Sürdürülebilir Kalkınma Amaçları

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• MakaleYayıncıda açık erişim2026/r/gumushane/13454
  Measurement-aware zero-phase iterative learning control for robust regulation of nonideal boost converters in electric vehicle fast charging

  Altan, Aytaç; Alzaidi, Mohammed S.; Yanarateş, Cağfer

  Integrating battery energy storage with DC-DC boost converters for electric vehicle fast charging exposes the regulator to ageing-induced parameter drift, periodic load pulses, and, critically, the nonidealities in the output-voltage sensing chain. This paper proposes a measurement-aware, zero-phase iterative learning control scheme for robust output-voltage regulation of a nonideal boost converter whose parameters are matched to those of a commercial Texas Instruments TPS6102x battery regulator. The controller combines an inner proportional-integral stabilizing loop with an outer zero-phase learning law that updates a feedforward correction based on the sensor-captured output trajectory; a forward-backward robustness filter suppresses the amplification of measurement noise and quantization across successive trials without introducing phase lag. The averaged small-signal model is validated against a 960 kHz switched simulation, with agreement within 0.2% at the operating point and a transient-waveform correlation of 0.91, while the bandwidth limitations imposed by the lightly damped resonance and the nonminimum-phase dynamics on feedback are quantified from the loop frequency response. Because feedback alone is bandwidth-limited, the learning feedforward recovers the intrinsic converter speed for the repetitive task and reduces the periodic tracking error by more than an order of magnitude relative to a fairly tuned baseline. A bounded-error convergence theorem shows that, under real sensing, the tracking error converges to a neighbourhood whose size is proportional to the measurement-disturbance level; this is confirmed numerically, with the asymptotic error scaling approximately linearly with the noise standard deviation once the noise dominates the filter bias. Systematic sweeps of converter resolution, noise, and sensor delay show that the zero-phase measurement-aware law remains bounded where a naive learning law diverges, and a 500-sample Monte Carlo study confirms robustness to component tolerances, load variation, and parasitic aging. The primary physical task is the rejection of a repeating high-current charging pulse: over successive charging cycles the scheme reduces the periodic output-voltage sag from 35% to below 0.01%, whereas the best feedback comparator leaves a 17% residual, and the sinusoidal reference used elsewhere serves as a spectral diagnostic that isolates the feedback-bandwidth ceiling at a single known frequency. The learning law is further verified in closed loop on the cycle-by-cycle switched large-signal converter, where it converges and stays bounded. A per-unit scale-invariance analysis then establishes the conditions under which the design obtained on the low-power benchmark transfers to fast-charging power levels, as confirmed on a 400 V, 6.1 kW stage that reproduces the benchmark convergence exactly.

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• MakaleAçık Erişim2024/r/gumushane/11525
  Atomic Comagnetometer Gyroscopes for Inertial Navigation Systems: A Review

  Karabi̇Naoğlu, Murat Salim; Çakir, Bekir; Başoğlu, Mustafa Engin

  In recent years, developments in quantum sensing, laser, and atomic sensor technologies have also enabled advancement in the field of quantum navigation. Atomic-based gyroscopes have emerged as one of the most critical atomic sensors in this respect. In this review, a brief technology statement of spin exchange relaxation free (SERF) and nuclear magnetic resonance (NMR) type atomic comagnetometer gyroscope (CG) is presented. Related studies in the literature have been gathered, and the fundamental compositions of CGs with technical basics are presented. A comparison of SERF and NMR CGs is provided. A basic simulation of SERF CG was carried out because of its high theoretic bias stability limit. Besides, some highly critical challenges for CGs were examined and reported with compensation methods. The objective of this review is to offer a guide for researchers to develop high-precision atomic gyroscopes and to encourage further research.

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• MakaleAçık Erişim2022/r/gumushane/13329
  Comparison of deep learning and regression-based MPPT algorithms in PV systems

  Karabi̇Naoğlu, Murat Salim; Çakir, Bekir; Başoğlu, Mustafa Engin; Kazdaloğlu, Abdülvehhab; Güneroğlu, Aziz

  Solar energy systems (SES) and photovoltaic (PV) modules should be operated at the maximum power point (MPP) to achieve the highest efficiency in the energy generation processes. Maximum power point tracking (MPPT) applications using conventional methods may not be able to follow the global MPP (GMPP) of the PV system under changing atmospheric conditions and they could oscillate around the local MPP. In this study, a machine learning and deep learning (DL) based long short-term memory (LSTM) model is proposed as an innovative solution for MPPT. Contrary to the traditional MPPT applications using current and voltage sensors, the output resistance of the PV module estimation was made by using environmental parameters (such as temperature and radiation) and artificial intelligence algorithms in this study.The LSTM model was compared with artificial neural networks (ANN) and regression methods regarding mean square error (MSE), root mean square error(RMSE) and mean absolute error (MAE) parameters. It has been determined that the LSTM model has a better performance and could more successfully follow MPP compared to the other methods. Finally, after the comparison with the ANN method, it is proved that LSTM gives 37%, 21%, and 31% more successful MSE, RMSE, and MAE results, respectively.

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• MakaleAçık Erişim2015/r/gumushane/15740
  Optimization of electrical power transmission lines' routing using AHP, fuzzy AHP, and GIS

  Eroʇlu, Hasan; Aydin, Musa

  With the increase of people's need for energy, electrical energy transmission has become a very important issue as well as electrical power generation. One of the most important problems in energy transmission is finding the best route in a very complex study area. To date, many people from different disciplines have come together to find the best routes by manual methods like using paper maps and deciding which route is the least-cost path (LCP) to the destination point. Today it is known that, in engineering problems, and especially in path-finding or site-placing problems, the Geographic Information System (GIS) is the most powerful tool. On the other hand, as finding the best route is a very complicated problem and many criteria should be considered, including slope, landslide, road/railway/pipeline crossing, ice zones, distances to roads, national parks, archaeological areas, residential areas forests, and river crossings, multicriteria decision methods like the analytic hierarchy process (AHP) and fuzzy analytic hierarchy process (FAHP) should be used to make the most accurate decisions. In this study, the AHP and FAHP are used to weight criteria relevant to energy transmission line (ETL) routing. According to the criteria, digital maps of the sample study area are edited, weighted, converted to raster-based format, and gathered using the Environmental Systems Research Institute's ArcGIS Desktop 10 software. After generation of the weighted surface map, the LCP tool is used to find the best route. By selecting different start and end points in the sample study area, accuracy and performance of the best routes according to the LCP algorithm are assessed and some problems of the routes for ETL routing are presented. With this study, mistakes by manual methods will decrease in ETL routing and other routing problems.

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• MakaleAçık Erişim2013/r/gumushane/13583
  Simulation of a large electric distribution system having intensive harmonics in the industrial zone of Konya

  Eroǧlu, Hasan; Aydin, Musa

  In this study, measurements were taken at difierent points of a sample distribution system in order to analyze the levels of harmonics that appear as a day-to-day increasing problem in electrical distribution systems. According to the measurement values, the dominant harmonics have been determined and an electrical model of the system has been prepared using the SimPowerSystems toolbox from MATLAB and Simulink. The accuracy of the electrical model has been verified by the measurement and simulation values. In addition, the transformer connection types' and impedances' simulations have been presented to understand and solve the harmonic problems in electrical distribution systems. With the help of this study, advanced analysis can be performed in the electrical distribution system for power quality analyses and this study can be a background for similar kinds of simulation studies.

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