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Öğe A Fuzzy Logic-Based Energy Management Approach for Fuel Cell and Photovoltaic Powered Electric Vehicle Charging Station in DC Microgrid Operations(Ieee-Inst Electrical Electronics Engineers Inc, 2025) Cakmak, Recep; Bayrak, Gokay; Koc, MehmetHydrogen and electric vehicles (EVs) stand out as two promising technologies with the potential to revolutionize the future of transportation. The adoption of hydrogen and EVs indicates a noteworthy shift in the transportation paradigm, offering the prospect of reshaping this sector. This paper introduces an energy management approach based on fuzzy logic for a charging station that combines fuel cell (FC) and photovoltaic (PV) to power electric vehicles (EVs). The study emphasizes developing and simulating a fuzzy logic-based energy management system tailored for DC microgrid operations, including on-grid and islanded microgrid schemes. The system is designed and simulated in a digital simulation environment. The findings indicate that the fuzzy logic approach significantly enhances the performance of PV and FC-based EV charging stations. This research contributes to developing sustainable and efficient solutions for integrating electric vehicles and hydrogen-powered supply systems.Öğe A new Fuzzy&Wavelet-based adaptive thresholding method for detecting PQDs in a hydrogen and solar-energy powered EV charging station(Pergamon-Elsevier Science Ltd, 2023) Bayrak, Gokay; Yilmaz, Alper; Cakmak, RecepThis study presents a hybrid fuzzy decision-maker (FDM) and un-decimated wavelet transform (UWT)-based method for detecting power quality disturbances (PQDs) in a developed hydrogen and solar energy-powered electric vehicle (EV) charge station. The proposed adaptive FDM&UWT-based hybrid method eliminated the lack of performance of threshold-based signal analysis methods in noise-containing signals and it is implemented for a reliable PQD detection and integration in a developed microgrid. Also, the proposed method has eliminated the need for a processing-intensive filtering process to reduce noise from the signal. With this adaptive approach, detection errors in boundary condi-tions in threshold value methods are avoided and at the same time, cost and computa-tional burden are minimized by using only the peak values in the detail coefficients of the voltage signal. The mean test accuracy is 96.13% for the FDM method using pyramidal UWT in noise-free conditions. Besides, the pyramidal UWT-FDM has a mean classification accuracy of 94.96% under 20-40 dB high-level noise conditions. The effectiveness of the UWT-FDM method is also tested using an experimental setup. The mean test accuracy for experimental data is 96.66%.(c) 2022 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.Öğe A new intelligent charging strategy in a stationary hydrogen energy-based power plant for optimal demand side management of plug-in EVs(Pergamon-Elsevier Science Ltd, 2024) Cakmak, Recep; Meral, Hasan; Bayrak, GokayStationary hydrogen energy-based power plants generating electricity to supply high-powered plug-in electric vehicles (PEVs) have recently become popular in renewable energy-based power plants. Besides, in a PEV charging station, various types of powered charge devices can be established such as DC fast chargers or 3.7 kW, 7.4 kW, 11 kW, and 22 kW AC chargers. This paper introduces a demand-side management-oriented optimal charging strategy that includes two stages for PEVs in a hydrogen energy-based microgrid. The paper focuses on two stages to execute an optimal charging of PEVs in compliance with their users' requests and satisfaction and considering the power system loading. It is assumed that there are three types of chargers in the PEV charging station and the users. In the first stage randomly created requests are classified by an ensemble learning classifier method that performs higher performance classification by combining the results from multiple classifiers in a machine learning classification. The second stage schedules the PEVs according to the classification results and users' requests. To test the proposed system, first random requests are created then they are sent to the classifier, and the results of classifiers are scheduled in each other. The demand-side management-oriented charge scheduling and managing strategy which includes the proposed two stages has been compared with nonmanaged cases. Case study results reveal that the proposed approach provides 52.1% peak load reduction and 72.3% valley filling improvement by the SOS algorithm. The results highlight the advantages of the proposed system in terms of peak reduction and valley filling.Öğe A new intelligent decision-maker method determining the optimal connection point and operating conditions of hydrogen energy-based DGs to the main grid(Pergamon-Elsevier Science Ltd, 2023) Bayrak, Gokay; Yilmaz, Alper; Calisir, AlperenThis study presents a new two-step intelligent decision-maker method using hydrogen energy-based distributed generators (HEDGs) to contribute to the reliability, durability, and stability of power transmission system in Bursa. In the first stage, the proposed method uses the power flow parameters evaluation (PFPE) algorithm to define the possible appropriate connection point of HEDGs by determining the electrical parameters. Then, to determine the conditions in which the HEDGs connected to the grid should be switched on, the power flow data such as load status, bus bar powers, and, line capacities are evaluated with the artificial neural network (ANN)-based method with a scaled conjugate gradient (SCG) algorithm. With the proposed intelligent two-step decision-maker method, HEDGs are connected to the points determined using the PFPE algorithm, and then the appropriate operating conditions for which HEDGs should be enabled are determined by the ANN with SCG. Different combinations of load status, bus bar powers, and line capacities values are applied to the ANN input and important features are determined. The ANN with SCG can predict the operating conditions of HEDGs with 96.8% accuracy in the test set and, 98.4% accuracy in the validation set. Thanks to the developed holistic PFPE & ANN approach, op-timum connection points and suitable operating conditions can be determined, which ensures reliability and safety for HEDGs in overload and/or failure conditions. & COPY; 2023 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.Öğe A new intelligent power quality disturbance classification in renewable and decentralized hydrogen-based energy systems using SwResNET hybrid model(Pergamon-Elsevier Science Ltd, 2025) Kucuker, Ahmet; Barakli, Burhan; Bayrak, Gokay; Basaran, Kivanc; Balaban, GeorgianaIn this study, a scalogram image-based Swin-Residual Network (SwResNET) hybrid method is proposed for the identification of power quality disturbances (PQDs) in a hydrogen energy-based distributed generator (HEBDGs). The proposed approach involves the creation of PQD scalogram images by applying spectrogram analysis to power signal data. This process generates a two-dimensional image that represents the frequency and time characteristics of the signal. These spectrogram images are then input into a SwResNET hybrid model for learning. The SwResNET hybrid model extracts features from the scalogram images and classifies the input signal based on the presence or absence of power quality disturbances. This paper used 21 different PQD events in HEBDGs for classification purposes. Furthermore, the proposed method was tested under noisy conditions. The data achieved from simulated results of the HEBDG system in Matlab/Simulink and empirical data collected in the laboratory collectively demonstrate that the proposed methodology exhibits exceptional performance in terms of 98.22 % accuracy and resistance to noise, surpassing existing state-of-the-art approaches.Öğe A Real-Time Improved ML Method for PQD Classification of a PV-Powered EV Charging Station(Ieee-Inst Electrical Electronics Engineers Inc, 2025) Yilmaz, Alper; Atesci, Tolga; Meral, Hasan; Bayrak, GokayThe installation of electric vehicle charging stations (EVCSs) that are powered by renewable energy sources has been growing rapidly. However, this has raised a crucial issue regarding the quality of power supplied to these stations. Due to the intermittent nature of renewable energy sources and the high-power requirements of EV charging, power quality disturbances (PQDs) occur more. This study proposes a new intelligent PQD classification method that considers feature extraction/selection based on pyramidal undecimated wavelet transform (p-UWT) and minimum redundancy maximum relevance (mRMR). The feature vector, derived through the application of mRMR, comprises a mere ten elements. The p-UWT-mRMR combination overcomes the problem of noise sensitivity inWTs. In addition, Bayesian optimization and UWT-mRMR have addressed hyperparameter selection difficulties and overfitting in support vector machine models. The proposed method demonstrated an impressive classification accuracy of 99.55% when faced with 30-dB noise. A prototype test platform is developed with EVCS-integrated PV systems in the laboratory to verify the performance of the proposed method in real-time cases. Dynamic analysis revealed that all PQDs have runtimes ranging from 5 to 10ms in experiments. The proposed method has been validated on a dataset of over 20 000 real-world signals with a test accuracy of 99.11%.Öğe Automated classification of power quality disturbances in a SOFC&PV-based distributed generator using a hybrid machine learning method with high noise immunity(Pergamon-Elsevier Science Ltd, 2022) Yilmaz, Alper; Kucuker, Ahmet; Bayrak, GokayIn this study, a new hybrid machine learning (ML) method is developed to classify the power quality disturbances (PQDs) for a hydrogen energy-based distributed generator (DG) system. The proposed hybrid ML method uses a new approach for the feature extraction by using a pyramidal algorithm with an un-decimated wavelet transform (UWT). The pyramidal UWT method is used and investigated with the Stochastic Gradient Boosting Trees (SGBT) classifier to classify PQD signals for a Solid Oxide Fuel Cell & Photovoltaic (SOFC&PV)-based DG. The overfitting problem of SGBT in noisy signals is eliminated with the features extracted by pyramidal UWT. Mathematical, simulative and real data results confirm that the developed UWT-SGBT method can classify PQDs with high accuracy of up to 99.59%. The proposed method is also tested under noisy conditions, and the pyramidal UWT-SGBT method outperformed other ML with wavelet transform (WT)-based methods in the literature in terms of noise immunity. (c) 2022 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.Öğe Deep learning-based multi-model ensemble method for classification of PQDs in a hydrogen energy-based microgrid using modified weighted majority algorithm(Pergamon-Elsevier Science Ltd, 2023) Bayrak, Gokay; Kucuker, Ahmet; Yilmaz, AlperIn this study, new multiple deep classifiers with a modified Weighted Majority Voting (WMV)-based method are proposed to identify power quality disturbances (PQDs) in a hydrogen energy-based microgrid. In the proposed approach, closed-loop deep LSTM (Long Short Time Memory), deep CNN (Convolutional Neural Network), and hybrid (CNN-LSTM) models are used for automatic feature extraction and classification. Then, a modified WMV method is employed to ensemble the outputs of the three deep learning (DL) classifier models. The enhanced WMV mechanism performs an automatically updated weighting based on the validation results of the DL classification models, unlike voting methods in the literature. The improved WMV mechanism eliminates the challenges of using multiple DL classifiers in the voting system. The mathematical data results in LabVIEW, simulation results in Matlab/Simulink, and real data results in the laboratory show that the proposed method shows superior performance in accuracy and noise immunity to state-of-the-art methods.(c) 2022 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.Öğe Design and Application of a Double-Inductor Boost Converter for Powertrain in Fuel Cell EVs(Ieee, 2025) Torbacioglu, Goksen Ali; Bayrak, GokayThe output voltage obtained from a fuel cell stack in Fuel Cell Electric Vehicles (FCEVs) constantly changes, and this variable output voltage must be converted to the appropriate voltage for power transmission in FCEVs. This study proposes a new double inductor DC-DC boost converter capable of adjusting the voltage from 25 to 48 V DC from a 1 kW fuel cell output to the 96 V DC level required for an FCEV powertrain system. First, the design and analysis of the proposed double inductor DC-DC boost converter are performed. Then, the designed converter is also experimentally implemented and compared with the results obtained mathematically. The obtained results show that the proposed converter is applicable in practice with an efficiency of 92.3%. It is thought that this structure, which can be used in practice in power transmission applications in FCEVs, can be used in many applications, especially in electric and hybrid energy vehicles, by increasing its power with scaling.Öğe Effectiveness of un-decimated wavelet transform in time-series forecasting: A PV power calculation case study in BTU(Pergamon-Elsevier Science Ltd, 2026) Albayram, Mehmet; Yilmaz, Alper; Bayrak, Gokay; Basaran, Kivanc; Popescu, Luminita GeorgetaThis study explored the effectiveness of Un-Decimated Wavelet Transform (UWT) in time-series applications, using photovoltaic (PV) calculation as a case study. Real-time measurements of irradiance, ambient temperature, module temperature, and humidity were collected at 5-min intervals from a 1.2 kW rooftop PV system at Bursa Technical University. Wavelet-based features extracted with both UWT and the conventional Discrete Wavelet Transform (DWT) were combined with regression and tree-based learners to build 16 hybrid models. The results show that the shift-invariant UWT significantly improves both feature extraction and prediction accuracy compared to the DWT approach. The UWT-DT model achieved the highest accuracy, with the lowest MSE (0.0001), the lowest RMSE (0.0118) and the highest R-2 coefficient (0.9986). A Wilcoxon signed-rank test applied to paired RMSE values confirmed that these improvements were statistically significant (p value < 0.05 for UWTDT vs DWT-DT). In terms of computational complexity, the 'a` trous' algorithm used in UWT requires convolution operations at every level, resulting in higher processing costs than DWT (12 ms feature extraction per 1024-sample input). However, the full-resolution features provided by UWT significantly reduced the error rates of treebased models, raising R-2 above 0.99.Öğe Eliminating Meteorological Dependencies in Solar Power Forecasting: A Deep Learning Solution With NeuralProphet and Real-World Data(Ieee-Inst Electrical Electronics Engineers Inc, 2025) Aksoy, Necati; Yilmaz, Alper; Bayrak, Gokay; Koc, MehmetForecasting solar power generation is essential for efficient energy management and grid stability. However, existing predictive models often rely on external datasets, such as meteorological and sensor data, to make accurate predictions. This dependency introduces complexities and limits their application in data-sparse scenarios. In this study, we propose a novel forecasting approach based on the NeuralProphet algorithm, a deep learning model that predicts solar power generation solely from its historical data, eliminating reliance on additional input data. To evaluate the proposed approach, we conducted two case studies. The first utilized a 10-month dataset from a 1.2 kW small-scale solar power unit at Bursa Technical University's Smart Grids laboratory, recorded at 15-minute intervals. Despite the limited dataset, the model achieved an R-squared value exceeding 0.74, demonstrating promising predictive capability. The second case study applied the NeuralProphet-based model to a large-scale dataset of nationwide solar power generation in Germany, spanning five years and collected at 15-minute intervals. Models trained on this dataset achieved R-squared values exceeding 0.99, highlighting the algorithm's capacity to effectively capture seasonal and temporal patterns at a national scale. Our results indicate that the NeuralProphet-based forecasting approach offers a viable and efficient alternative for solar power prediction, achieving high accuracy without external data dependencies.Öğe Enhancing grid resilience and cybersecurity considering visible light communication in electric vehicle charging units powered by green hydrogen(Pergamon-Elsevier Science Ltd, 2025) Bayrak, Gokay; Das, MuratThe complexity of the power grid has increased due to the unpredictability of renewable energy sources and the risk of cyber attacks targeting smart grids through devices like inverters and energy meters. Besides, the operating frequency and voltage must comply with IEEE 929-2000 in case of abnormal grid faults. This study introduces a new monitoring and protection system with visible light communication (VLCom) to address these issues in a green hydrogen-powered smart grid. The proposed system provides optical isolation from both cyberattacks and abnormal grid operating conditions, and it has a new VLCom approach that considers the grid protection of EV charging units in the literature. A developed VLCom grid protection relay enhances the smart grid's resilience and cybersecurity. The developed system is implemented in real-time with LabVIEW and is tested for abnormal voltage and frequency conditions. The results show that the proposed system is reliably within the limits specified in IEEE 929-2000 standards with 98.23 % signal transmission accuracy.Öğe Enhancing power quality in vehicle-to-grid (V2G) operations of FCEVs through the integration of real-time digital IIR filters in power calculations(Pergamon-Elsevier Science Ltd, 2024) Aslankaya, Emrullah; Yilmaz, Alper; Bayrak, GokayFuel cell electric vehicles (FCEVs) are estimated as the future's mobile distributed generators with vehicle-to-grid (V2G) functions. Besides, V2G topologies are required to be designed with an appropriate analog filter by accurately measuring the reference current and voltage values of inverters and converters. Thus, the highest accuracy is required in control structures for charging and grid connection of FCEVs to improve the power quality (PQ). In this study, PQ improvement of V2G operation of FCEVs is proposed by considering the developed real-time digital infinite impulse response (IIR) filters in generating reference signals in the power calculation stage. A band-pass digital IIR filter design is realized with the inverter using LabVIEW infrastructure, and reference signals are smoothed by using IIR filters. The performances of developed different IIR filters' results are examined in the study. Developed filters are designed using the order estimation method. Elliptic, Chebyshev, and Butterworth filters are applied to the voltage and current signals, and the best results are obtained in reference power signals with the Butterworth filter. The Butterworth filter maintains stability in current RMS, induces a 0.09 % decrease in voltage RMS, and shows a slight 0.30 % increase in active power. The proposed approach removes distortions in reference power signals by using a designed filter for a reliable V2G operation of FCEVs.Öğe Integrating hydrogen-powered fuel cell electric buses into grid-forming microgrids: A solution for emergency energy needs(Pergamon-Elsevier Science Ltd, 2025) Bayrak, Gokay; Basaran, Kivanc; Lazaroiu, Alexandra CatalinaFuel cell electric (FCE) buses have high-capacity batteries reaching up to 250-300 kW and high energy densities with hydrogen, so they can be used as a Mobile Microgrid (MoMG) by being supported by renewables. In this study, an FCE bus comprising a SOFC fuel cell stack and a battery is modeled as a Mobile Microgrid (MoMG) using MATLAB/Simulink to deliver mobile electrical energy support to regions inaccessible during disaster situations. To develop the V2L, V2V, and V2G functions of the MoMG, an Enhanced Grid Forming Control (EGFC) method is proposed, considering the IEEE 2800-2022 standards to ensure inverter-grid synchronization. In the proposed model, sudden changes in hydrogen power, evaluation of battery energy storage system (BESS) response during sudden load change, islanding conditions, and temporary and permanent faults are investigated. EGFC enables the FCE bus to maintain operation under both steady-state and transient conditions, achieving +1.5 % in grid voltage and current, +3 % grid active power and reactive power, and +0.01 Hz in frequency. The obtained results show that the proposed EGFC provides a reliable and stable grid integration of an FCE bus, providing the MoMG with low-voltage ride-through (LVRT) and fault ride-through (FRT) capabilities.Öğe Sizing and Techno-Economic Analysis of Utility-Scale PV Systems with Energy Storage Systems in Factory Buildings: An Application Study(Mdpi, 2025) Basaran, Kivanc; Ozdemir, Mahmut Temel; Bayrak, GokayIn recent years, PV power plants have been widely used on the roofs of commercial buildings with grid connections, primarily to enhance self-consumption in distributed energy systems. In addition, installing PV plants on commercial buildings' roofs is becoming increasingly important, especially in crowded cities where land is limited. Since the Sun is an intermittent energy source, PV power plants cause frequency and voltage fluctuations in the grid. The way to avoid this problem is to install PV plants together with battery storage systems. Battery storage systems prevent frequency and voltage fluctuations in the grid and provide economic benefits. This article presents the sizing and techno-economic analysis of a factory building's rooftop PV system with a battery. The amount of energy produced by the PV plant, PV temperature, and irradiation were recorded in a data logger obtained by various sensors. These real-time measurements were continuously collected and analyzed to evaluate system performance and assess seasonal variations.Load demand data were collected through an automatic meter reading system. The installed capacity of the PV power plant is 645 kW. The optimum battery capacity determined for this factory is 130 kW for 5 h. Techno-economic analysis was carried out using metrics such as the payback period, net present value, and levelized cost of energy. As a result of the analysis using various input variables, LCOE, NPV, and PBP were determined as 0.1467 $/kWh, 4918.3 $, and 7.03 years, respectively.
