Electrical Engineering最新文献

In this paper, two step-down converters with a high reduction ratio based on the concept of switched inductors and switched capacitors are analyzed, modeled, and controlled. These topologies can achieve a high-voltage step-down ratio compared to a conventional buck converter. The proposed topologies have the same conversion ratio in terms of the duty cycle, however, they have different operating principles, and thus, different design, modeling, and control strategies must be followed. Therefore, the guidelines for the design of these types of converters are proposed here. Furthermore, to show the benefits of selected topologies, a comparison between them and other topologies with similar voltage ratios is presented. The comparison is made regarding the number of semiconductors, number of passive elements, and switch electrical stress. Besides, model-based control strategies are proposed for both converters. The modeling process yields a second-order system for the switched inductor-based converter structure and a fourth-order system for the switched capacitor-based converter. Based on these models, control laws are designed resulting in multi-loop controllers, formed by inner and outer control loops. These control schemes are aimed to guarantee output voltage regulation and, therefore, zero steady-state error of the states in each system. Finally, the performance of the proposed control strategies and converters is evaluated on an experimental setup.

The deployment of grid connected photovoltaic (PV) systems has become increasingly vital in the pursuit of sustainable and renewable energy sources. As the global demand for electricity rises, the efficient and reliable incorporation of PV power into electrical grid is of paramount importance. An elementary Luo converter is employed here to enhance the resultant voltage of PV array. To further improve the system’s performance, a Grey Wolf optimized radial basis function neural network (GWO-RBFNN) is employed for maximum power point tracking (MPPT). The GWO algorithm is employed to fine-tune output of RBFNN, making it capable of adaptively extract maximum power. According to the obtained MPP, the input signals to the pulse width modulation generator is tuned using the proposed hybrid MPPT controller. These pulses regulates the operation of elementary Luo converter and guarantees maximum energy conversion efficiency. The converter’s DC link voltage is subsequently subject into grid through a single-phase voltage source inverter which is synchronized with the grid. To facilitate seamless grid integration and synchronization, a conventional proportional integral (PI) controller is deployed. The simulation outputs attained using Matlab results in a robust and efficient system, capable of contributing reliable renewable energy to the grid. The tracking efficiency of the proposed hybrid MPPT controller reaches up to 98.1% and the THD value is reduced to 2.95% which indicates the power quality of the proposed system.

The eddy-current losses of the solid rotor of homopolar inductor alternator (HIA) used for the high-power pulsed power supply (PPS) draw sufficient attention for serious temperature rising and efficiency problem. This paper introduces a novel hybrid-magnetic-field machine (HMFM) to address this issue. The novel HMFM is a hybrid of a conventional HIA and a reluctance-synchronous machine (RSM), integrating both the motor and generator functions. The rotor of the novel HMFM is made of silicon steel sheet rather than alloyed steel in order to reduce the rotor losses. For high-power PPS, the novel HMFM does not require an external driven motor to accelerate the rotor to store the inertia energy. Its design considerations for high-power PPS are presented in, and the comparison between the two PPSs is discussed in this paper. The results show that the rated field current of HMFM with silicon-steel-sheet rotor is about 12.5% larger than the HMFM with solid rotor. And the PPSs based on novel HMFM are much more efficient than conventional HIA. What’s more, its continuous ability is much better than the conventional HIA.

Isolated DC microgrid-based renewable energy sources (RES) require a concurrent high energy and high-power density presence. The most effective approach is employing a hybrid energy storage system (HESS). Supercapacitors (SCs) are used to recover braking energy during fast power changes to improve battery performance and decrease battery stress. Additionally, SCs can be utilized to keep batteries charged for prolonged periods and store surplus energy generated by RES. This paper suggests an energy management technique EMT to facilitate energy sharing among RES, HESS, and the demand load. The EMT ensures that the state of charge SOC of the HESS remains within the limitation range (80% ≥ SOC ≥ 20%). Furthermore, the EMT regulates the DC link voltage and tracks the battery and SC with desired current references in twelve different scenarios. The effectiveness of the suggested EMT is verified through simulated tests performed under various fluctuations and operating situations. The EMT demonstrates successful power sharing among the components and achieves faster DC link voltage regulation with less than 2% overshoot. Moreover, the EMT enhances power quality attributes in the DC microgrid and enables smooth mode transitions.

To tackle the issue of inadequate filtering of current harmonics in high-speed permanent magnet synchronous motors (HSPMSMs) caused by inverter nonlinearities and motor design, a harmonic suppression strategy based on the super twisting sliding mode control algorithm is proposed. Firstly, a mathematical model of HSPMSM containing harmonics was established; finally, a harmonic extraction module based on the Butterworth filter was designed, which obtains the DC quantity of a specific order of harmonics by setting the frequency in the d–q coordinate system. Experimental results demonstrate that implementing this harmonic suppression algorithm significantly reduces the amplitudes of the fifth and seventh harmonic currents from 38.4 and 28.8 dB to 21.3 and 13.2 dB, respectively. This method improves the suppression of fifth and seventh harmonics, thereby enhancing the operational stability of HSPMSM.

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.