Iranian Journal of Science and Technology-Transactions of Electrical Engineering最新文献

Abstract

An integrated switched-capacitor (SC), voltage multiplier (VM) cell and switched-inductor (SL) based high gain DC-DC converter is put forward here. This suggested converter boosts the low voltage obtained from fuel cell or solar photo voltaic (PV) to make it suitable for grid integration. This converter is highly efficient with a gain of 89 when operated with 0.8 duty ratio. The suggested converter switches and diodes are under low voltage stress. In order to validate simulation results, a 100 W, 200 V prototype model is set up in the laboratory for 24 V input. According to the experimental findings, the intended converter is highly effective, owing to its high gain, low switching stress and low duty cycle.

This paper presents a comprehensive analysis of an Externally Excited Synchronous Motor (EESM) integrated with sensorless Field-Oriented Control (FOC) using the stator and rotor mutual inductance method focusing on its impact on electric vehicle (EV) performance, energy efficiency, and optimization considerations. The investigation on the performance analysis of the EV shows that there is an improvement in the performance of the EV in terms of the efficiency of the three-phase Voltage Source Inverter (VSI) as 95.23, 95.07, and 94.51% across IHDC, WLTP, and NEDC drive cycles respectively. The efficiency of the traction motor, EESM, is 89.61, 89.92, and 88.46% for the same cycles, highlighting the efficacy of sensorless FOC in optimizing power and energy consumption. The research analysis reveals that the energy consumption rates of 280, 190, and 140 W/km for IHDC, WLTP, and NEDC cycles, with corresponding running costs of 1.19, 0.81, and 0.59 INR/km respectively. The analysis also uncovers driving ranges, with IHDC offering a maximum of 197 km, while WLTP and NEDC provide 290 and 400 km respectively. Additionally, the research study evaluates greenhouse gas emissions, with EESM-based EVs demonstrating substantial emission reductions. Specifically, IHDC records emissions of 198.8 g/km, WLTP at 134.9 g/km, and NEDC at 99.4 g/km, compared to 158.7 g/km for petrol cars and 145.25 g/km for diesel cars respectively. This research highlights the potential of EESM-based EVs with sensorless FOC control to enhance efficiency, reduced energy consumption and reduce emissions, making them a promising choice for sustainable transportation. The proposed research work carried out using Matlab/Simulink and results are presented to validate the proposed work.

This paper proposes a self tuning intelligent fuzzy logic controller (STIFLC) based reduced component dual input nine-level switched-capacitor multilevel inverter for induction heating (IH) applications. Moreover, to generate the curve producer referring to setting of rise time along with target temperature as being a feedback reference to the fuzzy logic controller (FLC) is adopted in this work. To acquire this goal different quantization factors in FLC must be fixed as per the system requirement. To address this drawback all the quantization factors are analysed precisely and STIFLC is designed which is capable for controlling the induction heating temperature and to perform in real time situation. Additionally, the control performance of traditional FLC and STIFLC is compared and power loss equations are derived which determines the system efficiency. Finally, simulation and experimental tests were carried out on a 5kVA inverter prototype including the IH system to confirm the potency of the proposed system. The findings demonstrate particularly the proposed STIFLC including multilevel inverter significantly increased the controllers adaptability and control capacity.

Emotion recognition is vital for augmenting human–computer interactions by integrating emotional contextual information for enhanced communication. Hence, the study presents an intelligent emotion detection system developed utilizing hybrid stacked gated recurrent units (GRU)-recurrent neural network (RNN) deep learning architecture. Integration of GRU with RNN allows the system to make use of both models’ capabilities, making it better at capturing complex emotional patterns and temporal correlations. The EEG signals are investigated in time, frequency, and time–frequency domains, meticulously curated to capture intricate multi-domain patterns. Then, the SMOTE-Tomek method ensures a uniform class distribution, while the PCA technique optimizes features by minimizing data redundancy. A comprehensive experimentation including the well-established emotion datasets: DEAP and AMIGOS, assesses the efficacy of the hybrid stacked GRU and RNN architecture in contrast to 1D convolution neural network, RNN and GRU models. Moreover, the “Hyperopt” technique fine-tunes the model’s hyperparameter, improving the average accuracy by about 3.73%. Hence, results revealed that the hybrid GRU-RNN model demonstrates the most optimal performance with the highest classification accuracies of 99.77% ± 0.13, 99.54% ± 0.16, 99.82% ± 0.14, and 99.68% ± 0.13 for the 3D VAD and liking parameter, respectively. Furthermore, the model’s generalizability is examined using the cross-subject and database analysis on the DEAP and AMIGOS datasets, exhibiting a classification with an average accuracy of about 99.75% ± 0.10 and 99.97% ± 0.03. Obtained results when compared with the existing methods in literature demonstrate superior performance, highlighting potential in emotion recognition.

This paper presents soft computing-based optimization techniques for the cogging torque reduction and thermal characterization by finite element analysis in a permanent magnet brushless DC motor (BLDC). Stator and rotor structure of BLDC motor are optimized to reduce the cogging torque, noise, and vibration by using the design parameters namely: length of magnet, length of air gap and opening in the stator slot which are selected by performing variance-based sensitivity analysis. The proposed method is suitable in the preliminary design phase of the motor to determine the optimal structure to improve the efficiency. The comparison of results obtained using firefly algorithm , ant colony optimization algorithm and Bat algorithm indicate that Firefly-based optimization algorithm is capable of giving improved design parameter output. Cogging torque is created due to the interaction of magnets in the rotor and the stator slot of the motor. Thorough thermal analysis is also conceded out to investigate the thermal characteristics at dissimilar portions of the motor namely: stator core, stator slot, rotor core and permanent magnet at different operating environments in the continuous operation mode. Thermal investigation is required for the various high speed e-vehicle applications. The usefulness of the designed machine simulation is compared with the results obtained from hardware analysis. The outcomes attained from software simulation studies are validated through experimental hardware setup.

This paper presents an FPGA hardware implementation of two different components of a microgrid’s electrical systems. Small and isolated microgrids are currently a frequent solution for electricity coverage in remote areas, where detailed studies, simulations, and realistic emulations are required to design appropriate systems according to each location. Hardware implementations based on a Labview environment of 240 W and 330 W generic photovoltaic generators, a Boost converter, and P &O algorithm were performed. The results obtained from the panels and converter were compared to the Simulink response. Unified systems, with and without controls, were compared. The systems were first compiled individually on an FPGA NI PCIe 7841R and later unified. The data and performance obtained from the emulated environment were verified using Simulink models, and the desired correspondence was obtained.

Abstract

In this paper analytical approach to design a broadband microstrip quadrature 3-dB power splitter is proposed and validated. The proposed quadrature splitter in this paper utilizes a double-stage coupled line Wilkinson power divider in conjunction with a modified phase shifter to induce phase delay at one output port. Compared with the conventional T- shaped stub, the proposed configuration of Tweaked T-shaped stubs exhibits better performance without adding any fabrication complexity, which in turn improves the performance of the quadrature power splitter. In testing, the proposed quadrature Wilkinson power divider covers 2–4.5 GHz (fractional bandwidth FBW = 76.9%) working frequency with 10-dB impedance and 15-dB isolation bandwidth. Furthermore, this design has a very good phase and amplitude balance, with a 7° phase and 1 dB amplitude imbalance.

The performance of solar photovoltaic (PV) panels is entirely determined by ambient temperature, solar irradiance, and dynamic environmental conditions. As a result, the photovoltaic system exhibits multiple peaks in the I-V and P-V curves during partial shading conditions (PSC), which significantly reduces power output. The maximum power point tracking (MPPT) method is essential for extracting maximum power from the PV panel during PSC. With conformist MPPT algorithms, determining the maximum power point is unrealistic. To overcome the constraints, this paper proposes the grasshopper optimisation algorithm (GOA), which imitates the behaviour of grasshopper swarms in nature and is capable of extracting maximum power even during unfavourable shading conditions. The performance assessment of GOA method has been carried out in the MATLAB/SIMULINK environment. This algorithm effectiveness is validated by comparing its performance with conventional and other most prominent global search counterparts. The proposed algorithm is validated in real-time hardware with boost converter through different PV array pattern. The outcome demonstrates the effectiveness of the proposed algorithm which drastically reduces the computation time and performs rapidly and precisely to extract the global maximum peak with minimal oscillations.

The worldwide growing concern of environmental degradation due to the burning of fossil fuels and their near exhaustion has resulted in a rise in the use of renewable energy sources (RES) for electricity generation. Due to the stochastic and intermittent nature of these sources along with their significant proportion into the modern power systems poses various operational challenges. Reliability evaluation has been extensively studied for operation and planning of the composite power systems during the last few decades. Evaluation of reliability is crucial for planning, design, and management of power systems integrated with RES. The objective of a reliability evaluation is to provide suitable measures, criteria, and indices of reliable and secure performance based on system historical data, component outage data and configuration. The fundamental technique for reliability evaluation is analytical assessment, however simulation techniques have been developed to deal with complex systems and the unpredictable behavior of systems and their constituent components. The studies are scrutinized in order to evaluate the indices, such as LOLP, EENS, LOLE. Recently, hybrid techniques that include the benefits of the conventional methods were developed. This paper presents a comprehensive and systematic review of the studies carried out for evaluation of reliability for composite power systems integrated with renewables particularly the wind and solar energy sources covering the peer-reviewed articles published between 2000 and 2021. According to the literature, a paradigm shift in reliability management is required to efficiently combine large number of RES and the requisite flexibility services. The review gives the reader a general overview of reliability of power systems, concepts of adequacy and security, different modelling and evaluation techniques and explanation on various reliability indices. The key aspects of different research works are discussed including comparison of different wind and solar power models, evaluation methods, application areas and the level of studies involved.

This article proposes a two-stage time-to-digital converter with a novel method for enhancing resolution and using digital error correction called time-to-digital converter with enhanced resolution (TDC-ER). The proposed TDC is composed of two Vernier TDCs and operating in two stages. The first stage uses a normal Vernier TDC with a 512 ps range, and the second stage employs a 2D Vernier TDC with a new delay element. The second stage can achieve a fine resolution of 2 ps. This study presents a novel idea for boosting the resolution by analyzing D flip-flop (DFF) outputs in the metastability state. In the end, it is shown that this method can achieve a 1 ps resolution. The TDC-ER offers the benefits of new digital error correction, reducing the connection error between two stages and increasing linearity. A new calibration idea is presented in this work. This circuit is designed and simulated using a 65-nm standard CMOS technology, and the simulation result demonstrates a 1.56 ps effective resolution and a 9-bit range. It operates at 250 MS/s while consuming about 0.5 mW power from a 1.2-V supply.