Drowsiness has become a significant contributing factor to traffic accidents in modern times, posing a major concern to society. Driver fatigue or sleepiness leads to decreased reaction time, diminished attention, and compromised decision-making abilities, thereby affecting the overall driving experience. This paper addresses this issue by proposing a drowsiness detection system based on image processing, utilizing a cascade of classifiers built on Haar-like features. The system effectively detects the eyes, allowing for determination of eye closure or opening, which serves as an indicator of driver drowsiness.
Voltage instability is a serious condition that can occur in a power system. An imbalance in reactive power, inadequate utilization of voltage control devices, loss of a component or an abrupt rise in load demand can cause this entire disturbance which leads a system to blackout, either partial or complete. In order to avoid the condition of voltage collapse, we need to predict the state of buses in the system so that we can prevent the occurrence of major outages. This research puts forward two methods for voltage collapse prediction. The first one is to compute a new line stability index (NLSI_1) through an artificial neural network, and the other one is to present a normalized power change index (NPCI) for the prediction. These indices are applied and examined on the IEEE-14 bus system; they check the state of the buses and tell us about the stability of the system. A detailed methodology and explanation are given in the following sections. According to the neural network outcomes, the normalized power change index (NPCI) proves to be more accurate than NLSI_1 for the test system.
Solar irradiance, the power of sunlight received on a given surface area during a specific time, is crucial in determining the efficiency and performance of solar power systems, as it directly influences the electricity units generated by photovoltaic (PV) cells. In recent years, deep learning and machine learning techniques have been leveraged to enhance the accuracy of solar adsorption and wind power forecasting. In this context, this study presents a comparative study of various deep learning models for very short term solar irradiance forecasting, aiming to find the most effective model for this specific purpose for our local city Karachi. The key findings indicate that the LSTM model outperforms the other architectures, achieving the highest R-squared value and the lowest RMSE. These results emphasize the importance of accurate forecasting models in optimizing renewable energy generation and grid management and their potential applications in various sectors.
A Multilevel Inverter (MLI) meets the power requirements of most power electronic applications. It can provide an AC supply with better power quality. The higher number of levels in an MLI results in better power quality output. The fundamental question that comes to mind is what the optimal number of levels can be in designing an MLI for better performance. This paper aims to provide a comparison of three-, five-, and seven-level schemes based on a “Diode Clamped MLI” and will analyze their performance on the reduction of Total Harmonic Distortion (THD).
: The classical problem of islanding detection in distributed generation falls into the commonly used categories known as passive, active, and hybrid techniques. These approaches vary in terms of their accuracy, security, and dependability. Detecting islanding in modern inverter-based distribution systems is of the utmost importance to ensuring the protection of equipment, ensuring the safety of workers, and preventing operational and cascaded faults when the system is specially subjected to renewable integration. This research paper presents a technical comparison of the afore-mentioned techniques, discussing their detection rate, Non-Detection Zone (NDZ), distinct topologies, and their effectiveness in integration for low-frequency grids. The review offers a thorough analysis comparing the key attributes found in the current literature while also highlighting the forthcoming needs for advanced management, optimization, and control technologies. These technologies are crucial to effectively tackling the difficulties that arise from integrating renewable energy sources into established grid systems.
