.23666..

[16] R. S. Dornelas and D. A. Lima, “Correlation Filters in Machine Learning Algorithms to Select De-mographic and Individual Features for Autism Spectrum Disorder Diagnosis,” Journal of Data Science and Intelligent Systems, vol. 3, no. 1, pp. 7–9, Jun. 2023. doi: 10.47852/bonviewJDSIS32021027.

[17] S. Tang, Y. Zhu, and S. Yuan, “Intelligent fault diagnosis of hydraulic piston pump based on deep learning and Bayesian optimization,” ISA transactions, vol. 129, no. 1, pp. 555–563, Jun. 2022. doi: 10.1016/j.isatra.2022.01.013.

[18] Z. You and C. Lu, “A heuristic fault diagnosis approach for electro-hydraulic control system based on hybrid particle swarm optimization and Levenberg–Marquardt algorithm,” Journal of Ambient Intelligence and Humanized Computing, vol. 14, no. 11, pp. 14873–14882, Aug. 2023. doi: 10.1007/s12652-018-0962-5.

[19] T. C. S. Rao, S. S. Tulasi Ram, and J. B. V. Subrahmanyam, “Neural network with adaptive evolutionary learning and cascaded support vector machine for fault localization and diagnosis in power distribution system,” Evolutionary Intelligence, vol. 15, no. 2, pp. 1171–1182, Feb. 2022. doi: 10.1007/s12065-020-00359-y.

[20] L. Zhang, Z. Zhao, D. Zhang, C. Luo, and C. Li, “Particle swarm optimization pattern recognition neural network for transmission lines faults classification,” Intelligent Data Analysis, vol. 26, no. 1, pp. 189–203, Jan. 2022. doi: 10.3233/IDA-205695.