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• W2000577495 abstract "The safe operation of AC-DC systems requires the Monitoring of appropriate system signals, the accuracy and rapid classification of any perturbations so that protective control decisions can be made. In case of fast acting HVDC transmission system, such decisions must often be made within tens of milliseconds to guarantee safe operation from disturbances such as the common commutation failures. The detection and fast clearance of faults are important for safe and optimal operation of power systems. Due to the integration of fast acting HVDC systems in ac power systems, it is necessary to detect, classify and clear the faults as fast as possible. The source and cause of disturbances or faults must be known before appropriate mitigation action be taken. For secure operation of a system, a feasible approach is to monitor the signals so that accurate and rapid classification of fault is possible for making correct protective control decisions. However, fast and reliable fault identification is still a big challenge. It is not easy to identify HVDC faults by using pure frequency or pure time domain based methods. The pure frequency domain based methods are not suitable for the time-varying transients and the pure time domain based methods are very easily influenced by noise. Recently, due to advancement of power electronics technology, High Voltage Direct Current (HVDC) transmission technology has been utilized to identify the faults in power system. The HVDC Transmission system is very reliable, flexible and cost effective. Advances in artificial intelligence techniques such as Fuzzy, Neural and ANN etc. and Power Semiconductor devices have made tremendous impact in the identifying of faults in HVDC system. A case is made to present overview of the artificial intelligence techniques to identify the faults in HVDC transmission system." @default.
• W2000577495 created "2016-06-24" @default.
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• W2000577495 date "2012-01-01" @default.
• W2000577495 modified "2023-10-14" @default.
• W2000577495 title "Fault identification in HVDC using artificial intelligence &#x2014; Recent trends and perspective" @default.
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• W2000577495 doi "https://doi.org/10.1109/epscicon.2012.6175256" @default.
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