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Öğe Adaptive Virtual Impedance Control with MPC’s Cost Function for DG Inverters in a Microgrid with Mismatched Feeder Impedances for Future Energy Communities(Multidisciplinary Digital Publishing Institute (MDPI), 2024) Khan, Mubashir Hayat; Zulkifli, Shamsul Aizam; Tutkun, Nedim; Burgio, AlessandroMore and more distributed generations (DGs), such as wind, PV or battery bank sources, are connected to electric systems or customer loads. However, the locations of these DGs are based on the highest energy that can be potentially harvested for electric power generation. Therefore, these locations create different line impedances based on the distance from the DGs to the loads or the point of common coupling (PCC). This paper presents an adaptive virtual impedance (AVI) in the predictive control scheme in order to ensure power sharing accuracy and voltage stability at the PCC in a microgrid network. The reference voltage from mismatched feeder impedances was modified by utilizing the suggested AVI-based predictive control for creating equal power sharing between the DGs in order to avoid overburdening any individual DG with low-rated power. The AVI strategy used droop control as the input control for generating equal power sharing, while the AVI output was used as the reference voltage for the finite control set–model predictive control (FCS-MPC) for creating a minimum voltage error deviation for the cost function (CF) for the inverter’s vector switching pattern in order to improve voltage stability at the PCC. The proposed AVI-based controller was tested using two DG inverter circuits in a decentralized control mode with different values of line impedance and rated power. The performance of the suggested controller was compared via MATLAB/Simulink with that of a controller based on static virtual impedance (SVI) in terms of efficiency of power sharing and voltage stability at the PCC. From the results, it was found that (1) the voltage transient magnitude for the AVI-based controller was reduced within less than 0.02 s, and the voltage at the PCC was maintained with about 0.9% error which is the least as compared with those for the SVI-based controller and (2) equal power sharing between the DGs increased during the change in the load demand when using the AVI-based controller as compared with using the SVI-based controller. The proposed controller was capable of giving more accurate power sharing between the DGs, as well as maintaining the voltage at the PCC, which makes it suitable for the power generation of consumer loads based on DG locations for future energy communities.Öğe Breast Cancer Mass Classification Using Machine Learning, Binary-Coded Genetic Algorithms and an Ensemble of Deep Transfer Learning(OXFORD UNIV PRESS, 2023) Tiryaki, Volkan Mujdat; Tutkun, NedimThe diagnosis of breast cancer (BC) as early as possible is crucial for increasing the survival rate. Mammography enables finding the breast tissue changes years before they could develop into cancer symptoms. In this study, machine learning methods for BC mass pathology classification have been investigated using the radiologists' mass annotations on the screen-film mammograms of the Breast Cancer Digital Repository (BCDR). The performances of precomputed features in the BCDR and discrete wavelet transform followed by Radon transform have been investigated by using four sequential feature selections and three genetic algorithms. Feature fusion from craniocaudal and mediolateral oblique views was shown to increase the performance of the classifier. Mass classification has been implemented by deep transfer learning (DTL) using the weights of ResNet50, NASNetLarge and Xception networks. An ensemble of DTL (EDTL) was shown to have higher classification performance than the DTL models. The proposed EDTL has area under the receiver operating curve (AUC) scores of 0.8843 and 0.9089 for mass classification on the region of interest (ROI) and ROI union datasets, respectively. The proposed EDTL has the highest BC mass classification AUC score on the BCDR to date and may be useful for other datasets.Öğe Decentralized Virtual Impedance Control for Power Sharing and Voltage Regulation in Islanded Mode with Minimized Circulating Current(MDPI, 2024) Khan, Mubashir Hayat; Zulkifli, Shamsul Aizam; Tutkun, Nedim; Ekmekçi, İsmail; Burgio, AlessandroIn islanded operation, precise power sharing is an immensely critical challenge when there are different line impedance values among the different-rated inverters connected to the same electrical network. Issues in power sharing and voltage compensation at the point of common coupling, as well as the reverse circulating current between inverters, are problems in existing control strategies for parallel-connected inverters if mismatched line impedances are not addressed. Therefore, this study aims to develop an improved decentralized controller for good power sharing with voltage compensation using the predictive control scheme and circulating current minimization between the inverters' current flow. The controller was developed based on adaptive virtual impedance (AVI) control, combined with finite control set-model predictive control (FCS-MPC). The AVI was used for the generation of reference voltage, which responded to the parameters from the virtual impedance loop control to be the input to the FCS-MPC for a faster tracking response and to have minimum tracking error for better pulse-width modulation generation in the space-vector form. As a result, the circulating current was maintained at below 5% and the inverters were able to share an equal power based on the load required. At the end, the performance of the AVI-based control scheme was compared with those of the conventional and static-virtual-impedance-based methods, which have also been tested in simulation using MATLAB/Simulink software 2021a version. The comparison results show that the AVI FCS MPC give 5% error compared to SVI at 10% and conventional PI at 20%, in which AVI is able to minimize the circulating current when mismatch impedance is applied to the DGs.Öğe The improved low cost grid connected EV charging station with PV and energy storage systems(Institute of Electrical and Electronics Engineers Inc., 2023) Tutkun, Nedim; Zulkifli, Shamsul Aizam; Ahmad, Zarafi BinRecently electric vehicles (EVs) have increasingly been used for transportation due to low cost operation and less carbon emission. However, major disadvantages of EVs are charging time, range and overloading the grid, and the latter may lead to instability in the grid when a vast number of EVs are simultaneously charged from the grid. One solution to this may be to charging times and to share amount of charging power through photovoltaic (PV) and energy storage (ES) systems with minimum cost. In this study, a 20 kW grid tied charging station with PV and ES systems is designed to charge EVs with minimum cost for the hourly changing electricity price through a variety of charge options such as grid to EV, ES to EV, PV to EV, EV to EV etc. This is achieved by optimizing charge start times and facilities using metaheuristic based computational algorithms. The proposed approach worked well and results obtained are encouraging and meaningful for the case studyÖğe Improved Low-Cost Home Energy Management Considering User Preferences with Photovoltaic and Energy-Storage Systems(MDPI, 2023) Tutkun, Nedim; Scarcello, Luigi; Mastroianni, CarloWith smart appliances, it has been possible to achieve low-cost electricity bills in smart grid-tied homes including photovoltaic panels and an energy-storage system. Apparently, many factors are important in achieving this and the minimization problem formulated requires a solution depending on a certain number of constraints. It should also be emphasized that electricity tariffs and the appliance operation type and range play a major role in this cost reduction, in particular, with dynamic electricity pricing usually available in a smart-grid environment. A limited number of metaheuristic methods are used to solve such a minimization problem, in which the start time of a controllable smart home appliance is the variable. However, the datasets used in many studies are different from each other and it is mostly unclear which of the proposed methods is better in this regard. In this study, we aim to minimize the daily energy consumption cost in a typical smart home with an energy-storage system integrated into a photovoltaic system under dynamic electricity pricing. While minimizing the daily energy consumption cost only, the user’s discomfort and the peak-to-average ratio inevitably tend to increase, as expected. Therefore, a balance can be established among the objectives using multi-objective optimization. Solving this problem helps comparatively reduce the daily energy consumption cost, the peak-to-average ratio and the user’s discomfort. The results are meaningful and encouraging for the optimization problem under consideration.Öğe Intelligent scheduling of smart home appliances based on demand response considering the cost and peak-to-average ratio in residential homes(MDPI, 2021) Tutkun, Nedim; Burgio, Alessandro; Jasinski, Michal; Leonowicz, Zbigniew; Jasinska, ElzbietaAbstract: With recent developments, smart grids assured for residential customers the opportunity to schedule smart home appliances’ operation times to simultaneously reduce both the electricity bill and the PAR based on demand response, as well as increasing user comfort. It is clear that the multiobjective combinatorial optimization problem involves constraints and the consumer’s preferences, and the solution to the problem is a difficult task. There have been a limited number of investigations carried out so far to solve the indicated problems using metaheuristic techniques like particle swarm optimization, mixed-integer linear programming, and the grey wolf and crow search optimization algorithms, etc. Due to the on/off control of smart home appliances, binary-coded genetic algorithms seem to be a well-fitted approach to obtain an optimal solution. It can be said that the novelty of this work is to represent the on/off state of the smart home appliance with a binary string which undergoes crossover and mutation operations during the genetic process. Because special binary numbers represent interruptible and uninterruptible smart home appliances, new types of crossover and mutation were developed to find the most convenient solutions to the problem. Although there are a few works which were carried out using the genetic algorithms, the proposed approach is rather distinct from those employed in their work. The designed genetic software runs at least ten times, and the most fitting result is taken as the optimal solution to the indicated problem; in order to ensure the optimal result, the fitness against the generation is plotted in each run, whether it is converged or not. The simulation results are significantly encouraging and meaningful to residential customers and utilities for the achievement of the goal, and they are feasible for a wide-range applications of home energy management systems.Öğe Multi-Objective Operation of PV-ESU Powered EV Charging Station(Institute of Electrical and Electronics Engineers Inc., 2024) Tutkun, Nedim; Zulkifli, Shamsul A.; Şimşir, MehmetRecently electric vehicles (EV s) have been popular choice in many countries due to low carbon emission and less operation cost. The rapid increase in EV s inevitably increases the number of charging stations used to charge them, and this naturally leads to more power demand at certain times of the day. It is also apparent that the increase in energy demand leads to an increase in electricity prices, as well as an increase in power loss in transmission lines. This may reduce the current aura of EV s, as higher electricity prices mean more expensive charging costs. Therefore, creating more competitive conditions at existing charging stations for lower charging costs is essential for a sustainable future. In this study, the primary objective is to reduce the charging cost by integrating a 20-kWp photovoltaic (PV) array and a 20-kWh energy storage unit (ESU) into an existing charging station fed from the grid and considering the overload of the grid and user charging preference. This multi-objective problem is solved for optimal daily cost using the binari-coded genetic algorithm (BCGA). The results show that proposed optimization model worked well, and the charging cost decreased depending on user preferences. © 2024 IEEE.
