Yazar "Inanc, Nihat" seçeneğine göre listele

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    Control and performance analyses of a DC motor using optimized PIDs and fuzzy logic controller
    (Elsevier, 2023) Manuel, Nelson Luis; Inanc, Nihat; Lily, Murat
    Based on the no-free-lunch theorem, researchers have been proposing optimization algorithms for solving complex engineering problems. This paper analyzes the performance of five metaBased Optimization (TLBO), Differential Evolution (DE), and Genetic Algorithm (GA) in finetuning the gains of a Proportional-Integral-Derivative (PID) to control the speed of a DC motor. The selected metaheuristics, in addition to being from distinct classes, are well established in their respective groups. The methods and findings of this study can be summarized in three phases. First, the mathematical model of the DC motor is deduced. Second, detailed descriptions of the aforementioned algorithms are presented. Furthermore, the structures of the applied controllers are discussed. Third, comparisons based on statistical indicators and analyses in the time and frequency domains, in addition to robustness and load disturbance tests, are performed. The results revealed that if a sufficient number of runs is given for each metaheuristic, despite being in different runs, all algorithms are able to propose the same optimal gain values. TLBO presented the highest speed, while GA and DE were the slowest in finding optimal values. Additionally, the results were compared with the Opposition-Based Learning Henry Gas Solubility Optimization (OBL/HBO)-based PID, reported to have better results than some previously published works on this topic, and a Fuzzy Logic Controller (FLC). The five optimized controllers obtained approximately the same results and outperformed the OBL/HGO-based PID, but the FLC was superior compared to the metaheuristic-based PIDs.
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    Design of a robust hybrid fuzzy super-twisting speed controller for induction motor vector control systems
    (Springer London Ltd, 2022) Nurettin, Abdulhamit; Inanc, Nihat
    This paper deals with a new design of a hybrid fuzzy super-twisting sliding mode controller (HFSTSMC) for a three-phase induction motor (IM) controlled by the rotor flux orientation technique. Super-twisting sliding mode control is employed as a potential solution to limit the inherent chattering effect in the conventional sliding mode control without affecting the tracking accuracy and robustness. The super-twisting sliding mode control (STSMC) scheme is a modified second-order sliding mode control (SOSMC) scheme that does not need the information of any derivative of the sliding surface, but the experimental control coefficients found in the control law have an obvious effect on limiting chattering and the system response speed. Therefore, a robust hybrid controller was proposed based on the fuzzy logic control (FLC) approach to optimally tuning these coefficients. Whereas, the fuzzy logic controller is used as a supervisory controller to adjust the value of the gains according to the state of the system. Thus, providing high dynamic performance and achieving the highest rates of robustness in transient and uncertain conditions. On the other hand, increasing tracking accuracy and chattering phenomena reduction in steady states. The validation of the suggested scheme is verified by experimental approximating of simulations using MATLAB/SIMULINK and also compared with conventional and advanced controllers. The obtained results confirm the reduction of the chattering phenomenon and thus reduction of the total harmonic distortion (THD) in the motor current, and the effectiveness of the proposed scheme in various operating conditions.
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    Designing an Intuitive Algorithm Based Load Frequency Controller for Electrical Power Systems
    (Taylor & Francis Inc, 2023) Soyacikgoz, Kursat; Inanc, Nihat
    In the interconnected system, changes in energy consumption and random energy generation by renewable energy sources cause an increase or decrease in frequency and bus voltages. Load frequency control (LFC) that cannot be controlled within certain limits may cause serious problems. Therefore, LFC is required to keep the interconnection frequency and power sharing of the interconnection line at a certain value. Due to the importance of this issue, researchers have been working on numerous studies to improve LFC. In this article, a cascaded FOPID+(III) controller consisting of a fractional-order PID and three integrators is designed for a two-zone power system, including thermal power plants, electric vehicles using the vehicle-to-grid (V2G) technique and renewable energy sources such as wind farms and photovoltaic panels. Particle Swarm Optimization and Gray Wolf Optimization are used to determine the gain parameters in our new design. The effectiveness and efficiency of the FOPID+(III) controller are tested with load variation, parameter variation in the designed model and RES power variation. As a result of the experiments, it was observed that the PSO-based FOPID+(III) controller provided a 54% improvement in settling time and a 55% improvement in maximum frequency overshoot compared to other controllers.
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    Direction of arrival estimation in time modulated linear arrays using matrix pencil method with single snapshot and optimized time steps
    (Elsevier Gmbh, 2022) Aytas, Nilay; Afacan, Erkan; Inanc, Nihat
    The direction of arrival estimation by the time modulated linear array with a proposed novel approach has been analyzed and proven. By merging Matrix Pencil method with time modulated linear array, a great advantage has been obtained by the virtue of using a single snapshot. A new formulation is developed by the authors which combines the Time Modulation and Matrix Pencil methods. The time steps were optimized using differential evolution algorithm for the estimation of the angle of incidence. The effects of the noise level, the number of signal sources, the number of antenna elements and different angles of incidence were examined with the simulations and comparisons were presented. Compared to traditional methods, the simulation results show that the proposed novel approach maintains significant advantages from the viewpoint of estimation accuracy, especially for scenarios with single snapshot number and low SNR.
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    Forecasting Electricity Consumption for Accurate Energy Management in Commercial Buildings With Deep Learning Models to Facilitate Demand Response Programs
    (Taylor & Francis Inc, 2024) Erten, Mustafa Yasin; Inanc, Nihat
    In the context of rapidly increasing energy demands and environmental concerns, optimizing energy management in commercial buildings is a critical challenge. Smart grids, empowered by advanced Energy Management Systems (EMS), play a pivotal role in addressing this challenge through Demand Side Management (DSM). However, the efficiency of DSM-based building EMS is often limited by the accuracy of load forecasting. This paper addresses this gap by exploring load forecasting models within DSM-based building EMS, focusing on a case study in a commercial building in Ankara, Turkey. Employing Deep Learning (DL) models for load forecasting, we provide inputs for rule-based controllers to enhance energy efficiency. Our major contribution is the development of the ANFIS-IC algorithm, aimed at maximizing demand response participation in commercial buildings. ANFIS-IC, integrating ANFIS controllers with LSTM-based load consumption forecasts, leads to a 33.14% reduction in energy consumption and a 39.22% decrease in energy costs, surpassing the performance of rule-based controllers alone which achieve reductions of 25.34% in energy consumption and 34.03% in energy costs. These findings not only highlight the potential of integrating rule-based controllers with deep learning algorithms but also underscore the importance of accurate load forecasting in improving the effectiveness of DSM-based building EMS.
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    High-Performance Induction Motor Speed Control Using a Robust Hybrid Controller With a Supertwisting Sliding Mode Load Disturbance Observer
    (IEEE-Inst Electrical Electronics Engineers Inc, 2023) Nurettin, Abdulhamit; Inanc, Nihat
    To enhance the speed control performance of a three-phase induction motor controlled by the vector control strategy, a new design of a hybrid controller (HC) is proposed based on the supertwisting algorithm (STA) and fuzzy approach. STA is chosen for its ability to decrease the ingrained chattering phenomenon in the classical sliding mode control with maintaining tracking precision and robustness. Nevertheless, the control gains included in the control law have an evident impact on suppressing the chattering phenomenon and increasing the system's dynamic response speed. Therefore, first, a robust HC based on the fuzzy logic control approach that operates as a fuzzy supervisor to online self-tune the value of the gains according to the system states is suggested to achieve high dynamic performance and limit the chattering effect. Second, to enhance the disturbance refusal capability, a supertwisting sliding mode load disturbance observer is developed to estimate the load torque disturbances. Then, the estimated disturbance is introduced into the equivalent control law. Subsequently, the system stability is verified by the Lyapunov theorem. Finally, the superiority of the proposed scheme is validated through comparison with the advanced and traditional controllers in simulation and experimental studies.