Electrical Engineering最新文献

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.

This paper proposes a new approach for interconnecting Distributed Energy Resources (DERs) in low-voltage distribution networks, focusing on integrating photovoltaic (PV) generation systems and Battery Energy Storage (BES). To optimize the integration of DERs into distribution energy systems, distinct voltage profiles of customer’s nodes and energy losses along the grid have been analyzed. The applied methodology utilizes the load flow study module of the PSS/E software, in conjunction with developed Python algorithms to study several studies over 24-h periods. The main parameters analyzed include: the number of clients with a connected PV system, customer-installed power, and load profiles of all clients connected to the same source. Furthermore, two battery energy storage strategies have been examined: centralized energy storage and distributed energy storage. An independent voltage control has been implemented to assess the benefits of PV systems. The obtained results contribute to ongoing efforts toward more sustainable and efficient distribution energy systems and provide valuable insights to overcome barriers to DER penetration in low-voltage distribution networks.

This paper introduces an innovative demand response energy management system tailored for smart homes, aimed at optimizing appliance usage in real time. The system considers dynamic pricing tariffs, device characteristics, usage patterns and user behavior to achieve efficient energy management. Unlike conventional systems, the proposed approach integrates a novel fuzzy logic-based pricing system that combines real-time pricing, multi-time pricing and load-dependent inclining block rate coefficients. This integration enhances cost reduction effectiveness for both homeowners and grid operators. Furthermore, appliance runtime optimization is achieved through linear programming, enhancing consumer behavior and domestic energy efficiency. By merging mathematical optimization methods with AI-enabled smart pricing coefficients, practical applications in real-world energy management scenarios are demonstrated. Moreover, a user-friendly interface is designed to facilitate real-time multitasking optimization steps using MATLAB, thus advancing the application of Internet of things (IoT) beyond data storage and communication to include intelligent real-time optimizations. The effectiveness of the proposed system is evaluated in various usage scenarios, including an analysis of the impact of comfort parameters and user behaviors. Additionally, savings effectiveness is compared with existing pricing systems. Results show that the proposed system optimizes energy usage effectively, leading to significant cost savings for consumers and improved grid management for operators. The analysis highlights the system’s adaptability to various usage scenarios and its potential to enhance user comfort and energy efficiency, thus presenting a robust solution for demand response in residential settings.

The placement and scale of virtual power plants (VPPs) in distribution networks are the only topics covered in this article that pertain to the resilience of the grid to severe weather. This problem is framed as a two-objective optimization, where the expected energy that the network would not deliver in the case of an earthquake or flood (expected energy not-supplied), and the annual planning cost of the VPP, are the two objective functions to be minimized. Noted that the expected energy not-supplied in the earthquake or flood condition is considered as the resiliency index. The constraints include the formula for VPP planning, limitations on network operation and resilience, and equations for AC power flow. Uncertainties about demand, renewable power, energy prices, and the supply of network hardware and VPP components are all taken into account in stochastic programming. The proposed technique achieves a single-objective formulation in the subsequent stage by the use of a Pareto optimization strategy based on the ε-constraint method. This article uses a solver based on a hybrid of Crow search algorithm (CSA) and sine cosine algorithm (SCA) to achieve the trustworthy optimal solution with lowest dispersion in the final response. In order to tackle the problem, the proposed system looks at how the VPP affects network resilience, scales it, and combines it with the hybrid evolutionary algorithm. In the end, with the implementation of the proposed design on the distribution network of 69 buses, the obtained numerical results confirm the ability of optimal placement and dimensions of VPPs in improving the economic status, utilization and resilience of the distribution network.

This paper presents the design and implementation of an LED driver utilizing an interleaved Watkins–Johnson converter (ILWJC). ILWJC is proposed for the control of LED system. The converter's architecture, incorporating multiple phases with diodes, switches, and coupled inductors, allows for efficient voltage transformation and precise current control. Its interleaved configuration minimizes current ripple and enhances performance efficiency, ideal for applications demanding strict current regulation. This research, employing MATLAB's SIMSCAPE for simulations, focuses on a 12-V, 2.4-W chip-on-board injection type LED module. The study assesses the performances of proportional–integral–derivative (PID) and fractional order PID (FOPID) control systems in managing the ILWJC, with the FOPID controller showing superior outcomes in terms of faster response times, reduced steady-state error, lower peak overshoot, and improved voltage and current ripple control. These results underline the FOPID controller’s potential to enhance responsiveness, accuracy, and stability in LED lighting systems. The research also identifies an optimal phase shift for interleaving at 240 degrees, achieving the lowest current ripple at 63 mA, thereby enhancing conversion efficiency. A 12-W LED driver was successfully implemented in hardware, demonstrating the practical viability of the ILWJC for real-world applications.

In recent times, mounting concerns about environmental sustainability and a rising need for superior electrical power quality have propelled substantial progress in renewable energy and power quality enhancement technologies. As a consequence, the proposed work involves the fusion of Photovoltaic (PV) systems with Unified Power Quality Conditioners (UPQC). This pioneering approach not only facilitates the generation of eco-friendly and sustainable energy but also effectively tackles the critical challenge of enhancing Power Quality (PQ) within contemporary electrical grids. By incorporating PV-generated power, UPQC shunt compensator effectively mitigates load-side PQ issues. Simultaneously, series compensator ensures perfect in-phase alignment between load and source voltages. To integrate PV system with UPQC, a Modified Z-Source Single-Ended Primary-Inductance Converter (SEPIC) with hybrid Bald Eagle Search-Optimized Adaptive Neuro-Fuzzy Inference System (BESO-ANFIS) Maximum Power Point Tracking (MPPT) technique is employed, enabling optimal performance under both Partial Shading Condition (PSC) and uniform insolation conditions. For effective control of UPQC, a (dq) theory-based approach is adopted, complemented by an Adaptive Proportional-Integral (PI) controller. This control mechanism ensures seamless operation of the PV-based UPQC. The performance and dynamics of proposed system are extensively assessed through simulations in MATLAB. The developed hybrid MPPT technique and integrated PV-based UPQC hold significant promise for enhancing power quality while harnessing renewable energy sources efficiently.