Search

Background: Hepatitis C virus HCV infection is one of the main causes of chronic liver disease worldwide, Egypt has the highest national-level HCV prevalence in the world, Sofosbuvir/ Daclatasvir (SOF/DAC) regimen widely used to treat HCV infection in Egypt. Aims: to identify the predictors of treatment failure with Sofosbuvir/Daclatasvir regimen for 12 weeks in HCV Egyptian patients. Methodology: A retrospective cohort, we investigated the predictors of treatment failure in 2300 HCV patients received the SOF/DAC regimen in Egypt. Results: Patients who completed the treatment and follow up visits were 2079 patients,4 patients stopped treatment due to side effects, 35 due to incompliance and 182 patients missed their follow up visits after treatment, out of the 2079 patients 2043 (98.3%) patients responded to treatment with SVR12 and just 36 (1.7%) patients had treatment failure, presence of Hypertension (p=0.001), Diabetes Miletus (p=0.001), ALT level >51 (p=0.013) , AST level >60 (p=0.002), AFP > 6 (p=0.000) and abnormal liver echo-pattern all were found to have an impact on SVR12 rates, some other factors were found to be insignificant as age, sex, BMI, special habits, liver function tests, haemoglobin, total leucocytic count and serum creatinine, by multivariate analysis AFP was found to be the strongest predictor of treatment failure with cut off value >6 with sensitivity 66.67% and specificity 77.31%. Conclusion: This study shows that ALT, AST, AFP, Abnormal Liver Echo-pattern and the Presence of Diabetes Miletus or Hypertension were found to affect SVR rates, but the strongest predictor was AFP level with a cutoff value of >6.

Background The production of biopolymers from waste resources is a growing trend, especially in high-population countries like Egypt. Beta-glucan (β-glucan) belongs to natural polysaccharides that are derived from plant and microbial origins. In this study, following increasing demands for β-glucan owing to its bioactive properties, a statistical model to enhance microbial β-glucan production was evaluated for its usefulness to the food and pharmaceutical industries. In addition, a trial to convert β-glucan polymer to nanostructure form was done to increase its bioactivity. Results Ingredients of low-cost media based on agro-industrial wastes were described using Plackett–Burman and central composite design of response surface methodology for optimizing yeast β-glucan. Minerals and vitamin concentrations significantly influenced β-glucan yield for Kluyveromyces lactis and nitrogen and phosphate sources for Meyerozyma guilliermondii. The maximum predicted yields of β-glucan recovered from K. lactis and M. guilliermondii after optimizing the medium ingredients were 407 and 1188 mg/100 ml; respectively. For the first time, yeast β-glucan nanoparticles (βGN) were synthesized from the β-glucan polymer using N-dimethylformamide as a stabilizer and characterized using UV–vis spectroscopy, transmission electron microscope (TEM), dynamic light scattering (DLS) and Fourier transform infrared spectroscopy (FT-IR). The average size of βGN was about 300 nm as determined by DLS. The quantitative variation of functional groups between β-glucan polymer and βGN was evaluated by FT-IR for explaining the difference in their biological activity against Normal Homo sapiens-Hela contaminant and Hepatic cancer cell lines. Conclusions Enriching the low-cost media based on agro-industrial wastes with nutritional ingredients improves the yield of yeast β-glucan. The present study succeeds to form β-glucan nanoparticles by a simple method.

The integration of renewable distributed generation (RDG) into distribution networks is promising and increasing nowadays. However, high penetration levels of distributed generation (DG) are often limited as they may have an adverse effect on the operation of distribution networks. One of the operation challenges is the interaction between DG and voltage-control equipment, e. g., an under-load tap changer (ULTC), which is basically designed to compensate for voltage changes caused by slow load variations. The integration of variable DGs leads to rapid voltage fluctuations, which can negatively affect the tap operation of ULTC. This paper investigates the impact of high penetration levels of RDG on the tap operation of ULTC in distribution networks through simulations. Various mitigation techniques that can alleviate this impact are also examined. Among these techniques, constant power-factor mode is regarded as the best trade-off between the simplicity and effectiveness of minimizing the number of tap operations. Simulations are performed on a Canadian benchmark rural distribution feeder using OpenDSS software.

Additional file 1: Figure (S1). Plot of significance effects and Pareto chart of the variables influencing β-glucan biosynthesis by Kluyveromyces lactis. Figure (S2). Plot of significance effects and Pareto chart of the variables influencing β-glucan biosynthesis by Meyerozyma guilliermondii. Figure (S3). Response optimization plots of the most significant variables affecting β-glucan biosynthesis. A: Minerals and vitamins concentration for Kluyveromyces lactis. B: Nitrogen and Phosphate concentrations for Meyerozyma guilliermondii. Figure S4. Phylogenetic tree showing the relationship of the selected yeast Kluyveromyces lactiswith other related fungal species relatives from Genbank based on their sequence homology of 18S rRNA. Figure S5. Phylogenetic tree showing the relationship of the selected yeast Meyerozyma guilliermondii with other related fungal species relatives from Genbank based on their sequence homology of 18S rRNA.

This paper presents a novel robust current droop controller (RCDC) using a single droop loop. This scheme is unified supporting dual mode of operation for micro-grids (MGs), including grid connected mode (GCM) and islanded mode (ISM) while ensuring seamless transition between the two modes with proportional power sharing maintained. The proposed controller is further incorporated with an improved maximum power point tracking (MPPT) technique presented for the parallel operation of single-stage inverters fed by multi-string PV array topology. In addition, an improved phase-locked-loop-less (PLL-less) method is presented supporting self-synchronization strategy of the parallel operation of PV-inverters with the main grid while maintaining the full capabilities of the unified control architecture. This obviates the usage of conventional PLLs, which are widely used with active synchronization techniques. The performance of the proposed control scheme is validated using real time simulations (RTS) developed by dSPACE MicroLabBox.

Wireless Sensor Networks (WSNs) have been around for over a decade and have been used in many important applications. Energy and reliability are two of the major problems with these kinds of applications. Reliable data delivery is an important issue in WSNs because it is a key part of how well data are sent. At the same time, energy consumption in battery-based sensors is another challenge. Therefore, efficient clustering and routing are techniques that can be used to save sensors energy and guarantee reliable message delivery. With this in mind, this paper develops an energy-efficient and reliable clustering protocol (ERCP) for WSNs. First, an efficient clustering technique is proposed for sensor nodes' energy savings considering different clustering parameters, including the link quality metric, the energy, the distance to neighbors, the distance to the sink node, and the cluster load metric. The proposed routing protocol works based on the concept of a reliable inter-cluster routing technique that saves energy. The routing decisions are made based on different parameters, such as the energy balance metric, the distance to the sink node, and the wireless link quality. Many experiments and analyses are examined to determine how well the ERCP performs. The experiment results showed that the ECRP protocol performs much better than some of the recent algorithms in both homogeneous and heterogeneous networks.

This paper introduces a new framework for optimum design and operation of hybrid renewable energy plants (HREP) augmented with battery energy storage systems (BESS). A new renewable energy management system (REMS) is developed comprising three components: 1) Enhanced joint forecasting of wind and solar outputs based on deep neural networks and also multiplicative weights update (MWU); 2) an advanced optimization model for sizing the HREP-BESS components and the policy of BESS operation; and 3) Augmenting the rolling hourly dispatch for HREP-BESS with a novel dynamic ramping limit and a criterion for reduction of deviations from the hour-ahead dispatch schedule. The proposed REMS tool enables maintaining the inter-hourly ramping of the HREP-BESS output within a threshold. In this context, a novel dynamic ramp limit is proposed to minimize the energy curtailment during operation and maximize energy sales to the power grid. The advantage of the proposed REMS tool over the classical renewable energy systems operation scheme is the mitigation of the volatility of renewable energy sources (RES) by suppressing extreme ramping events with minimum curtailment. Moreover, the costs and revenues of the HREP-BESS design and operation are assessed over a 25 years period. The design problem is solved for different scenarios, and the optimal solution always encloses a hybrid mix of renewables where the share of the PV plant can reach up to 37.1% of the total plant size. With the proposed REMS, the curtailment of RES never exceeds 12.9% even when the HREP is operated without a reserve margin. For the selected design, the optimum BESS capacity is 12.9% of the HREP capacity. The number of hours which observe a ramping violation event is 2.4% of the season's length (2184 hours). 99% of all ramping events fall within the defined ramping limits. The use of the MWU method increases the total profit by 2.53% compared with adopting the average forecast.

A routine task faced by Microgrid (MG) operators is to optimally allocate incoming power demand requests while accounting for the underlying power distribution network and the associated constraints. Typically, this has been formulated as an offline optimization problem for day-ahead scheduling, assuming perfect forecasting of the demands. In practice, however, these loads are often requested in an ad-hoc manner and the control decisions are to be computed without any foresight into future inputs. With this in view, the present work contributes to the modeling and algorithmic foundations of real-time load scheduling problem in a demand response (DR) program. We model the problem within an AC Optimal Power Flow (OPF) framework and design an efficient online algorithm that outputs scheduling decisions provided with information on past and present inputs solely. Furthermore, a rigorous theoretical bound on the competitive ratio of the algorithm is derived. Practicality of the proposed approach is corroborated through numerical simulations on two benchmark MG systems against a representative greedy algorithm.

In this article, a novel step-up transformerless inverter topology is presented for the grid-connected solar PV system. The proposed topology has the features of a dc–dc step-up converter and two-level inverter. First, it offers the transformerless grid connection of solar PV system with negligible leakage current and higher efficiency, and second, it provides inherent high gain step-up so that low-voltage solar PV modules can be directly connected. In addition, the proposed topology has low-voltage stress, less power losses, and requires fewer independent gate drive signals compared to the other existing transformerless inverter configurations with voltage boosting capability. The design, modes of operation, analysis, and developed PWM strategy, for the proposed inverter topology, are presented in detail. Furthermore, the system performance is validated experimentally through a 400 VA laboratory prototype. The experimental investigation demonstrates that the proposed topology results in transformerless voltage boosting while achieving improved system efficiency.

Nowadays, multilevel inverters (MLIs) are gaining huge popularity for high power transformerless PV applications. Among the traditional MLIs, the cascaded H-bridge (CHB) MLI accommodates lower voltage rated input dc sources, which reduce the voltage stress across the devices. However, the CHB MLI requires multiple PV sources as separate dc-link voltage sources, which create more paths for leakage currents. Therefore, it is a challenging task to deal with the leakage currents in the case of CHB MLIs. In this article, a novel three-phase transformerless inverter topology for grid-connected solar PV application is introduced. This proposed that the inverter topology has six switches per phase, and it has the combined advantages of dc-bypass and ac-bypass circuit configurations. A new modulation strategy is developed for the proposed topology; it is based on a sine triangle pulsewidth modulation technique combined with the dedicated logic functions. The gate pulses for all switches are provided by using these dedicated logic functions. The switching pulses obtained from the dedicated logic functions control all the inverter switches so that the variation of common-mode voltage (CMV) is constant during the inverter operation. This results in reduced leakage current throughout the operation of the inverter. The theoretical analysis, simulations, and experimental results are presented to validate the proposed topology concept for a 3-kVA grid-connected system. Both the simulation and experimental results show that the proposed solution can well attenuate the leakage current; all the results are presented in the article.

This article introduces a new time-domain fault location algorithm for two-terminal parallel transmission lines connected to large scale wind farms. The proposed algorithm employs only a half-cycle data window of synchronized current samples at both line terminals to avoid inaccurate estimation of current phasors due to the generated sub- and inter-harmonics currents by the wind farms. The proposed algorithm does not need any transformation method to decouple the double-circuit transmission line. Moreover, it takes into consideration the effect of the line asymmetry and the potential couplings between the six phases. The fault location equation is deduced by equalizing the differential components of the calculated instantaneous voltages at the fault point, and then the fault distance is estimated directly without any iterative algorithm. The two-terminal parallel transmission line is emulated by PSCAD/EMTDC platform utilizing the frequency-dependent phase model, and the required calculations for fault location are conducted by MATLAB software. The proposed algorithm is tested for several fault resistances and fault locations, and all fault types, including cross-circuit faults. In addition, the effect of measurement, synchronization, and line parameters errors on the fault location accuracy is investigated. The obtained results confirm acceptable accuracy of the proposed fault location algorithm.

The cigarette beetle, Lasioderma serricorne (F.) is a serious economic insect pest of many stored products, spices and dried fruits. This work was carried out in the Laboratory of Plant Protection Department, Faculty of Agriculture, Zagazig University, Egypt, to study the effect of eight different botanical foods as insect diets (standard insect rearing diet, wheat germ, oat grains, cumin seeds, corn flakes, chamomile flowers, roselle flowers and fenugreek seeds) on some biological aspects of this insect, population growth after one and three months as well as percentage of weight loss. Also, the effect of the heavy insect infestation on certain chemical constituents of the tested foods as food moisture content, total carbohydrates, total proteins, ash, total fats and total fibers was determined and compared to control after three months of storage. The standard insect rearing diet and wheat germ were the best foods since the insect larval period was very short (14.45 and 15.36 days, respectively), while fenugreek was less preferred food as larval diet (30.55 days). Pupal period ranged from 7.18 to 11.81 days on wheat germ and corn flakes, respectively. The shortest complete developmental period was 30.67 days on wheat germ and the longest one was 53.21 days on fenugreek seeds. Values of the susceptibility index ranged from 3.03 to 8.08% for fenugreek seeds and wheat germ, respectively. The standard insect rearing diet and wheat germ were the best food kinds since they produced the highest mean progeny number after one and three months of storage (204.33, 151.67 and 3151.30, 4313.30 adults, respectively). Mean weight loss percentage ranged from 0.52 to 5.20% on corn flakes and oat grains after one month from insect infestation, respectively. However, it reached its maximum value (31.13%) on the standard insect rearing diet and minimum value on cumin seeds (1.23%) after three months. The highest relative weight loss per each adult insect was 23.75% on chamomile flowers and the lowest one valued 2.66% on corn flakes after one month of storage. After three months the value increased on corn flakes, standard insect rearing diet, roselle flowers and cumin seeds, while decreased on the other tested food kinds. A positive correlation was found between the heavy insect infestation on some foods and the chemical constituents as moisture content, total carbohydrate, total proteins, total fats, ash and total fibers. In contrast a negative one was recorded on the other foods. Positive correlation coefficients with all tested chemical constituents on sound and infested foods were detected by highly significant for total carbohydrates and total proteins as well as significant for total fats. The heavy insect infestation increased both the progeny number and the incurred weight loss and affected the chemical constituents of the different tested food kinds compared to control. All tested food kinds were infested by the cigarette beetle L. serricorne and no immune food was found free from the insect infestation with preferable some tested food kinds to the insect. Moreover, the heavy insect infestation after three months affected the chemical constituents of the tested food kinds.

EUS-guided cystogastrostomy is a well-established advanced endoscopic technique with a steep-learning curve which necessitates an ex-vivo simulator that would allow for adequate training. The aim of this study is to evaluate the feasibility of the model in allowing training for EUS-guided cystogastrostomy using lumen-apposing metal stent (LAMS).The model was created by ROEYA Training Center, Egypt, using native porcine tissue to create fluid collections simulating both cystic and solid lesions. It was designed and tested in advance while the hydrogel was added on-site. The simulator was evaluated prospectively in five training sessions involving 17 international experts. The task was to successfully deploy the LAMS to drain the created cyst. After using the simulator, the experts were asked to fill a questionnaire to assess their experience. The primary endpoint was overall satisfaction with the model as a training tool.All of the experts were satisfied with the model as a tool to train endoscopists for the technique. 76.5% (n = 11) of the experts thought the model to be moderately realistic. Proper visualization was reported by 94.1% of the experts. All experts believed the lesions to be either slightly like or very similar to real lesions. The model was graded "easy" in difficulty by 11 of the experts.In all parameters assessed, the experts thought the model to be a useful tool for future training. This preliminary study suggests that the aforementioned simulator can be used to train endoscopists on using LAMS in a risk-free environment.

Objectives: To evaluate outcome of spreader flaps versus the spreader grafts in reconstruction of the nasal dorsum after large hump resection. This includes both functional and aesthetic aspects.Patients and Methods: Thirty patients seeking rhinoplasty for dorsal nasal hump. All surgeries were done through the closed approach. Patients were randomly divided into two groups and accordingly to the modality of nasal dorsal reconstruction after hump resection. Group (A) patients were scheduled for reconstruction using spreader grafts while in Group (B) spreader flaps were used. Evaluation included functional and aesthetic considerations. Functional subjective evaluation was done by Nasal Obstruction Symptom Evaluation (NOSE) score and objective assessment by active anterior rhinomanometry (AAR). Aesthetic evaluation included patients’ assessment of their problem according to Visual Analogue scale (VAS) while objective evaluation was done based on a suggested scoring system designed by the authors using pre and post-operative photography. The evaluating surgeons were blinded regarding the vault reconstructive modality.Results: In both groups significant improvement in both functional and aesthetic outcome - after a minimum of three months follow up- when comparing the pre and postoperative data that was obtained. There was no statistically significant difference in outcome between both groups.Conclusion: The spreader flap is a good alternative to the spreader graft in nasal dorsal reconstruction after large hump resection giving similar positive results both functionally and aesthetically however, the authors find the autospreader flap more superior to spreader graft technique as it does not require cartilage harvest.

This study proposes a new differential relay characteristic to detect internal faults on bipolar HVDC (high voltage direct current) transmission lines and discriminate accurately and in a timely manner both internal and external faults. In comparison with the conventional differential protection characteristic, the proposed relay characteristic avoids the need for an intentional time delay typically required to avoid nuisance fault detection. The sampling currents of the positive and negative poles are obtained from both line ends, and the noise is eliminated from the current signals using a stationary wavelet transform. Initially, a startup unit is utilised to activate the proposed protection scheme. The proposed current differential scheme adopts a biasing technique, which is based on the inherent transient behaviour of the bipolar HVDC system during both external and internal faults. The HVDC transmission line is implemented on the PSCAD/EMTDC platform utilising the frequency-dependent phase model, and it is tested considering numerous internal and external faults. Finally, the proposed current differential relay characteristic is tested considering different fault resistances up to 1000 Ω as well as different fault locations, including internal HVDC line faults and external faults at both rectifier and inverter sides. The obtained results confirm the robustness of the proposed protection scheme for bipolar HVDC transmission lines fault detection.

This letter proposes a hybrid Beta-kernel density estimation (KDE) model for solar irradiance probability density estimation. The model combines parametric and nonparametric approaches to avoid KDE boundary bias and obtain a more reliable statistical model of solar irradiance. The performance of the hybrid model is assessed via comparisons with the Beta distribution and two KDE models that employ different bandwidth selection methods. The assessment is carried out using the Kolmogorov–Smirnov goodness-of-fit test, and four measures of error: root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and mean bias error (MBE). Results confirm the accuracy of the hybrid model for solar irradiance modeling with percentage improvements over the Beta distribution of up to 13.8% (RMSE), 11.7% (MAE), 19.3% (MAPE), and 72.5% (MBE). The K–S test results show that the proposed Beta-KDE hybrid is the only model for which the null hypothesis is not rejected for any of the eight datasets considered in this study.

A pot experiment was consummated on Rhaphiolepis umbellata seedlings grown under seran greenhouse at Al-Zohriya Garden, Hort. Res. Inst., ARC, Giza, Egypt during the two consecutive seasons 2017 and 2018, to study effects of five foliar spray treatments of a mixture containing three growth regulators (PGR), at the same concentration for each, on plant growth and chemical composition. The three-tested PGR were NAA, GA3 and BA, while the five concentrations for each were 0 (control treatment), 100, 200, 300 and 400 ppm. Obtained results indicated that, generally, all recorded vegetative and root growth traits; i.e., plant height, stem diameter, numbers of branches and number of leaves/plant, leaf area, root length and number of roots/plant increased significantly under the effect of all tested PGR treatments, compared to the control plants. The highest increases in this regard were recorded in plants sprayed with PGR at 200 ppm, while the least values were found in the untreated control plants, followed by plants sprayed with the highest PGR concentration of 400 ppm for the two seasons. The same trend was noticed in respect of leaf chemical constituents, as plants that recorded the highest vegetative and root growth (treated 200 ppm PGR) also contained the highest values of total chlorophyll and carotenoids (mg/g f.w.) as well as percentages oftotal carbohydrate, N, P and K in leaf tissues. Accordingly, it could be recommended to apply the mixture of these three growth regulators 200 ppm on seedlings of Rhaphiolepis umbellata, two times monthly during the growing season to get the best plant growth and quality.

Solar energy is one of the most important and widely utilized renewable energy resource. Despite the many attractive features of solar-based renewables, the wide scale integration of solar generation into electric power systems presents significant challenges to network planners and operators, mainly due to the intermittent nature of solar energy. Most published work in this area is, however, focused on short-term forecasting studies used for unit commitment and energy market decisions with very little attention paid to solar irradiance probability density estimation needed for network planning and design studies. In this paper, a reliable nonparametric model of solar irradiance probability density is proposed based on the application of local linear regression in tandem with a root transformation method, introduced here for the first time. The performance of the proposed estimator is assessed via comparisons with the parametric Beta distribution (conventionally employed to model solar irradiance probability density) and two nonparametric kernel density estimation models; using the Kolmogorov–Smirnov (K–S) goodness-of-fit test, coefficient of determination ( $R^2$ ), and a number of error metrics. Results confirm the suitability and accuracy of the proposed method for solar irradiance probability density estimation.

Wireless Sensor Networks (WSNs) is the base for many critical applications where a large number of nodes are deployed in the monitored field. Those nodes suffer from limited processing capabilities as well as energy. They are also required to live for a long time; sensors send their data to the sink node through multi-hop routing. The sink node is responsible for making the appropriate decision based on the received data. This shows how critical the information fusion process at the sink node. Such fusion is affected by the quality of the collected information from the deployed sensors. In addition, information handling in the WSN is also affected by environmental conditions, sensors energy, and data reliability, which play important roles in the eminence of information fusion. This paper introduces an efficient environment-aware fusion-based reliable routing algorithm named E3AF. The algorithm takes different parameters into consideration, including environmental metrics, energy consumption balance among sensor nodes, and network and data reliability. The algorithm models the environment to allow the routing algorithm to avoid going through areas marked dangerous. The paper formulates the problem into an optimization problem, which is structured as an Integer Linear programming (ILP) to help grasp and obtain the optimum solution. Furthermore, the paper presents swarm intelligence as a heuristic algorithm when the optimal solution is not possible. In comparison to EFMRP, some of the used equations were corrected, and more realistic conditions and constraints were added. With extensive experiments, the proposed algorithm is evaluated and proved its efficiency.

Internet of things (IoT) is one of the leading technologies that have been used in many fields, such as environmental monitoring, healthcare, and smart cities. The core of IoT technologies is sensors; sensors in IoT form an autonomous network that is able to route messages from one place to another to the base station or the sink. Recently, due to the rapid technological development of sensors, wireless sensor networks (WSNs) have become an important part of IoT. However, in applications such as smart cities, WSNs with one sink might not be suitable due to the limited communication range of sensors and the wide area to be covered. Therefore, multi-sink WSN solutions seem to be suitable for such applications. The multi-sink WSNs are gaining popularity because they increase network throughput, network lifetime, and energy usage. At the same time, multi-hop routing is essential for the WSNS to collect data from sensor nodes and route it to the sink node for decision-making. Many routing algorithms developed for multi-sink WSNs focus on being energy efficient to extend the network lifetime, but the delay was not the main concern. However, these algorithms are unable to deal with such applications in which the data packets have to reach sink nodes within predefined real-time information. On the other hand, in the most existing routing schemes, the effects of the external environmental factors such as temperature and humidity and the reliability of real-time data delivery have largely been ignored. These issues can dramatically influence the network performance. Therefore, this paper designs a routing algorithm that satisfies three critical conditions: energy-efficient, real-time, environment-aware, and reliable routing. Therefore, the routing decisions are made according to different parameters. Such parameters include environmental impact metrics, energy balance metrics to balance the energy consumption among sensor nodes and sink nodes, desired deadline time (required delivery time), and wireless link quality. The problem is formed in integer linear programming (ILP) for optimal solution. The problem formulation is designed to fully understand the problem with its major constraints by the sensor networks research community. In addition, the optimal solution for small-scale problems could be used to measure the quality of any given heuristic that might be used to solve the same problem. Then, the paper proposes swarm intelligence to solve the optimization problem for large-scale multi-sink WSNs as a heuristic algorithm. The proposed algorithm is evaluated and analyzed compared with two recent algorithms, which are the most related to our proposal, SMRP and EERP protocols using an extensive set of experiments. The obtained results prove the superiority of the proposed algorithm over the compared algorithms in terms of packet delivery ratio, deadline miss ratio, average end-to-end delay, network lifetime, and energy imbalance factor under different aspects. In particular, the proposed algorithm requires more computational energy compared to comparison algorithms.

A high gain multilevel inverter (HGMLI) based solar power transfer system (SPTS) is proposed in this paper. The five-level symmetrical source topology consists of eight semiconductor switches and two non-isolated DC sources. The HGMLI is comprised of a multilevel generator unit and a polarity alternating unit. Four switches in the circuit operate to achieve unipolar multiple levels and another four switches change the alternate polarity to get the desired five-level AC output voltage. Four switches are operated in level-shifted pulse width modulation and the other four are at the fundamental frequency. Moreover, the polarity alternating switches are operated at zero voltage levels to reduce the switching losses. A voltage booster circuit extracts maximum power from the photovoltaic (PV) array and exhibits a high gain in the voltage transformation for obtaining the desired DC-link voltage even for a low input voltage. The MLI is controlled by a second-order generalized integral-based filter to integrate active power to the grid. At the point of common coupling (PCC), a nonlinear harmonic load is connected, and its impact on grid power quality is mitigated by the SPTS operation maintaining within IEEE limits. Hence, maximum power extraction, active power injection, and power quality enhancement are the major objectives of this work. The proposed system performance is validated at different operating conditions with extensive analysis in the MATLAB/Simulink model.