Electrical Engineering最新文献

The economic dispatch of power has evolved, shifting focus from cost optimization to prioritizing emission reduction from traditional fossil-fueled generators. Utilities now integrate renewable energy sources (RES) to mitigate emissions and address fossil fuel depletion. This paper introduces a social network search (SNS) algorithm tailored to address dynamic dispatch challenges in microgrids, with a specific focus on integrating RES such as solar and wind power. Through the analysis of four distinct test cases, the efficiency of the proposed SNS algorithm is rigorously demonstrated. Initially, the study addresses economic load dispatch (ELD), emission dispatch (EMD), and combined economic and emission dispatch (CEED) within an isolated microgrid setting, emphasizing RES integration. Subsequently, a comparative analysis of two CEED methods, penalty price factor (PPF) and fractional programming (FP), is conducted to determine optimal strategies for minimizing generation costs and emissions. Further exploration in test cases 3 and 4 examines the SNS algorithm’s effectiveness in tackling complex and non-convex dynamic dispatch problems by incorporating valve point loading (VPL) effects and ramp rate constraints. The results underscore the positive impact of RES integration on microgrid management and emissions reduction. Notably, RES integration leads to a 5.25% and 5.33% reduction in generation costs for ELD and CEED, respectively, alongside a 5.62% decrease in emissions. Moreover, the results highlight the advantages of the FP method in minimizing pollutant emissions and PPF in minimizing generation costs. Additionally, the simulation and statistical analyses demonstrate that the proposed SNS algorithm consistently yields high-quality solutions, surpassing other implemented and reported algorithms.

This article introduces an innovative three-load AC–AC converter topology and employs a hybrid control technique, incorporating pulse frequency modulation and asymmetrical duty cycle control. The innovation addresses inherent limitations in conventional induction heating systems. The proposed topology incorporates three legs, delivering power to multiple loads operating at distinct frequencies based on the unique physical characteristics of each load. The first converter leg maintains a fixed 50% duty cycle, optimising output through the implementation of PFM. Meanwhile, the remaining two converter legs operate by ADC to attain maximum power with independent power control for different vessels. The primary objective is to efficiently heat both non-ferromagnetic and ferromagnetic vessels. The PSIM platform simulation results are in close agreement with hardware results, validating the effectiveness of the proposed approach.

In the swiftly evolving arena of energy management and distribution, the integration of internet of things (IoT) technology stands as a dynamic promoter, especially within the environment of smart grid systems. Smart grids use IoT-enabled sensors to facilitate the seamless exchange of critical information through web applications and the internet, ushering in an era of enhanced grid management. These systems represent a critical aspect of modern energy infrastructure, aiming to address pressing issues such as energy efficiency, sustainability, and reliability. This integration ensures cost-effectiveness, intelligent features, and reliability while reducing the need for human intervention. IoT in smart grids emphasizes two-way communication among various devices and components. This proposed presents a novel approach to smart grid systems incorporating renewable photovoltaic (PV) and wind systems, alongside battery storage. Continuous monitoring of parameters such as V_PV, I_PV, V_DC, V_g, I_g and battery state-of-charge (SOC) is crucial for optimizing system performance. To transmit this data efficiently, suitable protocols are required. In this work, hybrid Adaptive Neuro Fuzzy Inference System-Sea Lion Optimization (ANFIS-SLnO) for effective data routing, which results in improved energy efficiency, and network lifetime. Moreover, a robust key management using 128-bit cryptography keys is implemented for secured data transfer, assuring data integrity, authentication, and enhanced protection. The outcomes of proposed smart grid system are evaluated using MATLAB and the parameters monitored using sensors is displayed via the Adafruit web application. In comparative evaluations, the proposed approach consistently outperforms existing methods, establishing itself as an efficient and resilient solution for secure data transfer within smart grids with a reduced delay of 0.10 s and packet loss of 3.54%. The time taken by the proposed work for encryption and decryption are given by 0.0022 s and 0.00315 s, respectively.

A feasible and efficient resolution to the challenges posed by the dependence of renewable energy sources (RES) on weather conditions and their intermittent behavior is the adoption of a hybrid energy system (HES). This study thoroughly investigates HES, incorporating an energy storage system to enhance RES integration into the power grid. HES integrates more than two renewable or non-renewable sources, thereby enhancing system stability and efficiency. The article delivers a comprehensive overview of HES, covering aspects such as system architecture, power converter structures, various energy storage systems and optimization objectives. Inverters, as a critical component, need to be selected judiciously for the system. Multilevel inverters (MLI) are favored for renewable energy integration, particularly over two-level converters, owing to their lower harmonic injection at low switching frequencies and suitability for high-power applications. The reduced switch multilevel inverter (RSMLI) has garnered notable interest in power conditioning for renewable energy sources. This article explores various reduced switch structures, comparing them based on the number of switches, drivers, diodes, capacitors and total blocking voltage for HES. The review underscores the technical advantages, future prospects and challenges associated with MLI-based HES. Cost and reliability pose major concerns in HES development, and this article delves into objectives related to reliability and cost optimization. Aiming to be a comprehensive resource, the article serves as a singular reference point for researchers in the realm of MLI-based HES.

Graphical Abstract

Many researchers have investigated estimating and forecasting load power by utilizing many approaches and techniques in neural networks. In this case study, a novel method is proposed to achieve higher accuracy in load-predicting performance in the smart solar microgrid. The K-means cluster is optimized with a density-based spatial cluster and is then utilized to determine the center points in the radial basis function neural network. The proposed method is analyzed and evaluated in the dataset, which is accumulated from the advanced meter infrastructure (AMI) in the smart solar microgrid in 6 months. The proposed methodology is deployed in load power forecasting in various horizons ranging from 10, 20, and 30 min. This optimized technique was inspected and compared against persistence methods, which only apply K-means cluster for center selection in RBF neural network, by using MATLAB simulations. The experimental results proved that the developing enhancement could achieve the maximum improvement of 7.432% R-square, 70.519% mean absolute percentage error (MAPE), and 80.769% root mean squared error (RMSE). The optimized algorithm could effectively eliminate the maximum average of 2.418% of the outer points in the dataset, which decreased the learning time during the modeling process and acquired better convergent velocity and stability compared with the persistent method. Moreover, when combined with enhanced methodology, the 10-min interval data had higher effectiveness and accuracy than the 20-min and 30-min data.

The demand for energy on the global is rising quickly, and the majority of that demand is met by the production of traditional fossil fuels. An original idea for incorporating renewable and hybrid energy sources to a grid was known as microgrid model. For proper power sharing between each component in the microgrid to ensure efficient, dependable, and cost-effective operation, Energy Management Systems (EMS) were crucial in microgrids through multiple energy resources and storage systems. Improper source prediction at the appropriate period was the issue that occurred in the EMS. This problem with efficiency causes a number of power-related issues on the load side and raises electricity costs. To mitigate this impacts, a novel deep learning controller-based EMS was proposed to manage the power flows at all period and reduce the cost of end users. Minimization of microgrid total electricity cost and total annual emission were considered as the primary objectives of the proposed model. Microgrid was designed with PV, tidal, grid, and battery, and in the demand side both hospital and home usages were considered. An actual dataset was developed according to the load activation power demand with its corresponding source power cost. Using this dataset, the deep learning controller was designed, and its performance was further improved through the coati optimization algorithm. The designed controller was fit in the EMS to select the proper source at the appropriate load demand period. The working states of the proposed model were observed under grid linked, and grid disliked mode of operation. The proposed deep learning controller offers 99.7% accuracy and 99.5% precision, and the results were compared to several other existing approaches. The outcomes demonstrate that the deep learning EMS approach was capable of interacting with many power sources and offer effective power management at a reasonable cost.

The insulating structure near the slot outlet of high-voltage generator stator coils is the typical bushing structure, which is prone to corona and has a decisive impact on the safety of the generator. In engineering, nonlinear resistance anti-corona tapes are usually bound with around the main insulation surface of the stator coils near the slot, to achieve the effect of homogenizing the electric field. Usually, the resistance nonlinearity is increased by adding semi conductive or conductive materials into the anti-corona tape. However, after the process of adding semi conductive or conductive materials, the breakdown strength of anti-corona tape is often reduced, resulting in that anti-corona tapes with good nonlinear cannot be applied. In order to have both good nonlinear resistance and breakdown strength, epoxy resin (EP) is used as a matrix, which is blended with one-dimensional structured carboxyl-functionalized multi-walled carbon nanotubes (MWCNTs) and zero-dimensional structured polyaniline (PANI) to obtain nonlinearly good materials in this paper. The nonlinear conductivity characteristics and breakdown characteristics were tested separately. The results show that compared to MWCNTs/EP composite materials, PANI-MWCNTs/EP composite materials have higher nonlinear coefficients and breakdown strength. The breakdown field strength of 0.5wt% MWCNTs/EP composites is 2.11 kV/mm, and the nonlinear coefficient is 1.41. In contrast, the breakdown field strength of 3wt% PANI-0.5wt% MWCNTs/EP was increased by 106.16%, and the nonlinear coefficient is as high as 5.32. In addition, with the increase in PANI doping amount, the nonlinear coefficient of PANI-MWCNTs/EP gradually increases, and the breakdown strength also gradually increases. It can be seen that doping PANI can improve the breakdown strength while maintaining the range of resistivity variation within the nonlinear material working field strength. This discovery can provide reference for the development of nonlinear anti-corona materials for subsequent high-voltage generators.

This manuscript proposes an optimization method for direct torque control for an induction motor drive with an open-end winding. The proposed method is the Cheetah Optimization Algorithm (COA). The proposed method’s primary goal is to maximize system efficiency and reduce power losses. The COA reduces power loss in the IM by optimizing the control factors such as the inductance of the rotor, the stator resistance, and so forth. This study provides an improvised loss analysis for an OEWIM drive with three levels of dual-inverter feeding and direct torque control (DTC), and comparative loss analysis for decoupled and alternative systems is examined. There are two types of pulse-width modulation schemes: space vector and discontinuous, both based on inverter switching and varying with modulation index. The proposed technique is implemented on the MATLAB platform and compared with current methods. The THD value of proposed technique is 0.99%, and the efficiency is 99.8%, compared with other existing techniques, such as gray wolf optimization, particle swarm optimization, and Capuchin Search Algorithm, the Total Harmonic Distortion (THD) of proposed approach is low. The simulation outcomes indicate that the proposed approach outperforms the existing ones in terms of performance.

Undesired tripping may occur in protection relays due to the power swings leading to shutting down of power utility equipment. Conventionally, the rate of speed of the impedance locus measured by the relay is utilised to differentiate the fault and power swing. Nowadays, the inertia of the system is lowered due to the proliferation of distributed generators and therefore it is very difficult to adopt a threshold limit to distinguish the fault from power swing. A setting or threshold free approach based on support vector machine to detect power swing is proposed in this work. The prominent SVM feature like load angle calculated indirectly from relay impedance is a novel way adopted in this scheme. The remaining SVM features selected are also a good combination of statistical and electrical parameters. Results also show that the scheme is capable to identify both symmetrical and asymmetrical faults during power swing. All the simulated case studies are also tested in a transmission line prototype set-up in the laboratory.

Existing fault situation frameworks conventionally use different ABC-domain or sequence network equivalent circuits for different fault types. The environmental conditions lead to changes in the parameters of the double-circuit transmission lines, and these incorrect parameters cause errors in the fault situation frameworks. The best tool for fault situation and protection of double-circuit transmission lines is the use of frameworks that work independently of the line parameters. In this article, fault situation for double-circuit transmission lines is implemented based on the measured voltage and current of each line, utilizing an Extreme Learning Machine capable of identifying nonlinear equations between measured values and fault situation. First, all types of faults were simulated at different distances in a power grid with a double-circuit transmission line. Then, the information obtained is utilized to train intelligent tools. Finally, the fault situations for different distances and resistances are estimated to assess the suggested method. To assess the superiority of the suggested framework over other intelligent frameworks, the outcomes of this article are compared with the outcomes obtained from two intelligent tools, artificial neural networks and support vector machines, which show more precision and reliability of the Extreme Learning Machine than other tools.