As the future electric power grid will be driven by distributed renewable energy sources, the deployment of grid-connected power converters will also grow to enable seamless grid and energy source interaction. To provide the reliable operation of these converters, the estimation of fundamental grid parameters is important. The most common estimation techniques are a phase-locked loops (PLL) and a frequency-locked loops (FLL). However, those techniques encounter challenges in conducting parameter estimation when the input signal is unbalanced due to DC-offset, harmonics, signal sags, and frequency and phase variations. This paper presents an enhanced FLL loop enriched with an additional loop for estimation and rejection of the DC-offset. Active and reactive power calculations in grid-connected microgrids by using the modified FLL loops with DC-offset rejection is a novel application introduced in this paper. Experimental verification has demonstrated that the enhanced FLL loop provides fast and reliable parameter estimation as well as stable and robust power calculations, even in the presence of a DC-offset.

This paper presents the implementation of the Binary Search Algorithm (BSA) to determine the Maximum Power Point (MPP) of a photovoltaic (PV) system under variable weather conditions. Additionally, the conventional well-known Perturb and Observe (P&O) algorithm is also implemented to be compared with the binary search based Maximum Power Point Tracking (MPPT) algorithm. Both algorithms are implemented in real time in MATLAB/Simulink environment. The experimental study is performed using the two 260 W series connected PV modules, the buck converter, and Humusoft MF 634 card to enable real-time operation. The value of the duty cycle for the buck converter is being updated in each step moving the operation point closer to MPP. The obtained experimental results demonstrate that the binary search based MPPT algorithm is more efficient and accurate when compared to the P&O MPPT algorithm.

For many years now MATLAB has been considered the academia standard when it comes to technical computing and simulation. Many university and college courses rely on multiple tool-boxes and ad-dons that MATLAB provides. With its relatively simple syntax, and large user community it has been, for so many years, a logical choice for academia. However, more often than not, students fresh out of university have been facing a new software that has very quickly become an industry standard in many areas of electrical engineering. On a simple example of DC motor control, this paper aims to showcase advantages of early adoption and using LabViewfor programming and simulation purposes in academia.

Gesture recognition is a field of study that involves recognizing human movements and gestures through sensors. In this paper, a basic gesture recognition system is proposed that uses three Time-of-Flight (TOF) VL53L0X distance sensors positioned in an L-shape able to recognize gesture through cover glass and up to 40 cm of distance. The system is capable of recognizing four basic gestures: swipe right, swipe left, swipe up and swipe down. This system can be applied in various fields such as Human-Computer Interaction (HCI), Gaming, Virtual Reality (VR) and Robotics, this paper will focus on the implementation and evaluation of the proposed system. The inspiration for the system is to simplify interaction with medical panel PCs and monitors while improving the hygienic aspect of the same, while taking into consideration data privacy.