Elektrik Elektronik Mühendisliği Bölümü Yayın Koleksiyonu

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https://hdl.handle.net/20.500.12885/1713

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Fault ride-through capability enhancement of hydrogen energy-based distributed generators by using STATCOM with an intelligent control strategy
(Elsevier Ltd, 2023-12-25) Bayrak, Gökay; Yılmaz, Alper; Demirci, Eren
This paper presents an intelligent adaptive neuro-fuzzy inference (ANFIS)-based control method for increasing the Fault Ride-Through (FRT) capability of hydrogen energy-based distributed generators. The static synchronous compensator (STATCOM) system is integrated into the modeled power system with the Solid Oxide Fuel Cell system. To investigate the FRT capability of the proposed method, fault scenarios under different grid conditions are generated. The proposed ANFIS-based method is compared with the conventional PI-based STATCOM model and the system without any flexible AC transmission system devices. When the obtained results are analyzed, with the developed intelligent control method, an improvement of at least 8% and a maximum of 12.6% is achieved in the voltage value, depending on the type of failure that occurs. Besides, there is an improvement of at least 10% and at most 16.6% in the settling time values. The voltage fluctuations and sudden peaks in the system with the proposed method are less than in the other systems and it provides voltage support to the system successfully. The transient response of the Solid Oxide Fuel Cell system provides sustainable and stable reactive power support to the grid. Besides, the proposed method not only contributes to the FRT capability of system but also minimizes voltage changes that may reduce the life of the distributed generator or cause it to malfunction
A novel switched-capacitor and fuzzy logic-based quadratic boost converter with mitigated voltage stress, applicable for DC micro-grid
(Springer, 2022) Tekin, Hakan; Bulut, Kübra; Ertekin, Davut
High-voltage and efficient power converter topologies equipped with the simple and practical controller circuits are necessary, especially for integration between the low-power and low-voltage renewable energy sources (RESs) like the photovoltaic (PV) arrays and the grid. These converters can be used widely in electrical vehicles (EVs) or charging stations, aquatic, medical, transportation application and other cases. This study proposes a switched capacitor (SC)-based quadratic boost converter (QBC) structure that provides high-voltage gain at low duty cycles equipped with the fuzzy logic control (FLC) technique. The output gain of the proposed converter is higher than a second-order step-up converter or a conventional QB circuit thanks to the presented switched-capacitor topology and the manipulation of the switches in conventional QBC. By using the second switch to the conventional QBC, the voltage stress across the main power switch will decrease that enhance the reliability and long-life of the converter. Since the SC block acts as an intermediate layer between the QB and load through the capacitors and diodes of this block, the voltage and current stresses of the power switches and diodes on the QB side are less than stresses for semiconductors for classical QB and boost converter. In this study, the proposed QBC and controller system are analyzed mathematically in detail and in MATLAB/SIMULINK environment. A 200 W prototype was developed in the laboratory to validate the proposed converter and computerized analysis. Finally, the theoretical and experimental results were compared and verified.
A demand-side management assessment of residential consumers by a clustering approach
(Springer, 2022) Oguz, Eray; Tekdemir, İbrahim Gürsu; Gozel, Tuba
Residential consumers have a significant share in total energy demand today. Demand-side management is a collection of processes which makes providing large amounts of energy less problematic. Identifying demand characteristics of energy consumers is a remarkable part of this process. Data clustering methods have recently been proposed as beneficial tools at that point. In this study, a novel parametric representation of residential energy consumption data is proposed. For that purpose, eleven specific parameters are proposed first for extraction of features in data. Next, principal component analysis is used for dimension reduction. Finally, k-means algorithm is applied for clustering. Two residential energy consumption datasets are used for validation. Analyses are carried out in MATLAB and R. Data clustering is realized on a monthly basis by using daily load curves and clustering performance is compared with another study. It is found that the proposed approach leads to the formation of meaningful clusters of residential consumers. It is also possible to observe demand tendency on a daily basis since daily consumption data is used during the process. Performance evaluation scores show that energy consumption data fit better into clusters when it is compared with another study in the literature.
A design for switched capacitor and single-switch DC-DC boost converter by a small signal-based PI controller
(Wiley, 2022) Ghaderi, Davood; Bulut, Kübra; Tekin, Hakan
In this study, a switched capacitor (SC)-based single-switch DC-DC boost converter structure operating under the high voltage gain and the low duty ratio is proposed using the PI control technique. High current and voltage stresses across the power switches and power diodes can be reduced by using the projected SC block. In addition, the proposed converter can achieve high voltage gain through shorter duty cycles, which directly reduces the voltage stress and dynamic losses in the power semiconductors. On the other hand, because the proposed converter includes a single power switch under different output powers and different loads, the control process is simpler than multiswitch structures. With the proposed converter, an output voltage of 10 times greater rather than the input voltage is obtained at 0.57 of the duty cycle. In this study, the fundamental functions of the proposed converter and the controller design steps are analyzed mathematically and tested in MATLAB/SIMULINK environment. As a result of the analysis, it was determined that the proposed topology works with a high performance at high frequency and variable load ranges. To validate the proposed converter and theoretical calculations, a 200-W prototype was established under a continuous conduction mode (CCM) working state, with 48-VDC input voltage and 400-VDC output voltage. Finally, the simulation results were tested and verified through the experimental results.
Fault ride-through (FRT) capability and current FRT methods in photovoltaic-based distributed generators
(Elsevier, 2020) Bayrak, Gökay; Ghaderi, Davood; Sanjeevikumar P.
In this chapter, current methods used for fault ride-through (FRT) capability are examined by researching studies on the capacity of FRT in grid-connected photovoltaic (PV) systems after any failures. These methods have been researched to show the best strategy that can be applied for PV systems by analyzing the FRT methods currently used. FRT methods are classified as external and internal (inverter-resident) methods in the chapter. Energy storage-based methods, flexible alternating current transmission system (FACTS)-based methods, and inverter-resident methods are investigated for PV-based distributed generators. Energy storage-based methods are expensive, and the life cycle of used units is short. Besides, energy storage-based methods are easy to implement. The total cost and complexity of FACTS-based methods were found to be the highest, among others. Regarding grid regulation compatibility, inverter-resident methods are highly effective, and a modified inverter controller method has been found to be the best solution among existing methods.
A Demand Side Management Controller Configuration for Interleaved DC-DC Converters Applicable for Renewable Energy Sources
(Wiley, 2021) Ertekin, Davut; Bayrak, Gökay; Subramaniam, Umashankar
In Micro-grid applications, accuracy and sensitivity of the Demand Side Management (DSM) process decrease when the load impedance changes. In this study, the impact of the DSM is analyzed and the interleaved structure is presented for DC-DC converter blocks equipped with adaptive PI controllers. This approach reinforces a same voltage source that can be a serial and parallel connection of Photovoltaic (PV) panels in different power rates as the input voltage source to enhance the voltage to the micro-grid DC level and is modeling the power transmission in Renewable Energy Sources (RESs) that they produce limited amounts of power. For the voltage droop problem, the Power-Voltage (P-V) approach is selected. Since the resistive loads are considered in this study, this approach can control DC currents based and depending on the DC voltages in DC micro-grid applications. For per controller block, different values of the gain coefficients are tested and the optimal droop coefficients are presented. All simulations have been done in MATLAB/SIMULINK and a prototype by power around 1kW is tested. The results of the hardware implementation confirm the theoretical and simulation outcomes.
A new signal processing-based islanding detection method using pyramidal algorithm with undecimated wavelet transform for distributed generators of hydrogen energy
(PERGAMON-ELSEVIER SCIENCE LTD, 2022) Yılmaz, Alper; Bayrak, Gökay
Machine learning-based fault detection methods are frequently combined with wavelet transform (WT) to detect an unintentional islanding condition. In contrast to this condition, these methods have long detection and computation time. Thus, selecting a useful signal processing-based approach is required for reliable islanding detection, especially in real-time applications. This paper presents a new modified signal processing-based islanding detection method (IDM) for real-time applications of hydrogen energy-based distributed generators. In the study, a new IDM using a modified pyramidal algorithm approach with an undecimated wavelet transform (UWT) is presented. The proposed method is performed with different grid conditions with the presence of electric noise in real-time. Experimental results show that oscillations in the acquired signal can be reduced by the UWT, and noise sensitivity is lower than other WT-based methods. The non-detection zone is zero and the maximum detection and computational time is also 75 ms at a close power match.(c) 2022 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
Consideration of graphene material in PCM with aluminum fin structure for improving the battery cooling performance
(WILEY, 2022) Aslan, Eyyüp; Aydın, Yusuf; Yasa, Yusuf
Phase change material (PCM) based battery thermal management system (BTMS) provides even heat distribution and lower maximum temperature, but it suffers from low thermal conductivity. In this study, the impact of graphene additive on PCM was analyzed by presenting three experiments with various structures to solve PCM's low thermal conductivity problem. The results demonstrate that there is no positive impact of graphene additive in the first and third structures. The PCM-graphene additive between the second structure's fins significantly improves the battery heat transfer by allowing the battery to cool down 1500 seconds earlier than the graphene-free structure. Moreover, a thermal equivalent circuit model was derived for the second structure because of its enhanced performance. It is shown that the model works accurately and proves its ability to control not only temperature fluctuations but also transient behavior of the battery. This model provides that the battery temperature can be analyzed without experimentation for different charge-discharge scenarios in lithium-ion batteries with a shorter computation time.
Cross-entropy method for distribution power systems reconfiguration
(Wiley, 2019) Sebaa, Karim; Gelen, Ayetül; Nouri, Hassan
Cross-entropy (CE) is a powerful simulation method for the solution of continuous and combinatory optimization problems. The work presented here utilizes the CE method for the optimal topology of distribution power systems (DPSs). The optimal network switches are determined for the reduction of active power loss. The adapted CE method is tested on three case studies, namely, the 33-node, 83-node, and 880-node DPSs. The results are compared with other reconfiguration algorithms to demonstrate the superiority of the proposed algorithm. The impact of the distributed generation is also investigated. The effective integration of the photovoltaic panels at midday, when their production is highest and meets the peak demand, is showed. Finally, the real-time reconfiguration strategy based on the switching effort reduction is proposed and enhanced via an adequate selection of the initial switch states.
Frequency Estimation Methods for Smart Grid Systems
(Springer, 2019) Mengüç, Engin Cemal; Acır, Nurettin
Frequency is one of the most significant parameters in the smart grid systems. Thus, accurate frequency estimation becomes an essential task for monitoring, controlling and protecting a real-time smart grid system. In this chapter, we present an overview of the frequency estimation methods in the smart grid system with a focus on real-time adaptive estimation algorithms. Primarily, in Sects. 5.1 and 5.2, the importance of the frequency estimation in the smart grid systems and the challenges encountered in its real-time applications are introduced in detail. In Sect. 5.3, a three-phase power system is then formulated as a two-phase system in the complex domain by using the well-known Clarke’s transformation so as to be able to estimate the frequency of the smart grid system in the real time. For this purpose, the adaptive real-time frequency estimation algorithms are comparatively presented as strictly and widely linear algorithms in Sect. 5.4. The strictly linear algorithms yield optimal solutions only under balanced three-phase systems, whereas the widely linear algorithms give a better solution under both balanced and unbalanced conditions due to the fact that they take into account all statistical information of the system. Considering smart grid applications in real time, the mentioned properties of these algorithms under both balanced and unbalanced conditions are proven in Sect. 5.5.
An improved automated PQD classification method for distributed generators with hybrid SVM-based approach using un-decimated wavelet transform
(Elsevier, 2021) Yılmaz, Alper; Kucuker, Ahmet; Bayrak, Gökay; Ertekin, Davut; Shafie-Khah, Miadreza; Guerrero, Josep M.
Artificial intelligence (AI) approaches are usually coupled with the wavelet transform (WT) for feature extraction to classify the power quality disturbances (PQDs). Therefore, selecting a useful WT-based signal processing approach is required for a reliable classification, especially in real-time applications. In this study, a new hybrid, un-decimated wavelet-transform (UWT)-based feature extraction method using a support vector machine (SVM) with a "' a trous" algorithm is proposed to classify PQDs in distributed generators (DGs). The proposed method was performed in a real-time application of a DG system to classify PQDs. The derived features were tested on five different machine learning (ML) models by determining the most appropriate classification technique for the proposed UWT-based feature extraction method. An experimental DG system is constituted in the laboratory using a LabVIEW environment, and the proposed method is tested under different grid conditions. Besides, other well-known and studied conventional ML methods were also tested under 25 dB, 30 dB, and 40 dB noise and compared to the developed method. The experimental and simulation results show that the features extracted with the proposed UWT-based method provide much more successful results in classification than the existing wavelet methods in the literature. Furthermore, the proposed method's noise sensitivity performance is much better than other conventional wavelet algorithms, especially in real-time applications.
A novel biometric identification system based on fingertip electrocardiogram and speech signals
(Academic Press, 2021) Guven, Gokhan; Guz, Umit; Gürkan, Hakan
In this research work, we propose a one dimensional Convolutional Neural Network (CNN) based biometric identification system that combines speech and ECG modalities. The aim is to find an effective identification strategy while enhancing both the confidence and the performance of the system. In our first approach, we have developed a voting-based ECG and speech fusion system to improve the overall performance compared to the conventional methods. In the second approach, we have developed a robust rejection algorithm to prevent unauthorized access to the fusion system. We also presented a newly developed ECG spike and inconsistent beats removal algorithm to detect and eliminate the problems caused by portable fingertip ECG devices and patient movements. Furthermore, we have achieved a system that can work with only one authorized user by adding a Universal Background Model to our algorithm. In the first approach, the proposed fusion system achieved a 100% accuracy rate for 90 people by taking the average of 3-fold cross-validation. In the second approach, by using 90 people as genuine classes and 26 people as imposter classes, the proposed system achieved 92% accuracy in identiying genuine classes and 96% accuracy in rejecting imposter classes.
Driver Drowsiness Detection using MobileNets and Long Short-term Memory
(Institute of Electrical and Electronics Engineers Inc., 2021) Aydemir, Gürkan; Kurnaz, Oguzhan; Bekiryazıcı, Tahir; Avcı, Adem; Kocakulak, Mustafa
Deep learning has been studied extensively for driver drowsiness detection using video data. However, since the proposed deep learning methods are computationally cumbersome, the commercial driver drowsiness detection methods are still using hand-crafted features such as lane deviation and percentage of eye closure. This study investigates a deep learning model that provides a fair drowsiness detection performance with a lightweight architecture. In the proposed method, Dlib library was used to detect the driver's face in individual frames of video data. The detected faces are fed into a pre-defined convolutional neural network architecture. Then, a long short-term memory network was used to capture the temporal information between the frame sequences to assess the state of drowsiness. The proposed model achieves a detection accuracy of 80% in a popular benchmark dataset. It was also verified that the model could be implemented on a commercial and inexpensive development board with a frame rate of 5 frames per second.
Double Compressed AMR Audio Detection Using Spectral Features With Temporal Segmentation
(Institute of Electrical and Electronics Engineers Inc., 2021) Büker, Aykut; Hanilçi, Cemal
Double compressed (DC) AMR audio detection is an important audio forensic problem which is used to authenticate the originality of an auido recording. Majority of the existing studies use audio features extracted from the AMR encoder parameters such as linear prediction (LP) coefficients. Recently, we proposed to use the long-term average spectrum (LTAS) features for DC AMR audio detection and promising results were achieved. In this paper, we propose a novel feature extraction techniques which does not require any prior knowledge about the details of the encoding and decoding processes of the AMR codec. The proposed features are extracted from the temporal segmentation of the short-term Fourier transform (STFT) representation of the audio signal. The proposed features are then classified using deep neural network (DNN) classifier. Experimental results conducted on two different databases show that the proposed features considerably outperform the long-term average spectrum (LTAS) features. The average detection rate is improved from 92.44% to 96.48% on MDSVC dataset and from 80.95% to 83.67% on TIMIT database with the proposed features.
Design and Validation of an Effective Temperature Compensated-Based FBG Sensor Package for Air Vehicle Applications
(Institute of Electrical and Electronics Engineers Inc., 2021) Arslan, Mehmet Mücahit; Bayrak, Gökay
The use of Fiber Bragg Gratings (FBGs) sensors is increasing day by day because of its unique properties such as lightness, immunity to EM and RF signals, minimized harness complexity, and suitability to array manufacturing. Thereby, in the last decades, it has become the first choice in terms of data collection and long-term health monitoring in the aerospace field. On the other hand, like other conventional sensors, Fiber Bragg Grating sensors are also gets affected by the rapid change of environmental conditions such as temperature. In this context, to avoid false alarm situations and compensate for the effect of environmental condition changes, in this study a newly designed temperature compensation metal package structure has been explained and discussed in detail. Obtained results showed that, with the newly designed package structure, the temperature effect had been reduced with success over %95, and the proposed package had been eligible to operate up to 80°C without needing any external reference sensors.
An Efficient Full-wave and Switched-capacitor-based AC-DC-DC Converter Configuration; Applicable for High Voltage Gain Industrial Utilizations
(Institute of Electrical and Electronics Engineers Inc., 2021) Ertekin, Davut; Bilgiç, Mesut Berke; Mutlu, Bülent
Power electronics circuits are one of the vital and important parts of the industrial applications, where different levels of the DC and AC voltages and currents are necessary to be applied to the different electronic and electrical devices and machines including the test approaches or working under industrial voltages. Meanwhile, many of these devices are designed to work under a certain and fixed industrial level of voltages like 48VDC, 24VDC, 12VDC or 5VDC, but sometimes, another levels of the voltage for a special device can be an issue. Therefore, the converting an AC voltage to a fixed, controllable and robust DC voltage under different level of the load values is necessary. Furthermore, the variation of the input AC voltage also should be considered and the load DC voltages should be hold fixed. This paper uses a Full-wave rectifier circuit to convert the AC voltage to the DC voltage and in the next step this voltage is enhanced and fixed by using a DC-DC step-up converter. Both rectifier and boost converters are high gain since sometimes it is necessary to convert the grid 220VAC to a small DC voltage or enhance a small DC to a higher DC voltage. The proposed converter can be used separately as a high gain AC-DC rectifier or a high gain DC-DC converter, or the combination of the proposed converters can be used to reach a desired DC voltage through an input AC voltage. It can be considered depending on the area where the converter is used. Theoretical analysis is presented and simulation results confirm the correctness of the topology under different working conditions.
SA-net: A sequence aware network for the segmentation of the left atrium in cine MRI datasets
(IEEE Computer Society, 2021) Uslu, Fatmatülzehra; Varela, Marta
The segmentation of the left atrium (LA) in CINE MRI is a prerequisite for the calculation of LA functional parameters and may be useful when selecting treatments for atrial fibrillation patients. CINE MRI usually captures both the LA and the left ventricle. The similarities between the LA and other cardiac structures complicate the segmentation of the LA and can lead to poor performance of standard 2D segmentation networks. In this study, we present SA-Net, a deep network which implicitly discriminates LA slices from non-LA slices during segmentation, with a sequence modulator using interslice correlations in a global context. Our experiments, conducted on an in-house dataset with 4710-mm thick bSSFP MR image stacks, show that SA-Net leads to good quality segmentation of the LA, with a mean Dice score of 0.89 and a mean Jaccard index of 0.80, outperforming the U-Net.
Motor Imagery Signal Classification Using Constant-Q Transform for BCI Applications
(European Signal Processing Conference, EUSIPCO, 2021) Balim, Mustafa Alper; Hanilçi, Cemal; Acir, Nurettin
Electroencephalography (EEG) signals have been using for brain-computer interface applications for the last two decades. Motor imagery (MI) signals are one of the EEG signal types formed by imagining a limb's movement. Recently with the help of deep neural networks (DNN) for classifying MI signals using time-frequency (TF) features, considerable performance improvement has been reported. This paper proposes using a well-known TF representation technique called Constant-Q Transform (CQT) for the MI signal classification. Experiments conducted on BCI IV 2b dataset with DNN classifier using CQT spectrogram show that CQT outperforms traditional short-time Fourier transform (STFT) representation.
Modified window function for optically thick samples measured by a terahertz time-domain spectroscopic system using a multimode laser diode
(The Optical Society, 2021) Morikawa, Osamu; Hamada, Dai; Ozturk, Turgut; Yamamoto, Kohji; Kurihara, Kazuyoshi; Kuwashima, Fumiyoshi; Tani, Masahiko
A low-cost type terahertz time-domain spectroscopic system can be constituted using a multimode laser diode instead of a pulse laser. To suppress noise, a window function is usually used in the Fourier transformation. When this system is used to measure an optically thick sample, the obtained refractive index shows sinusoidal structures. This is caused by ingress of a signal fragment into the measured range and egress of another signal fragment out of the measured range. In addition, a broad positive background component appears in the imaginary part of the refractive index. This is because of the decreased amplitude of the central structure of the signal, which results from the time shift under the window function caused by sample insertion. These false structures can be eliminated by using a modified window function that is shifted with the signal when the sample is inserted.
Modelling and Performance Analysis of an Electric Vehicle Powered by a PEM Fuel Cell on New European Driving Cycle (NEDC)
(Springer Science and Business Media Deutschland GmbH, 2021) Işıklı, Fırat; Sürmen, Ali; Gelen, Ayetül
Modelling of a complete polymer electrolyte membrane fuel cell (PEMFC) power systems and performance of the models when subjected to common driving cycle are important research issues. In this study a complete PEMFC system, including air and hydrogen supply equipment, fuel cell stack, electrical system and a 75 kW car, is modelled. An efficiency map of a brand new electric motor is directly imported into the model for it. MATLAB & Simulink tools, based on this mathematical model of PEMFC, are used to establish a dynamic model for a vehicle which is electrically supplied by the fuel cell according to cruise characteristics of New European Driving Cycle (NEDC). Model results show significant instabilities during transient operation regarding the late response of the air supply system. Obtained stack characteristics are similar to those obtained in similar studies conducted previously. Performance results of the car based on energy consumption shows perfect agreement with the results of another model developed for an electric vehicle of the same weight and run also on NEDC.

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