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

Several studies have focused on the inactivation of biological microorganisms, such as viruses, using microwave exposure and have introduced some apparatuses. This paper represents a feasibility study of the inactivation of pathogens by fabricating a new, but very simple electromagnetic exposure test setup. The setup has the advantage of having uniform electric field radiation on multiple test tubes simultaneously. The test setup operates from 8 to 9 GHz. The radiation unit in the test setup consists of two circular parallel plates and can be considered a parallel-plane radial transmission line. Five equispaced holes are embedded in the setup to insert test tubes containing samples. The study also takes the distribution of the field inside the liquids of the test tubes. The results demonstrate that without inserting the test tubes, the TEM wave propagates in the radiation unit while inserting the test tubes creates a standing wave inside the test tubes. The setup can create E-field levels of more than 100 V/m from 0.5 Watts input power inside the liquid which is much better than using open-air methods.

Designing an efficient and advanced controlling technique for improving the power quality of grid integrated multilevel inverters is one of the challenging and demanding tasks in recent days. Because reduced Total Harmonic Distortions (THD), voltage sag, swell, and other power quality issues have a significant impact on the performance of the overall grid system. Hence, the different types of multilevel inverter topologies are implemented in the conventional works for solving the power quality problems of the grid-PV systems. Since, it limits with the drawbacks of increased system complexity, oscillations, loss of power, and presence of noise components. Therefore, the proposed work objects to develop an advanced and efficient optimization based controlling technique, named as, Swapped Probabilistic Search (SPS)—Linear Propagation of Differential Parameter (LPDP) Controller for the grid-PV systems. The main contribution of this work is to design and develop a Symmetric Switching based Multilevel Inverter (SSMI) for solving the power quality problems of grid systems. Moreover, a Nelder-Mead Maximum Power Point Tracking (NM-MPPT) algorithm is also employed for obtaining the maximum power yield from the solar PV panels during fluctuating climatic circumstances. The development of a new controlling algorithm for a multi-level inverter in order to enhance grid system power quality is the original research contribution of this work. It supports to increase PV output with minimal switching complexity by utilizing a cutting-edge converter. A new NM-MPPT controlling algorithm, SEPIC converter, SPS optimization, and LPDP controlling technique are used in this study work to achieve these goals. In order to improve the performance of SSMI, the controlling parameters are selected with the use of SPS optimization technique. Based on the LPDP controlling operations, the overall grid performance is improved with better power quality. The SPS-LPDP controlling technique helps to improve the power quality of grid by tuning the optimal controlling parameters. During evaluation, the performance of SPS-LPDP controlling technique is validated and compared by using various measures. By using SPS-LPDP controlling technique, the power tracking efficiency is improved to 99%, THD is reduced to 2.94%, and hardware performance rate is increased up to 98%.

Ensemble learning is a powerful technique for combining multiple classifiers to achieve improved performance. However, the challenge of applying ensemble learning to dynamic and diverse data, such as text in sentiment analysis, has limited its effectiveness. In this paper, we propose a novel reinforcement learning-based method for integrating base learners in sentiment analysis. Our method modifies the influence of base learners on the ensemble output based on the problem space, without requiring prior knowledge of the input domain. This approach effectively manages the dynamic behavior of data to achieve greater efficiency and accuracy. Unlike similar methods, our approach eliminates the need for basic knowledge about the input domain. Our experimental results demonstrate the robust performance of the proposed method compared to traditional methods of base learner integration. The significant improvement in various evaluation criteria highlights the effectiveness of our method in handling diverse data behavior. Overall, our work contributes a novel reinforcement learning-based approach to improve the effectiveness of ensemble learning in sentiment analysis.

Numerous processes in chemical and biological engineering make use of tubular reactors. A particular kind of tubular reactor was known as a chemical plug-flow reactor (PFR) when there is no diffusion term. This paper offers a nonlinear controller for a non-isothermal PFR governed by time-delay first-order partial differential equation (PDE). Due to, an Extended state-dependent differential Riccati equation (ESDDRE) of the PFR system, a sub-optimal nonlinear controller is suggested. At the first step, an Extended pseudo-linearization (EPL) presentation of the PFR with State-dependent coefficients (SDC) is designed. All of the time-delay terms in this representation are situated in the input vectors as well as the system matrices. Then, a control signal regard on the ESDDRE controller is generated by introducing a Hamiltonian equation and performance index according to the PDE systems. Moreover, the asymptotic stability regarded on ESDDRE controller is guaranteed by applying a suitable Lyapunov theory. Simulation results verify the efficiency of the recommended ESDDRE controller method for two EPL form presentations with different time delays.

A switched-capacitor-based multilevel inverter (MLI) topology is given in this study. The suggested MLI employs fewer power switches and has an extendable configuration that allows it to increase the number of output voltage levels while using a single DC source. To produce gate pulses, the nearest level control method is used. The proposed topology’s priority has been determined by comparing the proposed structure to other similar topologies. The suggested seven-level and extended 13-level MLIs are developed into a laboratory-scale prototype and evaluated under load variation and variation in the modulation index conditions. All the results demonstrate that the inverter topology operates effectively in various real-time environments.

India, being a land of diversified flora, has a promising resource of biomass, which, when utilized to its best, can cope with the elevating need for power. This research proposes a biomass-based energy generation system that can cater to the power needs of remote areas in tropical deciduous forests. A simulation and techno-economic and ecological investigation of eight hybrid energy generation architectures have been carried out. The model comprising a solar photovoltaic-biomass energy unit-storage unit was found to be the most appropriate system to cater to the domestic, agricultural, and commercial demands of a cluster of 10 villages in the tropical highlands of India. This configuration proposes the least cost of energy (0.077 $/kWh) at a computed net cost of $ 380,415. 30.3% (106,240 kWh/year) of power is generated by PV, and the remaining 69.7% (321,580 kWh/year) is generated by Biomass generators. Ecologically, the proposed architecture predicts a cutdown of GHG (CO₂) emissions to a mere 82 kg/year. The results can help policymakers, researchers, and designers on the latest constraints as well as policies for solar photovoltaic-biomass energy-based systems.

In this paper, the design, simulation and construction of two high-speed permanent magnet motors with an interior permanent magnet (IPM) rotor and two different stators with and without slot have been investigated. In high-speed motors, due to high excitation frequency, problems such as high mechanical loss, attraction force between rotor and stator, high core losses in stator and skin effect on wire etc. occur. In this article, it is proved that by removing the stator slots of high-speed motors, many of these mentioned problems are solved. The electromagnetic design of two motors was investigated according to nominal values and design equations. In this case, the number of poles, coils and outer diameter of two motors were assumed to be the same. Due to the elimination of the slots, the air gap flux density in the slot-less stator is lower than the structure with a slotted stator, which leads to the higher number of coils turns per phase. Then the simulation of the two motors was evaluated using the finite element method for comparison, and some parameters of the two motors such as air gap flux density, torque, Back-EMF, etc. were compared. By comparing the obtained results, it was concluded that the motor without slots provides a more suitable performance compared to the one with slot. Finally, the steps related to motor construction and experimental results were reviewed. Finally, it is proved that the experimental results are very close to the results presented in the analytical section.

Energy is the primary driving force in improvement of the human life cycle. All the activities for the betterment of human life are dependent on some form of energy. Conventional energy sources rely on fossil fuels which have limited reserves and we are bound to exhaust them soon. On the other hand, non-conventional/renewable energy sources are produced on a regular basis and are clean without any polluting emissions. These sources include solar, wind, hydraulic, biomass/bio gas, geothermal, tidal, etc. Solar energy is one of the primary sources in countries like India, but it does have drawbacks like high initial cost, dependency on weather, expensive storage, space requirement, etc. It is therefore imperative to create accurate solar radiation forecasting models to identify and address these issues. Forecasting models are created based on daily or hourly data and are location specific. In this work, binning based machine learning models are proposed for accurately forecasting hourly solar radiation. These models are data driven clustering based models. The clusters are identified based on geographic locations. The proposed approach also helps reduce the number of required models without compromising the high accuracy. In this work, global and diffuse solar radiation data, gathered from five geographically distinct stations from India, is analyzed. Validation of these models demonstrate increased performance. The number models required are also significantly smaller compared to the daily or hourly models.

Abstract

In this survey, the stability of input-constrained control for a widely used class of second-order systems is investigated. A continuous prediction-based approach is utilized to calculate the limited current control input by minimizing the next tracking error of nonlinear second-order system. The Karush–Kuhn–Tucker theorem is used to analytically solve the resulting constrained optimization problem. The constrained stability is analyzed by equating the constrained solution with the solution obtained from an optimal controller with time-varying weight on the control input. The proposed constrained controller adapts itself to real conditions by using information about the perturbations obtained from an extended state observer (ESO). Simulation studies for a lever arm indicates that the constrained controller presented in the closed form is much faster than the common nonlinear model predictive control method which requires an online dynamic optimization at each sampling time. Accordingly, experimental implementation of the proposed controller is conducted on a fabricated platform consisting of a lever arm. The results show that the proposed constrained controller can successfully track different time-varying positions for the arm by admissible torques generated by a DC motor. The comparative results with an adaptive backstepping controller indicate higher performance for the proposed ESO-based controller in compensating for the perturbations and external disturbance.

In this paper, a stochastic model predictive controller is designed for discrete time linear time invariant systems, considering additive disturbance and stochastic constraints. As we know, in practical applications, measuring all state information of a system is not generally possible or affordable. So, in this work, an output feedback law is assumed as the control law. By utilizing the Chebyshev inequality and Schur complement, it is tried to convert a stochastic non-convex optimization problem into a deterministic convex optimization problem. Simulation results demonstrate the effectiveness of the proposed methodology.