Fault Detection Method for Energy Routing Nodes of Smart Grids Oriented to Electricity Information Security

doi:10.23940/ijpe.19.12.p23.33043311

Abstract

Abstract: Current fault detection methods based on the immune mechanism for energy routing nodes of power grids have low peak strengths of estimated fault signals, resulting in a low probability of fault detection and fault location accuracy and a lack of fault isolation performance. A fault detection method for energy routing nodes of smart grids oriented to power information security is proposed. According to the fault characteristics of energy routing nodes of power grids, fault diagnosis criteria are given. The necessity and sufficiency of the fault diagnosis criteria are proven. The peak strengths of fault signals are estimated, and fault detection is realized. The fault line is judged by the natural frequency value of the fault traveling wave, and the traveling wave propagation that reflects the fault points is utilized. The intrinsic frequency value of the path can accurately calculate the fault distance and obtain the exact location of the fault. The fault isolation is accomplished by using the distributed power supply and combining it with the current power grid structure with the switch position. Experiments show that this method is superior to the current method in peak estimation strength, fault detection rate, and fault location, and the highest fault detection rate can reach 99%. The implementation of the proposed method can effectively improve the current situation of power grid fault detection and provide a more scientific basis for the development of this field.

Key words: smart grid, energy routing node, fault detection, power information security

Wenpeng Li, Sance Gao, Renjie Ding, Yanjun Hao, and Cheng Yang. Fault Detection Method for Energy Routing Nodes of Smart Grids Oriented to Electricity Information Security [J]. Int J Performability Eng, 2019, 15(12): 3304-3311.

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References

[1] Y. Lin, X. Zhu, Z. Zheng, Z. Dou,R. L. Zhu, “The Individual Identification Method of Wireless Device based on Dimensionality Reduction and Machine Learning,” Journal of Supercomputing, Vol. 75, No. 6, pp. 3010-3027, 2019
[2] Y. Lin, C. Wang, J. X. Wang,Z. Dou, “A Novel Dynamic Spectrum Access Framework based on Reinforcement Learning for Cognitive Radio Sensor Networks,” Sensors, Vol. 16, No. 10, pp. 1675, 2016
[3] J. Sneath and A. D. Rajapakse, “Fault Detection and Interruption in an Earthed HVDC Grid using ROCOV and Hybrid DC Breakers,” IEEE Transactions on Power Delivery, Vol. 31, No. 3, pp. 973-981, 2015
[4] P. M. Papadopoulos, L. Hadjidemetriou, E. Kyriakides,M. M. Polycarpou, “Robust Fault Detection, Isolation and Accommodation of Current Sensors in Grid Side Converters,” IEEE Transactions on Industry Applications, Vol. 10, No. 99, pp. 1-10, 2016
[5] X. Y. Tong, R. H. Wang, H. B. Wang,W. Lian, “Online Detection Algorithm of Power System Fault using Limited PMUs,” Power System Protection and Control, Vol. 44, No. 19, pp. 49-55, 2016
[6] A. Brancaccio and R. Solimene, “Fault Detection in Dielectric Grid Scatterers,” Optics Express, Vol. 23, No. 7, pp. 8200-8215, 2016
[7] T. Neumann, T. Wijnhoven, G. Deconinck,I. Erlich, “Enhanced Dynamic Voltage Control of Type 4 Wind Turbines During Unbalanced Grid Faults,” IEEE Transactions on Energy Conversion, Vol. 30, No. 4, pp. 1650-1659, 2015
[8] G. Yang and S. Liu, “Distributed Cooperative Algorithm for k-M Set with Negative Integer k by Fractal Symmetrical Property,”International Journal of Distributed Sensor Networks, Vol. 4, pp. 1-7, 2014
[9] U. M. Choi, J. S. Lee, F. Blaabjerg,K. B. Lee, “Open-Circuit Fault Diagnosis and Fault-Tolerant Control for a Grid-Connected NPC Inverter,” IEEE Transactions on Power Electronics, Vol. 31, No. 10, pp. 7234-7247, 2015
[10] F. B.Youssef and L. Sbita, “Sensors Fault Diagnosis and Fault Tolerant Control for Grid Connected PV System,” International Journal of Hydrogen Energy, Vol. 42, No. 13, pp. 8962-8971, 2016
[11] C. Guo, Y. Liu, C. Zhao, X. Wei,W. Xu, “Power Component Fault Detection Method and Improved Current Order Limiter Control for Commutation Failure Mitigation in HVDC,” IEEE Transactions on Power Delivery, Vol. 30, No. 3, pp. 1585-1593, 2015
[12] N. L. Georgijevic, M. V. Jankovic, S. Srdic,Z. Radakovic, “The Detection of Series Arc Fault in Photovoltaic Systems based on the Arc Current Entropy,” IEEE Transactions on Power Electronics, Vol. 31, No. 8, pp. 5917-5930, 2015
[13] L. Zhu, J. Li, Y. Liu,S. Ji, “Initial Features of the Unintended Atmospheric Pressure DC Arcs and Their Application on the Fault Detection,” IEEE Transactions on Plasma Science, Vol. 45, No. 4, pp. 742-748, 2017
[14] M. Dong, H. Dong, L. Wang, J. Yang, L. Li,Y. Wang, “A Simple Open-Circuit Detection Strategy for a Single-Phase Grid-Connected pv Inverter Fed from Power Optimizers,” IEEE Transactions on Power Electronics, Vol. 6, No. 99, pp. 1-1, 2017
[15] Y. Fan, C. Li, W. Zhu, X. Zhang, L. Zhang,M. Cheng, “Stator Winding Interturn Short-Circuit Faults Severity Detection Controlled by OW-SVPWM Without CMV of a Five-Phase FTFSCW-IPM,” IEEE Transactions on Industry Applications, Vol. 53, No. 1, pp. 194-202, 2016
[16] M. M. A.Mahfouz and M. A. H. El-Sayed, “Smart Grid Fault Detection and Classification with Multi-Distributed Generation based on Current Signals Approach,” IET Generation Transmission amd Distribution, Vol. 10, No. 16, pp. 4040-4047, 2016
[17] M. Liu, S. Liu, W. Fu,J. T. Zhou, “Distributional Escape Time Algorithm based on Generalized Fractal Sets in Cloud Environment,” Chinese Journal of Electronics, Vol. 24, No. 1, pp. 124-127, 2015
[18] S. Liu, “Introduction of Key Problems in Long-Distance Learning and Training,” Mobile Networks and Applications, Vol. 24, No. 1, pp. 1-4, 2019