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    A Case Study on National Electricity Blackout of Turkey
    (Mdpi, 2023) Saribulut, Lutfu; Ok, Gorkem; Ameen, Arman
    The necessary precautions should be taken in order to prevent service interruption during the maintenance and repairing of electricity networks. Among these measures, emergencies that may occur in the network should be foreseen, hazard scenarios should be created, and solutions should be developed. If these are not done, a blackout, which first follows the local regions and eventually results in the collapse of the national electrical network, may take place. In this study, the national blackout of Turkey that occurred on 31 March 2015 is examined. The information about Turkey's electrical infrastructure and its energy policies was provided, as well as the reliability assessment criteria for power systems and examples of significant blackouts that occurred worldwide. The direct relation between line voltage and system frequency was provided with mathematical derivation by using real data taken from a local industrial zone. Then, a case study is presented to demonstrate this direct relation. The causes, development process, and consequences of the blackout are discussed in detail, and some recommendations are offered to increase the security of the electrical infrastructure and to prevent future occurrences while ensuring the sustainability of the system.
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    A novel average filter based phase-locked loop for FACTS devices
    (Elsevier Science Sa, 2016) Saribulut, Lutfu
    The continuous phase-angle of electrical networks is critical information for the auxiliary devices used for improving the stability of the power system. A Phase-Locked Loop (PLL) is the most widely used method for extracting the grid phase accurately to synchronize these devices. In this study, a novel PLL, based on average filter algorithm (APLL), is proposed to extract the amplitude, phase and sequences of selected harmonic for the real time applications. It is actualized by integrating the average filters into the simple structure of adaptive notch filter. The accuracy of APLL is demonstrated by giving the mathematical derivation of each property and its validation is evaluated under the distorted system conditions by comparing its results with Fast Fourier Transform at the simulation and experimental case studies. (C) 2016 Elsevier B.V. All rights reserved.
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    A Review on Feature Extraction for Speaker Recognition under Degraded Conditions
    (Taylor & Francis Ltd, 2017) Disken, Gokay; Tufekci, Zekeriya; Saribulut, Lutfu; Cevik, Ulus
    Speech is a signal that includes speaker's emotion, characteristic specification, phoneme-information etc. Various methods have been proposed for speaker recognition by extracting specifications of a given utterance. Among them, short-term cepstral features are used excessively in speech, and speaker recognition areas because of their low complexity, and high performance in controlled environments. On the other hand, their performances decrease dramatically under degraded conditions such as channel mismatch, additive noise, emotional variability, etc. In this paper, a literature review on speaker-specific information extraction from speech is presented by considering the latest studies offering solutions to the aforementioned problem. The studies are categorized in three groups considering their robustness against channel mismatch, additive noise, and other degradations such as vocal effort, emotion mismatch, etc. For a more understandable representation, they are also classified into two tables by utilizing their classification methods, and used data-sets.
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    A simple power factor calculation for electrical power systems
    (Elsevier Sci Ltd, 2014) Saribulut, Lutfu
    The accurately and fast estimation of Phase Difference (PD) is required between the voltage and current of an AC electrical power system to calculate the Power Factor (PF) for defining how effectively the electrical energy is converted into the useful form. Many complex methods based on difficult mathematical equations are presented by the researchers to estimate the PD. In this study, a new and simple algorithm derived by using the trigonometric functions is proposed for PD estimation to calculate PF of a power system. With this method, the fast-time and unaffected by distorted sinusoids of PD estimation are carried out by decreasing the number of mathematical equations. The performance of the proposed method is evaluated under the various system conditions by performing the simulation case studies. The results of these studies are given to verify its effectiveness under the distorted system conditions. (C) 2014 Elsevier Ltd. All rights reserved.
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    Artificial neural network-based discrete-fuzzy logic controlled active power filter
    (Inst Engineering Technology-Iet, 2014) Saribulut, Lutfu; Teke, Ahmet; Tumay, Mehmet
    Artificial neural network (ANN) is a computational algorithm based on the structure and functions of biological neural networks. It is used for modelling of the non-linear systems that cannot be mathematically expressed by the formula and extraction of the system dynamics, expressed by using the complex mathematical equations, such as harmonics. To show the effective usage of ANNs in the power system, the fundamental harmonic of a load with six-pulse thyristor controlled rectifier is extracted with ANN by using the system variables that are difficult to express with each other. Then, a new approach is proposed to generate the reference signal for compensating the harmonics of the current by using discrete fuzzy logic in this study. In addition, a simple and useful method to determine the circuit parameters of the active power filter (APF) is proposed to reduce the rating of the required filter and the capacitor values without affecting its efficiency. Case studies are performed to test the performance of the proposed control algorithm for APF.
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    Day-ahead electricity price forecasting using artificial intelligence-based algorithms
    (Institute of Electrical and Electronics Engineers Inc., 2023) Yorat, Emre; Ozbek, Necdet Sinan; Zor, Kasim; Saribulut, Lutfu
    Deregulation and privatization of electricity markets has brought greater attention to electricity price forecasting (EPF) problem in day-ahead and intraday markets since a reliable forecast ensures market participants develop bidding strategies that aim to maximize their profit. Nonlinear and non-stationary characteristics of electricity prices ensemble a barrier in front of an accurate forecast and have required researchers to analyze the effects of exogenous variables such as economic metrics and neighboring countries' prices. In this paper, three different artificial intelligence-based algorithms namely multiple linear regression (MLR), autoregressive integrated moving average (ARIMA) with exogenous variables, and extreme gradient boosting decision trees (XGBoost) are applied to forecast day-ahead electricity prices of the Turkish electricity market by considering the aforementioned exogenous variables. Test results have shown that the XGBoost model has superior results in the error metrics than the other employed methods. Substantial error decrease in symmetric mean absolute percentage error, normalized root mean square error, normalized mean absolute error, and mean absolute scaled error metrics by 19.256%, 19.834%, 23.060%, and 23.016% is observed with respect to the closest performing MLR method on the test set. © 2023 IEEE.
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    Multi-functional static synchronous compensator for distribution systems
    (Gazi Univ, Fac Engineering Architecture, 2016) Saribulut, Lutfu; Teke, Ahmet; Latran, Mohammad Barghi
    Power quality problems cause the damage and inefficient operation of the electronic devices and sensitive loads, and lead to substantial economic losses. In order to eliminate these problems, Distribution Static Synchronous Compensator (D-STATCOM) is widely used due to the fast response to the changes in the system dynamics and the capability of controlling the power system parameters. In this research study, D-STATCOM, having a control method based on General Wavelet Transform, is firstly introduced and the balanced/unbalanced voltage fluctuations, current/voltage harmonics and reactive power that are most common types of power quality problems in the distribution systems are simultaneously compensated. With this study, it is the first time to formulate the general wavelet algorithm as mathematically in the literature. The performance of the proposed method is tested with several case studies by using PSCAD/EMTDC program.
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    Real-Time Speaker Independent Isolated Word Recognition on Banana Pi
    (IEEE, 2018) Disken, Gokay; Saribulut, Lutfu; Tufekci, Zekeriya; Cevik, Ulus
    Devices controlled with voice commands have gained popularity over the last decade. To recognize an utterance, they usually require an internet connection, or use commercial programming libraries. Therefore, their flexibility is low, and algorithm update opportunities are limited. In this study, a speaker independent isolated word recognition algorithm, embedded in a single board computer, is proposed to recognize utterances in real-time. The proposed system neither requires an internet connection, nor uses external libraries. Mel Frequency Cepstral Coefficients and their deltas are used as feature vectors. Gaussian mixture models are utilized to define word models. Digits and some confirmation words of Turkish language are recorded ten times in one session from twenty-four individuals. Seven of these records are used for training, and the others for testing the system. The off-line experimental results showed that the system is working with 99.98%. In real-time experiments, the system's recognition accuracy was proficient for controlled environments.
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    Real-Time Speaker Independent Isolated Word Recognition on Banana Pi
    (Institute of Electrical and Electronics Engineers Inc., 2018) Disken, Gokay; Saribulut, Lutfu; Tufekci, Zekeriya; Cevik, Ulus
    Devices controlled with voice commands have gained popularity over the last decade. To recognize an utterance, they usually require an internet connection, or use commercial programming libraries. Therefore, their flexibility is low, and algorithm update opportunities are limited. In this study, a speaker independent isolated word recognition algorithm, embedded in a single board computer, is proposed to recognize utterances in realtime. The proposed system neither requires an internet connection, nor uses external libraries. Mel Frequency Cepstral Coefficients and their deltas are used as feature vectors. Gaussian mixture models are utilized to define word models. Digits and some confirmation words of Turkish language are recorded ten times in one session from twenty-four individuals. Seven of these records are used for training, and the others for testing the system. The off-line experimental results showed that the system is working with 99.98%. In real-time experiments, the system's recognition accuracy was proficient for controlled environments. © 2018 IEEE.
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    Student Project-Based Space Vector Modulation Technique for Power Electronics Laboratory
    (Mdpi, 2023) Saribulut, Lutfu; Ameen, Arman
    Two-level DC/AC inverter topologies are widely used for low voltage and high voltage applications in power systems and industrial areas. Space Vector Modulation (SVM) is a popular Pulse-Width Modulation technique used for controlling the inverters and providing the efficient energy conversion from DC sources. However, applications of SVM-based studies are limited in the Power Electronics Laboratory (PEL) due to the vital risks associated with high voltage applications, and it is not easily learned through mathematical analysis and visual learning without implementation by undergraduate students. A simulation and experimental setup of an SVM-controlled two-level, three-phase inverter was presented in this study for undergraduate students to learn its basics in the PEL. Several programs were used to simulate the inverter in the classroom environment and to design a power circuit and microcontroller-based printed circuit board of the inverter for PEL experiments. The two case studies were given. In the case results, the output voltage waveforms of simulation and experimental inverters were compared to show the validation of simulation results. With this study, the students' experience is enhanced in electronic circuit design, programming, coordination with hardware and software development activities, self-learning, and teamwork. Additionally, practical applications increase undergraduate students' interest in Power Electronics Courses and reinforce their knowledge from lecture and laboratory studies.
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    Voltage Sag Detection and Compensation Signal Extraction for Power Quality Mitigation Devices
    (Mdpi, 2023) Saribulut, Lutfu; Ameen, Arman
    The importance of voltage quality is continuously increasing in electrical networks due to the rising manufacturing costs resulting from system faults and disturbances in utility dynamics. Researchers generally prefer reference-frame transformation-based methods to detect and mitigate these disturbances. However, these methods are adversely affected during unbalanced loading and disturbances due to their direct dependence on system dynamics (currents and voltages). In this study, a new and simple method based on Clarke transformation is proposed to detect disturbances and generate compensation signals for Power Quality Mitigation Devices. The aim is to address the deficiencies of existing approaches. Firstly, the Clarke transformation is introduced through the vector presentation. Then, the mathematical derivation of the proposed method is provided to enhance readers' understanding. The voltage sag detection and compensation signal extraction of its control algorithm for a Dynamic Voltage Restorer is illustrated graphically. Subsequently, a simple power system is created using a simulation program. Balanced and unbalanced voltage disturbances are applied to the test system to demonstrate the validation of the proposed method under distorted system conditions. The results of voltage sag detection and compensation signal extraction for both the proposed and existing methods are compared at the end of the case studies.
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    Voltage Sag Detection and Compensation Signal Extraction for Power Quality Mitigation Devices
    (Mdpi, 2023) Saribulut, Lutfu; Ameen, Arman
    The importance of voltage quality is continuously increasing in electrical networks due to the rising manufacturing costs resulting from system faults and disturbances in utility dynamics. Researchers generally prefer reference-frame transformation-based methods to detect and mitigate these disturbances. However, these methods are adversely affected during unbalanced loading and disturbances due to their direct dependence on system dynamics (currents and voltages). In this study, a new and simple method based on Clarke transformation is proposed to detect disturbances and generate compensation signals for Power Quality Mitigation Devices. The aim is to address the deficiencies of existing approaches. Firstly, the Clarke transformation is introduced through the vector presentation. Then, the mathematical derivation of the proposed method is provided to enhance readers' understanding. The voltage sag detection and compensation signal extraction of its control algorithm for a Dynamic Voltage Restorer is illustrated graphically. Subsequently, a simple power system is created using a simulation program. Balanced and unbalanced voltage disturbances are applied to the test system to demonstrate the validation of the proposed method under distorted system conditions. The results of voltage sag detection and compensation signal extraction for both the proposed and existing methods are compared at the end of the case studies.