02508nam a22001337a 4500 65891 65888 Bhellar, Naveed Ahmed 16MPE15 Supervisor - Prof. DR. Aslam Pervez Memon Analysis of HVDC transmission ststem using wavelet transform for proper and reliable solution of faultidentification Nawabshah QUEST 2019 170p. ABSTRACT The most efficient type of energy is electrical energy amongst all types of energy therefore, electrical power transmission must be so efficient in terms of electrical losses and capital cost from power generating station to load center. In this regard High Voltage Direct Current (HVDC) is most suitable option in both aspects. Nowadays, HVDC is vital choice to transmit power due to its high power carrying capacity as compared to High Voltage Alternating Current (l-IVAC) due to so many technical and economic advantages. Due to its high power transmission capacity, it is also subject to severe faults like DC fault and AC fault. Fault penetration is more severe in HVDC than HVAC due to power electronics based converters. h is necessary to protect the power converters and flVDC transformers due to their capital cost. Signal processing techniques help us to design protection system in a better way. Different signal processing techniques are being adopted such as Artificial Neural Network (ANN), Short Time Fourier Transform (STFT), Traveling wave and Wavelet Transformation (WT) Analysis. The HVDC fault problems like DC fault on rectifier side and L-G fault, L-L fault, and L-L-L fault occur on inverter side in the electrical transmission system. These faults can be sorted out and overcome by using proper and reliable fault detection and protection. The detection of these faults requires time-frequency signal processing technique in order to protect the system with reliability . This work proposes a simple technique of time-frequency analysis for fault identification of HVDC faults using a software approach of Matlab/Simulink . The simulation results show that the WT clearly indicates the occurrence of electrical faults within time-frequency ranges. This technique requires selection of suitable mother wavelet which uses haar, daubechies, symlets, coiflets, biorthogonal, reverse-biorthogonal and dmey with multiresolution analysis (MRA) algorithm of WT. Department of Electrical Engineering http://tinyurl.com/bdf2erzb THESIS 0 0 0 0 RESEARCH RESEARCH 2019-10-17 1 R/IMS-19 MP/51-619 2023-04-06 2023-04-06 00:00:00 2023-04-06 THESIS