Fault Classification in a DG Connected Power System using Artificial Neural Network

Distributed generation is playing an important role in power system to meet the increased load demand. Integration of Distributed Generator (DG) to grid leads to various issues of   protection and control of power system structure.  The effect of the distributed generators to the grid is changes the fault current level, which makes the fault analysis more complex. From the different fault issues occurs in a distributed generator integrated power system, classification of fault remains as one of the most vital issue even after years of in-depth research. This paper emphasis on the classification of faults in DG penetrated power system using Artificial Neural Network (ANN). Because researchers are attempting to detect and diagnose these faults as soon as possible in order to avoid financial losses, this work aims to investigate the sort of fault that happened in the hybrid system. This paper proposed artificial neural network based approaches for fault disturbances in a microgrid made up of wind turbine generators, fuel cells, and diesel generator. The voltage signal is retrieved at the point of common coupling (PCC). The extracted data are used for training and testing purpose.  Artificial neural network technique is utilized for the classification of fault in the simulated model. Furthermore, performance indices (PIs) such as standard deviation and skewness are calculated for reduction of data size and better accuracy. Both the fault and parameters are varied to check the usefulness of the proposed method. Finally, the results are discussed and compared with different DG penetration

Detection of islanding and fault disturbances in microgrid using wavelet packet transform

Fast detection of islanding is very important for effective operation and control in distributed generation (DG) penetrated distribution networks. The islanding detection techniques such as passive, active, communication, and hybrid have their own merits and demerits. This paper proposed wavelet transform (WT) and wavelet packet transform (WPT) based techniques for detection of islanding and fault disturbances in a microgrid consisting of resources like wind turbine generator, fuel cell (FC), and microturbine. Voltage signal is extracted at the point of common coupling (PCC) and is passed through these detection techniques to obtain the time-frequency multi-resolution analysis. Further, to validate the graphical study, performance indices (PIs) like standard deviation and entropy are calculated for the disturbance detection using suitable selection of threshold. A comparative analysis using WT and WPT is presented in the form of graphical simulation as well as in terms of PIs to analyse their effectiveness and robustness under different operating conditions. It is observed that WPT shows better detection capability in comparison to WT even under 20-dB noisy scenarios.National Research Foundation (NRF)Accepted versionAuthors acknowledge the support for research from the National Research Foundation, Prime Minister’s Office, Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) programme

Hybrid nanogenerator for self-powered object recognition

Energy harvesting systems, including piezoelectric (PENG), triboelectric (TENG), and pyroelectric (PYNG) nanogenerator technologies, have emerged as one of the major future energy solutions. Energy harvesting eliminates the need for conventional batteries and encourages eco-friendly alternatives. This study reports hydrothermally synthesized BaTiO3 (BTO) particles with a tetragonal symmetry for hybrid energy harvesting. BTO particles are incorporated with PDMS at various wt% to form a flexible composite film. The 15 wt% BTO-PDMS composite/Al hybrid device (PENG-TENG) produces a peak voltage of 100 V, a current of 980 nA, and a charge of 17 nC, generating a peak power output of 33.64 μW at 100 MΩ. Furthermore, integrating this HNG (external hybridization) yielded an output of 101 V and 980 nA, demonstrating practical applicability. HNG is also employed to interact by touching various objects at different temperatures. The pyroelectric behavior of BTO allows direct thermal sensing of the object. The signals produced are processed using a convolutional neural network (CNN)-based object recognition system, which achieved a remarkable classification accuracy of 99.27% for various objects. External hybridization improves energy efficiency, representing a huge step forward in sustainable technology applications. This research paves the way for developing hybrid energy harvesters and can be employed further for extremely precise battery-free object recognition systems. This unique hybrid nanogenerator, which combines pyroelectric, piezoelectric, and triboelectric components, represents a new method of self-powered object detection. External hybridization improves energy efficiency, representing a huge step forward in sustainable technology applications