Performance improvement of MO surge arrester using high gradient arrester block against VFTOs

It is well known that the metal oxide surge arrester is inoperative against very fast transients overvoltages (VFTOs) because of its strong stray capacitive effect. This stray effect causes a time lag between the peak of residual voltage and peak of the current surge and so there is a delay its response. In order to reduce the stray effect, high gradient material is used for preparing metal oxide arrester blocks with different compositions. For simulation study, the required electrical parameters of high gradient arrester blocks are calculated with estimated height of arrester. This model is simulated using Electromagnetic transient program (EMTP) for different arrester ratings against switching, lightning, steep and very fast transients. The simulated value of residual voltages are compared with experimental values. From the observed results, it is perceived that the newly developed high gradient arrester decreases the delay and so the dynamic performance of the arrester is improved especially against very fast transients

An Assessment of Onshore and Offshore Wind Energy Potential in India Using Moth Flame Optimization

Wind energy is one of the supremely renewable energy sources and has been widely established worldwide. Due to strong seasonal variations in the wind resource, accurate predictions of wind resource assessment and appropriate wind speed distribution models (for any location) are the significant facets for planning and commissioning wind farms. In this work, the wind characteristics and wind potential assessment of onshore, offshore, and nearshore locations of India—particularly Kayathar in Tamilnadu, the Gulf of Khambhat, and Jafrabad in Gujarat—are statistically analyzed with wind distribution methods. Further, the resource assessments are carried out using Weibull, Rayleigh, gamma, Nakagami, generalized extreme value (GEV), lognormal, inverse Gaussian, Rician, Birnbaum–Sandras, and Bimodal–Weibull distribution methods. Additionally, the advent of artificial intelligence and soft computing techniques with the moth flame optimization (MFO) method leads to superior results in solving complex problems and parameter estimations. The data analytics are carried out in the MATLAB platform, with in-house coding developed for MFO parameters estimated through optimization and other wind distribution parameters using the maximum likelihood method. The observed outcomes show that the MFO method performed well on parameter estimation. Correspondingly, wind power generation was shown to peak at the South West Monsoon periods from June to September, with mean wind speeds ranging from 9 to 12 m/s. Furthermore, the wind speed distribution method of mixed Weibull, Nakagami, and Rician methods performed well in calculating potential assessments for the targeted locations. Likewise, the Gulf of Khambhat (offshore) area has steady wind speeds ranging from 7 to 10 m/s with less turbulence intensity and the highest wind power density of 431 watts/m2. The proposed optimization method proves its potential for accurate assessment of Indian wind conditions in selected locations.publishedVersio

Smart performance optimization of energy-aware scheduling model for resource sharing in 5G green communication systems

This paper presents an analysis of the performance of the Energy Aware Scheduling Algorithm (EASA) in a 5G green communication system. 5G green communication systems rely on EASA to manage resource sharing. The aim of the proposed model is to improve the efficiency and energy consumption of resource sharing in 5G green communication systems. The main objective is to address the challenges of achieving optimal resource utilization and minimizing energy consumption in these systems. To achieve this goal, the study proposes a novel energy-aware scheduling model that takes into consideration the specific characteristics of 5G green communication systems. This model incorporates intelligent techniques for optimizing resource allocation and scheduling decisions, while also considering energy consumption constraints. The methodology used involves a combination of mathematical analysis and simulation studies. The mathematical analysis is used to formulate the optimization problem and design the scheduling model, while the simulations are used to evaluate its performance in various scenarios. The proposed EASM reached a 91.58% false discovery rate, a 64.33% false omission rate, a 90.62% prevalence threshold, and a 91.23% critical success index. The results demonstrate the effectiveness of the proposed model in terms of reducing energy consumption while maintaining a high level of resource utilization.© 2024 The Authors. The Journal of Engineering published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.fi=vertaisarvioitu|en=peerReviewed

Selective Harmonics Elimination in Multilevel Inverter Using Bio-Inspired Intelligent Algorithms

Multilevel inverters are powerful electronic devices that are used for the conversion of DC input voltage into AC output voltage and mostly used in medium and high voltage operations. In these operations, pulse width modulation (PWM) frequency is distorted because of electromagnetic interference (EMI) and switching losses which are caused by dv/dt stress. To achieve a pure sinusoidal waveform at output of multilevel inverter is a primary purpose so that a smaller number of harmonic contents are produced. Selective harmonic elimination PWM technique is used in cascaded multilevel inverter for the mitigation of lower harmonics by solving nonlinear transcendental equations and maintains the required fundamental voltage. An objective function is derived from SHE problem to calculate switching angles. For the solution of objective function, optimization approach such as bio-inspired intelligent algorithms are used. In this paper, Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Bee Algorithm (BA) are used to determine the optimum switching angles for cascaded multilevel inverters to get low total harmonic distortion (THD) in output voltage. These computed angles are analyzed in MATLAB simulation model to authenticate the results. And there will be direct comparison among these algorithms

An Evaluation on Wind Energy Potential using Multi-Objective Optimization-based Non-dominated Sorting Genetic Algorithm III

Wind energy is an abundant renewable energy resource that is extensively used worldwide in recent years. The present work proposes a new Multi-Objective Optimization (MOO) based genetic algorithm (GA) model for a wind energy system. The proposed algorithm consists of non-dominated sorting which focuses to maximize the power extraction of the wind turbine and the lifetime of the battery. Also, the performance characteristics of the wind turbine and battery energy storage system (BESS) are analyzed specifically torque, current, voltage, state of charge (SOC), and internal resistance. The complete analysis is carried out in the MATLAB/Simulink platform. The simulated results are compared with existing optimization techniques such as single-objective, multi-objective, and non-dominating sorting GA II (Genetic Algorithm-II). From the observed results, the NSGA III optimization algorithm offers superior performance notably higher turbine power output with higher torque rate, lower speed variation, and lesser degradation rate of the battery. This result attested to the fact that the proposed optimization tool can extract a higher rate of power from a self-excited induction generator (SEIG) when compared with a conventional optimization tool.publishedVersio

Modelling and validation of metal oxide surge arrester for very fast transients

Metal oxide surge arrester models that are currently on rounds suffer from certain deficiencies and as a consequence, they are not in position to portray accurately the arrester residual voltages. This drawback is mainly attributed to their inherent features. The prevailing pressing need demands accurate performance assessment of zinc oxide arrester under all types of overvoltages including very fast transient overvoltages (VFTOs). So immediate corrective measures like the formation of a new arrester model or rejig of the existing one are urgently warranted. This issue constitutes the centre piece/core of this study. In consideration of its various merits, which includes its capacity to bring out the virtual representation of arrester activities, electromagnetic transient programme software has been chosen for this study. With its aid, the arrester model recommended by IEEE Working Group has been aptly reshaped to suit the needs of VFTOs studies. The end results of the simulations thus carried out have clearly established its suitability for the studies involving nanosecond waves; additionally, it got its validation and endorsement from the test data obtained from different arrester manufacturers. The commendable endorsements got from these studies shed clear light on it. All these aspects are dealt with this work

Exploration of Machine Learning Approaches for Paddy Yield Prediction in Eastern Part of Tamilnadu

Agriculture is the principal basis of livelihood that acts as a mainstay of any country. There are several changes faced by the farmers due to various factors such as water shortage, undefined price owing to demand–supply, weather uncertainties, and inaccurate crop prediction. The prediction of crop yield, notably paddy yield, is an intricate assignment owing to its dependency on several factors such as crop genotype, environmental factors, management practices, and their interactions. Researchers are used to predicting the paddy yield using statistical approaches, but they failed to attain higher accuracy due to several factors. Therefore, machine learning methods such as support vector regression (SVR), general regression neural networks (GRNNs), radial basis functional neural networks (RBFNNs), and back-propagation neural networks (BPNNs) are demonstrated to predict the paddy yield accurately for the Cauvery Delta Zone (CDZ), which lies in the eastern part of Tamil Nadu, South India. The performance of each developed model is examined using assessment metrics such as coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), coefficient of variance (CV), and normalized mean squared error (NMSE). The observed results show that the GRNN algorithm delivers superior evaluation metrics such as R2, RMSE, MAE, MSE, MAPE, CV, and NSME values about 0.9863, 0.2295 and 0.1290, 0.0526, 1.3439, 0.0255, and 0.0136, respectively, which ensures accurate crop yield prediction compared with other methods. Finally, the performance of the GRNN model is compared with other available models from several studies in the literature, and it is found to be high while comparing the prediction accuracy using evaluation metrics

Design of Novel Modified Double-Ended Forward Converter for Stepper Motor Drive

This paper presents the design and analysis of a modified double-ended forward converter (DEFC) for stepper motor-based robotic applications. The proposed converter topology provides galvanic isolation between the input and output while also higher efficiency with a smooth operative system, making it suitable for use in robotic systems that require both power and control signals to be transmitted. The paper also discusses the control strategy for the converter, which uses Proportional Integral (PI) to regulate the output voltage and current. The control strategy is implemented using a microcontroller-based system, which provides precise control of the output parameters. The converter is tested using a stepper motor-based load, and the results demonstrate the effectiveness of the proposed topology and control strategy. In addition to the experimental results, the paper also presents a detailed analysis of the converter’s performance. The analysis includes the input voltage and current, capacitor voltage, MOSFET parameters, output voltage and current, and calculation of efficiency. The analysis results show that the proposed converter topology and control strategy offer high efficiency comparing to existing converting approaches. Overall, the proposed double-ended forward converter offers a suitable solution for stepper motor-based robotic applications, providing efficient and reliable power and control signals. The results demonstrate the effectiveness of the proposed converter topology and control strategy, making it a promising option for use in future robotic systems

Design and Modeling of Modified Interleaved Phase-Shifted Semi-Bridgeless Boost Converter for EV Battery Charging Applications

Electric vehicles (EVs) are set to become one of the domestic transportation systems that are highly preferred over conventional vehicles. Due to the huge demand for and cost of fuel, many people are switching over to EVs. Companies such as Tesla, BMW, Audi, and Mercedes have started marketing EVs. These EVs need charging stations to charge the batteries. The challenges for EV batteries require the implementation of features such as fast charging, long-run utilization, reduced heat emission, a light weight, and a small size. However, fast charging using conventional converters generates an imbalance in current injection due to the passive component selection. In this study, a converter is proposed that uses an interleaved network that provides a balanced current injection; i.e., an improved interleaved phase-shifted semi-bridgeless boost converter (IIPSSBBC) is designed for EV battery charging applications. The suggested approach is mathematically designed using MATLAB/Simulink (2021) software. The result shows that the battery charging current achieves about 16.5 A, which is relatively more than conventional systems. Moreover, the charging time of the proposed converter is about 6 hrs for a 50 Ah battery with a discharge load capacity of 5000 W, which is relatively less than the conventional method. In a nutshell, compared with conventional converters, the IIPSSBBC performs better, and, notably, the charging speed and current injection are increased by two times the amount. Further, a prototype hardware model is developed to assess the performance of the proposed converter