A new approach for the fault identi fi cation , localization , and classi fi cation in the power system

The recent structure of the monitoring, protection, and control of the power systems includes GPS timely synchronized measurement units (Phasor Measurement Units). With the implementation of these units, Wide-Area Monitoring, Protection and Control Systems are required to perform fast and efficient identification of the disturbances that may lead to cascade propagation and blackouts in the power system. The requirements furthermore enable appropriate actions, preventive and corrective measures to minimize effects of the occurring disturbances. This paper proposes the application of the discrete Teager Energy Operator for the power system fault identification, localization, and classification. Identification and localization of the disturbances are performed with the analysis of available signals with the application of the Teager Energy Operator and comparison of its peak values at several points in the system. The proposed classifier of the disturbances is based on the Teager Energy Operator analysis of available signals and values of indicator of active power unbalance at several points in the system. Simulations are performed in the New England 39 bus test system using DIgSILENT Power Factory software. The performance and the comparison of the applied techniques are assessed through a large number of the simulated faults for the specific fault type. Fault identification and localization results are compared with the results obtained in the analysis performed with Discrete Wavelet Transform and Hilbert-Huang Transform indicating on satisfactory performance of the proposed approach. Furthermore, the proposed approach provides notable results in the fault classification performed according to 141 simulated faults. Teager Energy Operator in the proposed method outperforms other techniques with less computational work and faster estimation, enabling the development of a relatively simple algorithm for the fast and efficient identification, localization, and classification of the disturbances in power system.