e-Prime - Advances in Electrical Engineering, Electronics and Energy最新文献_第4页

A novel combined KCL-KVL based zero bus distribution load flow with pre-defined energy contract scenarios and different load types 一种基于KCL-KVL组合的新型零母线配电潮流，具有预定义的能源合同方案和不同的负荷类型

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

Pub Date : 2025-09-12 DOI: 10.1016/j.prime.2025.101106

Anagha Rajendran K.P. , Soumyabrata Barik , Sudarshan Swain , Bikash Das

With the growing integration of distributed generation (DG) and the transition toward decentralized power systems, conventional load flow techniques face increasing difficulty in accurately representing modern distribution networks (DNs). These challenges become even more pronounced when operating under predefined power exchange constraints. To address this limitation, this paper presents a novel zero-bus load flow method for radial distribution networks (RDNs) using a KCL-KVL-based approach integrated with matrix algebra. The proposed method effectively models and manages predefined power exchanges between the grid and the RDN. It utilizes simple KCL and KVL equations, which makes it well-suited for RDNs with high R/X ratios. It eliminates the need for matrix inversion or admittance matrix formation, thereby addressing the drawbacks of the existing Newton–Raphson (N-R) load flow method and Voltage-Sensitivity (V-S) load flow method with zero bus. A distinctive aspect of the proposed approach lies in converting the topology matrix into a conversion matrix through a logical OR operation. The current injection at the zero bus is calculated to ensure that the predefined power exchange with the main grid is maintained. The proposed method is tested on 33, 69, and 118-bus DNs with different load types to analyze their performance. For a 33-bus system, the proposed method achieves 64.8% and 87.7% faster convergence than the N-R and V-S methods, respectively. For a 69-bus system, the improvement of the proposed method is 83% and 92.4% compared to N-R and V-S methods, respectively. While in a 118-bus system, the enhancement of the proposed method reaches 89.9% and 97.2% compared to N-R and V-S methods, respectively. These results reveal that the proposed method is both robust and time-efficient when compared to existing zero-bus load flow methodologies in the literature. This demonstrates its effectiveness and potential to significantly improve load flow analysis in distribution networks.

随着分布式发电集成度的不断提高和向分散式电力系统的过渡，传统的潮流技术越来越难以准确地表示现代配电网。当在预定义的功率交换约束下运行时，这些挑战变得更加明显。为了解决这一限制，本文提出了一种基于kcl - kcl的径向配电网零母线潮流方法，该方法与矩阵代数相结合。该方法有效地对电网和RDN之间的预定义功率交换进行建模和管理。它使用简单的KCL和KVL方程，这使得它非常适合具有高R/X比的rdn。它消除了矩阵反演或导纳矩阵形成的需要，从而解决了现有的零母线牛顿-拉夫森（N-R）潮流法和电压敏感（V-S）潮流法的缺点。该方法的一个独特之处在于通过逻辑或操作将拓扑矩阵转换为转换矩阵。计算零汇流线上的电流注入，以确保与主电网保持预定的电力交换。在33、69和118总线不同负载类型的DNs上进行了测试，以分析其性能。对于33总线系统，该方法的收敛速度分别比N-R和V-S方法快64.8%和87.7%。对于一个69总线系统，与N-R和V-S方法相比，该方法分别提高了83%和92.4%。而在118总线系统中，与N-R和V-S方法相比，该方法的增强率分别达到89.9%和97.2%。这些结果表明，与文献中现有的零总线负载流方法相比，所提出的方法既鲁棒又省时。这表明了它的有效性和潜力，可以显著改善配电网的潮流分析。

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引用次数: 0

Lifetime prediction of Lithium-ion cells using electrochemical modeling with combined calendar and cyclic aging effects 结合日历和循环老化效应的锂离子电池电化学模型寿命预测

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

Pub Date : 2025-09-08 DOI: 10.1016/j.prime.2025.101103

Prasath Raj , Ernst Richter , Kai Schofer , Julian Kempf , Florence Michel , Alexander Fill , Kai Peter Birke

Developing an aging model for Lithium-ion batteries (LIBs) that captures multifaceted degradation mechanisms and their interdependencies under real-world conditions is a complex challenge. This study introduces an advanced electrochemical model for a 43 Ah automotive-grade Lithium-ion pouch cell, parameterized at the anode, cathode, and electrolyte levels to predict both calendar and cyclic aging across diverse operating conditions. The model quantifies solid electrolyte interface (SEI) growth, distinguishing between passive formation during storage and accelerated growth during cycling, and incorporates a detailed representation of Lithium plating, assessing its influence under different charging rates and temperatures.

To validate the model, realistic driving profiles emulating Plug-in Hybrid Electric Vehicle (PHEV) usage were incorporated, ensuring direct comparison with empirical data. The model successfully captures capacity fade across different conditions varying in temperature, State of Charge (SoC), and applied current, replicating aging pathways dominated by SEI formation and Lithium plating. The ability to describe these fundamentally different degradation modes within a unified framework underscores the model’s robustness and predictive capability.

By accurately differentiating between calendar-induced SEI thickening and cycling-accelerated SEI growth, the model provides a mechanistic estimation of capacity loss without reliance on purely empirical fitting. The results reinforce the necessity of considering both SEI formation and Lithium plating in aging models to achieve a comprehensive and predictive understanding of Li-ion cell degradation.

开发锂离子电池（lib）的老化模型是一项复杂的挑战，该模型可以捕获现实条件下多方面的降解机制及其相互依赖性。本研究引入了一种先进的43 Ah汽车级锂离子袋状电池电化学模型，在阳极、阴极和电解质水平上进行参数化，以预测不同操作条件下的日历和循环老化。该模型量化了固体电解质界面（SEI）的生长，区分了存储期间的被动形成和循环期间的加速生长，并结合了锂电镀的详细表示，评估了其在不同充电速率和温度下的影响。为了验证该模型，采用了插电式混合动力汽车（PHEV）的实际驾驶工况，确保与经验数据进行直接比较。该模型成功捕获了温度、荷电状态（SoC）和施加电流等不同条件下的容量衰减，复制了以SEI形成和锂电镀为主导的老化途径。在一个统一的框架内描述这些根本不同的退化模式的能力强调了模型的鲁棒性和预测能力。通过准确区分日历引起的SEI增厚和周期加速的SEI增长，该模型提供了对产能损失的机制估计，而不依赖于纯粹的经验拟合。这些结果强调了在老化模型中同时考虑SEI形成和锂镀层的必要性，以实现对锂离子电池降解的全面和预测性理解。

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引用次数: 0

"Bridging complexity and accessibility: A novel model for PV and BESS capacity estimation in rural microgrids near the equatorial region" “弥合复杂性和可及性：赤道地区农村微电网光伏和BESS容量估算的新模型”

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

Pub Date : 2025-09-06 DOI: 10.1016/j.prime.2025.101107

Hironobu Matsuo , Yash Pandey , Md Imtiaz Kabir , Sourasis Chattopadhyay

This study introduces a straightforward and effective methodology for determining the optimal capacities of photovoltaic (PV) systems and battery energy storage systems (BESS) in microgrids integrated with backup power sources. Utilizing load profiles and global horizontal irradiance (GHI) data, the method focuses on rural electrification scenarios. Simulation analyses were conducted using HOMER software, incorporating load profile data from Katurukila village and meteorological data from the Morogoro region of Tanzania. A microgrid configuration including PV systems, BESS, and diesel generators (DG) was modeled to systematically evaluate various scenarios and variations in both load profiles and GHI parameters. The study resulted in the derivation of equations capable of estimating optimal PV and BESS capacities based on differentiated daytime and nighttime electricity consumption patterns, specifically tailored to enhance cost optimization under sufficient resilience. Moreover, this research develops and validates a simplified numerical model that effectively integrates load profiles with GHI data, providing practical insights and solutions for designing cost-effective microgrids, particularly beneficial in rural electrification. A key contribution is the development of simple empirical equations for rapid capacity estimation, enabling technically sound decisions in resource-limited settings without the need for complex simulation tools. The model demonstrated strong predictive performance with a mean absolute percentage error (MAPE) of 2.4% & 2.3% across PV and BESS estimations respectively compared to HOMER results. Validated over daily energy consumption ranging from 100 to 2500 kWh and GHI values between 3 and 8 kWh/m²/day, the method applies to diverse load profiles, including total load (TL), residential load (RL), and commercial load (CML). The derived linear regression equations enable instantaneous capacity estimation with minimal input data. The approach is positioned as a bridge between accessibility and technical rigor for early stage microgrid planning. The outcomes of this research hold relevance for both on-grid and off-grid microgrid applications for the region near the equator, where seasonal variations in load and temperature are not significant, emphasizing their practical utility in rural settings.

本研究介绍了一种简单有效的方法，用于确定与备用电源集成的微电网中光伏（PV）系统和电池储能系统（BESS）的最佳容量。利用负荷分布和全球水平辐照度（GHI）数据，该方法侧重于农村电气化情景。利用HOMER软件进行模拟分析，结合来自Katurukila村的负荷剖面数据和来自坦桑尼亚莫罗戈罗地区的气象数据。本文对包括光伏系统、BESS和柴油发电机（DG）在内的微电网配置进行了建模，系统地评估了负载分布和GHI参数的各种情况和变化。该研究的结果是推导出了能够根据不同的白天和夜间电力消耗模式估计最佳光伏和BESS容量的方程，专门针对充分弹性下的成本优化进行了定制。此外，本研究开发并验证了一个简化的数值模型，该模型有效地将负载分布与GHI数据集成在一起，为设计具有成本效益的微电网提供了实用的见解和解决方案，尤其有利于农村电气化。一个关键的贡献是开发了用于快速容量估计的简单经验方程，使得在资源有限的情况下无需复杂的模拟工具就可以做出技术上合理的决策。该模型显示出较强的预测性能，与HOMER结果相比，PV和BESS估计的平均绝对百分比误差（MAPE）分别为2.4%和2.3%。该方法的日能耗范围为100至2500千瓦时，GHI值为3至8千瓦时/平方米/天，适用于各种负荷，包括总负荷（TL）、住宅负荷（RL）和商业负荷（CML）。所导出的线性回归方程能够以最小的输入数据进行瞬时容量估计。该方法被定位为早期微电网规划的可访问性和技术严谨性之间的桥梁。这项研究的结果与赤道附近地区的并网和离网微电网应用相关，在那里负荷和温度的季节性变化并不显著，强调了它们在农村环境中的实际效用。

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引用次数: 0

Fault diagnosis in renewable-integrated distribution systems using EMD-GAF and ANN 基于EMD-GAF和ANN的可再生集成配电系统故障诊断

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

Pub Date : 2025-09-04 DOI: 10.1016/j.prime.2025.101101

Dhanunjayudu N. , Eswaramoorthy K. Varadharaj , Mohana Rao M. , Krishnaiah J.

The increasing integration of distributed renewable energy sources and dynamic loads has made fault detection in modern distribution systems significantly more challenging. Traditional protection schemes often fail to accurately distinguish between faults and non-fault disturbances such as switching events, islanding, or power quality anomalies, which can lead to delayed or incorrect responses. This paper proposes a fast and reliable fault diagnosis technique integrating Empirical Mode Decomposition (EMD), Gramian Angular Fields (GAF), and Artificial Neural Networks (ANN) to detect, classify, and locate faults in renewable-integrated distribution networks. Three-phase current and voltage signals are first decomposed using EMD to extract low-frequency residues, that are then transformed into two-dimensional GAF visual patterns. Cosine similarity compares these patterns against reference healthy conditions for fault detection.

For fault localization, an ANN is trained using statistical features from four levels of EMD residues. The proposed method achieves over 99.5% accuracy in fault detection and classification using only 0.25 cycles of post-fault data and single-point current and voltage measurements at the substation, even under noisy (20 dB SNR) and high-impedance (up to 5

Ω

) conditions. It outperforms existing signal-analysis-based and visual-pattern-based techniques by accurately distinguishing faults from switching and islanding events, making it a robust and scalable solution for real-time smart grid protection. Furthermore, the method achieves up to 99.04% bus-level fault localization accuracy and reduces distance-to-fault errors by over 25% compared to existing techniques, further enhancing suitability for protection and precise fault location.

分布式可再生能源与动态负荷的日益融合，使现代配电系统的故障检测变得更加具有挑战性。传统的保护方案往往不能准确区分故障和非故障干扰（如开关事件、孤岛或电能质量异常），从而导致响应延迟或错误。本文提出了一种结合经验模态分解（EMD）、格拉曼角场（GAF）和人工神经网络（ANN）的快速、可靠的故障诊断技术，用于可再生综合配电网的故障检测、分类和定位。首先使用EMD对三相电流和电压信号进行分解，提取低频残基，然后将其转化为二维GAF视觉图形。余弦相似度将这些模式与故障检测的参考健康状况进行比较。对于故障定位，使用四层EMD残差的统计特征来训练人工神经网络。即使在噪声（20 dB信噪比）和高阻抗（高达5 Ω）条件下，该方法仅使用0.25个故障后数据和变电站单点电流和电压测量，在故障检测和分类中也能达到99.5%以上的准确率。它通过准确区分开关和孤岛事件的故障，优于现有的基于信号分析和基于视觉模式的技术，使其成为实时智能电网保护的鲁棒性和可扩展性解决方案。此外，与现有技术相比，该方法可实现高达99.04%的母线级故障定位精度，将故障距离误差降低25%以上，进一步提高了保护的适用性和故障定位的精度。

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引用次数: 0

Optimal defense against security threat on stability of grid-integrated synchronous generator during FDI attack FDI攻击下并网同步发电机稳定性安全威胁的最优防御

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

Pub Date : 2025-09-01 DOI: 10.1016/j.prime.2025.101097

Fariya Tabassum , M.S. Rana , Tasnim Sarker Joyeeta , Md. Mahmudul Hasan

Recent internet of energy (IoE)-based transactive energy frameworks (TrEFs) have introduced a wide array of information and communication technology (ICT) devices into conventional power systems, significantly increasing their vulnerability to cyber-attacks. The emergence of prosumers – entities that both consume and generate energy at the distribution level – has further intensified this risk. While considerable research has focused on mitigating attacks on the distribution side of the network, the internal dynamics of power-generating units under such threats have received comparatively less attention. This work addresses this gap by proposing an optimal transient control strategy based on a linear-quadratic-Gaussian (LQG) controller to enhance the resilience of power-generating units during cyber-attacks within TrEFs. The control approach is implemented and tested on a single-machine infinite-bus structure. Designed controller’s performance is justified by comparing with other typical and advanced controllers. The robustness of the proposed scheme is evaluated under worst-case conditions, as well as in the other cases, including the simultaneous occurrence of severe system faults and various false data injection (FDI) attacks. Simulation results demonstrate that the proposed controller can regain its marginal operating conditions within 0.93, 0.53 and 0.47 s, when the system is considered under scale, pulse and ramp FDI attack respectively highlighting its effectiveness and reliability.

最近基于能源互联网（IoE）的交互式能源框架（tref）将广泛的信息和通信技术（ICT）设备引入传统电力系统，大大增加了其对网络攻击的脆弱性。产消者——在分配层面上既消费能源又生产能源的实体——的出现进一步加剧了这种风险。虽然大量的研究集中在减轻对网络配电侧的攻击上，但发电机组在这种威胁下的内部动态受到的关注相对较少。这项工作通过提出基于线性二次高斯（LQG）控制器的最优暂态控制策略来解决这一差距，以增强tref内网络攻击期间发电机组的弹性。该控制方法在单机无限总线结构上进行了实现和测试。通过与其它典型控制器和先进控制器的比较，验证了所设计控制器的性能。该方案在最坏情况下以及其他情况下的鲁棒性进行了评估，包括同时发生严重系统故障和各种虚假数据注入（FDI）攻击。仿真结果表明，当系统分别考虑尺度、脉冲和斜向FDI攻击时，所提控制器能在0.93、0.53和0.47 s内恢复其边际运行状态，突出了所提控制器的有效性和可靠性。

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引用次数: 0

Retraction Notice to “Enhancing DC Microgrids Cluster Performance with Distributed Event-Triggered Consensus Protocols” [e-Prime - Advances in Electrical Engineering, Electronics and Energy 10 (2024)100773] 关于“利用分布式事件触发共识协议增强直流微电网集群性能”的撤回通知[e-Prime -电气工程，电子与能源进展10 (2024)100773]

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

Pub Date : 2025-09-01 DOI: 10.1016/j.prime.2025.101092

Hanan Mohammed Jawad Ghanimi, Hoda Ghoreishy, Seyyed Mehdi Mirimani

引用次数: 0

Integrated techno-economic optimization and metaheuristic benchmarking of grid-connected hybrid renewable energy systems using real-world load and climate data 基于真实负荷和气候数据的并网混合可再生能源系统综合技术经济优化和元启发式基准测试

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

Pub Date : 2025-09-01 DOI: 10.1016/j.prime.2025.101099

Aykut Fatih Güven , Onur Özdal Mengi , Mohit Bajaj , Ahmad Taher Azar , Walid El-Shafai

This study proposes an integrated optimization framework for the techno-economic sizing and performance evaluation of a grid-connected hybrid renewable energy system (HRES) comprising photovoltaic (PV) panels, wind turbines (WT), battery storage (BTS), and a diesel generator (DG). A real-world case study is conducted on a university campus in Turkey using high-resolution hourly meteorological and load data over a full year (8760 h). The objective is to minimize the annualized cost of the system (ACS), levelized cost of energy (LCOE), and total net present cost (TNPC), while ensuring high reliability through a constraint on the loss of power supply probability (LPSP) at 0.5 %. The decision variables include the optimal capacities of PV, WT, DG, BT, and inverter components, bounded by technical, economic, and operational constraints, including a minimum renewable energy fraction (REF) requirement. The system's energy production, storage, and grid interactions are modeled using detailed mathematical formulations. Optimization is performed using the Moth-Flame Optimization Algorithm (MFOA) and benchmarked against the Whale Optimization Algorithm (WOA), Flower Pollination Algorithm (FPA), and Genetic Algorithm (GA). Simulation results identify the PV/WT/BT configuration as the most cost-effective and reliable, achieving an LCOE of $0.1342/kWh, a TNPC of $3.2542 × 10⁶, and an ACS of $2.9214 × 10⁵. These values reflect a 33 % cost reduction compared to the off-grid configuration. Additionally, the system enables annual grid electricity purchases of up to 4.4086 × 10⁵ kWh and sales of up to 1.2114 × 10⁶ kWh. Notably, the achieved LCOE is significantly lower than the prevailing commercial grid tariff of $0.35/kWh in Turkey, demonstrating the financial competitiveness of the proposed system for institutional and commercial users. In terms of algorithmic performance, MFOA outperforms the other methods by delivering the fastest convergence, highest optimization stability, and a fully renewable solution (REF = 100 %) without DG operation. This solution achieves an LCOE of $0.1443/kWh and a TNPC of $3.5085 × 10⁶, which is slightly higher than the absolute minimum cost but demonstrates the ability to reach 100 % renewable penetration without diesel usage. The system also reports the shortest execution time (336.5 s), confirming its suitability for real-time or iterative design tasks. Overall, the proposed HRES configuration offers a technically feasible, economically advantageous, and environmentally sustainable solution for campus electrification and broader smart grid applications, and serves as a replicable decision-support model for renewable energy planning in regions with high electricity tariffs.

本研究提出了一个由光伏（PV）面板、风力涡轮机（WT）、电池存储（BTS）和柴油发电机（DG）组成的并网混合可再生能源系统（HRES）的技术经济规模和性能评估的集成优化框架。在土耳其的一所大学校园进行了一个真实的案例研究，使用了一整年（8760小时）的高分辨率每小时气象和负荷数据。目标是最小化系统的年化成本（ACS）、平准化能源成本（LCOE）和总净当前成本（TNPC），同时通过将电源损失概率（LPSP）限制在0.5%来确保高可靠性。决策变量包括PV， WT， DG， BT和逆变器组件的最佳容量，受技术，经济和操作约束的限制，包括最低可再生能源分数（REF）要求。该系统的能源生产、储存和电网相互作用使用详细的数学公式进行建模。优化使用蛾焰优化算法（MFOA）进行，并与鲸鱼优化算法（WOA），花卉授粉算法（FPA）和遗传算法（GA）进行基准测试。仿真结果表明PV/WT/BT配置最具成本效益和可靠性，LCOE为0.1342美元/千瓦时，TNPC为3.2542美元× 10⁶，ACS为2.9214美元× 10 5。这些数值表明，与离网配置相比，成本降低了33%。此外，该系统每年可使电网购电高达4.4086 × 10 5千瓦时，售电高达1.2114 × 10 26千瓦时。值得注意的是，实现的LCOE明显低于土耳其现行的0.35美元/千瓦时的商业电网电价，这表明拟议系统对机构和商业用户具有财务竞争力。在算法性能方面，MFOA优于其他方法，具有最快的收敛速度、最高的优化稳定性和完全可再生的解决方案（REF = 100%），无需DG操作。该解决方案的LCOE为0.1443美元/千瓦时，TNPC为3.5085美元× 10 26，略高于绝对最低成本，但证明了在不使用柴油的情况下实现100%可再生能源渗透的能力。该系统还报告了最短的执行时间（336.5秒），确认其适合于实时或迭代设计任务。总体而言，拟议的HRES配置为校园电气化和更广泛的智能电网应用提供了技术上可行、经济上有利和环境可持续的解决方案，并可作为高电价地区可再生能源规划的可复制决策支持模型。

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引用次数: 0

Reinforcement learning based control approach for PMSM drives — Theory, concept, design and realizations 基于强化学习的永磁同步电机驱动控制方法-理论，概念，设计和实现

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

Pub Date : 2025-09-01 DOI: 10.1016/j.prime.2025.101095

Nándor Szécsényi, Péter Stumpf

With the recent advancements made in Artificial Intelligence, it is possible that Reinforcement Learning based data driven methods can become a next generation technology to control electrical drives instead of the classical model-based techniques. However, providing the correct setup and hyperparameters for training the agent is challenging and usually not evident. The manuscript aims to present the main steps of the design workflow to apply Reinforcement Learning for controlling the current of a PMSM drive. Along these steps, every major design consideration is summarized in addition to providing the necessary theoretical background. Furthermore, a thorough examination of the hyperparameter search stage is provided with several experiments in Python on the most influential parameters of the environment. The most optimal setup based on the compared results is selected and evaluated using a testing environment in Matlab that resembles a real-life application scenario. The paper summarize the findings in the form of practical guidelines that are crucial for achieving a high level of performance, and they also minimize the time needed for the implementation and training of the RL agent.

随着人工智能的最新进展，基于强化学习的数据驱动方法有可能成为下一代控制电力驱动的技术，而不是传统的基于模型的技术。然而，为训练代理提供正确的设置和超参数是具有挑战性的，而且通常不明显。该手稿旨在介绍设计工作流程的主要步骤，以应用强化学习来控制PMSM驱动器的电流。沿着这些步骤，除了提供必要的理论背景外，还总结了每个主要的设计考虑。此外，对超参数搜索阶段进行了彻底的检查，并在Python中对环境中最具影响力的参数进行了几个实验。根据比较结果选择最优设置，并使用类似于现实应用场景的Matlab测试环境进行评估。本文以实用指南的形式总结了这些发现，这些指南对于实现高水平的性能至关重要，并且它们还最大限度地减少了RL代理的实施和培训所需的时间。

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引用次数: 0

Robust H∞ control for master-slave power converters in renewable energy systems 可再生能源系统主从电源变换器的鲁棒H∞控制

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

Pub Date : 2025-09-01 DOI: 10.1016/j.prime.2025.101104

Hawraa Q. Hameed, Fadhil A. Hasan, Lina J. Rashad

This paper proposes a robust H∞ control technique to enhance the stability and performance of master-slave power converters in renewable energy systems. The microgrid's parameters often vary continuously due to the changes in generation patterns, load fluctuations, and grid impedance uncertainties. The proposed method presents a mathematical analysis and derivation of the H∞ controller for the master-slave converters that can face the parameter uncertainty and external disturbances. The detailed modeling and simulation of the system were illustrated and validated using the MATLAB toolbox and SIMULINK. The simulation results show that the designed H∞ controller outperforms conventional PI control in all tested scenarios in terms of time and frequency responses. Compared to the PI controller, the proposed controller shows superior dynamic performance by reducing the rise and settling times by about 60 % and 80 %, respectively, with the absence of overshoot and undershoot. Besides, the disturbance rejection performance shows an enhancement in transient time, settling time, and peak deviation by about 56 %, 55 %, and 66 %, respectively. Notably, while the H∞ controller improves power control, its effectiveness in voltage regulation is limited across various operating modes, where the voltage profile varies by about ±6 % from its nominal value, except in the voltage restorer mode, where the voltage can be controlled precisely.

提出了一种鲁棒H∞控制技术，以提高可再生能源系统中主从功率变换器的稳定性和性能。由于发电方式的变化、负荷的波动和电网阻抗的不确定性，微电网的参数往往是连续变化的。该方法对主从变换器在参数不确定性和外部干扰下的H∞控制器进行了数学分析和推导。利用MATLAB工具箱和SIMULINK对系统进行了详细的建模和仿真，并进行了验证。仿真结果表明，在所有测试场景下，所设计的H∞控制器在时间和频率响应方面都优于传统的PI控制。与PI控制器相比，该控制器在无超调和欠调的情况下，将上升和沉降时间分别缩短了约60%和80%，具有优越的动态性能。此外，扰动抑制性能在瞬态时间、稳定时间和峰值偏差方面分别提高了约56%、55%和66%。值得注意的是，虽然H∞控制器改善了功率控制，但其电压调节的有效性在各种工作模式下受到限制，其中电压分布从其标称值变化约±6%，但电压恢复模式除外，电压可以精确控制。

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引用次数: 0

Corrigendum to “Optimization of microstrip antenna S11 gain using fuzzy rough set-based pseudocode algorithm” [e-Prime - Advances in Electrical Engineering, Electronics and Energy, Volume 13, 2025, 101061] “使用基于模糊粗糙集的伪码算法优化微带天线S11增益”的勘误表[e-Prime -电气工程，电子和能源进展，第13卷，2025,101061]

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

Pub Date : 2025-09-01 DOI: 10.1016/j.prime.2025.101084

Fredelino A. Galleto Jr. , Aaron Don M. Africa

引用次数: 0