Wavelet-ANN Based Detection of Fault Location of Renewable Energy Sources Integrated Power Transmission System

TheWavelet interconnectionInterconnection ofRenewable energy sources non-conventionalNon-Conventional energyFault location sourcesEnergy source necessitatesPower supplyingTransmission system uninterruptedUninterrupted and high power qualityPower quality to consumersConsumer located in a vast geothermalGeothermal area to meet the increased demand. Because of these aspects, the study of powerPower flows, faultFault calculations, and protection aspects in the presence of renewable energy sourcesRenewable energy sources assumes a vital role. The complexity of the power systemPower systems increases with an increase in penetration of conventionalConventional and non-conventionalNon-Conventional energy sourcesEnergy source to meet the increasing load demandLoad demand over hundreds of kilometers long. There will be protection challenges due to different powerPower ratings and fault current levelsFault current levels of both types of sources. Thus, it is necessary to develop a suitable algorithmAlgorithms to identify the location of the faultFault accurately in the presence of conventionalConventional and renewable energy sourcesRenewable energy sources integrated powerPower transmission systemsTransmission system for long and short distances. In this chapter, a novel WaveletWavelet-Artificial Neural NetworkArtificial Neural Network (ANN) based method is developed where the waveletWavelet multi-resolution analysisMulti-Resolution Analysis is used to obtainDetailed Coefficients Detailed (D1) coefficients from the faultFault current signalsCurrent signals and this data is used for training and testing ANN. The fault locationFault location is carried out in the presence of conventionalConventional and renewable energy sourcesRenewable energy sources on a 4-bus connected transmission systemTransmission system. A 4-bus transmission systemTransmission system is simulated using simulation softwareSimulation software and the analysisAnalysis of faultFault is done by using current signalsCurrent signals of various faultsFault. This analysisAnalysis is worked out almost within half a cycle of current signalsCurrent signals. The proposed waveletWavelet-ANN-based algorithmAlgorithms is tested for all faultFault conditions with different powerPower ratings of conventionalConventional and renewable energy sourcesRenewable energy sources at various distances. The proposed method provided the best results for the fault locationFault location at different fault impedancesFault impedances, generator capacities, and fault inception anglesFault inception angles.