Stenosis and Aneurysm of Coronary Arteries in A Patient with Behcet’s Disease

Coronary artery disease is extremely rare in patients with Behçet’s disease. We report the case of a patient with a history of Behçet’s disease who was admitted in our hospital with instable angina pectoris. The patient’s electrocardiogram was normal. Coronary angiography revealed aneurysm of the distal right coronary artery with a tight stenosis of the proximal part of the posterolateral branch. These two conditions were initially treated with immunosuppressive treatment. Three years later coronary angiography showed a total occlusion of the right coronary artery treated with medical therapy. More than fourteen cases of coronary involvement were reported in the literature but the etiopathogeny and the treatment are yet unknow

Coordinated Control and Load Shifting-Based Demand Management of a Smart Microgrid Adopting Energy Internet

High renewable energy penetration worsens systems instability. Balancing consumption energy and generation output energy reduces this instability. This paper introduces coordination control to coordinate the flow of electricity between MG buses and to stabilize the system under variable load, generation conditions. The adopted MG regulates the bidirectional DC/AC main converter using digital proportional resonant controllers in a synchronous reference frame. A maximum power point tracker-based boost DC/DC converter enables the wind turbine and solar photovoltaic to harvest maximum power. Traditional methods such as perturb and observe and incremental conductance maximum power trackers cannot solve nonlinearity and inaccurate responses. This work provides a hybrid maximum power tracker strategy to modify the responses of standard maximum power point techniques based on particle swarm optimization-trained adaptive neuro-fuzzy inference system (ANFIS-PSO) to achieve quick and maximum solar power with minimal oscillation tracking. Concerning the management system, this paper adopts a recent meta-heuristic algorithms-based DSM program to modify consumers’ electricity use by shifting the load appliances to off-peak demand periods. The adopted algorithms for DSM are sparrow search algorithm (SSA), binary orientation search algorithm (BSOA), and cockroach algorithm (CA). Finally, based on energy Internet technology, ThingSpeak cloud-based MATLAB is adopted to gather and display real-time data streams and generate graphical analyses. The simulation results reveal that the recommended coordinating control produces quick grid frequency responsiveness and zero steady-state errors. The optimal demand management program minimizes peak energy consumption from 5.2 kWh to 4.6 kWh. All DSM methods cost 439.1 $per month, compared to 484.4$ for the nonscheduling load profile

A Comparison between Particle Swarm and Grey Wolf Optimization Algorithms for Improving the Battery Autonomy in a Photovoltaic System

This research focuses on a photovoltaic system that powers an Electric Vehicle when moving in realistic scenarios with partial shading conditions. The main goal is to find an efficient control scheme to allow the solar generator producing the maximum amount of power achievable. The first contribution of this paper is the mathematical modelling of the photovoltaic system, its function and its features, considering the synthesis of the step-up converter and the maximum power point tracking analysis. This research looks at two intelligent control strategies to get the most power out, even with shading areas. Specifically, we show how to apply two evolutionary algorithms for this control. They are the “particle swarm optimization method” and the “grey wolf optimization method”. These algorithms were tested and evaluated when a battery storage system in an Electric Vehicle is fed through a photovoltaic system. The Simulink/Matlab tool is used to execute the simulation phases and to quantify the performances of each of these control systems. Based on our simulation tests, the best method is identified

Least Square Estimation-Based Different Fast Fading Channel Models in MIMO-OFDM Systems

In cellular wireless communication systems, channel estimation (CE) plays a pivotal role as a crucial technique applied in orthogonal frequency division multiplexing (OFDM) modulation. CE utilizes a variety of methods, including decision-directed channel estimation, pilot-assisted channel estimation (PACE), and blind channel estimation. Among these options, PACE is widely favored for its remarkable stability and consistent superior performance. The idea of massive multiple-input multiple-output (MIMO) shows tremendous potential for the future of wireless communications. However, existing massive MIMO systems face challenges with their high computational complexity and intricate spatial structures, preventing efficient utilization of channel and sparsity features in these multiantenna systems. In communication channels, the signal received is often influenced by the characteristics of the channel and noise present at the receiver. To address this issue, an efficient dataset is utilized, employing the least square (LS) algorithm for minimization. OFDM is a commonly and widely used modulation method in communication systems utilized to specifically combat resonance fading in wireless channels. In wireless communication systems employing OFDM-MIMO, frequency selectivity and time-varying attributes due to multipath channels cause Intercarrier Interference (ICI) among symbols. Channel estimation is a vital aspect for mitigating the effects of fading channels. This investigation focuses on the application of a method examined in the study, which involves a block-type pilot symbol-assisted estimation technique for Rayleigh and Rician fading channel models. The research assesses the performance of the least square (LS) channel estimators in fast-fading channel models while employing various symbol mapping techniques focusing on bit error rate, throughput, and mean square error. The results indicate that the LS estimator exhibits excellent performance in Rayleigh and AWGN channels within the pedestrian A (PedA) model for both uplink and downlink scenarios. It outperforms the PedA model without channel estimation

MPPT of PEM Fuel Cell Using PI-PD Controller Based on Golden Jackal Optimization Algorithm

Subversive environmental impacts and limited amounts of conventional forms of energy necessitate the utilization of renewable energies (REs). Unfortunately, REs such as solar and wind energies are intermittent, so they should be stored in other forms to be used during their absence. One of the finest storage techniques for REs is based on hydrogen generation via an electrolyzer during abundance, then electricity generation by fuel cell (FC) during their absence. With reference to the advantages of the proton exchange membrane fuel cell (PEM-FC), this is preferred over other kinds of FCs. The output power of the PEM-FC is not constant, since it depends on hydrogen pressure, cell temperature, and electric load. Therefore, a maximum power point tracking (MPPT) system should be utilized with PEM-FC. The techniques previously utilized have some disadvantages, such as slowness of response and largeness of each oscillation, overshoot and undershoot, so this article addresses an innovative MPPT for PEM-FC using a consecutive controller made up of proportional-integral (PI) and proportional-derivative (PD) controllers whose gains are tuned via the golden jackal optimization algorithm (GJOA). Simulation results when applying the GJOA-PI-PD controller for MPPT of PEM-FC reveal its advantages over other approaches according to quickness of response, smallness of oscillations, and tininess of overshoot and undershoot. The overshoot resulting using the GJOA-PI-PD controller for MPPT of PEM-FC is smaller than that of perturb and observe, GJOA-PID, and GJOA-FOPID controllers by 98.26%, 86.30%, and 89.07%, respectively. Additionally, the fitness function resulting when using the GJOA-PI-PD controller for MPPT of PEM-FC is smaller than that of the aforementioned approaches by 93.95%, 87.17%, and 87.97%, respectively

Distribution System Service Restoration Using Electric Vehicles

Nowadays the utilization of Electric Vehicles (EVs) has greatly increased. They are attaining greater attention due to their impacts on the grid at the distribution level. However, due to the increased need for electricity, EVs are also used to serve the load in the instance of electrical failure in the distribution systems. This paper presents a new approach to a service restoration method for a low-voltage distribution network at the time of a power outage using existing EVs available in a parking place. The objective function formulated here was a constrained linear optimization model. It aimed to develop priority-based scheduling of the residential user appliances while meeting all the operational constraints if the EV’s power was in a deficit at the hour of the outage. Weight factors were assigned to various residential appliances to decide their priority while scheduling. To substantiate the proposed methodology, a day load profile of a 20 kVA distribution transformer feeding eight residential users is considered. This was tested during an hour-long power outage scenario in the MATLAB and LINGO platforms, with four EVs available during the outage period. This method restored the maximum power to the residential appliances

A Novel Electric Spring With Improved Range of Operation for Isolated Microgrid Systems

The smart load phenomenon of electric spring has been proven to be an important aid to the distribution network in terms of voltage stability. While the performance of ES is commendable, its operational range presents a significant challenge. The effectiveness of an ES depends on various factors, including the choice of control structure, smart load ratio, ES placement, and non-critical load power factor. To address the challenges regarding the noncritical load power factor, this study proposes a novel hybrid control structure that combines two well-established schemes: quadrature voltage injection and in-phase voltage injection. The performance of the proposed scheme is evaluated under variable non-critical load resistance, inductance, and power factors, with the results presented in this paper. Additionally, the sensitivity of the proposed scheme is analyzed for different scenarios, such as varying non-critical load power factors, resistances, and inductances using MATLAB/Simulink environment. A comparative sensitivity analysis is conducted between the proposed scheme and the quadrature and in-phase voltage injection schemes, and the findings are also documented. Further a real-time validation of the proposed control scheme, as well as the other schemes, is conducted using the OPAL-RT 4510 real-time simulator. At last, THD analysis along with a competitive study is done with other pre-published control structures to showcase the superiority of the proposed control scheme

Challenges and Opportunities in Green Hydrogen Adoption for Decarbonizing Hard-to-Abate Industries: A Comprehensive Review

The decarbonization of hard-to-abate industries is crucial for keeping global warming to below $2^{\circ }C$ . Green or renewable hydrogen, synthesized through water electrolysis, has emerged as a sustainable alternative for fossil fuels in energy-intensive sectors such as aluminum, cement, chemicals, steel, and transportation. However, the scalability of green hydrogen production faces challenges including infrastructure gaps, energy losses, excessive power consumption, and high costs throughout the value chain. Therefore, this study analyzes the challenges within the green hydrogen value chain, focusing on the development of nascent technologies. Presenting a comprehensive synthesis of contemporary knowledge, this study assesses the potential impacts of green hydrogen on hard-to-abate sectors, emphasizing the expansion of clean energy infrastructure. Through an exploration of emerging renewable hydrogen technologies, the study investigates aspects such as economic feasibility, sustainability assessments, and the achievement of carbon neutrality. Additionally, considerations extend to the potential for large-scale renewable electricity storage and the realization of net-zero goals. The findings of this study suggest that emerging technologies have the potential to significantly increase green hydrogen production, offering affordable solutions for decarbonization. The study affirms that global-scale green hydrogen production could satisfy up to 24% of global energy needs by 2050, resulting in the abatement of 60 gigatons of greenhouse gas (GHG) emissions - equivalent to 6% of total cumulative $CO_{2}$ emission reductions. To comprehensively evaluate the impact of the hydrogen economy on ecosystem decarbonization, this article analyzes the feasibility of three business models that emphasize choices for green hydrogen production and delivery. Finally, the study proposes potential directions for future research on hydrogen valleys, aiming to foster interconnected hydrogen ecosystems