02635nam a22001457a 4500999001700000100007900017245012200096260002700218300001000245500202500255700004202280856003202322942001102354952012402365  c65891d65888  a Bhellar, Naveed Ahmeda16MPE15aSupervisor - Prof. DR. Aslam Pervez Memon  aAnalysis of HVDC transmission ststem using wavelet transform for proper and reliable solution of faultidentification   aNawabshahbQUESTc2019  a170p.  a
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.
  aDepartment of Electrical Engineering   uhttp://tinyurl.com/bdf2erzb  cTHESIS  00104070aRESEARCHbRESEARCHd2019-10-17l1oR/IMS-19pMP/51-619q2023-04-06r2023-04-06 00:00:00s2023-04-06yTHESIS