Design and Evaluation of High Efficiency Power Converters Using Wide-Bandgap Devices for PV Systems

The shortage of fossil resources and the need for power generation options that produce little or no environmental pollution drives and motivates the research on renewable energy resources. Power electronics play an important role in maximizing the utilization of energy generation from renewable energy resources. One major renewable energy source is photovoltaics (PV), which comprises half of all recently installed renewable power generation in the world. For a grid-connected system, two power stages are needed to utilize the power generated from the PV source. In the first stage, a DCDC converter is used to extract the maximum power from the PV panel and to boost the low output voltage generated to satisfy the inverter side requirements. In the second stage, a DC-AC inverter is used to convert and deliver power loads for grid-tied applications. In general, PV panels have low efficiency so high-performance power converters are required to ensure highly efficient PV systems. The development of wide-bandgap (WBG) power switching devices, especially in the range of 650 V and 1200 V blocking class voltage, opens up the possibility of achieving a reliable and highly efficient grid-tied PV system. This work will study the benefits of utilizing WBG semiconductor switching devices in low power residential scale PV systems in terms of efficiency, power density, and thermal analysis. The first part of this dissertation will examine the design of a high gain DC-DC converter. Also, a performance comparison will be conducted between the SiC and Si MOSFET switching devices at 650 V blocking voltage regarding switching waveform behavior, switching and conduction losses, and high switching frequency operation. A major challenge in designing a transformerless inverter is the circulating of common mode leakage current in the absence of galvanic isolation. The value of the leakage current must be less than 300mA, per the DIN VDE 0126-1-1 standard. The second part of this work investigates a proposed high-efficiency transformerless inverter with low leakage current. Subsequently, the benefits of using SiC MOSFET are evaluated and compared to Si IGBT at 1200 V blocking voltage in terms of efficiency improvement, filter size reduction, and increasing power rating. Moreover, a comprehensive thermal model design is presented using COMSOL software to compare the heat sink requirements of both of the selected switching devices, SiC MOSFET and Si IGBT. The benchmarking of switching devices shows that SiC MOSFET has superior switching and conduction characteristics that lead to small power losses. Also, increasing switching frequency has a small effect on switching losses with SiC MOSFET due to its excellent switching characteristics. Therefore, system performance is found to be enhanced with SiC MOSFET compared to that of Si MOSFET and Si IGBET under wide output loads and switching frequency situations. Due to the high penetration of PV inverters, it is necessary to provide advanced functions, such as reactive power generation to enable connectivity to the utility grid. Therefore, this research proposes a modified modulation method to support the generation of reactive power. Additionally, a modified topology is proposed to eliminate leakage current

Integration of OOFDM with RoF for High Data Rates Long-Haul Optical Communications

One of the advanced technologies in optical fiber communication systems that support efficient convergence of wireless and optical access network structure is Radio over Fiber (RoF). In RoF, light is modulated by using a radio signal and sent over an optical fiber link to simplify wireless access. The demand for high-speed wireless communications is increasing rapidly. To increase the capacity and bandwidth of the optical fiber communication system a Wavelength-Division Multiplexing (WDM) is used. In WDM, multiple optical carrier signals are multiplexed and transmitted over one optical fiber. Optical Orthogonal Frequency Division Multiplexing (OOFDM) technology commits to be a fundamental technique for accomplishing high data when is integrated with RoF. OOFDM is an effective method to overcome different restrictions of optical fiber transmission systems such as chromatic dispersion, polarization mode dispersion, and modal dispersion. Therefore, the combination of OOFDM and RoF will enhance the system flexibility and help to cover a very large area without increasing the system complexity and cost very much. This thesis investigates the integration of OOFDM with RoF for achieving high data rates and the transmission of the signal over long haul optical fiber. Results from the OptiSystem model shows the performance of OOFDM signals through the WDM RoF is studied by using a simulation tool called OptiSystem version 12

Evaluation of Machine Learning Models for Smart Grid Parameters: Performance Analysis of ARIMA and Bi-LSTM

The integration of renewable energy resources into smart grids has become increasingly important to address the challenges of managing and forecasting energy production in the fourth energy revolution. To this end, artificial intelligence (AI) has emerged as a powerful tool for improving energy production control and management. This study investigates the application of machine learning techniques, specifically ARIMA (auto-regressive integrated moving average) and Bi-LSTM (bidirectional long short-term memory) models, for predicting solar power production for the next year. Using one year of real-time solar power production data, this study trains and tests these models on performance measures such as mean absolute error (MAE) and root mean squared error (RMSE). The results demonstrate that the Bi-LSTM (bidirectional long short-term memory) model outperforms the ARIMA (auto-regressive integrated moving average) model in terms of accuracy and is able to successfully identify intricate patterns and long-term relationships in the real-time-series data. The findings suggest that machine learning techniques can optimize the integration of renewable energy resources into smart grids, leading to more efficient and sustainable power systems.© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

Neuro-Fuzzy Based High-Voltage DC Model to Optimize Frequency Stability of an Offshore Wind Farm

Lack of synchronization between high voltage DC systems linking offshore wind farms and the onshore grid is a natural consequence owing to the stochastic nature of wind energy. The poor synchronization results in increased system disturbances, grid contingencies, power loss, and frequency instability. Emphasizing frequency stability analysis, this research investigates a dynamic coordination control technique for a Double Fed Induction Generator (DFIG) consisting of OWFs integrated with a hybrid multi-terminal HVDC (MTDC) system. Line commutated converters (LCC) and voltage source converters (VSC) are used in the suggested control method in order to ensure frequency stability. The adaptive neuro-fuzzy inference approach is used to accurately predict wind speed in order to further improve frequency stability. The proposed HVDC system can integrate multiple distributed OWFs with the onshore grid system, and the control strategy is designed based on this concept. In order to ensure the transient stability of the HVDC system, the DFIG-based OWF is regulated by a rotor side controller (RSC) and a grid side controller (GSC) at the grid side using a STATCOM. The devised HVDC (MTDC) is simulated in MATLAB/SIMULINK, and the performance is evaluated in terms of different parameters, such as frequency, wind power, rotor and stator side current, torque, speed, and power. Experimental results are compared to a conventional optimal power flow (OPF) model to validate the performance.© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

Parents’ self-directed practices towards the use of antibiotics for upper respiratory tract infections in Makkah, Saudi Arabia

Abstract Background Excessive and inappropriate antimicrobial use in the community is one risk factor that can result in the spread of antimicrobial resistance. Upper respiratory tract infections are most frequently reported among children and mainly of viral origin and do not require antibiotics. We have conducted Knowledge, Attitude and Perception (KAP) survey of parents to explore the parent’s knowledge, attitude & perception of Saudi parents. Methods A knowledge attitude perception questioner was adopted from a previous study conducted in Greece by Panagakou et al. Raosoft online sample size calculator calculated the sample size by adding the total estimated Makkah population of 5,979,719 with a response rate of 30%, 5% margin of error and 99% confidence interval. Based on the described criteria five hundred & fifty-eight was the required sample size of the study. Incomplete questioners were excluded from the statistical analysis. SPSS version 21 was used to analyse data and to produce descriptive statistics. Results Most of the mothers (95%) responded among parents. 67% had no health insurance to cover medications costs. Most of them (74%) were related to medium income level. Seventy per cent of the parents believed physicians as a source of information for judicious antibiotics use. Interestingly, only 8% were agreed that most of the upper respiratory tract infections are caused by viral reasons. Majority of Saudi parents (53%) expect pediatricians to prescribe antimicrobials for their children for symptoms like a cough, nose discharge, sore throat and fever. Moreover, most the parents had the poor knowledge to differentiate commonly used OTC medications for URTI and antibiotics like Augmentin (Co-amoxiclav), Ceclor (cefaclor) and Erythrocin (Erythromycin). While comparing males and female’s knowledge level, few males have identified Amoxil (Amoxicillin). Similarly, parents of age 20–30 years have good knowledge about the antibiotics. Conclusions Majority of Saudi parents believe in pediatricians and use antibiotics on physician’s advice. Most of them expect antibiotics from their physicians as a primary treatment for upper respiratory tract infections. There is need for more educational activities to parents by the pharmacists to prevent antibiotics overuse among children

Grid Distribution Fault Occurrence and Remedial Measures Prediction/Forecasting through Different Deep Learning Neural Networks by Using Real Time Data from Tabuk City Power Grid

Modern societies need a constant and stable electrical supply. After relying primarily on formal mathematical modeling from operations research, control theory, and numerical analysis, power systems analysis has changed its attention toward AI prediction/forecasting tools. AI techniques have helped fix power system issues in generation, transmission, distribution, scheduling and forecasting, etc. These strategies may assist today’s large power systems which have added more interconnections to meet growing load demands. They make it simple for them to do difficult duties. Identification of problems and problem management have always necessitated the use of labor. These operations are made more sophisticated and data-intensive due to the variety and growth of the networks involved. In light of all of this, the automation of network administration is absolutely necessary. AI has the potential to improve the problem-solving and deductive reasoning approaches used in fault management. This study implements a variety of artificial intelligence and deep learning approaches in order to foresee and predict the corrective measures that will be conducted in response to faults that occur inside the power distribution network of the Grid station in Tabuk city with regard to users. The Tabuk grid station is the source of the data that was gathered for this purpose; it includes a list of defects categorization, actions and remedies that were implemented to overcome these faults, as well as the number of regular and VIP users from 2017 to 2022. Deep learning, the most advanced method of learning used by artificial intelligence, is continuing to make significant strides in a variety of domain areas, including prediction. This study found that the main predictors of remedial measures against the fault occurring in the power systems are the number of customers affected and the actual cause of the fault. Consequently, the deep learning regression model, i.e., Gated Recurrent Unit (GRU), achieved the best performance among the three, which yielded an accuracy of 92.13%, mean absolute error (MAE) loss of 0.37%, and root mean square error (RMSE) loss of 0.39% while the simple RNN model’s performance is not up to the mark with an accuracy of 89.21%, mean absolute error (MAE) loss of 0.45% and root mean square error (RMSE) loss of 0.34%. Significance of the research is to provide the maximum benefit to the customers and the company by using different AI techniques

An Improved Proposed Single Phase Transformerless Inverter with Leakage Current Elimination and Reactive Power Capability for PV Systems Application

Single-phase transformerless inverters are broadly studied in literature for residential-scale PV applications due to their great advantages in reducing system weight, cost and elevating system efficiency. The design of transformerless inverters is based on the galvanic isolation method to eliminate the generation of leakage current. Unfortunately, the use of the galvanic isolation method alone cannot achieve constant common mode voltage (CMV). Therefore, a complete elimination of leakage current cannot be achieved. In addition, modulation techniques of single-phase transformerless inverters are designed for the application of the unity power factor. Indeed, next-generation PV systems are required to support reactive power to enable connectivity to the utility grid. In this paper, a proposed single-phase transformerless inverter is modified with the clamping method to achieve constant CMV during all inverter operating modes. Furthermore, the modulation technique is modified by creating a new current path in the negative power region. As a result, a bidirectional current path is created in the negative power region to achieve reactive power generation. The simulation results show that the CMV is completely clamped at half the DC link voltage and the leakage current is almost completely eliminated. Furthermore, a reactive power generation is achieved with the modified modulation techniques. Additionally, the total harmonic distortion (THD) of the grid current with the conventional and a modified modulation technique is analyzed. The efficiency of the system is enhanced by using wide-bandgap (WBG) switching devices such as SiC MOSFET. It is observed that the efficiency of the system decreased with reactive power generation due to the bidirectional current path, which leads to increasing conduction losses

Comparative Analysis of Si- and GaN-Based Single-Phase Transformer-Less PV Grid-Tied Inverter

Recently, the interest in grid-tied PV transformer-less inverters has increased rapidly, because of their higher efficiency and lower cost compared to traditional line transformer inverters. This paper presents a new modified transformer-less topology derived from H5 inverter, and provides a detailed comparison between the use of GaN and Si devices for the proposed topology. Detailed operation modes, inverter structure and switching strategy of the proposed topology are presented. Datasheet information, conduction losses, switching losses, and heat sink requirements are studied and analyzed to provide an accurate comparison between GaN and Si power devices for the proposed topology operating at unity power factor. The results show that, GaN power devices significantly reduce the power losses in the system, which consequently allow a significant increase in either inverter power rating or switching frequency. Thus, the use of GaN power devices for the proposed inverter can be more appealing and cost-effective approach

Techno-Economic and Environmental Analysis of Grid-Connected Electric Vehicle Charging Station Using AI-Based Algorithm

The rapid growth of electric vehicles in India necessitates more power to energize such vehicles. Furthermore, the transport industry emits greenhouse gases, particularly SO2, CO2. The national grid has to supply an enormous amount of power on a daily basis due to the surplus power required to charge these electric vehicles. This paper presents the various hybrid energy system configurations to meet the power requirements of the electric vehicle charging station (EVCS) situated in the northwest region of Delhi, India. The three configurations are: (a) solar photovoltaic/diesel generator/battery-based EVCS, (b) solar photovoltaic/battery-based EVCS, and (c) grid-and-solar photovoltaic-based EVCS. The meta-heuristic techniques are implemented to analyze the technological, financial, and environmental feasibility of the three possible configurations. The optimization algorithm intends to reduce the total net present cost and levelized cost of energy while keeping the value of lack of power supply probability within limits. To confirm the solution quality obtained using modified salp swarm algorithm (MSSA), the popularly used HOMER software, salp swarm algorithm (SSA), and the gray wolf optimization are applied to the same problem, and their outcomes are equated to those attained by the MSSA. MSSA exhibits superior accuracy and robustness based on simulation outcomes. The MSSA performs much better in terms of computation time followed by the SSA and gray wolf optimization. MSSA results in reduced levelized cost of energy values in all three configurations, i.e., USD 0.482/kWh, USD 0.684/kWh, and USD 0.119/kWh in configurations 1, 2, and 3, respectively. Our findings will be useful for researchers in determining the best method for the sizing of energy system components