Profile URL: https://hdl.handle.net/20.500.13091/12037
Job Title:Dr. Öğr. Gör.
Email Address:asozer@ktun.edu.tr
Main Affiliation:07. 12. Department of Electronics and Automation
Status: Current Staff
ORCID:
0000-0002-8430-2887
0000-0002-8430-2887YÖK Akademik: F753AD7F1C92A7A3
Google Scholar:
3ArxAccAAAAJ
3ArxAccAAAAJWeb of Science ID:
GQZ-2616-2022
GQZ-2616-2022Name Variants:
Özer, Ali S. Ozer, Ali Sait
12 results
Scholarly Output Search Results
Now showing 1 - 10 of 12
Article Citation - WoS: 1Citation - Scopus: 1Power Quality Improvement of DSTATCOM Using Fast Hybrid-PLL-Based Control Method for Wind Energy Applications(Power System Protection & Control Press, 2026) Ozer, Ali Sait; Karaca, Hulusi; Sevilmis, Fehmi; Ahmed, HafizIn self-excited induction generator (SEIG)-based wind energy systems, voltage and frequency fluctuate with variations in wind speed and load, reducing power quality and efficiency. A distribution static synchronous compensator (DSTATCOM) is an effective solution to mitigate these fluctuations but requires real-time and accurate control, including precise estimation of voltage and current parameters. This paper proposes a fast hybrid-phase locked loop (FH-PLL)-based DSTATCOM control algorithm, offering superior filtering capabilities and enhanced sensitivity in detecting amplitude, frequency, and phase angle variations. The proposed method significantly improves the performance of DSTATCOM-assisted SEIG energy systems. Unlike conventional alternatives, which often suffer from either low estimation accuracy or high computational complexity, the proposed approach achieves an optimal balance between computational efficiency and estimation precision, making it a superior alternative to existing control algorithms. Comprehensive comparative performance evaluations under various challenging conditions such as non-linear loads, unbalanced loads, open-circuit faults, and measurement offsets, demonstrate that the proposed method achieves the lowest total harmonic distortion (THD) and total demand distortion (TDD) compared to state-of-the-art techniques, including the enhanced phase locked loop (EPLL), second-order generalized integrator (SOGI), and conventional synchronous reference frame PLL (SRF-PLL), while remaining compliant with the relevant IEEE 519-2014 standards.Article Power Quality Enhancement in Wind Energy Applications through E2PLL-Based DSTATCOM Control(Public Library Science, 2026) Özer, Ali Sait; Sevilmiş, Fehmi; Karaca, Hulusi; Ahmed, HafizThe distribution static compensator (DSTATCOM) is widely employed to regulate the voltage of self-excited induction generators (SEIGs) in wind energy systems. It supplies the reactive power demanded by both the SEIG and the connected load, drawing on the DC bus voltage for this purpose. Effective operation requires maintaining the DC bus voltage at a constant reference value and accurately determining the reactive power demand. This, in turn, depends on precise estimation of the reference source currents. Under nonlinear and unbalanced loading conditions, inadequate filtering of load currents can lead to errors in separating their active and reactive components, thereby degrading system performance. To address this challenge, this paper proposes an extended enhanced phase-locked loop (E2PLL)-based control algorithm for DSTATCOM-supported SEIGs operating with nonlinear and unbalanced loads. The proposed E & sup2;PLL achieves an effective balance between structural complexity and filtering capability, enabling accurate estimation of load current magnitude and frequency. This allows the DSTATCOM to maintain balanced and sinusoidal source currents even under nonlinear and unbalanced loading conditions. The proposed approach is comprehensively validated through real-time hardware-in-the-loop system (OPAL-RT) under these adverse conditions. In addition, robustness evaluations under practical operating disturbances, including wind-speed variations, measurement noise, and DC-link voltage ripple, further demonstrate the reliability of the proposed controller. The results demonstrate its effectiveness in ensuring accurate current estimation and stable system operation.Article Citation - WoS: 9Citation - Scopus: 11Enhanced Control Method for Voltage Regulation of Dstatcom Based Seig(Elsevier, 2022-11-01) Özer, Ali Sait; Sevilmiş, Fehmi; Karaca, Hulusi; Arabacı, HayriSelf-excited induction generator (SEIG)-based wind energy conversion systems (WECS) are very popular for feeding standalone loads in remote areas where there is no grid. SEIG needs adjustable reactive power to regulate terminal voltage and frequency. Usually, a combination of a distributed static compensator (DSTATCOM) and a fixed capacitor bank is used to meet this reactive power demand. DSTATCOM is the most suitable option for power quality compensation. The performance of DSTATCOM depends on its control algorithm that generates the proper switching signals for voltage source inverter (VSI). Many control algorithms have been proposed for DSTATCOM in the literature. Among them, the most frequently used algorithms are synchronous reference frame (SRF), instantaneous reactive power (IRP), and current synchronous detection (CSD) algorithms. An effective control algorithm must accurately estimate the amplitude of the terminal voltage without being affected by harmonics, DC-offset, and frequency variation. For this purpose, an enhanced phase locked loop (EPLL)-based CSD control algorithm is proposed to estimate the amplitudes of individual phase voltages, and to filter SEIG voltages in case of harmonics, DC offset, and frequency variation. The proposed algorithm has been tested under linear and nonlinear load conditions. The obtained results clearly demonstrate the effectiveness of the proposed EPLL-based CSD control algorithm. (C) 2022 The Author(s). Published by Elsevier Ltd.Conference Object Enhanced DSOGI-PLL Based Control Strategy for DSTATCOM(IEEE, 2025-08-05) Ozer, Ali Sait; Karaca, HulusiA self-excited induction generator (SEIG) is highly preferred in standalone wind power generation systems due to its robust structure. In order for the voltage and frequency generated by SEIG to be stable and constant, active and reactive power control is required. For this purpose, DSTATCOM is widely used. The effectiveness of the performance of DSTATCOM depends on the control algorithm used. The control algorithm generates reference currents using load currents, SEIG voltages, and frequency. When the SEIG is fed with nonlinear and unbalanced loads, if the load currents are not well filtered, the active components, reactive components, and frequency can be misestimated. In this paper, an enhanced DSOGI-PLL based control algorithm with superior filtering capability is proposed for voltage and frequency control of SEIG. The proposed algorithm is tested under nonlinear load and nonlinear unbalanced load conditions. The results clearly demonstrated the superiority of the EDSOGI-PLLL based algorithm.Article Moving Average Filter Based Dstatcom Control Approach(2025) Özer Ali Sait; Karaca Hulusi; Özer, Ali Sait; Karaca, Hulusi; Sevilmiş, FehmiSelf-excited induction generators (SEIGs) are commonly preferred in standalone renewable energy systems due to their low cost and robust construction. However, the voltage and frequency stability at the SEIG output may be adversely affected by varying reactive and active power demands depending on the load profile. In such systems, DSTATCOM is used to provide voltage and frequency regulation, and its performance largely depends on the applied control algorithm. In this study, a moving average filter (MAF) based dq control algorithm is proposed for DSTATCOM supported SEIG systems. The MAF structure contributes to more accurate calculation of reference source currents by reducing the effect of harmonic content. The proposed control method is tested under nonlinear load and unbalanced-nonlinear load conditions. The results show that the system maintains voltage and frequency stability and successfully suppresses disturbances.Article DSOGI-PLL and Moving Average Filter Based Control Method for Shunt Active Power Filter(2026) Özer, Ali SaitThe increasing penetration of nonlinear loads and power electronic converters in modern power systems has led to a significant rise in current harmonics and power quality problems. The shunt active power filter (SAPF) has emerged as an effective solution to mitigate these issues. However, the performance of SAPF systems largely depends on the accuracy of the reference current generation method and the effectiveness of the synchronization algorithm. In conventional synchronous reference frame (SRF–dq) based SAPF control methods, low-pass filters (LPFs) are commonly used to extract the fundamental component, while high-pass filters (HPFs) are employed to obtain harmonic components. Nevertheless, these filtering structures may introduce undesirable effects such as time delay and phase shift, which degrade the dynamic performance of the control system.In this study, an improved dq-based control strategy is proposed for SAPF applications by combining a Dual Second-Order Generalized Integrator Phase-Locked Loop (DSOGI-PLL) based synchronization technique with a Moving Average Filter (MAF) based harmonic extraction approach. In the proposed method, the DSOGI-PLL structure ensures accurate phase angle estimation even under distorted grid conditions, while the MAF structure enables the extraction of the fundamental current component without introducing the delay and phase shift problems associated with conventional LPF and HPF filters. The effectiveness of the proposed control method is evaluated through simulation studies under different operating conditions. In the case of sinusoidal grid voltage and nonlinear load, the total harmonic distortion (THD) of the source current is reduced from approximately 19.19% to 0.79%. Furthermore, under distorted grid voltage and nonlinear load conditions, the source current THD values decrease to the range of 1.55–1.89%. These results demonstrate that the proposed method significantly enhances harmonic mitigation performance and ensures compliance with the limits specified in the IEEE 519-2014 standard.Conference Object Machine Learning Evaluation of Servo Motor Performance for Efficient Control Strategies(Institute of Electrical and Electronics Engineers Inc., 2026) Ozer, Ali Sait; Cinar, Ilkay; Unal, CihanArticle Real-Time and Fully Automated Robotic Stacking System with Deep Learning-Based Visual Perception(MDPI, 2025) Ozer, Ali Sait; Cinar, IlkayHighlights The proposed framework represents a fully deployable AI-driven automation system that enhances operational accuracy, flexibility, and efficiency. It establishes a benchmark for smart manufacturing solutions that integrate machine vision, robotics, and industrial communication technologies. The study contributes to the advancement of Industry 4.0 practices by validating an intelligent production model applicable to real industrial environments. What are the main findings? A real-time image processing framework was developed in Python using the YOLOv5 models and directly integrated into an industrial production line. The system successfully combined object classification results with a Siemens S7-1200 PLC via Profinet communication, enabling synchronized control of the robotic arm, conveyor motors, and sensors. What are the implications of the main findings? The integration of deep learning-based visual perception with PLC-controlled automation enables seamless communication between vision and mechanical components in industrial settings. The validated framework demonstrates scalability and real-world applicability, offering an effective solution for multi-class object detection and robotic stacking in manufacturing environments.Highlights The proposed framework represents a fully deployable AI-driven automation system that enhances operational accuracy, flexibility, and efficiency. It establishes a benchmark for smart manufacturing solutions that integrate machine vision, robotics, and industrial communication technologies. The study contributes to the advancement of Industry 4.0 practices by validating an intelligent production model applicable to real industrial environments. What are the main findings? A real-time image processing framework was developed in Python using the YOLOv5 models and directly integrated into an industrial production line. The system successfully combined object classification results with a Siemens S7-1200 PLC via Profinet communication, enabling synchronized control of the robotic arm, conveyor motors, and sensors. What are the implications of the main findings? The integration of deep learning-based visual perception with PLC-controlled automation enables seamless communication between vision and mechanical components in industrial settings. The validated framework demonstrates scalability and real-world applicability, offering an effective solution for multi-class object detection and robotic stacking in manufacturing environments.Highlights The proposed framework represents a fully deployable AI-driven automation system that enhances operational accuracy, flexibility, and efficiency. It establishes a benchmark for smart manufacturing solutions that integrate machine vision, robotics, and industrial communication technologies. The study contributes to the advancement of Industry 4.0 practices by validating an intelligent production model applicable to real industrial environments. What are the main findings? A real-time image processing framework was developed in Python using the YOLOv5 models and directly integrated into an industrial production line. The system successfully combined object classification results with a Siemens S7-1200 PLC via Profinet communication, enabling synchronized control of the robotic arm, conveyor motors, and sensors. What are the implications of the main findings? The integration of deep learning-based visual perception with PLC-controlled automation enables seamless communication between vision and mechanical components in industrial settings. The validated framework demonstrates scalability and real-world applicability, offering an effective solution for multi-class object detection and robotic stacking in manufacturing environments.Abstract This study presents a fully automated, real-time robotic stacking system based on deep learning-driven visual perception, designed to optimize classification and handling tasks on industrial production lines. The proposed system integrates a YOLOv5s-based object detection algorithm with an ABB IRB6640 robotic arm via a programmable logic controller and the Profinet communication protocol. Using a camera mounted above a conveyor belt and a Python-based interface, 13 different types of industrial bags were classified and sorted. The trained model achieved a high validation performance with an mAP@0.5 score of 0.99 and demonstrated 99.08% classification accuracy in initial field tests. Following environmental and mechanical optimizations, such as adjustments to lighting, camera angle, and cylinder alignment, the system reached 100% operational accuracy during real-world applications involving 9600 packages over five days. With an average cycle time of 10-11 s, the system supports a processing capacity of up to six items per minute, exhibiting robustness, adaptability, and real-time performance. This integration of computer vision, robotics, and industrial automation offers a scalable solution for future smart manufacturing applications.Article Robust Control of Distribution Static Compensator in Self-Excited Induction Generator-Based Wind Energy Systems Under Sensor Failures and Abnormal Load Conditions(MDPI, 2026) Özer, Ali Sait; Karaca, HulusiSelf-excited induction generators (SEIGs) used in wind energy systems suffer from poor voltage and frequency regulation due to varying active/reactive power demands of nonlinear and unbalanced loads. The distribution static compensator (DSTATCOM) provides an effective solution through reactive power support and harmonic mitigation. However, its performance strongly depends on the robustness of the control algorithm against harmonics, load imbalance, and sensor-induced measurement errors such as DC offset, which degrade reference current generation. This study proposes an Advanced Dual Fourth-Order Generalized Integrator (ADFOGI)-based control algorithm to improve voltage and frequency regulation of SEIG-DSTATCOM systems under such adverse conditions. The proposed method inherently rejects DC offset components and enables accurate reference current generation even under severe harmonic distortion, load imbalance, and transient disturbances. The effectiveness of the approach is validated on an OPAL-RT real-time platform under three scenarios: nonlinear load, unbalanced nonlinear load, and one-phase open-circuit condition, where DC offset is intentionally introduced to emulate sensor errors. Under the most severe case, where load current THD reaches 16.23%, SEIG current THD is reduced to 3.71% and voltage THD to 1.66%. In all scenarios, harmonic levels remain below the IEEE-519-2022 limit of 5%, confirming the robustness and effectiveness of the proposed control strategy.Conference Object A Speed Detection Method for Bldc Motor(IEEE, 2024) Sevilmis, Fehmi; Ozer, Ali Sait; Karaca, HulusiIt is very important to accurately determine the position and speed of BLDC motor. The use of an encoder to precisely control the motor is a suitable solution, but the placement of the encoder on the motor shaft causes the volume of the system to increase. In addition, the high cost of the encoder is another disadvantage. To overcome these problems, sensorless control algorithms can be used for position and speed detection. However, the complex structure of sensorless control methods and their inadequate performance at low speeds cause them not to he preferred in practical applications. For these reasons, hall-effect sensor control algorithms come to the forefront due to their simple structure and low cost. Since the resolution of hall sensor signals is low, these signals must be processed properly in speed detection. For this purpose, MAF-PLL-based speed detection method is proposed in this study. The proposed method is tested under different conditions. The results obtained show the accuracy of the proposed speed detection algorithm.
Research Topics
Domains
Physical SciencesSocial Sciences
Fields
EngineeringComputer ScienceDecision Sciences
Subfields
Electrical and Electronic EngineeringControl and Systems EngineeringComputer Networks and CommunicationsManagement Science and Operations Research
Specific Research Areas
Wind Turbine Control Systems
Microgrid Control and Optimization
Power Quality and Harmonics
Sensorless Control of Electric Motors
Energy Efficient Wireless Sensor Networks
Multi-Criteria Decision Making
Sustainable Development Goals
7AFFORDABLE AND CLEAN ENERGY
6
Research Products
9INDUSTRY, INNOVATION AND INFRASTRUCTURE
1
Research Products
This researcher does not have a Scopus ID.
Documents
5
Citations
15
Publication Collaboration
| Affiliation Name | Count |
|---|---|
| Konya Technical University | 12 |
| Selçuk University | 8 |
| Universities UK | 3 |
| Hacettepe University | 1 |
| University of Sheffield | 1 |
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Data obtained from OpenAlex
| Journal | Count |
|---|---|
| Sensors | 2 |
| 3rd Conference on Information Technology and Data Science -- AUG 26-28, 2024 -- HUNGARY | 1 |
| Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi | 1 |
| Electrical Engineering and Energy | 1 |
| Energy Reports | 1 |
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Scholarly Output
12
Articles
9
Views / Downloads
19/22
Supervised MSc Theses
0
Supervised PhD Theses
0
WoS Citation Count
11
Scopus Citation Count
12
Patents
0
Projects
1
WoS Citations per Publication
0.92
Scopus Citations per Publication
1.00
Open Access Source
8
Supervised Theses
0
Scopus Quartile Distribution
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