Safety Protection Technology of Power Monitoring System Based on Feature Extraction Algorithm
The network security protection technology of power monitoring systems is of great significance. Aiming at the power network monitoring and protection technology problem, the paper proposes an active monitoring and protection strategy based on a feature extraction algorithm. The algorithm can calculate the transfer degree of security incidents based on evidence theory. First, the paper obtains a specific state transition diagram based on the security topology of a generalized random power communication network. Then, we analyze the relationship between power system information security and engineering security based on the system’s operating results and feature extraction algorithms. The experimental results demonstrate the rapid effectiveness of this method.
Nasir, V., Cool, J., & Sassani, F. Acoustic emission monitoring of sawing process: artificial intelligence approach for optimal sensory feature selection. The International Journal of Advanced Manufacturing Technology. 102(9), pp. 4179–4197, 2019.
Liu, S., You, S., Yin, H., Lin, Z., Liu, Y., Yao, W., & Sundaresh, L. Model-free data authentication for cyber security in power systems. IEEE Transactions on Smart Grid. 11(5), pp. 4565–4568, 2020.
Ghadimi, N., Akbarimajd, A., Shayeghi, H., & Abedinia, O. Application of a new hybrid forecast engine with feature selection algorithm in a power system. International Journal of Ambient Energy. 40(5), pp. 494–503, 2019.
Michau, G., Hu, Y., Palmé, T., & Fink, O. Feature learning for fault detection in high-dimensional condition monitoring signals. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability. 234(1), pp. 104–115, 2020.
Guo, W., Li, B., & Zhou, Q. An intelligent monitoring system of grinding wheel wear based on two-stage feature selection and Long Short-Term Memory network. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture. 233(13), pp. 2436–2446, 2019.
Lai, C. F., Chien, W. C., Yang, L. T., & Qiang, W. LSTM and edge computing for big data feature recognition of industrial electrical equipment. IEEE Transactions on Industrial Informatics. 15(4), pp. 2469–2477, 2019.
Akrivopoulos, O., Amaxilatis, D., Mavrommati, I., & Chatzigiannakis, I. Utilising fog computing for developing a person-centric heart monitoring system. Journal of Ambient Intelligence and Smart Environments. 11(3), pp. 237–259, 2019.
Sassi, P., Tripicchio, P., & Avizzano, C. A. A smart monitoring system for automatic welding defect detection. IEEE Transactions on Industrial Electronics. 66(12), pp. 9641–9650, 2019.
Kumar, P., & Hati, A. S. Review on machine learning algorithm based fault detection in induction motors. Archives of Computational Methods in Engineering. 28(3), pp. 1929–1940, 2021.
Selvarajan, S., Shaik, M., Ameerjohn, S., & Kannan, S. Mining of intrusion attack in SCADA network using clustering and genetically seeded flora-based optimal classification algorithm. IET Information Security. 14(1), pp. 1–11, 2020.
Mohanraj, T., Shankar, S., Rajasekar, R., Deivasigamani, R., & Arunkumar, P. M. Tool condition monitoring in the milling process with vegetable based cutting fluids using vibration signatures. Materials Testing. 61(3), pp. 282–288, 2019.
Wu, X., Han, X., & Liang, K. X. Event-based non-intrusive load identification algorithm for residential loads combined with underdetermined decomposition and characteristic filtering. IET Generation, Transmission & Distribution. 13(1), pp. 99–107, 2019.