Yazar "Khaki, Behnam" seçeneğine göre listele

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    Öğe
    Active Fault Tolerant Control of Grid-Connected DER: Diagnosis and Reconfiguration
    (Ieee, 2019) Khaki, Behnam; Kilic, Heybet; Yilmaz, Musa; Shafie-khah, Miadreza; Lotfi, Mohamed; Catalao, Joao P. S.
    In this paper, we propose an active fault tolerant control (FTC) to regulate the active and reactive output powers of a voltage source converter (VSC) in the case of actuator failure. The active fault tolerant controller of the VSC which connects a distributed energy resource to the distribution power grid is achieved through the fault diagnostic and controller reconfiguration units. The diagnostic unit reveals the actuator failure by comparing the known inputs and measured outputs of VSC with those of the faultless model of the system and testing their consistency. In the case of actuator failure, the reconfiguration unit adapts the controller to the faulty system which enables the VSC to track the desired active and reactive output powers. The reconfiguration unit is designed using the virtual actuator which does not interfere with the regular controller of the VSC. The effectiveness of the proposed active FTC is evaluated by the numerical simulation of a VSC connected to the AC distribution grid.
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    Fault detection in photovoltaic arrays via sparse representation classifier
    (Institute of Electrical and Electronics Engineers Inc., 2020) Kılıç, Heybet; Khaki, Behnam; Gümüş, Bilal; Yılmaz, Musa; Palensky, Peter; 0000-0002-6119-0886
    In recent years, there has been an increasing interest in the integration of photovoltaic (PV) systems in the power grids. Although PV systems provide the grid with clean and renewable energy, their unsafe and inefficient operation can affect the grid reliability. Early stage fault detection plays a crucial role in reducing the operation and maintenance costs and provides a long lifespan for PV arrays. PV Fault detection, however, is challenging especially when DC short circuit occurs under the low irradiance conditions while the arrays are equipped with an active maximum power point tracking (MPPT) mechanism. In this case, the efficiency and power output of a PV array decrease significantly under hard-to-detect faults such as active MPPT and low irradiance. If the hard-to-detect faults are not detected effectively, they will lead to PV array damage and potential fire hazard. To address this issue, in this paper we propose a new sparse representation classifier (SRC) based on feature extraction to effectively detect DC short circuit faults of PV array. To verify the effectiveness of the proposed SRC fault detection method, we use numerical simulation and compare its performance with the artificial neural network (ANN) based fault detection.
  • Küçük Resim
    Öğe
    Fault detection in photovoltaic arrays via sparse representation classifier
    (IEEE-Institute of Electrical Electronics Engineers INC., 2020) Kılıç, Heybet; Khaki, Behnam; Gümüş, Bilal; Yılmaz, Musa; Palensky, Peter
    In recent years, there has been an increasing interest in the integration of photovoltaic (PV) systems in the power grids. Although PV systems provide the grid with clean and renewable energy, their unsafe and inefficient operation can affect the grid reliability. Early stage fault detection plays a crucial role in reducing the operation and maintenance costs and provides a long lifespan for PV arrays. PV Fault detection, however, is challenging especially when DC short circuit occurs under the low irradiance conditions while the arrays are equipped with an active maximum power point tracking (MPPT) mechanism. In this case, the efficiency and power output of a PV array decrease significantly under hard-to-detect faults such as active MPPT and low irradiance. If the hard-to-detect faults are not detected effectively, they will lead to PV array damage and potential fire hazard. To address this issue, in this paper we propose a new sparse representation classifier (SRC) based on feature extraction to effectively detect DC short circuit faults of PV array. To verify the effectiveness of the proposed SRC fault detection method, we use numerical simulation and compare its performance with the artificial neural network (ANN) based fault detection.
  • [ X ]
    Öğe
    Stability Analysis of Islanded Microgrid with EVs
    (Ieee, 2018) Kilic, Heybet; Khaki, Behnam; Gumus, Bilal; Yilmaz, Musa; Asker, M. Emin
    Renewable energy resources (RESs) and electric vehicles (EVs) have emerged as powerful concepts which can replace the conventional energy and transportation systems with more flexibility and efficiency. Due to low inertia of microgrid and intermittent nature of RESs, the rapid increase in the penetration level of RESs and EVs in smart grids may lead to frequency stability issue. However, EVs, as the mobile energy storage system, can contribute to the improvement of the frequency fluctuation and stability. This paper proposes a method to control the EVs integrated in an islanded microgrid so that they participate in load frequency control (LFC). On the contrary to the approaches proposed in the literature, the proposed control method enables the investigation of time delay effect on LFC and considers the communication latency on the LFC stability. The delay-dependent stability criterion of the proposed method is derived based on Lyapunov theory in the form of linear matrix inequality (LMI). The LMI is solved to find the parameters impacting the maximum allowable delay (MAD) and to obtain the MAD by which the stability of the LFC system is guaranteed. The effectiveness of the proposed method is validated through numerical simulation of a case study.