e-Prime - Advances in Electrical Engineering, Electronics and Energy最新文献_第5页

Enhancing grid-connected photovoltaic systems' power quality through a dynamic voltage restorer equipped with an innovative sliding mode and PR control system

e-Prime - Advances in Electrical Engineering, Electronics and Energy

Pub Date : 2024-12-03 DOI: 10.1016/j.prime.2024.100875

Negin Shahidi, Ebrahim Salary

This study focuses on enhancing power quality in on-grid photovoltaic (PV) schemes through an innovative dynamic voltage restorer (DVR) that integrates two control strategies with a multi-level inverter. The DVR, a power electronic compensator, corrects voltage disturbances such as sag and swell by adjusting the voltage at the point of common coupling (PCC). It utilizes sliding mode control (SMC) for normal operations and proportional resonance (PR) during voltage disruptions, ensuring accurate voltage correction. The system's multi-level inverter offers a distinct advantage over traditional high-frequency inverters, which typically suffer from significant switching losses in high-power applications. The multi-level inverter effectively detects and mitigates voltage issues while minimizing harmonic distortion. The study evaluates the DVR's performance under various conditions, including voltage swell, disruption, mild sag, and severe sag. Simulation results using the Simulink tool demonstrate that the proposed DVR significantly reduces voltage disturbances and enhances power quality in the PV panel's output, the PCC, and the injected voltage. The findings suggest that this dual-control DVR designe, with its efficient use of a multi-level inverter, is a promising solution for improving power quality and stability in on-grid PV arrangements.

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引用次数: 0

AI and Machine Learning in V2G technology: A review of bi-directional converters, charging systems, and control strategies for smart grid integration V2G技术中的人工智能和机器学习：双向转换器、充电系统和智能电网集成控制策略综述

e-Prime - Advances in Electrical Engineering, Electronics and Energy

Pub Date : 2024-12-01 DOI: 10.1016/j.prime.2024.100856

Nagarajan Munusamy, Indragandhi Vairavasundaram

Electric Vehicles (EVs) are transforming the transportation sector, and their integration with the grid is crucial for a sustainable energy future. EVs can serve as distributed energy resources, aiding in peak shaving, frequency management, and voltage support, thus enhancing grid stability. This comprehensive review explores the transformative potential of EVs in the power grid, focusing on Vehicle-to-Grid (V2 G) technology. We discuss different bidirectional Converter types, including AC-DC and DC-DC converters, to optimize power flow and voltage regulation. AC-DC converters rectify AC grid power for DC charging, while DC-DC converters optimize DC power flow and voltage regulation. Charging station safety is paramount, with electrical shock protection, fire protection, and cybersecurity measures essential for ensuring safe and reliable charging. The review also delves into energy trading and security in blockchain management, highlighting the use of blockchain technology to address hacking vulnerabilities. We explore the potential of Artificial Intelligence (AI) and Machine Learning (ML) algorithms to optimize V2 G performance. By leveraging AI and ML, we can improve the efficiency, reliability, and scalability of V2 G systems. AI-powered predictive analytics can forecast energy demand and supply, enabling proactive charging and discharging strategies. ML algorithms can optimize charging rates, battery health, and grid stability while also detecting anomalies and preventing potential faults. By integrating AI and ML into V2 G systems, we can unlock new possibilities for sustainable energy management, grid resilience, and electric vehicle adoption.

电动汽车（ev）正在改变交通运输行业，它们与电网的整合对于可持续能源的未来至关重要。电动汽车可以作为分布式能源，帮助调峰、频率管理和电压支持，从而提高电网的稳定性。这篇全面的综述探讨了电动汽车在电网中的变革潜力，重点是车辆到电网（V2 G）技术。我们讨论了不同的双向变换器类型，包括AC-DC和DC-DC变换器，以优化功率流和电压调节。AC-DC变换器将交流电网的电力整流为直流充电，而DC-DC变换器优化直流潮流和电压调节。充电站安全至关重要，电击保护、防火和网络安全措施对确保安全可靠的充电至关重要。该审查还深入研究了区块链管理中的能源交易和安全问题，强调了区块链技术的使用，以解决黑客漏洞。我们探索人工智能（AI）和机器学习（ML）算法优化V2 G性能的潜力。通过利用AI和ML，我们可以提高V2 G系统的效率、可靠性和可扩展性。人工智能预测分析可以预测能源需求和供应，实现主动充电和放电策略。机器学习算法可以优化充电率、电池健康状况和电网稳定性，同时还可以检测异常并防止潜在故障。通过将AI和ML集成到V2 G系统中，我们可以为可持续能源管理、电网弹性和电动汽车采用开辟新的可能性。

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引用次数: 0

A novel low complexity, low latency rate 1/2 FEC code 一种新颖的低复杂度，低延迟率1/2 FEC代码

e-Prime - Advances in Electrical Engineering, Electronics and Energy

Pub Date : 2024-12-01 DOI: 10.1016/j.prime.2024.100838

Maan A.S. Al-Adwany, Mohammed H. Al-Jammas, Hind Th. Hamdoon

In this paper, a relatively simple and low complexity rate

1 / 2

FEC (Forward Error Correction) code has been proposed. The proposed encoder combines the effect of the low cross correlation of two orthogonal sequences along with the effect of the quadrature phase to achieve the desired performance. A mathematical modeling for the proposed code has been accomplished which indicates that the code is able to deliver a

3 dB

coding gain. The obtained results revealed that the performance of the proposed code is comparable to that of the Convolutional Codes (CCs). Interestingly, the latency analysis showed that, unlike polar codes and convolutional codes where latency is correlated with the data block size or traceback depth (

TB

), the proposed code exhibits a decoding latency of a single clock cycle. Furthermore, the proposed code and the CC have been implemented on an Field Programmable Gate Array (FPGA) platform to evaluate the overhead in terms of usability of hardware resources. The experimental results showed that the proposed code can achieve a

3 dB

coding gain, which is in agreement with the outcomes of the mathematical analyses. Moreover, the proposed code showed relatively fewer usability of hardware resources. Accordingly, the proposed code is suitable for applications that require a good balance between error correction and data rate.

本文提出了一种相对简单且复杂度较低的1/2前向纠错码。该编码器结合了两个正交序列的低互相关效应和正交相位效应，达到了理想的性能。对所提出的代码进行了数学建模，表明该代码能够提供3dB的编码增益。实验结果表明，该编码的性能与卷积编码相当。有趣的是，延迟分析表明，与延迟与数据块大小或回溯深度（TB）相关的极性码和卷积码不同，所提出的代码显示出单个时钟周期的解码延迟。此外，所提出的代码和CC已在现场可编程门阵列（FPGA）平台上实现，以评估硬件资源可用性方面的开销。实验结果表明，该编码可实现3dB的编码增益，与数学分析结果一致。此外，所建议的代码显示出相对较少的硬件资源可用性。因此，建议的代码适用于需要在纠错和数据速率之间取得良好平衡的应用程序。

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引用次数: 0

Multi-scale energy planning for the global transition: Local, regional, and global insights

e-Prime - Advances in Electrical Engineering, Electronics and Energy

Pub Date : 2024-12-01 DOI: 10.1016/j.prime.2024.100841

Felipe Feijoo , Matteo Giacomo Prina , Marko Mimica , Neven Duić

The global shift towards sustainable energy systems and decarbonization presents a complex set of challenges that require technological innovation, policy integration, and regional adaptation. This editorial synthesizes the contributions from four papers in this special issue of e-Prime - Advances in Electrical Engineering, Electronics and Energy, each providing unique insights into key aspects of the energy transition. The first paper assesses the economic feasibility of floating offshore wind energy projects in the European Atlantic and Mediterranean, emphasizing regional cost differences and resource availability. The second paper examines the dynamics of renewable energy investments in Croatia, highlighting the critical role of policy support, technology costs, and energy flexibility in driving the transition. The third paper utilizes a simulation-based optimization approach to explore global decarbonization pathways, employing Integrated Assessment Models (IAMs) to analyze policy trade-offs and long-term impacts. Finally, the fourth paper focuses on the energy transition challenges of Mediterranean islands, exploring their dependency on imported fossil fuels and the role of local governance in promoting renewable energy solutions. Together, these studies underscore the need for an integrated, multi-faceted approach that combines policy, technology, and localized strategies to accelerate the transition towards a sustainable and resilient global energy future. Hence, this editorial discusses that while technological advancements are critical, only a combined strategy involving regulation, technology, and societal engagement will ensure the global success of the energy transition.

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引用次数: 0

Fast frequency response constrained stochastic scheduling of flexible loads in low inertia grids 低惯量电网中柔性负荷的快速频率响应约束随机调度

e-Prime - Advances in Electrical Engineering, Electronics and Energy

Pub Date : 2024-12-01 DOI: 10.1016/j.prime.2024.100852

Ashish Mathur, Sumit Nema, Vivek Prakash, Jyotsna Singh

This paper presents a modeling approach to address fast frequency response (FFR) requirements within a stochastic scheduling framework. The work integrates FFR response from flexible loads and propose advance modeling techniques to characterize renewable generation uncertainty. The study incorporates scenario-based uncertainty modeling to capture the inherent unpredictability of renewable energy resources (RES), enhancing the accuracy of scheduling in low inertia grids. Central to the proposed methodology is the integration of flexible loads such as interruptible loads (ILs), deferrable loads (DLs), and electric vehicles (EVs) strategically modeled to contribute to FFR capabilities. By embedding uncertainty modeling within the proposed stochastic scheduling framework, the research offers a comprehensive strategy to effectively handle the challenges posed by renewable generation fluctuations and reduces the RES curtailment. The paper underscores the significance of FFR in maintaining grid balancing, particularly in the presence of RES. The inclusion of flexible loads further contributes to enhancement of grid resilience by enabling rapid adjustments in response to frequency disturbances. The proposed framework not only accommodates renewable generation uncertainties but also leverages smart flexible loads to bolster FFR capabilities paving the way for a reliable and sustainable power systems.

本文提出了一种在随机调度框架下解决快速频率响应（FFR）要求的建模方法。这项工作集成了柔性负载的FFR响应，并提出了表征可再生能源发电不确定性的先进建模技术。该研究采用基于场景的不确定性建模来捕捉可再生能源（RES）固有的不可预测性，提高了低惯性电网调度的准确性。所提出方法的核心是集成灵活的负载，如可中断负载（ILs）、可延迟负载（dl）和电动汽车（ev），通过战略建模来促进FFR能力。通过在随机调度框架中嵌入不确定性模型，为有效应对可再生能源发电波动带来的挑战和减少可再生能源弃风提供了一种综合策略。本文强调了FFR在维持电网平衡方面的重要性，特别是在res存在的情况下。柔性负载的包含进一步有助于通过对频率干扰的快速调整来增强电网的弹性。拟议的框架不仅可以适应可再生能源发电的不确定性，还可以利用智能灵活负载来增强FFR能力，为可靠和可持续的电力系统铺平道路。

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引用次数: 0

Advanced parameter extraction optimization technique for the four-diode model approach

e-Prime - Advances in Electrical Engineering, Electronics and Energy

Pub Date : 2024-12-01 DOI: 10.1016/j.prime.2024.100861

Bhanu Prakash Saripalli , Bharati Gamgula , Revathi Ravilisetty , Prashant Kumar , Gagan Singh , Sonika Singh

Accurate performance estimation of photovoltaic devices is important in optimizing the efficiency and cost of the photovoltaic systems. The one-diode and two-diode models are used because they concisely represent the current-voltage (I-V) variations. However, these models mainly focus on the fundamental mechanism of diffusion and Shockley-Read-Hall recombination. Four-diode model (FDM) is developed from the standard three-diode model used to enhance the precision of PV system performance estimations. However, the better-detailed FDM offers modeling of other recombination and leakage currents that occur in rather complex solar cells or advanced cells like heterojunction, multijunction, or perovskite ones. This model helps to get a more accurate picture of the PV cell operation as several diodes are added to model recombination processes and defects. This work makes use of sophisticated forms of parameter extraction aimed at promoting the optimization of algorithms such as the one known as Advanced Dynamic Inertia-Particle Swarm Optimization with Velocity Clamping or ADIPSO-VC. For comparison with FDM, a three-diode model (THDM) is utilized, and the outcome of the former is then analyzed against the latter. In addition, as a confirmation of the reliability and repeatability of the results obtained by applying the developed algorithm for parameter extraction, FDM is compared with classical methods. To demonstrate the efficacy of the proposed method it is tested against the other algorithms Simulated annealing, and conventional PSO. Based on the comparison, it is evident that ADIPSO-VC surpasses the other methods by demonstrating lower error rates and shorter computational time.

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引用次数: 0

Directivity enhancement of microstrip antennas for high-resolution brain tumor imaging using characteristic modes theory and the confocal microwave image reconstruction algorithm 基于特征模理论和共聚焦微波图像重建算法的微带天线高分辨率脑肿瘤成像指向性增强

e-Prime - Advances in Electrical Engineering, Electronics and Energy

Pub Date : 2024-12-01 DOI: 10.1016/j.prime.2024.100854

Mouad El Moudden , Badiaa Ait Ahmed , Ibtisam Amdaouch , Mohamed Zied Chaari , Juan Ruiz-Alzola , Otman Aghzout

The increasing prevalence of brain tumors necessitates the development of advanced diagnostic techniques to enhance detection and characterization. This paper presents innovative methodologies for designing and optimizing antenna characteristics using characteristic modes theory (CMT), specifically adapted for high-resolution imaging in medical applications. Our research focuses on the critical goal of improving the accuracy and precision of brain tumor detection through a confocal microwave image reconstruction algorithm. The study begins with an in-depth modeling of essential antenna elements, examining their behavior to understand their interactions within the overall structure. This comprehensive analysis enhances our understanding of antenna performance and characteristics. The introduction of CMT is pivotal, as it facilitates the identification of resonance frequencies that exhibit exceptional radiation efficiency. Moreover, the antenna’s directivity is significantly enhanced through a thorough investigation of the effects of various substrate materials and patch shapes on the performance of the radiated antenna modes. This study prioritizes the optimization of the dominant directive mode to improve tumor imaging resolution, ultimately leading to superior quality imaging results. To compare and analyze the impact of different antenna directivity modes on the imaging resolution of brain tumors, two optimized antennas with distinct patch shapes and radiation patterns are integrated into a microwave imaging system. This advanced system is carefully designed to accurately locate and characterize brain tumors, enhancing diagnostic precision. The confocal imaging algorithm demonstrates that the dominant mode with high directivity radiation produces high-resolution images that significantly improve tumor detection and diagnosis.

脑肿瘤的日益流行需要发展先进的诊断技术来加强检测和表征。本文介绍了利用特征模式理论（CMT）设计和优化天线特性的创新方法，特别适用于医疗应用中的高分辨率成像。我们的研究重点是通过共聚焦微波图像重建算法提高脑肿瘤检测的准确性和精密度。该研究从对基本天线元件的深入建模开始，检查它们的行为以了解它们在整体结构中的相互作用。这种全面的分析增强了我们对天线性能和特性的理解。CMT的引入是至关重要的，因为它有助于识别具有特殊辐射效率的共振频率。此外，通过深入研究各种衬底材料和贴片形状对辐射天线模式性能的影响，天线的指向性得到了显著增强。本研究优先优化主导指示模式，提高肿瘤成像分辨率，最终获得高质量的成像结果。为了比较和分析不同天线指向性模式对脑肿瘤成像分辨率的影响，将两种具有不同贴片形状和辐射方向图的优化天线集成到微波成像系统中。这种先进的系统经过精心设计，可以准确定位和表征脑肿瘤，提高诊断精度。共聚焦成像算法表明，具有高指向性辐射的优势模式产生高分辨率图像，显着提高了肿瘤的检测和诊断。

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引用次数: 0

Multi-port network based modeling and selection of capacitor for desired voltage regulation of a standalone six-phase short-shunt induction generator for application in remote areas

e-Prime - Advances in Electrical Engineering, Electronics and Energy

Pub Date : 2024-12-01 DOI: 10.1016/j.prime.2024.100859

Saikat Ghosh, S.N. Mahato

This paper gives a straightforward method to determine the values of excitation capacitors of a standalone short-shunt six-phase induction generator (SPIG) to maintain the voltage profile within predetermined percentage of voltage deviation (VD). In this envisioned study, the value of the capacitor is meticulously chosen to optimize the number of capacitor switching, ensuring minimal system cost and complexity. The theory of multi-port network analysis has been applied for modelling of the SPIG, thus, the complex mathematical derivation to obtain the model equations is avoided. The system is expressed as a multivariable nonlinear optimization problem. The resultant admittance of the SPIG is calculated from its per phase equivalent circuit and is used as an objective function, which is solved using Binary Search Algorithm (BSA). The main novelty of this work is the determination of the model equations of the SPIG system in an efficient and simple way using the multi-port network analysis approach. Along with this, the BSA is employed for optimal selection of excitation capacitors because of its simplicity and less computational time. The results, on a 3.7 kW induction machine, reveal that to maintain a 4 % VD, a fixed series capacitor of 140 µF and two switched shunt capacitors (34.4 µF, 91.8 µF) are required. For 2 % VD, four shunt capacitors (24.2µF, 36.2µF, 64.7µF, 91.2µF) are necessary. The performance of the machine is evaluated with the help of magnetic characteristics and other equations obtained from its per phase equivalent circuit. The experimentation has been carried out in a hardware prototype system developed in the laboratory. The experimental and the simulated results are compared and found that both are nearly same for different operating conditions, which indicates the efficacy of the proposed approach.

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引用次数: 0

Modern deep neural networks for Direct Normal Irradiance forecasting: A classification approach 直接正常辐照度预测的现代深度神经网络：一种分类方法

e-Prime - Advances in Electrical Engineering, Electronics and Energy

Pub Date : 2024-12-01 DOI: 10.1016/j.prime.2024.100853

Muhammad Saud Ul Hassan , Kashif Liaqat , Laura Schaefer , Alexander J. Zolan

The escalating energy demand and the adverse environmental impacts of fossil-fuel use necessitate a shift towards cleaner and renewable alternatives. Concentrated Solar Power (CSP) technology emerges as a promising solution, offering a carbon-free alternative for power generation. The efficiency and profitability of CSP depend on the Direct Normal Irradiance (DNI) component of solar radiation; hence, accurate DNI forecasting can help optimize CSP plants’ operations and performance. The unpredictable nature of weather phenomena, particularly cloud cover, introduces uncertainty into DNI projections. Existing DNI forecasting models use meteorological factors, which are both challenging to estimate numerically over short prediction windows and expensive to model through data at a sufficiently high spatial and temporal resolution. This research addresses the challenge by presenting a novel approach that formulates DNI prediction as a multi-class classification problem, departing from conventional regression-based methods. The primary objective of this classification framework is to identify optimal periods aligning with specific operational thresholds for CSP plants, contributing to enhanced dispatch optimization strategies. We model the DNI classification problem using four advanced deep neural networks – rectified linear unit (ReLU) networks, 1D residual networks (ResNets), bidirectional long short-term memory (BiLSTM) networks, and transformers – achieving accuracies up to 93.5% without requiring meteorological parameters.

不断上升的能源需求和化石燃料使用对环境的不利影响，使我们必须转向更清洁和可再生的替代品。聚光太阳能发电（CSP）技术作为一种很有前途的解决方案出现，为发电提供了一种无碳替代方案。CSP的效率和盈利能力取决于太阳辐射的直接正常辐照度（DNI）成分；因此，准确的DNI预测可以帮助优化CSP电厂的运营和性能。天气现象的不可预测性，尤其是云层，给DNI预估带来了不确定性。现有的DNI预报模式使用气象因子，在短的预报窗口内进行数值估计具有挑战性，并且通过足够高的时空分辨率的数据进行建模的成本很高。本研究通过提出一种新颖的方法来解决这一挑战，该方法将DNI预测作为一个多类分类问题，与传统的基于回归的方法不同。该分类框架的主要目标是确定与CSP电厂特定运行阈值相一致的最佳周期，有助于增强调度优化策略。我们使用四种先进的深度神经网络——整流线性单元（ReLU）网络、一维残余网络（ResNets）、双向长短期记忆（BiLSTM）网络和变压器——对DNI分类问题进行建模，在不需要气象参数的情况下，准确率高达93.5%。

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引用次数: 0

A deep learning model for fault detection in distribution networks with high penetration of electric vehicle chargers 用于检测电动汽车充电器高度普及的配电网络故障的深度学习模型

e-Prime - Advances in Electrical Engineering, Electronics and Energy

Pub Date : 2024-11-20 DOI: 10.1016/j.prime.2024.100845

Seyed Amir Hosseini , Behrooz Taheri , Seyed Hossein Hesamedin Sadeghi , Adel Nasiri

Integration of a significant number of domestic electrical vehicle (EV) charging stations into the power distribution infrastructure can give rise to several protection problems. Therefore, we propose a new method to detect short-circuit faults in distribution networks with high penetration of residential EV chargers. In this method, first, the features of voltage and current waveforms in various operational scenarios are extracted through a two-dimensional modeling. These features are then used to train a deep learning model based on black widow optimization bi-directional long short-term memory (BWO-BiLSTM) technique. In contrast with the conventional adaptive protection schemes, the proposed method can perform accurately in the presence of fast and unpredictable network topology, without requiring to determine a large number of threshold values to detect a fault, or relying on communication links. The effectiveness of the proposed method is investigated through a series of case studies on a modified IEEE 69-bus distribution network with a substantial penetration of residential EV chargers. The results show the proposed method's ability to detect all types of faults within 5 ms. Since it employs a machine learning algorithm for fault detection, the method's accuracy is 98.5 %, surpassing the accuracy of k-nearest neighbors (KNN) and conventional LSTM models. Additionally, the results confirm its optimal performance under noisy conditions. Even with noise in the sampled signals at a level of 10 dB, the method's accuracy remains higher than that of other methods, with a value of 96.9 %.

大量家用电动汽车（EV）充电站并入配电基础设施后，可能会产生一些保护问题。因此，我们提出了一种新方法来检测住宅电动汽车充电器普及率较高的配电网络中的短路故障。在该方法中，首先通过二维建模提取各种运行场景下的电压和电流波形特征。然后，利用这些特征来训练基于黑寡妇优化双向长短期记忆（BWO-BiLSTM）技术的深度学习模型。与传统的自适应保护方案相比，所提出的方法可以在网络拓扑快速且不可预测的情况下准确执行，无需确定大量阈值来检测故障，也不依赖于通信链路。我们通过一系列案例研究，对住宅电动汽车充电器大量普及的改进型 IEEE 69 总线配电网络进行了调查，以了解所提方法的有效性。结果表明，所提出的方法能够在 5 毫秒内检测出所有类型的故障。由于采用了机器学习算法进行故障检测，该方法的准确率达到 98.5%，超过了 k-nearest neighbors (KNN) 和传统 LSTM 模型的准确率。此外，结果还证实了该方法在噪声条件下的最佳性能。即使采样信号中的噪声为 10 dB，该方法的准确率仍高于其他方法，达到 96.9%。

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引用次数: 0