Elektrik ve Enerji Bölümü Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12514/174

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Citation - WoS: 26
Citation - Scopus: 28
Developed Analytical Expression for Current Harmonic Distortion of the Pv System's Inverter in Relation To the Solar Irradiance and Temperature
(Springer, 2021) Cangi, Hasan; Eid, Bilal; Yilmaz, Ahmet Serdar; Adak, Suleyman
This paper deals with modeling and simulation of the total harmonic distortion of the current (THDI) dispatched from the inverter and connected to nonlinear load. The change of THD(I)was examined in relation to the ambient temperature (T) and solar irradiance (G). The developed model is being used to extract parameters for a given THD(I)as a function of temperature and solar radiation. This study outlines the working principle of photovoltaic (PV) panel as well as PV array. Off-grid PV system is modeled by using Matlab/Simulink program, and detailed analytical study has been carried out in this work. The design, modeling and simulation of this study are performed from 50 up to 988 W/m(2)for solar irradiance. Harmonic components have negative effects on the steady-voltage stability of the PV system. Therefore, analytical expression is needed for steady-state stability analysis in order to reduce negative effects. Hence, two analytical expressions of THD(I)were obtained by two new different methods which are statistical package for the social sciences program and genetic expression programming. Eventually, two different methods have been verified by the Matlab/Simulink program in order to find out THD(I)and demonstrated the effectiveness of the proposed strategy. As a result of this study, it is observed that input current THDI of nonlinear load is too high at low irradiance. It is suggested that active harmonic filters should be used at low irradiance in order to produce better quality energy and avoid damages in the PV system.
Citation - WoS: 32
Citation - Scopus: 32
Development software program for extraction of photovoltaic cell equivalent circuit model parameters based on the Newton–Raphson method
(SpringerLink, 2022) Adak, Süleyman; Cangi, Hasan; Yılmaz, Ahmet Serdar; Arifoğlu, Uğur
Finding the equivalent circuit parameters for photovoltaic (PV) cells is crucial as they are used in the modeling and analysis of PV arrays. PV cells are made of silicon. These materials have a nonlinear characteristic. This distorts the sinusoidal waveform of the current and voltage. As a result, harmonic components are formed in the system. The PV cell is the smallest building block of the PV system and produces voltages between 0.5 V and 0.7 V. It serves as a source of current. The amount of radiation hitting the cell determines how much current it produces. In an ideal case, a diode and a parallel current source make up the equivalent circuit of the PV cell. In practice, the addition of a series and parallel resistor is made to the ideal equivalent circuit. There are many equivalent circuits in the literature on modeling the equivalent circuit of a PV cell. The PV cell single–diode model is the most used model due to its ease of analysis. In this study, the iterative method by Newton–Raphson was used to find the equivalent circuit parameters of a PV cell. This method is one of the most widely used methods for determining the roots of nonlinear equations in numerical analysis. In this study, five unknown parameters (Iph, Io, Rs, Rsh and m) of the PV cell equivalent circuit were quickly discovered with the software program prepared based on the Newton–Raphson method in MATLAB
Citation - WoS: 10
Citation - Scopus: 12
Development software program for finding photovoltaic cell open-circuit voltage and fill factor based on the photovoltaic cell one-diode equivalent circuit model
(Springer, 2024) Adak, Süleyman; Cangi, Hasan
The photovoltaic (PV) cell is the smallest building block of the PV solar system and produces voltages between 0.5 and 0.7 V. It acts as a current source in the equivalent circuit. The amount of radiation hitting the cell determines how much current it produces. The equivalent circuit of an ideal PV cell consists of a diode and a parallel current source. In order to express losses in applications, series and parallel resistance are added to the ideal equivalent circuit of the PV cell. There are many equivalent circuits in the literature for modeling the equivalent circuit of a PV cell. The single-diode equivalent circuit is the most widely used model because of its simplicity and ease of analysis. There are several methods available to estimate and analyze the parameters of PV cell models, such as Newton Raphson method, Lambert-W function, etc. In this study, the Newton Raphson method was used to find the equivalent circuit parameters of a PV cell. Fill factor is used to determine the quality of electricity generated by the photovoltaic cell. Open-circuit voltage is the maximum voltage value that the PV cell can transmit. The analysis of PV cell fill factor and open-circuit voltage was carried out using the developed software program. Then, the open-circuit voltage and fill factor were found using the software program prepared in MATLAB and given in Appendix.
Multi-task learning for arousal and sleep stage detection using fully convolutional networks
(2023) Zan, Hasan; Yıldız, Abdulnasır
Objective. Sleep is a critical physiological process that plays a vital role in maintaining physical and mental health. Accurate detection of arousals and sleep stages is essential for the diagnosis of sleep disorders, as frequent and excessive occurrences of arousals disrupt sleep stage patterns and lead to poor sleep quality, negatively impacting physical and mental health. Polysomnography is a traditional method for arousal and sleep stage detection that is time-consuming and prone to high variability among experts. Approach. In this paper, we propose a novel multi-task learning approach for arousal and sleep stage detection using fully convolutional neural networks. Our model, FullSleepNet, accepts a full-night single-channel EEG signal as input and produces segmentation masks for arousal and sleep stage labels. FullSleepNet comprises four modules: a convolutional module to extract local features, a recurrent module to capture long-range dependencies, an attention mechanism to focus on relevant parts of the input, and a segmentation module to output final predictions. Main results. By unifying the two interrelated tasks as segmentation problems and employing a multi-task learning approach, FullSleepNet achieves state-of-the-art performance for arousal detection with an area under the precision-recall curve of 0.70 on Sleep Heart Health Study and Multi-Ethnic Study of Atherosclerosis datasets. For sleep stage classification, FullSleepNet obtains comparable performance on both datasets, achieving an accuracy of 0.88 and an F1-score of 0.80 on the former and an accuracy of 0.83 and an F1-score of 0.76 on the latter. Significance. Our results demonstrate that FullSleepNet offers improved practicality, efficiency, and accuracy for the detection of arousal and classification of sleep stages using raw EEG signals as input.
Citation - WoS: 11
Citation - Scopus: 13
Multi-task learning for arousal and sleep stage detection using fully convolutional networks
(IOP Publishing, 2023) Zan, Hasan; Yıldız, Abdulnasir
Objective: Sleep is a critical physiological process that plays a vital role in maintaining physical and mental health. Accurate detection of arousals and sleep stages is essential for the diagnosis of sleep disorders, as frequent and excessive occurrences of arousals disrupt sleep stage patterns and lead to poor sleep quality, negatively impacting physical and mental health. Polysomnography is a traditional method for arousal and sleep stage detection that is time-consuming and prone to high variability among experts. Approach: In this paper, we propose a novel multi-task learning approach for arousal and sleep stage detection using fully convolutional neural networks. Our model, FullSleepNet, accepts a full-night single-channel EEG signal as input and produces segmentation masks for arousal and sleep stage labels. FullSleepNet comprises four modules: a convolutional module to extract local features, a recurrent module to capture long-range dependencies, an attention mechanism to focus on relevant parts of the input, and a segmentation module to output final predictions. Main results: By unifying the two interrelated tasks as segmentation problems and employing a multi-task learning approach, FullSleepNet achieves state-of-the-art performance for arousal detection with an area under the precision-recall curve of 0.70 on Sleep Heart Health Study and Multi-Ethnic Study of Atherosclerosis datasets. For sleep stage classification, FullSleepNet obtains comparable performance on both datasets, achieving an accuracy of 0.88 and an F1-score of 0.80 on the former and an accuracy of 0.83 and an F1-score of 0.76 on the latter. Significance: Our results demonstrate that FullSleepNet offers improved practicality, efficiency, and accuracy for the detection of arousal and classification of sleep stages using raw EEG signals as input.
Citation - WoS: 17
Citation - Scopus: 19
The Quality Problems at Low Irradiance in the Grid-Connected Photovoltaic Systems
(Springer, 2024) Adak, Suleyman; Cangi, Hasan
Solar photovoltaic (PV) energy is one of the most prominent topics that have attracted the attention of researchers in recent years. The use of solar energy is increasing rapidly in the world. Although using PV energy has various advantages, it has some disadvantages. Among these disadvantages, power factor (PF) and total harmonic distortion (THD) issues are discussed in this article. When solar PV systems are integrated into the grid, various power quality problems arise. In addition, due to low power quality and high harmonics, power system components overheat and start operating in undesirable regions; causes great damage. The magnitude of PF and THD is dependent on solar irradiation values. In order to determine how the power quality in the grid-connected solar system is affected by changes in solar irradiation (G), results for various irradiation situations are presented and analyzed. In addition, at low irradiance values, the amplitude of harmonic components and reactive power increases, whereas the power factor of the PV system decreases. Low power factor and high amplitude of harmonics cause the efficiency of the solar system to decrease. In this study, PF and THDI values were measured on a particular cloudy day for analysis. An analysis of the solar PV system was conducted using Matlab/simulation program to model the grid-connected PV system. Thus, the analytical expression of the PF and THDI, which are dependent on irradiation, was found with a new method by using the Statistical Package for the Social Sciences (SPSS) program and the curve fitting method. Obtaining the analytical expressions for both solar irradiation (G) and power factor (PF) used the SPSS program and also solar irradiation (G) and total harmonic distortion (THDI) used the MATLAB curve fitting method which contributed to the science comparing to the existing literature. It can be prevented the low power quality by using such these expressions at low solar irradiation cases.

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