Yazar "Lashab, Abderezak" seçeneğine göre listele

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    A Deep GMDH Neural-Network-Based Robust Fault Detection Method for Active Distribution Networks
    (Mdpi, 2023) Celik, Ozgur; Farkhani, Jalal Sahebkar; Lashab, Abderezak; Guerrero, Josep M.; Vasquez, Juan C.; Chen, Zhe; Bak, Claus Leth
    The increasing penetration of distributed generation (DG) to power distribution networks mainly induces weaknesses in the sensitivity and selectivity of protection systems. In this manner, conventional protection systems often fail to protect active distribution networks (ADN) in the case of short-circuit faults. To overcome these challenges, the accurate detection of faults in a reasonable fraction of time appears as a critical issue in distribution networks. Machine learning techniques are capable of generating efficient analytical expressions that can be strong candidates in terms of reliable and robust fault detection for several operating scenarios of ADNs. This paper proposes a deep group method of data handling (GMDH) neural network based on a non-pilot protection method for the protection of an ADN. The developed method is independent of the DG capacity and achieves accurate fault detection under load variations, disturbances, and different high-impedance faults (HIFs). To verify the improvements, a test system based on a real distribution network that includes three generators with a capacity of 6 MW is utilized. The extensive simulations of the power network are performed using DIgSILENT Power Factory and MATLAB software. The obtained results reveal that a mean absolute percentage error (MAPE) of 3.51% for the GMDH-network-based protection system is accomplished thanks to formulation via optimized algorithms, without requiring the utilization of any feature selection techniques. The proposed method has a high-speed operation of around 20 ms for the detection of faults, while the conventional OC relay performance is in the blinding mode in the worst situations for faults with HIFs.
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    Power System Integration of Electric Vehicles: A Review on Impacts and Contributions to the Smart Grid
    (Mdpi, 2024) Inci, Mustafa; Celik, Ozgur; Lashab, Abderezak; Bayindir, Kamil Cagatay; Vasquez, Juan C.; Guerrero, Josep M.
    In recent years, electric vehicles (EVs) have become increasingly popular, bringing about fundamental shifts in transportation to reduce greenhouse effects and accelerate progress toward decarbonization. The role of EVs has also experienced a paradigm shift for future energy networks as an active player in the form of vehicle-to-grid, grid-to-vehicle, and vehicle-to-vehicle technologies. EVs spend a significant part of the day parked and have a remarkable potential to contribute to energy sustainability as backup power units. In this way, EVs can be connected to the grid as stationary power units, providing a range of services to the power grid to increase its reliability and resilience. The available systems show that EVs can be used as alternative energy sources for various network systems like smart grids, microgrids, and virtual power plants besides transportation. While the grid-EV connection offers various contributions, it also has some limitations and effects. In this context, the current study highlights the power system impacts and key contributions of EVs connected to smart grids. Regarding the power system impacts in case of EV integration into smart grids, the challenges and difficulties are categorized under the power system stability, voltage/current distortions, load profile, and power losses. Voltage/current distortions like sags, unbalances, harmonics, and supraharmonics are also detailed in the study. Subsequently, the key contributions to the smart grid in terms of energy management, grid-quality support, grid balancing, and socio-economic impacts are explained. In the energy management part, issues such as power flow, load balancing, and renewable energy integration are elaborated. Then, the fault ride-through capability, reactive power compensation, harmonic mitigation, and grid loss reduction are presented to provide information on power quality enhancement. Lastly, the socio-economic impacts in terms of employment, net billing fees, integration with renewable energy sources, and environmental effects are elucidated in the present study.
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    Real-Time HIL Simulation for Frequency Regulation in DFIG with AGC: An Egyptian Case Study
    (IEEE Computer Society, 2023) Abubakr, Hussein; Lashab, Abderezak; Celik, Ozgur; Vasquez, Juan C.; Guerrero, Josep M.
    A sudden change in loads creates troubles in grids/microgrids (MGs) such as frequency fluctuations. For balancing between generation and demand, a Doubly fed induction generator (DFIG) has been incorporated into the Egyptian power system (EG-EPS) to provide additional inertia. The studied EG-EPS consists of three conventional power plants (reheat, non-reheat, and hydro) as the main sources of generation, with 20% DFIG participation. The existing literature highlights that the control methods employ for LFC and DFIG in MGs are insufficiently coordinated for minimizing deviations in frequency and power. This limitation arises due to narrow control constraints and inadequate sensitivity to disturbances. To this regard, this paper proposes an adaptive control-based balloon effect modulation (BE) to tune the PID controller for the Automatic Generation Control (AGC) and to tune the PI controller of the DFIG speed controller. The balloon effect tool is used to minimize the impact of system load disturbance by increasing the optimizer sensitivity and traceability. The EG-EPS is examined under the influence of a 2% step load perturbation and random load variations using dSPACE DS1006 and real-time digital simulator (RTDS) as a Hard-In-Loop (HIL) test platform. The proposed controller demonstrates superior performance compared to the conventional one in terms of overshoot, undershoot, and stabilizing time. © 2023 IEEE.