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In this study, the effects of grilles in a vehicle on the aerodynamic drag coefficient and energy loss have been investigated. For this purpose, grilles have been placed horizontally to the front of the vehicle. Firstly, the aerodynamic drag coefficient and aerodynamic energy loss of the vehicle model without grilles have been determined numerically. Then, the aerodynamic loss coefficient and the aerodynamic energy loss of the vehicle model with grilles have been determined. Thus, the effect of the grilles placed in the front of vehicle on aerodynamic resistance and energy loss is compared to the vehicle model without grilles. For all estimates, the vehicle models have been modeled in SolidWorks software. ANSYS-Fluent software has been used for numerical analysis for the modeled vehicles. It has been determined that the grilles placed in the front of vehicle increased the aerodynamic drag coefficient of the vehicle and thus the aerodynamic energy loss of the vehicle increased. Thus, it has been shown that it is possible to reduce a certain extent the additional aerodynamic losses in the vehicle by approaching the grilles designs closer to the vehicle model without grilles.

In this study, the effects of grilles in a vehicle on the aerodynamic drag coefficient and energy loss have been investigated. For this purpose, grilles have been placed horizontally to the front of the vehicle. Firstly, the aerodynamic drag coefficient and aerodynamic energy loss of the vehicle model without grilles have been determined numerically. Then, the aerodynamic loss coefficient and the aerodynamic energy loss of the vehicle model with grilles have been determined. Thus, the effect of the grilles placed in the front of vehicle on aerodynamic resistance and energy loss is compared to the vehicle model without grilles. For all estimates, the vehicle models have been modeled in SolidWorks software. ANSYS-Fluent software has been used for numerical analysis for the modeled vehicles. It has been determined that the grilles placed in the front of vehicle increased the aerodynamic drag coefficient of the vehicle and thus the aerodynamic energy loss of the vehicle increased. Thus, it has been shown that it is possible to reduce a certain extent the additional aerodynamic losses in the vehicle by approaching the grilles designs closer to the vehicle model without grilles.

Among all the control methods developed for Induction Motor (IM) drivers, the hysteresis controller based Direct Torque Control (DTC) method has an important place. This control method does not require other rotor and stator parameters except the stator resistance and does not require position or velocity sensors. However, there are some disadvantages of the DTC method, such as high torque, flux and current ripples. In this study, in order to reduce the high torque ripples occurring in an induction motor that is controlled by the hysteresis controller based conventional DTC method, a simple and effective Sugeno type Neuro-Fuzzy Torque Controller (NFTC) is proposed. This proposed controller is used instead of hysteresis controller. An experimental setup consisting of 1.1 kW induction motor, current and voltage measurement, DS1103 control card and two-level voltage source inverter was installed. To evaluate the performance of the proposed controller structure, various experimental studies were performed. Results obtained from the proposed NFTC based structure and conventional hysteresis controller based DTC structures are given comparatively. By the obtained experimental results, it was confirmed that the proposed NFTC-based controller structure considerably reduced flux and torque ripples in the motor.

Among all the control methods developed for Induction Motor (IM) drivers, the hysteresis controller based Direct Torque Control (DTC) method has an important place. This control method does not require other rotor and stator parameters except the stator resistance and does not require position or velocity sensors. However, there are some disadvantages of the DTC method, such as high torque, flux and current ripples. In this study, in order to reduce the high torque ripples occurring in an induction motor that is controlled by the hysteresis controller based conventional DTC method, a simple and effective Sugeno type Neuro-Fuzzy Torque Controller (NFTC) is proposed. This proposed controller is used instead of hysteresis controller. An experimental setup consisting of 1.1 kW induction motor, current and voltage measurement, DS1103 control card and two-level voltage source inverter was installed. To evaluate the performance of the proposed controller structure, various experimental studies were performed. Results obtained from the proposed NFTC based structure and conventional hysteresis controller based DTC structures are given comparatively. By the obtained experimental results, it was confirmed that the proposed NFTC-based controller structure considerably reduced flux and torque ripples in the motor.

This paper presents a novel method for online power quality data analysis in transmission networks using a machine learning-based classifier. The proposed classifier has a bundle structure based on the enhanced version of the Extreme Learning Machine (ELM). Due to its fast response and easy-to-build architecture, the ELM is an appropriate machine learning model for power quality analysis. The sparse Bayesian ELM and weighted ELM have been embedded into the proposed bundle learning machine. The case study includes real field signals obtained from the Turkish electricity transmission system. Most actual events like voltage sag, voltage swell, interruption, and harmonics have been detected using the proposed algorithm. For validation purposes, the ELM algorithm is compared with state-of-the-art methods such as artificial neural network and least squares support vector machine.

This paper presents a novel method for online power quality data analysis in transmission networks using a machine learning-based classifier. The proposed classifier has a bundle structure based on the enhanced version of the Extreme Learning Machine (ELM). Due to its fast response and easy-to-build architecture, the ELM is an appropriate machine learning model for power quality analysis. The sparse Bayesian ELM and weighted ELM have been embedded into the proposed bundle learning machine. The case study includes real field signals obtained from the Turkish electricity transmission system. Most actual events like voltage sag, voltage swell, interruption, and harmonics have been detected using the proposed algorithm. For validation purposes, the ELM algorithm is compared with state-of-the-art methods such as artificial neural network and least squares support vector machine.

Monitoring Power Quality Events (PQE) is a crucial task for sustainable and resilient smart grid. This paper proposes a fast and accurate algorithm for monitoring PQEs from a pattern recognition perspective. The proposed method consists of two stages: feature extraction (FE) and decision-making. In the first phase, this paper focuses on utilizing a histogram based method that can detect the majority of PQE classes while combining it with a Discrete Wavelet Transform (DWT) based technique that uses a multi-resolution analysis to boost its performance. In the decision stage, Extreme Learning Machine (ELM) classifies the PQE dataset, resulting in high detection performance. A real-world like PQE database is used for a thorough test performance analysis. Results of the study show that the proposed intelligent pattern recognition system makes the classification task accurately. For validation and comparison purposes, a classic neural network based classifier is applied.