Evaluation of Flashover Voltage Levels of Contaminated Hydrophobic Polymer Insulators Using Regression Trees, Neural Networks, and Adaptive Neuro-Fuzzy

Polluted insulators at high voltages has acquired considerable importance with the rise of voltage transmission lines. The contamination may lead to flashover voltage. As a result, flashover voltage could lead to service outage and affects negatively the reliability of the power system. This paper presents a dynamic model of ac 50Hz flashover voltages of polluted hydrophobic polymer insulators. The models are constructed using the regression tree method, artificial neural network (ANN), and adaptive neuro-fuzzy (ANFIS). For this purpose, more than 2000 different experimental testing conditions were used to generate a training set. The study of the ac flashover voltages depends on silicone rubber (SiR) percentage content in ethylene propylene diene monomer (EPDM) rubber. Besides, water conductivity (μS/cm), number of droplets on the surface, and volume of water droplet (ml) are considered. The regression tree model is obtained and the performance of the proposed system with other intelligence methods is compa ed. It can be concluded that the performance of the least squares regression tree model outperforms the other intelligence methods, which gives the proposed model better generalization ability

Implementation of MPPT Algorithm for Single-Stage Grid-Connected Photovoltaic system by using incremental conductance method

This paper presents simulation and implementation of maximum power point tracking algorithm for single stage three-phase grid-connected PV system by using incremental conductance method. The maximum efficiency is realized when PV works at its maximum power point, which is contingent on irradiation and temperature. Since the irradiation and temperature always change with time, a PV system which able to track the maximum power point needs to be established to produce more energy. the IC method shows a superior performance, lower oscillation and it took 3second to match MPP that abest time to give stability to the system. the control strategy is supported out using MATLAB/Simulink and experimentally validated with a dSPACE MicroLabBox controller.</p

Identification of Putative SNP Markers Associated with Resistance to Egyptian Loose Smut Race(s) in Spring Barley

Loose smut (LS) disease is a serious problem that affects barley yield. Breeding of resistant cultivars and identifying new genes controlling LS has received very little attention. Therefore, it is important to understand the genetic basis of LS control in order to genetically improve LS resistance. To address this challenge, a set of 57 highly diverse barley genotypes were inoculated with Egyptian loose smut race(s) and the infected seeds/plants were evaluated in two growing seasons. Loose smut resistance (%) was scored on each genotype. High genetic variation was found among all tested genotypes indicating considerable differences in LS resistance that can be used for breeding. The broad-sense heritability (H2) of LS (0.95) was found. Moreover, genotyping-bysequencing (GBS) was performed on all genotypes and generated in 16,966 SNP markers which were used for genetic association analysis using single-marker analysis. The analysis identified 27 significant SNPs distributed across all seven chromosomes that were associated with LS resistance. One SNP (S6_17854595) was located within the HORVU6Hr1G010050 gene model that encodes a protein kinase domain-containing protein (similar to the Un8 LS resistance gene, which contains two kinase domains). A TaqMan marker (0751D06 F6/R6) for the Un8 gene was tested in the diverse collection. The results indicated that none of the Egyptian genotypes had the Un8 gene. The result of this study provided new information on the genetic control of LS resistance. Moreover, good resistance genotypes were identified and can be used for breeding cultivars with improved resistance to Egyptian LS

Oscillation Damping Neuro-Based Controllers Augmented Solar Energy Penetration Management of Power System Stability

The appropriate design of the power oscillation damping controllers guarantees that distributed energy resources and sustainable smart grids deliver excellent service subjected to big data for planned maintenance of renewable energy. Therefore, the main target of this study is to suppress the low-frequency oscillations due to disruptive faults and heavy load disturbance conditions. The considered power system comprises two interconnected hydroelectric areas with heavy solar energy penetrations, severely impacting the power system stabilizers. When associated with appropriate controllers, FACTs technology such as the static synchronous series compensator provides efficient dampening of the adverse power frequency oscillations. First, a two-area power system with heavy solar energy penetration is implemented. Second, two neuro-based controllers are developed. The first controller is constructed with an optimized particle swarm optimization (PSO) based neural network, while the second is created with the adaptive neuro-fuzzy. An energy management approach is developed to lessen the risky impact of the injected solar energy upon the rotor speed deviations of the synchronous generator. The obtained results are impartially compared with a lead-lag compensator. The obtained results demonstrate that the developed PSO-based neural network controller outperforms the other controllers in terms of execution time and the system performance indices. Solar energy penetrations temporarily influence the electrical power produced by the synchronous generators, which slow down for uncomfortably lengthy intervals for solar energy injection greater than 0.5 pu. © 2023 by the authors.Ministry of Science, ICT and Future Planning, MSIP: 2019M3F2A1073164; National Research Foundation of Korea, NRFThis research was supported by the Energy Cloud R&D Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT (2019M3F2A1073164)

Integrated supply–demand energy management for optimal design of off-grid hybrid renewable energy systems for residential electrification in arid climates

The growing research interest in hybrid renewable energy systems (HRESs) has been regarded as a natural and yet critical response to address the challenge of rural electrification. Based on a Bibliometric analysis performed by authors, it was concluded that most studies simply adopted supply-side management techniques to perform the design optimization of such a renewable energy system. To further advance those studies, this paper presents a novel approach by integrating demand-supply management (DSM) with particle swarm optimization and applying it to optimally design an off-grid hybrid PV-solar-diesel-battery system for the electrification of residential buildings in arid environments, using a typical dwelling in Adrar, Algeria, as a case study. The proposed HRES is first modelled by an in-house MATLAB code based on a multi-agent system concept and then optimized by minimizing the total net present cost (TNPC), subject to reliability level and renewable energy penetration. After validation against the HOMER software, further techno-economic analyses including sensitivity study are undertaken, considering different battery technologies. By integrating the proposed DSM, the results have shown the following improvements: with RF = 100%, the energy demand and TNPC are reduced by 7% and 18%, respectively, compared to the case of using solely supply-side management. It is found that PV-Li-ion represents the best configuration, with TNPC of $23,427 and cost of energy (COE) of 0.23$/kWh. However, with lower RF values, the following reductions are achieved: energy consumption (19%) and fuel consumption or CO 2 emission (57%), respectively. In contrast, the RF is raised from 15% (without DSM) to 63% (with DSM). It is clear that the optimal configuration consists of wind-diesel, with COE of 0.21 $/kWh, smaller than that obtained with a stand-alone diesel generator system. The outcomes of this work can provide valuable insights into the successful design and deployment of HRES in Algeria and surrounding regions

Alternative Control of Tomato Wilt Using the Aqueous Extract of Calotropis procera

Calotropis procera (Aiton) Dryand (commonly known as the apple of sodom, calotrope, and giant milkweed) is an evergreen, perennial shrub of the Apocynaceae family, and is mainly found in arid and semi-arid regions. Previous studies have established the toxic effects of Calotropis procera (C. procera) (Aiton). Its extract is used as one of the vital alternatives to pesticides due to its effective impacts on several pathogens and to combat insect pests that cause severe damage to several crops, but so far, its effects on combating Fusarium oxysporum f. sp. lycopersici (FOL) have not yet been evaluated. Therefore, the current study focuses on evaluating the effects of the aqueous extract of this plant on FOL by studying physicochemical indicators. In Saudi Arabia and many other countries, such disease is considered as one of the critical factors affecting tomato growth and production. For the control of Fusarium wilt disease (Fwd), many strategies have been developed, but are limited in their use. Ten isolates of the pathogens were obtained from diseased tomato plants. These isolates were able to exhibit symptoms of tomato wilt, and they varied in their virulence. Isolate No. 5 caused the highest severity of the disease (73%). According to these results, we used this isolate in the following experiment, which was identified by ITS sequences as F. oxysporum f. sp. lycopersici. Following intensive screening studies, we selected aqueous plant extract of C. procera to study its effectiveness against Fwd of tomato plants and their antifungal activity or induce systemic resistance in plants. All concentrations of C. procera extracts suppressed growth of the pathogen. The highest reduction of mycelia growth was obtained by aqueous extract at 15%, it reduced the pathogen growth to 70.2%, relative to the control. In greenhouse conditions, the aqueous C. procera extract at 5, 10, and 15% significantly reduced Fwd of the tomato compared to the infected control (52.01, 69.86, and 83.6%, respectively). The use of aqueous C. procera extract at 5, 10, and 15% significantly enhanced the fresh and dry weight of tomato plants (g plant&minus;1) compared to inoculated plants (for fresh weight: 50, 66.6, and 86.6 and for dry weight: 60, 100, and 120%, respectively). Total phenols and flavonoids as well as antioxidant enzymes were increased in inoculated or non-inoculated tomato plants after being treated with aqueous extracts of C. procera

Identification of Putative SNP Markers Associated with Resistance to Egyptian Loose Smut Race(s) in Spring Barley

Loose smut (LS) disease is a serious problem that affects barley yield. Breeding of resistant cultivars and identifying new genes controlling LS has received very little attention. Therefore, it is important to understand the genetic basis of LS control in order to genetically improve LS resistance. To address this challenge, a set of 57 highly diverse barley genotypes were inoculated with Egyptian loose smut race(s) and the infected seeds/plants were evaluated in two growing seasons. Loose smut resistance (%) was scored on each genotype. High genetic variation was found among all tested genotypes indicating considerable differences in LS resistance that can be used for breeding. The broad-sense heritability (H2) of LS (0.95) was found. Moreover, genotyping-by-sequencing (GBS) was performed on all genotypes and generated in 16,966 SNP markers which were used for genetic association analysis using single-marker analysis. The analysis identified 27 significant SNPs distributed across all seven chromosomes that were associated with LS resistance. One SNP (S6_17854595) was located within the HORVU6Hr1G010050 gene model that encodes a protein kinase domain-containing protein (similar to the Un8 LS resistance gene, which contains two kinase domains). A TaqMan marker (0751D06 F6/R6) for the Un8 gene was tested in the diverse collection. The results indicated that none of the Egyptian genotypes had the Un8 gene. The result of this study provided new information on the genetic control of LS resistance. Moreover, good resistance genotypes were identified and can be used for breeding cultivars with improved resistance to Egyptian LS