 Subjects -> ELECTRONICS (Total: 207 journals)
| A B C D E F G H I J K L M N O P Q R S T U V W X Y Z | | The end of the list has been reached or no journals were found for your choice. |
|
|
- Analysis of Power and Torque for the IPM Motors with High Flux Density in
Stator
Authors: Vuong Quoc Dang, Hung Duc Bui, Dinh Minh Bui
Abstract: The new idea of this paper is to focus on investigating the influence of characteristics on the power and torque of an Interior Permanent Magnet (IPM) motor designed by the Tesla rear-drive. The detail of improvement designs of double V (2V) shape and inverter delta (VI) shape has been proposed for electric vehicles taking a high constant torque in a wide range speed into account. The torque ripple, output power and torque density are developed and evaluated in different topologies via the finite element method. The two-layered rotor structure with the 2V and VI shapes is also designed to give the suitable choices for manufacturing in mass production. For the higher torque density and efficiency, the two-layered 2V or VI magnets of IPM motor with 72 slots/8 poles can be adjusted with the sinusoidal step skewing to minimize the torque ripple, harmonic components and back electromotive force. The developed method is performed on the practical problem of the IPM motor of 200 kW-450 Nm, which is applied to the single drive system delivers.
PubDate: 2023-08-08
Issue No: Vol. 21 (2023)
- Proposals for the Modernization of TPP Turbogenerators
Authors: Valentine Shevchenko, Alexander Minko, Larisa Shilkova
Abstract: The purpose of this work is to develop proposals for modernizing the design of turbogenerators with a capacity of 200-300 MW in order to increase their power. To increase the power, it is proposed to increase the generator electromagnetic parameters (induction in the magnetic circuit and linear load of the generator). At the same time, the possibility of replacing hydrogen, which cools the generator internal volume, on the air was studied, which is a global trend for turbogenerators of thermal power plants with a capacity of 200-300 MW. A prerequisite was the preservation of the turbogenerator dimensions. The set goals were achieved by changing the geometry of the stator core tooth zone and intensifying cooling. To determine the necessary changes in the tooth zone with a change in the electromagnetic and thermal characteristics of the turbogenerator, a mathematical model was developed that makes it possible to determine by calculation the required dimensions and configuration of the stator slots and gas cooler elements at condition that the permissible thermal characteristics are maintained. The modelling of the stator core was carried out in order to select the dimensions of the slots and channels of axial ventilation for the required mode of the coolers operation, subject to an increase in power.
PubDate: 2023-08-08
Issue No: Vol. 21 (2023)
- Fuzzy Based Optimal Network Reconfiguration of Distribution System with
Electric Vehicle Charging Stations, Distributed Generation, and Shunt
Capacitors
Authors: Ajit Kumar Mohanty, Perli Suresh Babu
Abstract: Electric Vehicles (EVs) are gaining popularity due to their low maintenance, better performance and zero carbon emission. To expand their adoption, Electric Vehicles Charging Stations (EVCS) must be integrated with the distribution system constructively to charge EVs. This study suggests an RAO-3 based on the fuzzy classification technique for the optimum EVCS, Distributed Generations (DGs), and Shunt Capacitors (SCs) sizing and positioning for 69 bus radial distribution systems with network reconfiguration. The proposed method has the following advantages (i) lower active power loss, (ii) enhanced voltage profiles, (iii) improved power factor at the substation, and (iv) optimum distribution of EVs at charging stations. Characteristic curves of Li-Ion battery charging are utilised for load flow analysis to build EV battery charging loads models. The proposed simultaneous fuzzy multi-objective study with a reconfigured network can handle the optimal number of EVs in EVCS and maintain the substation power factor at the required level, yielding an impressive distribution system performance. For example, the minimum active power loss of 18.0884 kW is achieved with a minimum voltage enhanced to 0.9905 p.u., maintaining the bus voltages at their permissible limit. The numerical results indicate that using the RAO-3 algorithm, the simultaneous technique with system reconfiguration is computationally efficient and scalable, outperforming the two-stage methodology and the method without system reconfiguration.
PubDate: 2023-08-08
Issue No: Vol. 21 (2023)
- A Novel Quadruple Active Bridge DC Converter With Reduced Inductor Current
for EV Battery Charging
Authors: Anil Kumar Boya, Jyothi Batchalakura
Abstract: A bidirectional DC-DC converter can diminish the uncertainty of PV generation and enhance system reliability when integrated with energy storage systems. Usually, a Dual Active Bridge (DAB) converter with soft switching capability essentially matches the needs of an energy storage system. The DAB not able to reduce the inductor current stress when load is increased, and thus, the efficiency is decreased. To address this issue, we present a novel Quadruple Active Bridge (QAB) converter. In terms of enhancing the efficiency along with mitigating the current stress on the Low Voltage (LV) side, the LLC resonant converter with a constant pulse width modulation scheme was implemented. Finally, the QAB-LLC converter fed with Electric Vehicle’s (EV’s) Battery has been verified in MATLAB/Simulink and it results in a maximum State of Charge (SOC) and, maximum voltage capability which provides a high efficiency along with minimum inductor current stress on LV side.
PubDate: 2023-08-08
Issue No: Vol. 21 (2023)
- Robust Neural Controllers for Power System Based on New Reduced Models
Authors: Wissem Bahloul, Mohamed Chtourou, Mohsen Ben Ammar, Hsan Hadjabdallah
Abstract: This paper presents an advanced control method for the stabilization of Electric power systems. This method is a decentralized control strategy based on a set of neural controllers. Essentially, the large-scale power system is decomposed into a set of subsystems in which each one is constituted by a single machine connected to a variable bus. For each subsystem, a neural controller is designed to respond to a performance index. The neural controller is a feed-forward multi-layered one. Its training method is accomplished for different rates of desired terminal voltage and is based on the perturbed electrical power system model. For a single machine, the synaptic weights of corresponding neural controller are adjusted to force the machine outputs to converge into expected one obtained by the load flow program. To evaluate the performance and effectiveness of the proposed control method, it has been applied to the WSCC power system under severe operating conditions. The obtained results compared to the ones of conventional controllers proved the high quality of the proposed controller in terms of transient stability and voltage regulation of the considered electrical power system.
PubDate: 2023-08-08
Issue No: Vol. 21 (2023)
- Compact and Energy Efficient QCA Based Hamming Encoder for Error Detection
and Correction
Authors: Premananda Belegehalli Siddaiah, Megha Puttaswamy, Nagavika Kamat
Abstract: Quantum-dot Cellular Automata (QCA) are preferred for realizing logic circuits at nanoscale dimensions along with a high level of integration and minimal energy consumption. Hamming code is a set of entropy codes used for error detection and correction in communication systems. Error detection and correction is carried out with the help of parity bits which are appended with the original bits. Designing a Hamming encoder in nanoscale has its own merits such as optimized area, reduced energy dissipation and lower QCA cost. This work proposes two QCA-based (7, 4) Hamming encoder designs; multilayer (proposed-1) and coplanar (proposed-2) structure with area and energy analysis. Proposed-1 encoder has achieved reduction of cell area by 12.5%, 34.58% in terms of cell count, and reduction in total energy dissipation of 26.8% when compared to reference encoder. Proposed-2 encoder has achieved reduction of 18.75% in area, 44.15% in terms of cell count, and 16.5% in total energy dissipation when compared to reference encoder. In terms of QCA cost, reduction of 12.5% is achieved in case of proposed structures. The energy dissipated in the proposed designs is less compared to reference encoder. Proposed-2 structure is more efficient compared to multilayer and reference encoder in terms of cell count, cell area and QCA cost. The QCA circuits are realized in QCADesigner and analyzed energy in QCADesigner-E.
PubDate: 2023-08-08
Issue No: Vol. 21 (2023)
- Formulation of Pattern Recognition Framework - Analysis and Detection of
Tyre Cracks Utilizing Integrated Texture Features and Ensemble Learning
Methods
Authors: Vijayalakshmi Gopasandra Venkateshappa Mahesh, Alex Noel Joseph Raj
Abstract: For a safe drive with a vehicle and better tyre life, it is important to regularly monitor the tyre damages to diagnose its condition and chose appropriate solution. This paper proposes a framework based on pattern recognition utilizing the strength of texture attributes and ensemble learning to detect the damages on the tyre surfaces. In this paper, a concatenation of the statistical and edge response based texture features derived from Gray Level Co-occurrence Matrix and Local directional pattern are proposed to describe and represent the tyre surface characteristics and their variations due to any damages. The derived features are provided to train machine learning algorithms using ensemble learning methods for a better understanding to discriminate the tyre surfaces into normal or damaged. The experiments of tyre surface classification were conducted on the tyre surface images acquired from Kaggle tyre dataset. The results demonstrated the ability of the combined texture features and ensemble learning methods in effectively analysing the tyre surfaces and discriminate them with better performance provided by adaboost and histogram gradient boosting methods.
PubDate: 2023-08-08
Issue No: Vol. 21 (2023)
|
Open Access journal
