48 research outputs found

    Comparing of fuzzy logic controllers with PID controller in a thermic power plant

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    YÖK Tez ID: 168792ÖZET BULANIK MANTIK KONTROLÖRLER ÎLE PID KONTROLÖRÜN BÎR ELEKTRİKSEL TERMİK SANTRALDE KARŞILAŞTIRILMASI TİRYAKİ, Hasan Kırıkkale Üniversitesi Fen Bilimleri Enstitüsü Elektrik-Elektronik Anabilim Dalı, Yüksek Lisans Tezi Danışman: Prof. Dr. İlhan KOCAARSLAN Ocak 2005, 83 sayfa Birçok endüstriyel termik santralin dinamik davranışı, giriş ve çıkıştaki bozulmalardan, set noktalarındaki değişikliklerden ve işletme şartlarındaki bazı faktörlerden etkilenir. Bu durum özellikle büyük kömür yakıtlı güç santrallerinde görülür. Yükte meydana gelen değişiklikler, kömürün kalitesi, kazan yüzeyindeki kirlenmeler gibi etkiler karşısında klasik PID kontrolörler her zaman istenen performansı sağlayamamaktadır. Diğer yandan kompleks ve büyük modern elektrik santrallerindeki ihtiyaçların çoğalmasıyla, optimal ve esnek kontrolörlere ihtiyaç duyulmaktadır. Son yıllarda birçok araştırmacı simülasyon veya uygulamalarda farklı kontrol yöntemleri denemektedir. Bu yöntemlerden üzerine ilgi toplayanlardan biri de bulanık mantık kontrol yöntemidir. Bu yöntemin daha geliştirilmiş hali ise kazancı bulanık mantık kuralları ile ayarlanan yeni nesil PI kontrolörlerdir. Bu tezçalışmasında bir termik elektrik santralinin güç ve entalpi çıkışlarım kontrol etmek amacıyla bulanık PI kontrolör ile bulanık mantık kontrolör ve PID kontrolörler karşılaştınlmıştır. Simülasyon sonuçlan, bulanık PI kontrolörün tüm işletme şartlan altında, sisteme verdiği hızlı cevaplarla diğer kontrolörlerden daha iyi bir performansa sahip olduğunu göstermiştir. Anahtar Kelimeler : Termik Güç Santrali, Santral Modelleme, PID Kontrolör, Bulanık Mantık Kontrolör, Bulanık PI Kontrolör, Kazan Kontrolü IIABSTRACT COMPARING OF FUZZY LOGIC CONTROLLERS WITH PID CONTROLLER IN A THERMIC POWER PLANT TİRYAKİ, Hasan Kırıkkale University Graduate School of Natural and Applied Sciences Department of Electrical and Electronic Engineering, M. Sc. Thesis Supervisor: Prof. Dr. İlhan KOCAARSLAN January 2005, 83 pages The dynamic behavior of many thermic power plant heavily depends on iner and outer disturbances, set point changes and in particular on changes in operating points. This is especially the case for large coal-fired power plants. Due to load changes to cover power demands, quality differences of coal and contamination of boiler heating surfaces, conventional PID control will not reach a high degree of control perfonmance. On the other hand, the growing needs of complex and huge modern combinational power plants require optimal and flexible controllers. Not only due to the effects discussed above but also taking into account the expected economical benefits. During recent years many researchers try different control methods in simulation or applications. One of the popular methods is fuzzy logic controller. The IIInew generation of PI controllers which adjusted by fuzzy logic rules are the advanced condition of this method. In this thesis, fuzzy gain scheduled PI (fuzzy PI) controller is compared with fuzzy logic controller PID controller for controlling the power and enthalpy output of a thermic electrical power plant. Simulation results shown that, because of the fast response to the systems, fuzzy PI controller's performance is better than the other controllers under all the considered operating conditions. Key Words : Thermic Power Plant, Plant Modelling, PID Controller, Fuzzy Logic Controller, Fuzzy PI Controller, Boiler Control. I

    Bulanık mantık kontrolörler ile PID kontrolörün bir elektriksel termik santralde karşılaştırılması

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    Tez (Yüksek Lisans) -- Kırıkkale Üniversitesi78690

    Prediction of railway switch point failures by artificial intelligence methods

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    In recent years, railway transport has been preferred intensively in local and intercity freight and passenger transport. For this reason, it is of utmost importance that railway lines are operated in an uninterrupted and safe manner. In order to carry out continuous operation, all systems must continue to operate with maximum availability. In this study, data were collected from switch motors, which are the important equipment of railways, and the related equipment and these data were evaluated with sector experience and the results related to the failure status of the switch points were revealed. The obtained results were processed with support vector machines and artificial neural networks, which are artificial intelligence methods, and machine learning was performed. In the light of this learning, a decision support model, which predicts possible failures and gives information about the root cause of the failures that have occurred, was developed. This model aims to ensure that the data obtained in each movement of the railway switch point are processed and the necessary corrective and preventive actions are communicated to the maintenance personnel; thus, failures are eliminated before they affect the railway operation and the solution process of the failures that have occurred is shortened. Considering the six switch points from which the data were collected, the experimental results were predicted with 24% RMSE error rates in the SVM method, while they were successfully predicted with RMSE error rates ranging from 2.4% to 6.6% in the ANN method. Therefore, it is observed that the ANN method is more appropriate in the implementation of the established model

    Control of the Automatic Voltage Regulator System with a Novel Stability-based Artificial Intelligence Method

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    This paper proposes a novel Stability-Based Artificial Intelligence Method for predicting the optimum parameters of the proportional-integral-derivative controller in an automatic voltage regulator system. To implement the stability-based artificial intelligence method, first, parameters which are of great importance for the control of the system are applied to the system randomly, data are collected, and then artificial intelligence studies are carried out. The suggested approach has been applied to the system and compared with other control methods in the literature, namely the improved Kidney Inspired algorithm, Jaya algorithm, Tree Seed algorithm, Water Wave Optimization, and Biography-Based Optimization to test the robustness of the new method. The numerical results indicate that the proposed method significantly outperforms all other methods
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