Maximum power point tracking of photovoltaic module based on Particle Swarm Optimization enhanced with Quasi-Newton method.

Maximum Power Point Tracking (MPPT) is a promising technology for extracting peak power from single or multiple solar modules for improving Photovoltaic (PV) system performance and satisfying economic operation. The tracker should continuously follow the MPP of the PV module at all operating and weather conditions. The Particle Swarm Optimization (PSO) algorithm represents a powerful optimal MPP tracker due to its simplicity and has enhanced greatest exploration characteristics.

Various optimization algorithms for efficient placement and sizing of photovoltaic distributed generations in different networks.

Recent research has concentrated on emphasizing the significance of incorporating renewable distributed generations (RDGs), like photovoltaic (PV) and wind turbines (WTs), into the distribution system to address issues related to distributed generation (DG) allocation. The key implications of integrating RDGs include the improvement of voltage profiles and the minimization of power losses. Various optimization techniques, namely Salp Swarm Algorithm (SSA), Marine Predictor Algorithm (MPA), Grey Wolf Optimizer (GWO), Improved Grey Wolf Optimizer (IGWO), and Seagull Optimization Algorithm (SOA), have been applied to achieve optimal allocation and sizing of RDGs in radial distributed systems (RDS).

A novel inverter control strategy for maximum hosting capacity photovoltaic systems in distribution networks using power factor.

The conventional inverter is undergoing a transformation into a smart inverter, driven by the expanding penetration of Photovoltaic (PV) power production in Low Voltage (LV) systems. The adoption of smart inverters is on the rise. Power companies are keen on integrating them into their networks to acquire essential frequency and voltage support as required.

Evaluating Machine Learning and Deep Learning models for predicting Wind Turbine power output from environmental factors.

This study presents a comprehensive comparative analysis of Machine Learning (ML) and Deep Learning (DL) models for predicting Wind Turbine (WT) power output based on environmental variables such as temperature, humidity, wind speed, and wind direction. Along with Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Convolutional Neural Network (CNN), the following ML models were looked at: Linear Regression (LR), Support Vector Regressor (SVR), Random Forest (RF), Extra Trees (ET), Adaptive Boosting (AdaBoost), Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM). Using a dataset of 40,000 observations, the models were assessed based on R-squared, Mean Absolute Error (MAE), and Root Mean Square Error (RMSE).

Adaptive electronic relay for smart grid based on self-healing protection.

The protection system is crucial for grid stability and safeguarding essential components, including generators, transformers, transmission systems, and power connections. The smart grid system increases the flexibility and complexity of the power system, making fault detection and isolation the primary challenges for the protection system. This paper presents an optimal protection solution using an adaptive electronic relay to enhance reliability and enable self-healing.

Development of low-cost micro-fabrication procedures for planar micro-thermoelectric generators based on thin-film technology for energy harvesting applications.

With the rapid proliferation of portable and wearable electronics, energy autonomy through efficient energy harvesting has become paramount. Thermoelectric generators (TEGs) stand out as promising candidates due to their silent operation, high reliability, and maintenance-free nature. This paper presents the design, fabrication, and analysis of a micro-scale TEG for powering such devices.

Advanced control of split source inverter through finite control-set model predictive control for improved system performance.

Distributed power generation systems may necessitate connecting multiple independent energy sources that employ various converter topologies. A recent development in this field is the emergence of impedance source converters, offering the ability to deliver buck-boost functionality within a single stage. The split-source inverter (SSI) has been introduced as a novel choice in between this family.