A Constant Grid Interface Current Controller for DC Microgrid

With the increased percentage of distributed renewable energy sources (RES) connected to the power network, it is challenging to maintain the balance between the power generation and consumptions against the unpredictable renewable energy generation and load variations. Considering this, this study proposed a new DC microgrid control strategy to reduce the disturbance to the main power grid from the distributed generation and load variations within the DC microgrid. The DC microgrid model used in this study includes an energy storage unit (battery), a distributed generation unit (PV) and loads. A fuzzy logic controller (FLC) is used to actively regulate the battery charging/discharging current to absorb the power variation caused by PV generation and load changes. The proposed control strategy is validated by simulation in MATLAB/Simulink

Accelerated Particle Swarm Optimization for Photovoltaic Maximum Power Point Tracking under Partial Shading Conditions

This paper presents an accelerated particle swarm optimization (PSO)-based maximum power point tracking (MPPT) algorithm to track global maximum power point (MPP) of photovoltaic (PV) generation under partial shading conditions. Conventional PSO-based MPPT algorithms have common weaknesses of a long convergence time to reach the global MPP and oscillations during the searching. The proposed algorithm includes a standard PSO and a perturb-and-observe algorithm as the accelerator. It has been experimentally tested and compared with conventional MPPT algorithms. Experimental results show that the proposed MPPT method is effective in terms of high reliability, fast dynamic response, and high accuracy in tracking the global MPP

A Comprehensive Review of the Soiling Effects on PV Module Performance

Photovoltaic (PV) systems are a popular renewable energy source globally, owing to their beneficial environmental and economic properties. However, their efficiency is impacted by various environmental and weather conditions, including dust accumulation, which harms the performance of solar cells, particularly in hot and dry regions. Several researchers have studied how to clean and minimize dust on PV modules. This paper reviews recent studies on the effects of dust on PV systems and effective cleaning methods. Some locations experience power losses of over 1% each day and 80% monthly efficiency reduction due to dust, which is substantial. This paper delivers a thorough review of the issue of dust on PV modules. It analyzes previous research on how photovoltaic (PV) systems function when exposed to a mix of dust accumulation and other environmental factors. It also delves into the development of models to forecast dust accumulation. Furthermore, it examines various aspects of PV module design, including the frequency of cleaning methods, economic factors, and their advantages and disadvantages. Additionally, the study identifies several research gaps that require further exploration. These gaps include developing artificial intelligence-based models for reducing dust accumulation, dynamic optimization models for cleaning schedules, as well as advanced techniques for predicting dust accumulation, taking into account environmental conditions and ageing procedures. This information is essential for engineers, designers, and researchers who work on PV systems

AI Prediction of Brain Signals for Human Gait Using BCI Device and FBG Based Sensorial Platform for Plantar Pressure Measurements

Artificial intelligence (AI) in developing modern solutions for biomedical problems such as the prediction of human gait for human rehabilitation is gaining ground. An attempt was made to use plantar pressure information through fiber Bragg grating (FBG) sensors mounted on an in-sole, in tandem with a brain-computer interface (BCI) device to predict brain signals corresponding to sitting, standing and walking postures of a person. Posture classification was attained with an accuracy range between 87–93% from FBG and BCI signals using machine learning models such as K-nearest neighbor (KNN), logistic regression (LR), support vector machine (SVM), and naïve Bayes (NB). These models were used to identify electrodes responding to sitting, standing and walking activities of four users from a 16 channel BCI device. Six electrode positions based on the 10–20 system for electroencephalography (EEG) were identified as the most sensitive to plantar activities and found to be consistent with clinical investigations of the sensorimotor cortex during foot movement. A prediction of brain EEG corresponding to given FBG data with lowest mean square error (MSE) values (0.065–0.109) was made with the selection of a long-short term memory (LSTM) machine learning model when compared to the recurrent neural network (RNN) and gated recurrent unit (GRU) models

A grid interface current control strategy for DC microgrids

In this paper, a grid interface current control strategy is presented for a DC microgrid, which aims to reduce the disturbance from PV generation and the load variation to the main grid without a grid interface converter. The grid interface current is directly controlled by a battery DC-DC converter within the DC microgrid. Based on a comprehensive analysis of the battery DCDC converter and interface current control, the control system has been mathematically modelled. This enabled two transfer functions to be derived that reflect the dynamic response of the inductor current to the duty cycle variation (inner loop), and the dynamic response of the grid interface current to the inductor current variation (outer loop). Experimental study has been done to assess the effectiveness of the proposed control strategy. The experimental results indicate that the proposed control strategy has a good performance to control the grid interface current without an interface converter, regardless the variations of both PV and the load conditions

Smart city drivers and challenges in energy and water systems

At the turn of the 21st century, urban development has experienced a paradigm shift so that the quest for smarter cities has become a priority agenda, with the direct participation of industry, policy makers, practitioners, and the scientific community alike. The 2008 financial crisis, the exodus from rural areas, and the densification of urban centers coupled with environmental and sustainability concerns has posed enormous challenges to municipalities all over the globe. The United Nations predicts that the world population will reach 9.8 billion by 2050, a growth of 2.1 billion from the 2018 level. Almost all of this population growth will occur in urban areas and, consequently, stress already overloaded transportation systems

Smart city drivers and challenges in urbanmobility, health-care, and interdependent infrastructure systems

At the turn of the 21st century, urban development has experienced a paradigm shift so that the quest for smarter cities has become a priority agenda, with the direct participation of industry, policy makers, practitioners, and the scientific community alike. The 2008 financial crisis, the exodus from rural areas, and the densification of urban centers coupled with environmental and sustainability concerns has posed enormous challenges to municipalities all over the globe. The United Nations predicts that the world population will reach 9.8 billion by 2050, a growth of 2.1 billion from the 2018 level. Almost all of this population growth will occur in urban areas and, consequently, stress already overloaded transportation systems

SARS-CoV-2 vaccination modelling for safe surgery to save lives: data from an international prospective cohort study

Background: Preoperative SARS-CoV-2 vaccination could support safer elective surgery. Vaccine numbers are limited so this study aimed to inform their prioritization by modelling. Methods: The primary outcome was the number needed to vaccinate (NNV) to prevent one COVID-19-related death in 1 year. NNVs were based on postoperative SARS-CoV-2 rates and mortality in an international cohort study (surgical patients), and community SARS-CoV-2 incidence and case fatality data (general population). NNV estimates were stratified by age (18-49, 50-69, 70 or more years) and type of surgery. Best- and worst-case scenarios were used to describe uncertainty. Results: NNVs were more favourable in surgical patients than the general population. The most favourable NNVs were in patients aged 70 years or more needing cancer surgery (351; best case 196, worst case 816) or non-cancer surgery (733; best case 407, worst case 1664). Both exceeded the NNV in the general population (1840; best case 1196, worst case 3066). NNVs for surgical patients remained favourable at a range of SARS-CoV-2 incidence rates in sensitivity analysis modelling. Globally, prioritizing preoperative vaccination of patients needing elective surgery ahead of the general population could prevent an additional 58 687 (best case 115 007, worst case 20 177) COVID-19-related deaths in 1 year. Conclusion: As global roll out of SARS-CoV-2 vaccination proceeds, patients needing elective surgery should be prioritized ahead of the general population