Design and analysis of virtual impedance control scheme based on MESOGI for improving harmonic sharing of nonlinear loads.

Under the presence of nonlinear load, the most existing virtual impedance (VI) methods-based control solution performs poorly in reactive power sharing among droop-operated VSIs in microgrids (MGs). This may be due to the involved estimation techniques for extracting the current harmonics at selected frequencies, which suffer from either poor accuracy of the harmonic estimation and/or the effect of DC offset in the measurements. Such an issue may affect the performance of the virtual impedance control, hence, the system stability. To bridge this gap, the implementation of the virtual impedance based on multiple enhanced second-order generalized integrator (MESOGI) suitable for harmonics and DC-offset estimation/rejection, is proposed in this paper. The MESOGI can offer an accurate estimation of the current quadrature components free from DC offset at selected frequencies, required to implement the virtual impedance control. Therefore, it makes the designed virtual impedance-based control scheme robust to voltage distortions, immune to DC disturbance, and capable of sharing properly the power harmonics. As a result, this may contribute to improving the reactive and harmonic power-sharing between droop-controlled VSIs within an islanded MG. The modeling of the MESOGI scheme and its performance investigation is carried out. In addition, the mathematical model of the implemented virtual impedance is derived. Further, analysis based on the obtained model of the equivalent output impedance including virtual impedance is established to study its effect. Simulation and experimental tests are performed to prove the effectiveness of the control proposal in improving the reactive power sharing under nonlinear load operating conditions.

An efficient watermarking algorithm for digital audio data in security applications.

Transform-domain audio watermarking systems are more robust than time-domain systems. However, the main weakness of these systems is their high computational cost, especially for long-duration audio signals. Therefore, they are not desirable for real-time security applications where speed is a critical factor. In this paper, we propose a fast watermarking system for audio signals operating in the hybrid transform domain formed by the fractional Charlier transform (FrCT) and the dual-tree complex wavelet transform (DTCWT). The central idea of the proposed algorithm is to parallelize the intensive and repetitive steps in the audio watermarking system and then implement them simultaneously on the available physical cores on an embedded systems cluster. In order to have a low power consumption and a low-cost cluster with a large number of physical cores, four Raspberry Pis 4B are used where the communication between them is ensured using the Message Passing Interface (MPI). The adopted Raspberry Pi cluster is also characterized by its portability and mobility, which are required in watermarking-based smart city applications. In addition to its resistance to any possible manipulation (intentional or unintentional), high payload capacity, and high imperceptibility, the proposed parallel system presents a temporal improvement of about 70%, 80%, and 90% using 4, 8, and 16 physical cores of the adopted cluster, respectively.

Red-tailed hawk algorithm for numerical optimization and real-world problems.

This study suggests a new nature-inspired metaheuristic optimization algorithm called the red-tailed hawk algorithm (RTH). As a predator, the red-tailed hawk has a hunting strategy from detecting the prey until the swoop stage. There are three stages during the hunting process. In the high soaring stage, the red-tailed hawk explores the search space and determines the area with the prey location. In the low soaring stage, the red-tailed moves inside the selected area around the prey to choose the best position for the hunt. Then, the red-tailed swings and hits its target in the stooping and swooping stages. The proposed algorithm mimics the prey-hunting method of the red-tailed hawk for solving real-world optimization problems. The performance of the proposed RTH algorithm has been evaluated on three classes of problems. The first class includes three specific kinds of optimization problems: 22 standard benchmark functions, including unimodal, multimodal, and fixed-dimensional multimodal functions, IEEE Congress on Evolutionary Computation 2020 (CEC2020), and IEEE CEC2022. The proposed algorithm is compared with eight recent algorithms to confirm its contribution to solving these problems. The considered algorithms are Farmland Fertility Optimizer (FO), African Vultures Optimization Algorithm (AVOA), Mountain Gazelle Optimizer (MGO), Gorilla Troops Optimizer (GTO), COOT algorithm, Hunger Games Search (HGS), Aquila Optimizer (AO), and Harris Hawks optimization (HHO). The results are compared regarding the accuracy, robustness, and convergence speed. The second class includes seven real-world engineering problems that will be considered to investigate the RTH performance compared to other published results profoundly. Finally, the proton exchange membrane fuel cell (PEMFC) extraction parameters will be performed to evaluate the algorithm with a complex problem. The proposed algorithm will be compared with several published papers to approve its performance. The ultimate results for each class confirm the ability of the proposed RTH algorithm to provide higher performance for most cases. For the first class, the RTH mostly got the optimal solutions for most functions with faster convergence speed. The RTH provided better performance for the second and third classes when resolving the real word engineering problems or extracting the PEMFC parameters.

Energy management system in smart buildings based coalition game theory with fog platform and smart meter infrastructure.

This paper proposes a central energy management system (EMS) in smart buildings. It is based on the coalition method for optimal energy sharing between smart buildings. Game theory is applied to obtain an optimal allocation of the building's surplus energy on the deficient energy buildings using the Shapley value, which enables the unequal energy distribution based on the energy demand. The main objective is reducing energy waste while preserving the generation/demand balance. The fog platform with memory storage is applied, which handles all the measured data from the smart buildings through Wi-Fi-based communication protocol and performs the EMS program. The smart meter links the smart buildings with the fog-based EMS central unit. Two scenarios are implemented based on the difference between total deficient and surplus energy. Coalition game theory is applied for optimal surplus energy allocation on deficient buildings when the total energy surplus is lower than the total energy deficient. Also, there is a one-to-one relationship between the surplus and deficient building; if the surplus energy is larger than the deficit, the extra surplus energy is stored for further usage. The proposed EMS is applied and tested using a smart city with 10 buildings in the MATLAB program. A comparison between the result obtained with and without applying the proposed method is performed. The performance of the fog platform is introduced based on the run and delay time and the memory size usage. The results show the effectiveness of the proposed EMS in a smart building.

Modified bald eagle search algorithm for lithium-ion battery model parameters extraction.

Bald eagle search algorithm (BES) is a recent metaheuristic algorithm based on bald eagle hunting behavior. Like other metaheuristic algorithms, the BES algorithm is prone to entangle in local optimums due to limited diversity, sluggish convergence rate, or improper equilibrium between exploitation and exploration. Thus, adaptive parameters are injected into the original BES to overcome these shortcomings. These parameters are a function of the current and the max number of iterations. They provide the eagle with more diversity during the exploration and exploitation phases. The modified BES is tested on test functions provided by Congress on Evolutionary Computation 2020 and Congress on Evolutionary Computation 2022. The obtained results are compared to that of other reliable and recent algorithms. In addition, analysis of variance and Tuckey tests are utilized to confirm the results' significance. Due to its benefits, lithium-ion batteries are employed in more and more applications. However, extracting its parameters is challenging due to its complex model. Hence, the proposed algorithm will handle this task to approve its performance in complex problems. The significant benefit of this extraction method is its excellent precision, with fitness value declining (root mean square error) to 0.89 × 10-3 compared to the original BES (1.013 × 10-3) with a standard deviation of 1.12 × 10-3. To confirm the performance of mBES, a second battery was tested with the New European Driving Cycle profile. The mBES has the lowest fitness values (0.058896) and the least standard deviation (5.89 × 10-7).

A novel MPPT design based on the seagull optimization algοrithm for phοtovοltaic systems operating under partial shading.

The use of a maximum power point (MPP) tracking (MPPT) controller is required for photovoltaic (PV) systems to extract maximum power from PV panels. However, under partial shading conditions, the PV cells/panels do not receive uniform insolation due to several power maxima appear on the PV array's P-V characteristic, a global MPP (GMPP) and two or more local MPPs (LMPPs). In this scenerio, conventional MPPT methods, including pertub and observe (P&O) and incremental conductance (INC), fail to differentiate between a GMPP and a LMPP, as they converge on the MPP that makes contact first, which in most cases is one of the LMPPs. This results in considerable energy loss. To address this issue, this paper introduces a new MPPT method based on the Seagull Optimization Algorithm (SOA) to operate PV systems at GMPP with high efficiency. The SOA is a new member of the bio-inspired algorithms. When compared to other evolutionary techniques, it uses fewer operators and modification parameters, which is advantageous when considering the rapid design process. In this paper, the SOA-based MPPT scheme is first proposed and then implemented for an 80 W PV system using the MATLAB/SIMULINK environment. The effectiveness of the SOA based MPPT method is verified by comparing its performance with P& O and PSO (particle swarm optimization) based MPPT methods under different shading scenarios. The results demonstrated that the SOA based MPPT method performs better in terms of tracking accuracy and efficiency.

Robust sliding-Backstepping mode control of a wind system based on the DFIG generator.

This paper presents a new contribution in the field of the optimization of the techniques of control of the wind systems and the improvement of the quality of energy produced in the grid. The Sliding Mode control technique gives quite interesting results, but its major drawback lies in the phenomenon of chattering (oscillations), which reduces the system's precision. We propose in this work a solution to cancel this chattering phenomenon by the implication of the adaptive Backstepping technique to control the powers of the double-fed asynchronous generator (DFIG) connected to the electrical network by two converters (network side and side machine) in the nominal part of the sliding mode model. This hybrid technique will correct errors of precision and stability and the performance of the wind system obtained in terms of efficiency, active and reactive power is significant. First, a review of the wind system was presented. Then, an exhaustive explanation of the Backstepping technique based on the Lyapunov stability and optimization method has been reported. Subsequently, a validation on the Matlab & Simulink environment was carried out to test the performance and robustness of the proposed model. The results obtained from this work, either by follow-up or robustness tests, show a significant performance improvement compared to other control techniques.

A novel hybrid GWO-PSO-based maximum power point tracking for photovoltaic systems operating under partial shading conditions.

One of the major challenges in photovoltaic (PV) systems is extracting the maximum power from the PV array, especially when they operate under partial shading conditions (PSCs). To address this challenge, this paper introduces a novel hybrid maximum power point tracking (MPPT) method based on grey wolf optimization and particle swarm optimization (GWO-PSO) techniques. The developed MPPT technique not only avoids the common disadvantages of conventional MPPT techniques (such as perturb and observe (P&O) and incremental conductance) but also provides a simple and robust MPPT scheme to effectively handle partial shading in PV systems, since it requires only two control parameters, and its convergence to the global maximum power point (GMPP) is independent of the search process's initial conditions. The feasibility and effectiveness of the hybrid GWO-PSO-based MPPT method are verified via a co-simulation technique that combines MATLAB/SIMULINK and PSIM software environments, while comparing its performance against GWO, PSO and P&O based MPPT methods. The simulation results carried out under dynamic environmental conditions have shown the satisfactory effectiveness of the hybrid MPPT method in terms of tracking accuracy, convergence speed to GMPP and efficiency, compared to other methods.

Utilization of additive from waste products with gasoline fuel to operate spark ignition engine.

Impacts of blending fusel oil with gasoline on fuel combustion have been investigated experimentally in the current research to evaluate engine performance improvement and exhaust emission. Tested fuel include F10, F20 (10% and 20% of fusel oil by volume) and pure gasoline as baseline fuel have been used to operate 4-cylinder SI engine at increasing engine speed and constant throttle valve of 45%. The present results reveal a shorter combustion duration and better engine performance with F10 over engine speeds with maximum value of 33.9% for the engine brake thermal efficiency. The lowest BSFC of 251 g/kW h was recorded at 3500 rpm engine speed also with F10. All blended fuel have almost similar COVIMEP. Less NOx emission was measured with F10 at 4500 engine speed compared to gasoline. However, CO emissions reduced while higher CO2 was observed with introducing fusel oil in the blend. Moreover, HC emission increased an average by 11% over speed range and the highest value was achieved with 10% fusel oil addition compared to 20% and pure gasoline. Accordingly, higher oxygen content of fusel oil and octane number contribute to improve combustion of fuel mixture.

Design and processor in the loop implementation of an improved control for IM driven solar PV fed water pumping system.

In recent years, the improvement of photovoltaic water pumping system (PVWPS) efficiency takes the considerable interest of researchers due to its operating based on cleaner electrical energy production. In this paper, a new approach based on fuzzy logic controller incorporating loss minimization technique applied to the induction machine (IM) is developed for PVWPS applications. The proposed control selects the optimal flux magnitude by minimization of the IM losses. Moreover, Variable step size perturb and observe method is introduced. The suitability of the proposed control is approved by reducing the absorbed current; therefore, the motor losses are minimized and the efficiency is improved. The proposed control strategy is compared with the method without losses minimization. The comparison results illustrate the effectiveness of the proposed method based on losses minimization regarding the electrical speed, absorbed current, flow water and developed flux. A processor-in-the-loop (PIL) test is effectuated as an experimental test of the proposed method. It consists in implementing the generated C code on the STM32F4 discovery board. The obtained results from the embedded board are similar to numerical simulation results.

Purely mechanically driven door-controlled disinfection device for automatic COVID-19.

The COVID-19 outbreak is a sharp reminder that pandemics, like other rarely occurring catastrophes, have happened in the past and will continue to happen in the future. It is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which is an emerging disease. There has been a rapid increase in cases and deaths since it was identified in Wuhan, China. Even if we cannot prevent dangerous viruses from emerging, we should prepare to dampen their effects on society. The current outbreak has had severe economic consequences across the globe, and it does not look like any country will be unaffected. This not only has consequences for the economy; all of society is affected, which has led to dramatic changes in how businesses act and consumers behave. It is in this context that this paper describes a device based on a mechanical spray disinfection system that does not use an electrical source or detection sensor, thus facilitating the use of this system by anyone. The disinfection process is launched automatically by the movement of a door, which changes the position of a piston, triggering the spraying of a disinfectant liquid. This door-controlled automatic decontamination system could be an effective tool in the fight against the current COVID-19 pandemic and, in general, could help to address the health challenges related to hygiene and disease prevention at low cost.

Comparative study with practical validation of photovoltaic monocrystalline module for single and double diode models.

A photovoltaic (PV) module is an equipment that converts solar energy to electrical energy. A mathematical model should be presented to show the behavior of this device. The well-known single-diode and double-diode models are utilized to demonstrate the electrical behavior of the PV module. "Matlab/Simulink" is used to model and simulate the PV models because it is considered a major software for modeling, analyzing, and solving dynamic system real problems. In this work, a new modeling method based on the "Multiplexer and Functions blocks" in the "Matlab/Simulink Library" is presented. The mathematical analysis of single and double diodes is conducted on the basis of their equivalent circuits with simple modification. The corresponding equations are built in Matlab by using the proposed method. The unknown internal parameters of the PV panel circuit are extracted by using the PV array tool in Simulink, which is a simple method to obtain the PV parameters at certain weather conditions. Double-diode model results are compared with the single-diode model under various irradiances and temperatures to verify the performance and accuracy of the proposed method. The proposed method shows good agreement in terms of the I-V and P-V characteristics. A monocrystalline NST-120 W PV module is used to validate the proposed method. This module is connected to a variable load and tested for one summer day. The experimental voltage, current, and power are obtained under various irradiances and temperatures, and the I-V and P-V characteristics are obtained.

Preliminary design of an innovative, simple, and easy-to-build portable ventilator for COVID-19 patients.

This technical note describes the preliminary design of a simple, easy-to-use, and easy-to-build ventilator with an unique design that can be used for COVID-19 patients in emergencies and to prevent massive loss of life in resource-poor environments. It can be assembled by a nonexpert as a homemade solution, without the need for specific equipment or technology. The proposed system is novel, inexpensive, has a reduced reliance on external power, and is very easy to maintain.