Fault Detection in HVDC System with Gray Wolf Optimization Algorithm Based on Artificial Neural Network

• DOI: 10.3390/en15207775
• Publication Date: 2022-10-21T00:35:55Z
• Authors: Raad Salih Jawad; Hafedh Abid
• Keywords: Rectifier (neural networks); High-voltage direct current

Various methods have been proposed to provide the protection necessitated by high voltage direct current system. In this field, most of research is confined various types DC and AC line faults a maximum two switching converter faults. The main contribution study use new method for fault detection in HVDC systems, using gray wolf optimization along with artificial neural networks. Under method, help faulted non-faulted signals, features signals are extracted much shorter period signal. Subsequently, differences detected an network. studied system, behavior rectifier, its controllers required filters completely modeled. study, other methods, such as network, radial basis function, learning vector quantization, self-organizing map, were tested compared method. To demonstrate performance accuracy, sensitivity, precision, Jaccard, F1 score calculated obtained 99.00%, 99.24%, 98.74%, 98.00%, 98.99%, respectively. Finally, according simulation results, it became evident that could be suitable systems.