Yazar "Demir, Memnun" seçeneğine göre listele

Listeleniyor 1 - 6 / 6
  • [ X ]
    Öğe
    A Comparative Real-Time Speed Control of PMSM with Fuzzy Logic and ANN Based Vector Controller
    (Sirnak Üniversitesi, 2019) Güney, Ezgi; Karagöl, Serap; Demir, Memnun
    This paper presents, analyzed real-time speed control ofPermanent Magnet Synchronous Motor (PMSM) under constant load by using FuzzyLogic (FL) controller and recurrent Artificial Neural Network (ANN) controller.A closed loop PMSM drive system is improved using the mathematical model of thePMSM in Matlab / Simulink. Two types of controllers are used; the firstcontroller is the real-time FL controller and the second controller is areal-time recurrent ANN controller in terms of smoother speed response. Wholedrive systems is simulated in Matlab/Simulink program. The simulation resultsshow that the focused ANN controller produce considerable control performancecompare to the FL controller on controlling speed reference variations
  • [ X ]
    Öğe
    A Comparative Velocity Control Study of Permanent Magnet Tubular Linear DC Motor by Using PID and Fuzzy-PID Controllers
    (Ieee, 2017) Guney, Ezgi; Demir, Memnun
    This paper presents, PID and Fuzzy-PID (FPID) controller to control frequency of Permanent Magnet Tubular Linear Direct Current Motor (PMTLDCM). In the study firstly, is released by motor frequency can be controlled using PID and FPID controller. There are 27 fuzzy rules for FPID of each parameter of PID controller. FPID controller has two inputs as error and change of error and has output signal. After all, the control program is loaded to LPC1768FBD100 ARM microcontroller, the rotor frequency regulation system PID controller and FPID controller is applied and experimental results have been transferred to PC by MATLAB. Consequently, PID and FPID control based simulation and experimental results examine and the simulation demonstrate that the FPID controller has a better behavior of the PMTLDCM motor, an excellent frequency tracking with minimum overshoot and minimum steady state error and give better performance compared to conventional PID controller.
  • [ X ]
    Öğe
    Artificial Neural Network Based Real Time Speed Control of a Linear Tubular Permanent Magnet Direct Current Motor
    (Ieee, 2017) Guney, Ezgi; Dursun, Mahir; Demir, Memnun
    This paper presents a real-time speed control for a linear tubular permanent magnet direct current motor (LTPMDCM) by using artificial neural network (ANN). Firstly, a novel form of LTPMDCM prototype has been developed for linear motions. Then, a low cost motor drive has designed for expected rapid response and precise position control. Software developed in MATLAB environment is used for speed control. Through this software, real-time speed control of the motor with ANN has been performed. The effectiveness of the proposed control system is verified by experimental results and presented.
  • [ X ]
    Öğe
    Comparison of different heuristic algorithms for location and value determination of STATCOM providing minimum losses in power systems
    (Pamukkale Univ, 2017) Alcan, Yalcin; Ozturk, Ali; Dirik, Hasan; Demir, Memnun
    One of the most important issues of studies that have been done on the power systems is loss reduction, Flexible /-1. C transmission systems (FACTS) give significant opportunities to realize this aim, Static Synchronous Compensators (STATCOMs) are the most flexible and sophisticated structure as compared to the other FACTS devices in terms of operation, Total losses of u power system c hunge according to the location and reactive power output of STATCOM, In this work, it is aimed to find optimum locution and output value of a STATCOM that provide minimum kisses of power system by using four different heuristic algorithms and to compare these algorithms, Heuristic algorithms that ure used M this paper are Gravitational Search Algorithm (GSA), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC) and Ant Colony Algorithm (ACA) respectively, These methods were applied to IEEE-14 bus test system, and optimum locutions and output values of STATCOMs that provide minimum losses have been found, Also, results of all methods are compared and discussed in terms of finding proper value and convergence speed,
  • [ X ]
    Öğe
    Sinop İlinin Güneş Enerjisinden Elektrik Üretim Potansiyelinin Ülkemiz Ve Almanya İle Karşılaştırarak İncelenmesi
    (2018) Demir, Memnun; Alcan, Yalçın; Duman, Serhat
    Günümüzde enerjiye olan ihtiyaç geçmişte olduğu gibi, bugün de önemi korumuş ve daha da artmaya dadevam edeceği düşünülmektedir. Fosil yakıt rezerv sorunu, çevresel sorunlar gibi birçok etmenden dolayıelektrik üretimi için alternatif enerji kaynaklarına yönelim olmuştur. Doğal olan bu kaynaklar örneğin: güneş,rüzgar, hidrolik, jeotermal’dir. Çevre dostu, yenilenebilir oluşu, kolay elde edilebirliği, potansiyellerinin oluşubu kaynakları cazip kılmaktadır. Bu kaynaklardan güneş enerjisinden elektrik üretiminde daha fazlafaydalanabilmek için, dünyada ve ülkemizde çalışmalar devam etmektedir. Bir yerde güneş enerjisinden elektriküretim için faydalanılması istenildiğinde o yere ait güneş verilerini bilmek ve yorumlamak güneş enerjisipotansiyelinin belirlenmesi için gerekli ve önemlidir. Sinop ili, son dönemlerde termik ve nükleer santral ileenerji üretiminde adı çok sık projelerle anılmaktadır. Almanya fotovoltaik sistem kapasitesi bakımından sonyıllara kadar ilk sırada yer almıştır. Bu çalışmada Sinop ilinin güneş verileri Türkiye ve Almanya genelinebakılarak incelenmiş, kıyaslanılmıştır. Kıyaslanırken Solargis firması, World Weather and Climate Informationsitesi ve ülkemizdeki elektrik üretimi verilerinden faydalanılmıştır. Sonuçlar karşılaştırılarak tartışılmıştır.Ayrıca bu çalışmada Sinop ili için temiz, yenilenebilir ve alternatif bir üretim şekli olan güneş enerjisindenelektrik üretiminin hem potansiyel hem de kullanılabirliği hakkında bilgiler içermektedir.
  • [ X ]
    Öğe
    Time-Aware Machine Learning for Biomass Power Output Estimation Using SCADA Data
    (Springer Heidelberg, 2026) Guney, Ezgi; Demir, Memnun
    Accurate short-term estimation of electrical power output in biomass power plants remains challenging due to the nonlinear and dynamically coupled nature of thermochemical conversion processes, fuel heterogeneity, and pronounced thermal inertia. Conventional physics-based models, while effective for steady-state analysis, often fail to capture the high-frequency dynamics required for real-time monitoring and decision-support applications. This study proposes a data-driven framework for short-term power output estimation using high-resolution Supervisory Control and Data Acquisition (SCADA) data collected from an operational industrial biomass power plant. A large-scale SCADA dataset comprising several hundred thousand time-stamped records is used to model the relationship between seven key thermodynamic and operational variables and net electrical power output. Multi-layer perceptron (MLP), random forest (RF), gradient boosting regressor (GBR), and support vector regression (SVR) are evaluated under two distinct validation strategies: (i) a conventional random train-test split and (ii) a temporally blocked cross-validation scheme preserving causal order. Under random sampling, RF attains the highest apparent accuracy (R2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$<^>{2}$$\end{document} = 0.9687), whereas MLP exhibits lower performance (R2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$<^>{2}$$\end{document} = 0.8492), highlighting sensitivity to instantaneous regression assumptions. When temporal continuity is enforced, predictive performance improves consistently across all models. In the blocked validation stage, GBR and RF achieve R2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$<^>{2}$$\end{document} values of 0.9983 and 0.9973, respectively, while MLP demonstrates a substantial performance increase (R2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$<^>{2}$$\end{document} = 0.9865). Time-domain analysis further reveals that ensemble-based models provide smoother tracking of short-term fluctuations, whereas temporally aligned evaluation significantly improves the physical consistency of neural network predictions. These results demonstrate that temporally consistent validation is essential for reliable SCADA-based modeling of biomass power generation and provide a practical foundation for real-time monitoring and decision-support applications in industrial biomass power plants.