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Öğe Adaptive FEM-BPNN model for predicting underground cable temperature considering varied soil composition(Elsevier - Division Reed Elsevier India Pvt Ltd, 2024) Al-Dulaimi, Abdullah Ahmed; Guneser, Muhammet Tahir; Hameed, Alaa Ali; Marquez, Fausto Pedro Garcia; Gouda, Osama E.In underground cables of power systems, the maximum temperature of the cable is a crucial factor in determining its capacity. According to standards, the permissible operating temperature for the XLPE cable conductor under steady-state conditions is 90 degree celsius - a limit that should not be exceeded. Exceeding this temperature may lead to a thermal breakdown in the cable insulation, thereby resulting in interruption of the electrical power supply. Many factors affect the cable temperature, particularly through the processes of heat dissipation and diffusion from the cable into its surroundings. These factors include soil types and compositions, cable installation configuration, and thermo physical properties; therefore, accurate analysis of these factors is crucial for cable loading. In this study, the finite element method (FEM) is employed to predict the cable temperature considering different soil compositions and to present a new approach for the thermal analysis of an underground cable system. The novel approach considers various environmental conditions including single-layer and multi-layer soil types, homogeneous and non-homogeneous soil compositions, two configuration types - flat and trefoil - as well as two types of backfill materials, specifically sand-cement mixture backfill (SCMB) and fluidized thermal backfill (FTB), and dry zones to offer deeper insight into a thermal analysis. Given that the FEM requires the construction of a complex geometric model within an optimal operating condition to obtain results with high accuracy-a process that can often be complex as well as not adaptable because it depends on constant mathematical calculation-This paper presents a novel approach FEM-BPNN that uses an adaptive Backpropagation neural networks (BPNN) model as its mainstay. The proposed BPNN model exploits historical data from FEM to refine its predictive power, therefore, increasing its efficiency and accuracy. Furthermore, the model is subject to an optimization process, adjusting and refining its internal parameters in response to new data, with the ultimate goal of improving the predictive model capabilities for the temperature of underground power cables. The results underscored the high performance of FEM in the simulation, and it was observed that FEM yielded results closely aligned with those of the IEC standard. Moreover, the proposed FEM-BPNN demonstrated exceptional accuracy, achieving a low RMSE score of 0.008. It also exhibited impressive performance in the linear regression analysis, with an R-2 value of 0.99. These metrics collectively signify the robustness and efficacy of the model.Öğe Artificial intelligence solution to extract the dielectric properties of materials at sub-THz frequencies(Inst Engineering Technology-Iet, 2019) Guneser, Muhammet TahirMaterial characterisation plays a crucial role in many applications such as security, military, communication, bioengineering, medical treatment, food industry and material processing. Since it is useful to identify other properties of materials often tied to other useful parameters, such as stress-strain relation, bio content, moisture content, materials density and so on, the dielectric properties of materials should be achieved with high accuracy using appropriate measurement techniques and extraction techniques. There are many measurement methods to determine the dielectric properties of materials, which depend on parameters such as frequency range, material phase and temperature. In this study, the measurement methods and extraction techniques have been discussed, and alternative ways have been presented with experimental and simulation results. Furthermore, a new numerical extraction technique has been performed to achieve the dielectric properties of materials.Öğe Automated Classification of Snow-Covered Solar Panel Surfaces Based on Deep Learning Approaches(Tech Science Press, 2023) Al-Dulaimi, Abdullah Ahmed; Guneser, Muhammet Tahir; Hameed, Alaa Ali; Salman, Mohammad ShukriRecently, the demand for renewable energy has increased due to its environmental and economic needs. Solar panels are the mainstay for dealing with solar energy and converting it into another form of usable energy. Solar panels work under suitable climatic conditions that allow the light photons to access the solar cells, as any blocking of sunlight on these cells causes a halt in the panels work and restricts the carry of these photons. Thus, the panels are unable to work under these conditions. A layer of snow forms on the solar panels due to snowfall in areas with low temperatures. Therefore, it causes an insulating layer on solar panels and the inability to produce electrical energy. The detection of snow-covered solar panels is crucial, as it allows us the opportunity to remove snow using some heating techniques more efficiently and restore the photovoltaics system to proper operation. This paper presents five deep learning models,-16,-19, ESNET-18, ? ESNET-50, and ? ESNET-101, which are used for the recognition and classification of solar panel images. In this paper, two different cases were applied; the first case is performed on the original dataset without trying any kind of preprocessing, and the second case is extreme climate conditions and simulated by generating motion noise. Furthermore, the dataset was replicated using the upsampling technique in order to handle the unbalancing issue. The conducted dataset is divided into three different categories, namely; all_snow, no_snow, and partial snow. The five models are trained, validated, and tested on this dataset under the same conditions 60% training, 20% validation, and testing 20% for both cases. The accuracy of the models has been compared and verified to distinguish and classify the processed dataset. The accuracy results in the first case show that the compared models-16,-19, ESNET-18, andESNET-50 give 0.9592, while R ESNET-101 gives 0.9694. In the second case, the models outperformed their counterparts in the first case by evaluating performance, where the accuracy results reached 1.00, 0.9545, 0.9888, 1.00. and 1.00 for-16,-19, R ESNET-18 and R ESNET-50, respectively. Consequently, we conclude that the second case models outperformed their peers.Öğe A data-driven approach for diagnosing degradation in lithium-ion batteries using data transformation techniques and a novel deep neural network(Pergamon-Elsevier Science Ltd, 2024) Al-Dulaimi, Abdullah Ahmed; Guneser, Muhammet Tahir; Hameed, Alaa AliAccurate diagnosis of Lithium -ion batteries (Li -ion batteries) degradation plays a critical role in improving the maintenance of energy storage technology. This paper presents a method based on a novel deep network model combined with a data transformation technique to diagnose Li -ion battery degradation modes. Different from conventional studies based on specific experimental and numerical methods to estimate and predict the degradation, the proposed method is based on data -driven approach, by leveraging datasets consisting of voltage/capacity curves, these were converted into incremental capacity (IC) curves and then transformed into images using the gramian angular summation field (GASF) technique. The study adopted two models: Inception -v3 and the proposed model, both underwent fine-tuning and a subsequent transfer learning process. Degradation modes, namely loss of lithium inventory (LLI) and the loss of active materials in both the positive (LAMPE) and negative electrodes (LAMNE), were diagnosed in relation to IC curves. Finally, the model was tested using two different datasets, and the results showed that the proposed method achieved high performance, especially across three Li -ion batteries, three degradation modes, three cells, and various cycles (totaling 378 cases) the proposed method outperformed in 233 cases, thereby outperforming other methods in comparison. Our method provides a flexible data -driven approach that accurately predicts various degradation modes across different cell chemistries throughout their lifespan.Öğe Design and Analysis of Compact Triband Microstrip Antenna for X Band and Ku Band(Ieee, 2020) Guneser, Muhammet Tahir; Seker, Cihat; Kersolar, Seda; Telli, Ibrahim; Dogangun, Sevda Dilek; Shaaban, FatemaIn this paper presents a tri-band, monopole, compact microstrip antenna design that can operate in the X-band and Ku-band for fifth generation (5G) communication technology. The proposed antenna operates in the frequency range 10.37 to 10.69 GHz, 11.65 to 11.71 GHz in the X (8-12 GHz) band and in the range 14.14 to 14.20 GHz in the Ku (kurz-under 12-18 GHz) band. The proposed antenna is designed on a RO3210 plate with a dielectric constant (epsilon(r)) of 10.2 and a thickness of 1.27 mm In this design, circular slots are made on the radiation plane, thereby increasing the gain and bandwidth. The antenna was simulated in the HFSS simulation environment developed by Ansys. Compared with the other microstrip antennas in the literature, it is observed that the bandwidth and gain parameters are superior.Öğe Design and Comparative Analysis of a Microstrip Patch Antenna With Different Feed Technique at 2.4 GHz for Wireless Applications(Institute of Electrical and Electronics Engineers (IEEE), 2024-12-17) Hanashi, Salem Mohamed Al; Almohamad, Tarik Adnan; Aladwani, Azam Isam; Aziz, Ahlem; Guneser, Muhammet Tahir; Albreem, Mahmoud A.Nowadays, according to the wireless application's requirements, which grow very fast, the antenna designs must enhance their output parameters to be compatible with the new generation's demand and to have the advantages of being less weight, low cost and highly reliable. In this paper, we present the design and analysis of a Microstrip Patch Antenna (MPA) with different feeding techniques with the same parameters at 2.4 GHz for the applications of wireless communications, the CST 2020 software was used for antenna simulation, and the comparison was made among Microstrip Patch (MP) Antenna with Inset feed line and else with coaxial feeding. The results show that The MPA fed by a coaxial probe yields superior performance in the standing of return loss, bandwidth, and Voltage Standing Wave Ratio (VSWR) compared to the antenna utilizing an inset feed line.Öğe Development of extraction techniques for dielectric constant from free-space measured S-parameters between 50 and 170 GHz(Springer, 2017) Ozturk, Turgut; Elhawil, Amna; Uluer, Ihsan; Guneser, Muhammet TahirThis paper is a comprehensive study on Newton-Raphson technique used in millimeter wave frequencies for material characterization. Various algorithms are used for extracting the complex permittivity of a material from measured S-parameters. Efficiency of the methods depends on the initial guess and the accuracy of measured S-parameters for each thickness and frequency band. In this paper, we suggest the initial-value estimation method that helps to estimate a proper value for starting the algorithm. Moreover, an alternative extraction process is modelled that does not require new measured S-parameters or extracting process for each frequency band and thickness with a composed database. The estimation process is conducted partially as V (50-75 GHz), W (75-110 GHz), and D (110-170 GHz) frequency bands.Öğe Efficient 5.8 GHz Microstrip Antennas for Intelligent Transportation Systems: Design, Fabrication, and Performance Analysis(Mdpi, 2024) Guneser, Muhammet Tahir; Seker, Cihat; Guler, Mehmet Izzeddin; Fitriyani, Norma Latif; Syafrudin, MuhammadIn this study, we designed a high-performance, compact E-shaped microstrip antenna optimized for intelligent transportation systems, operating at 5.8 GHz. Utilizing simulation tools such as CST Studio Suite 2022 Learning Edition, Ansys HFSS 2022 R1, and MATLAB 2022b PCB Antenna Designer, we ensured consistent physical parameters. Fabricated with a 1.6 mm thick FR-4 substrate and a 50 Omega microstrip line-feeding technique, the antenna measures 35 x 50 x 1.6 mm(3), smaller than already existing designs. At 5.75 GHz, it exhibits a return loss of -23.68 dB and a VSWR of 1.140 dB, ensuring stable performance within the desired frequency band. Our findings recommend its integration into vehicle-to-infrastructure wireless communication systems. Comparison across simulation environments and laboratory measurements highlights the close alignment of results with those from Ansys HFSS 2022 R1, affirming its reliability.Öğe Energy Management Techniques in Off Grid Energy Systems: A Review(Springer International Publishing Ag, 2022) Elweddad, Mohamed; Guneser, Muhammet Tahir; Yusupov, ZiyodullaEnergy management system (EMS) algorithms and strategies are improved to make sure power continuity in all circumstances, minimizing energy production cost and protect grid components from being damaged. Energy management presents a viable solution to issues relating to the energy sector, such as rising demand, rising energy costs, sustainable supply, and environmental impact. The approaches performing energy management strategies, solution algorithms, and systems simulations to overcome many problems in low voltage distribution systems. Furthermore, in this paper some techniques and methodologies are considered to improve energy management of off-grid power systems with microgrid. The reviewed works in this paper cover the various structures of off-grid hybrid microgrids. The most common technologies and strategies have been used in the field of power management, in addition, providing of some future research directions.Öğe Frequency dependent electrical and dielectric properties of the Au/(RuO2: PVC)/n-Si (MPS) structures(Elsevier, 2023) Guneser, Muhammet Tahir; Elamen, Hasan; Badali, Yosef; Altindal, SemsettinIn this study, the electrical and dielectric characteristics of the Au/(RuO2:PVC)/n-Si structures were analyzed using the impedance spectroscopy method, including capacitance/conductance (C - G/omega) measurements in wide voltage and frequency ranges (+4 V, 5 kHz - 5 MHz) at room temperature. The main electrical parameters such as concentration of donor atoms (N-D), diffusion potential (V-D), depletion layer thickness (W-D), Fermi energy level (E-F), barrier height (phi(B)), and maximum electric field (E-m) were extracted for each measured frequency. The phi(B), W-D, and E-F values are increasing with increased frequency, while N-D and E-m exponentially decrease. The surfacestates (N-SS) were evaluated using the low-high-frequency capacitance technique. Furthermore, the basic dielectric parameters such as tangent-loss (tan delta), electrical conductivity (sigma(ac)), real and imaginary parts of epsilon*, electric-modulus (M*), and complex impedance (Z*) were investigated. The obtained results indicate that the N-SS, and RuO2:PVC organic interlayer are more effective on C and G/omega measurements.Öğe Grey wolf optimization for optimum sizing and controlling of a PV/WT/BM hybrid energy system considering TNPC, LPSP, and LCOE concepts(Taylor & Francis Inc, 2022) Tabak, Abdulsamed; Kayabasi, Erhan; Guneser, Muhammet Tahir; Ozkaymak, MehmetIn this study, energy demand of a faculty was aimed to supply with a hybrid energy system (HES) consisting of photovoltaic (PV) panels, wind turbine (WT) and bomass (BM) system with optimum power usage distribution and sized to reach a lowest cost and a reliable system. In this optimization, total net present cost (TNPC) for economic analysis, loss of power supply probability (LPSP) for reliability, and localized cost of energy (LCOE) for determining the unit energy cost were considered and an effective control algorithm was developed to decide the power source for improving system reliability. We used genetic algorithm (GA) and simulated annealing (SA), which are commonly used in the literature. On the other hand, we utilized the Grey Wolf Optimizer (GWO), which was recently found out and inspired by the hierarchy and hunting instincts of grey wolves. The results of GWO algorithm were also compared with GA and SA and confirmed that GWO is satisfying. GWO achieved better results to solve problems by setting LPSP to both 0.02 and 0.01 upper limits. When LPSP set to 0.02 maximum point, GWO suggested PV system at 86.39 kW power and BG at 50 kW power. Consequently, the energy requirement of a faculty was supplied by an optimized and designed PV/WT/BM HES. In addition, by the installation of optimized system, 144.29 tons of CO2 emissions per year will be reduced.Öğe The Importance of Mode Spacing of Multimode Laser Diode to Generate the THz Signal(Ieee, 2018) Ozturk, Turgut; Guneser, Muhammet TahirWhen examined Time Domain Spectroscopy system which uses multimode laser diodes (MLD) for THz signal generation, the importance of mode spacing of MLD is remarkable. When the previous studies are compared with each other, it has been determined that the wavelength distance between each mode of MLD should be small. In this context, the optical spectrum analyzer (OSA), which examine the mode spacing, can be used to predict the success of the MLD in the THz signal generation. Thus, in order to be able to choose the most suitable MLD (cheap and compact) to be used in a TDS system, the mode spacing analysis will be sufficed.Öğe Improving the Energy Management of a Solar Electric Vehicle(Univ Suceava, Fac Electrical Eng, 2015) Guneser, Muhammet Tahir; Erdil, Erzat; Cernat, Mihai; Ozturk, TurgutA solar electric vehicle (SEV) is an electric vehicle (EV) with onboard photovoltaic cells charging a set of batteries for extended driving range. This study aimed to improve the energy management system of a SEV, called YILDIZ, using a fuzzy logic control system (FLC). A MATLAB based simulation model of three basic components of a solar car: solar cell modules, batteries and motor drive system was performed. An original FLC was developed. For proving its applicability, the performances of the SEV were tested by simulation, in accordance with the standard test drive cycle ECE-15. The characteristics obtained with the original Proportional Integral Fuzzy Logic Control (PI-FLC) were compared with those obtained with a classical Proportional Integral (PI) controller. Using the designed model, we calculated the range of YILDIZ with and without PV feeding which gave us an opportunity to study and compare both SEV and EV models on real race-track situation. Then the optimum speed, at any time, which enabled the vehicle to reach a chosen destination as quickly as possible, while fully using the available energy, was calculated. Proposed solutions tested on YILDIZ. Results of simulations were compared with YILDIZ run on the Formula-G race track in Izmit, Turkey.Öğe An Induction Motor Design for Urban Use Electric Vehicle(Ieee, 2016) Guneser, Muhammet Tahir; Dalcali, Adem; Ozturk, Turgut; Ocak, Cemil; Cernat, MihaiBecause of fossil fuel shortage and its damages on environment, electric vehicles (EV) are going to be more popular in science and car industry as well. So, EV's take an opportunity to spread over the traffic finally. But battery, which is the main energy supplier of the motor, has disadvantages as big volume and weight. Scientists try to enhance energy densities, ranges and quicken charging. All of research results force us to drive EV in a loop, close to charging stations for near future. So EV's should be designed by considering urban road and speed conditions as well. In this survey, we designed a new two pole 7,5 kW induction motor for a certain electric vehicle, which was designed to use urban drive. We considered urban traffic rules and conditions, while designing the motor. We simulated and verified analytical and finite element analysis of the designed induction motor. We also modelled and simulated the EV with the induction motor on MATLAB to observe control performance of field oriented control (FOC) on several urban use scenarios. According to our results, EV with designed motor can be realized and controlled.Öğe Intelligent Energy Management and Prediction of Micro Grid Operation Based On Machine Learning and Genetic Algorithm(Int Journal Renewable Energy Research, 2022) Elweddad, Mohamed; Guneser, Muhammet TahirMicro grid energy management has become critically important due to inefficient power use in the residential sector. High energy consumption necessitates developing a strategy to manage the power flow efficiently. For this purpose, this work has been divided into two phases: The first is the ON/OFF operation, which has been executed using a genetic algorithm for the hybrid system, including diesel generator, solar photovoltaic (PV), wind turbine, and battery. Then, in the second phase, the output results were used as input in three algorithms to predict load and supply dispatch one month ahead. This study has two objectives; the first is to decide which energy source should meet the load one month ahead. The second is to compare the outcomes of machine-learning techniques, namely Random Forest (RF), Decision Tree (DT), and K-Nearest Neighbours (KNN), to determine the one that performs the best. The results indicated that the DT technique has the best performance in the application of classification with an accuracy of 100%. The findings also show that the RF approach gives acceptable results with an accuracy of up to 98%, and the KNN algorithm was poor in terms of accuracy with a value of 28%.Öğe Millimeter-wavepropagation modeling and characterization at 32GHzin indoor office for5Gnetworks(Wiley, 2020) Seker, Cihat; Guneser, Muhammet Tahir; Arslan, HuseyinSince it has a great bandwidth that supports gigabit communication, it is considered to use the millimeter-wave (mmWave) band in the fifth generation (5G) wireless communication. Therefore, an efficient, reliable, and accurate channel model is of vital importance in mmWave bands for indoor environments, especially in the 31.8 to 33.4 GHz band allocated by ITU for 5G communications. In this article, we performed modeling and characterization campaign at the 32 GHz in a typical indoor office environment on fourth floor of the Engineering Faculty in University of Karabuk, Turkey. The obtained results provide large-scale fadings such as path loss, shadowing, root mean square (RMS) delay spread, RMS angular spread, power angular spectrum, number of clusters, and Ricean K-factor in an open-plan indoor environment. Power angular spectrum is used to comprehend the propagation structure. We propose that the results obtained in this study will play a key role in simulating and planning systems at 32 GHz for 5G wireless communication.Öğe A Multi-Objective Optimization for enhancing the efficiency of Service in Flying Ad-Hoc Network Environment(Inst Computer Sciences, Social Informatics & Telecommunications Eng-Icst, 2023) Nahi, Hayder A.; Al-dolaimy, F.; Abbas, Fatima Hashim; Almohamadi, Mohammed; Hasan, Mustafa Asaad; Alkhafaji, Mohammed Ayad; Guneser, Muhammet TahirFlying Ad-hoc Network (FANET) is one among the emerging technology and it is used in the huge application of the intelligent communication system. FANETs are combined with multiple Unmanned Aerial Vehicles (UAVs) to control the complex environment. Due to high mobility in FANETs the computation overhead and computation delay of the network is greatly increased that reflects in the reduction of the performance of FANETs. So it becomes very essential to provide effective routing and optimization in FANETs to maintain the stable communication. For that purpose, in this paper MultiObjective Hybrid Optimization for Quality of Service (QoS) Assisted Flying Ad-Hoc Network (MOHOQFANET) approach is proposed with the combination of Ant colony optimization (ACO) and particle swarm optimization (PSO). To achieve effective routing in FANETs, reliability of ad-hoc that depend on demand vector routing (RAODV). In order to perform initial shortest path selection in FANETs, ACO algorithm is utilized. The PSO optimization is applied in FANETs to achieve the best optimal solution between the flying nodes during the time of communication between them. The MOHOQ-FANET technique is implemented using NS2 as the platform. As well as being compared to earlier studies like CSPO-FANET and OSNP-FANET, the performance of the FANETs is assessed using metrics like ratio of packet delivery, host-to-host delay, routing overhead, and network throughput. The outcomes have illustrated, as compared to earlier systems, the proposed MOHOQ-FANET approach delivers high packet delivery ratio and throughput as well as reduced host-to-host delay and routing overhead.Öğe Multi-Objective Optimization Method for Proper Configuration of Grid-Connected PV-Wind Hybrid System in Terms of Ecological Effects, Outlay, and Reliability(Springer Singapore Pte Ltd, 2021) Elbaz, Abdurazaq; Guneser, Muhammet TahirThe assessment of the performance of grid hybrid frameworks depends primarily on the costs and reliability, associated with reduced greenhouse gas (GHG) emissions of the system. In this work, with objectives based on the minimization of two optimization features, namely loss of power supply probability (LPSP) and cost of energy (COE), multi-objective optimization of a grid-connected PV/wind turbine framework was implemented in the Faculty of Engineering in Gharyan, Libya, with the aim of providing adequate electricity, while optimizing the system's renewable energy fraction (REF) was the third objective. This research also aimed to estimate the resulting amount of power produced by the hybrid system and mathematical models were submitted. The results showed the share of the total energy supplying the electricity demand for each part of the network. This study subsequently explored the interrelationship of the grid and the proposed hybrid system in relation to the capacity of the network to sell or obtain electricity from the hybrid system. In addition, multi-objective bat algorithm (MOBA) findings were divided into three dominant regions: the first region was the economically optimal solution (lowest COE), the second region was the conceptual perspective of utilizing renewable energies (highest REF), and the final region was the optimal solution with optimal environmental effects (lowest GHG emissions).Öğe Multi-objective Optimization of Combined Economic Emission Dispatch Problem in Solar PV Energy Using a Hybrid Bat-Crow Search Algorithm(Int Journal Renewable Energy Research, 2021) Elbaz, Abdurazaq; Guneser, Muhammet TahirThis paper deals with the multi-objective fuel cost optimization of a conventional power plant (CPP) and emission minimization in CPPs and solar PV power plants (SPVPPs) using a hybrid bat-crow search algorithm. To resolve this complicated, non-convex, and excessively nonlinear problem, a variety of meta-heuristic optimization algorithms are developed and effectively employed. To handle evolutionary multi-objective algorithms' inadequacies, such as early convergences, slowly meeting the Pareto-optimal front, and narrow trapping, applying a combination of different algorithms is unusual. This paper offers a hybrid evolutionary multi-objective optimization process based on combining the crow search optimization with the bat algorithm for dealing with the combined economic emission dispatch problem for SPVPPs. A hybrid technique combined with the proposed constriction handling method can balance exploitation and exploration tasks. Different IEEE standard bus systems were tested with the proposed hybrid method using the quadratic cost function and monitoring the transmission losses. The results of the proposed algorithm have also been compared with those of the bat, PSO, and crow search algorithms. The proposed method can be said to be effective considering the simulation results.Öğe Optimization and Evaluation of Hybrid PV/WT/BM System in Different Initial Costs and LPSP Conditions(Science & Information Sai Organization Ltd, 2017) Tabak, Abdulsamed; Ozkaymak, Mehmet; Guneser, Muhammet Tahir; Erkol, Huseyin OktayA modelling and optimization study was performed to manage energy demand of a faculty in Karabuk University campus area working with a hybrid energy production system by using genetic algorithm (GA). Hybrid system consists of photovoltaic (PV) panels, wind turbines (WT) and biomass (BM) energy production units. Here BM is considered as a back-up generator. Objective function was constituted for minimizing total net present cost (TNPC) in optimization. In order to obtain more accurate results, measurements were performed with a weather station and data were read from an electricity meter. The system was also checked for reliability by the loss of power supply probability (LPSP). Changes in TNPC and localized cost of energy (LCOE) were interpreted by changing LPSP and economic parameters such as PV investment cost, WT investment cost, BM investment cost, and interest rates. As a result, it was seen that a hybrid system consisted of PV and BM associated with an effective flow algorithm benefited from a GA meets the energy demand of the faculty.
