Digital signal processing

The work starts with a short overview of grid requirements for photovoltaic (PV) systems and control structures of grid-connected PV power systems. Advanced control strategies for PV power systems are presented next, to enhance the integration of this technology. The aim of this work is to investigate the response of the three-phase PV systems during symmetrical and asymmetrical grid faults.

Anomaly detection plays a crucial role in various domains, including but not limited to cybersecurity, space science, finance, and healthcare. However, the lack of standardized benchmark datasets hinders the comparative evaluation of anomaly detection algorithms. In this work, we address this gap by presenting a curated collection of preprocessed datasets for spacecraft anomalies sourced from multiple sources. These datasets cover a diverse range of anomalies and real-world scenarios for the spacecrafts.

Noise recognition plays an essential role in human-computer interaction and various technological applications. However, identifying individual speakers remains a significant challenge, especially in diverse and acoustically challenging environments. This paper presents the Enhanced Multi-Layer Convolutional Neural Network (EML-CNN), a novel approach to improve automated speaker recognition from audio speech. The EML-CNN architecture features multiple convolutional layers and a dense block, finely tuned to extract unique voice signatures from English speech samples.

PV Solar Power System Under Partial Shading Irradiance Conditions

Operation of a photovoltaic (PV) solar array connected to a variable DC source to plot the l-V and P-V characteristics under partial shading irradiance conditions.