HIGH IMPEDENCE FAULT DETECTION IN
DISTRIBUTED SYSTEM UNDER DISTRIBUTED
MAHANTESH CHIKKADESAIELECTRICAL AND ELECTRONICS DEPARTMENTKLS’s VISHWANATHRAO DESHPANDE RURAL INSTITUTE OF TECHNOLOGYHALIYAL.
Causes for HIFs
Methods to extract useful information from these high frequency components or harmonics are: 1)Fourier transform 2)Wavelet transform 3)Artificial neural network 4)Fuzzy logic or combination of these
HIF ModellingEmanuel arc model-proposed in 2003 The fault current flows towards the ground if the phase voltage >positive DC voltage Vp,The fault current reverses,if the phase voltage< negative DC voltage Vn,no fault current flows,for values of the phase voltage between Vn&Vp.
DISCRETE WAVELET TRANSFORM Feature extractionTransient voltages and currents during fault carry high frequency component or harmonics which carry important information regarding type and location of fault. The signal of the desired componentNumber of decomposition
Multi-resolution analysisLow frequency signals called approximations
High frequency signals called details
NEURAL NETWORKReliable method Algorithms use the gradient of the performance function to determine weights for better performance.
The gradient is determined using a technique called back propagation which involves performing computations backwards through the network.
The Implementation of Proposed Methodology
Conditions considered for training patterns data generationAnd Simulated wave forms
FEATURE EXTRACTIONUsed to extract raw fault signals
Outputs of this are inputs for ANN
Magnitude of transient energy of fault signal>non fault signal due to higher frequency
ADVANTAGES:Low cost.Multiple fault locations.Using standard back propagation approach was used to locate fault in a distribution network connected with distributed generators.
DISADVANTAGES:ANN is highly dependent on amount and quality of the well-trained ANN algorithm.Limited amount of information, or inaccurate information, will affect the performance of the algorithmIt has slow convergence ANN algorithm needs to be retrained whenever there are changes in the system
CONCLUSION This paper presents the application of wavelet multi resolution analysis in combination with artificial neural network for accurate classification and locating the fault.
Capabilities of neural network in pattern classification were utilized to classify the faults.
After successful classification details of fault signals are used to locate the fault. Simulation studies were performed for different fault conditions with faults at different phases.
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