International Journal of Electrical Power & Energy Systems最新文献

A data-driven droop control strategy for reactive power sharing and stability enhancement in islanded AC microgrids 孤岛交流微电网无功共享与稳定性增强的数据驱动下垂控制策略

IF 5 2区工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

International Journal of Electrical Power & Energy Systems

Pub Date : 2026-03-01 Epub Date: 2026-03-02 DOI: 10.1016/j.ijepes.2026.111736

Adham Osama , Tarek H.M. EL-Fouly , Hatem H. Zeineldin , Ehab F. El-Saadany

In islanded microgrids, distributed generation (DG) units employing conventional droop control often exhibit significant mismatches in reactive power sharing. This issue primarily arises from the mismatches in line impedances. These inaccuracies not only disrupt reactive power balance but also pose potential risks to system stability. This study proposes an adaptive droop control strategy that integrates conventional droop control with the Estimation of Signal Parameters via Rotational Invariance Technique (ESPRIT) for online dominant mode identification, enhanced by Particle Swarm Optimization (PSO). ESPRIT is selected for its low computational burden and robustness, while PSO efficiently handles nonlinear optimization problems. The proposed controller dynamically adjusts the reactive power droop gain to achieve accurate reactive power sharing and simultaneously enhance the microgrid stability margin. Effectiveness of the proposed approach is evaluated on a 34-bus microgrid benchmark and compared with conventional droop, virtual impedance-based control, and modified reactive power-voltage (Q-V) droop control strategies. Simulation results demonstrate that the approach achieves precise reactive power sharing and improves the stability margin by 12.36 times compared to conventional droop control. The controller also maintains robust performance under communication link failures. Real-time validation using the OPAL-RT simulator confirms practical applicability. The proposed strategy provides a scalable, communication-efficient solution for stability and reactive power sharing, enhances understanding of the interaction between stability and power sharing, and supports reliable operation and renewable integration. The study is limited to linear loads and does not consider communication delays, which will be addressed in future work.

在孤岛微电网中，采用传统下垂控制的分布式发电（DG）机组在无功共享中经常表现出明显的不匹配。这个问题主要是由线路阻抗不匹配引起的。这些不准确不仅会破坏无功平衡，而且会对系统稳定性造成潜在风险。本研究提出了一种自适应下垂控制策略，该策略将传统下垂控制与基于旋转不变性技术（ESPRIT）的信号参数估计相结合，用于在线优势模式识别，并通过粒子群优化（PSO）进行增强。ESPRIT算法计算量小，鲁棒性好，而粒子群算法能有效地处理非线性优化问题。该控制器可动态调节无功下垂增益，实现准确的无功分担，同时提高微网稳定裕度。在34总线微电网基准上对该方法的有效性进行了评估，并与传统的下垂、基于虚拟阻抗的控制和改进的无功电压（Q-V）下垂控制策略进行了比较。仿真结果表明，该方法实现了精确的无功分担，稳定裕度比传统下垂控制提高了12.36倍。该控制器在通信链路故障情况下也能保持良好的性能。使用OPAL-RT模拟器进行实时验证，证实了该方法的实用性。该策略为稳定性和无功共享提供了一种可扩展、通信高效的解决方案，增强了对稳定性和功率共享之间相互作用的理解，并支持可靠运行和可再生能源集成。该研究仅限于线性负载，没有考虑通信延迟，这将在未来的工作中得到解决。

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引用次数: 0

Debiased probabilistic reliability assessment of integrated heat-electricity systems with deep learning uncertainty 具有深度学习不确定性的综合热电系统去偏概率可靠性评估

IF 5 2区工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

International Journal of Electrical Power & Energy Systems

Pub Date : 2026-03-01 Epub Date: 2026-02-16 DOI: 10.1016/j.ijepes.2026.111647

Nan Lin, Keng-Weng Lao, Shaohua Yang, Zhanghao Huang, Yu Nie

Fast growing uncertainties in integrated heat-electricity systems (IHESs) raise a desire for reliability assessment in a shorter period. This paper proposes a debiased gated recurrent unit (GRU)-based model for the short-term probabilistic reliability assessment (PRA) of IHESs. First, numerous uncertainties in IHES, such as electric load, thermal load, heat pump generation, photovoltaic panel generation, etc., are considered to train a GRU-based PRA model. Different from conventional methods, the proposed method can provide the probabilistic distribution information of energy not supplied (ENS). Second, a cost-sensitive method based on kernel density estimation is proposed to correct the assessment tendency induced by the imbalance of the continued ENS labels, which can reduce 30% of mean absolute error. More importantly, in addition to the uncertainties in IHESs, the uncertainty induced by the GRU network is further considered. The upper and lower bounds of ENS are calculated to tolerate the uncertainty of the GRU network, thereby enhancing the credibility of the proposed PRA method. The proposed method is validated in a modified Barry Island energy system, showing a similar accuracy with Monte-Carlo Simulation but saving 99.9% of time consumption.

综合热电系统（IHESs）快速增长的不确定性引起了人们对较短时间内可靠性评估的渴望。提出了一种基于去偏门控循环单元（GRU）的IHESs短期概率可靠性评估模型。首先，考虑了电力负荷、热负荷、热泵发电、光伏板发电等IHES中的众多不确定性，训练了基于gru的PRA模型。与传统方法不同的是，该方法可以提供无供能的概率分布信息。其次，提出了一种基于核密度估计的代价敏感方法来纠正连续ENS标签不平衡导致的评估倾向，可将平均绝对误差降低30%；更重要的是，除了IHESs的不确定性外，还进一步考虑了GRU网络引起的不确定性。计算ENS的上界和下界以容忍GRU网络的不确定性，从而提高了所提出的PRA方法的可信度。该方法在改进的巴里岛能源系统中得到了验证，其精度与蒙特卡罗模拟相似，但节省了99.9%的时间消耗。

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引用次数: 0

Data mining-driven uncertainty analysis for the planning of photovoltaic-energy storage-charging station in power-traffic coupled networks 电力-交通耦合网络中光伏-储能-充电站规划的数据挖掘驱动不确定性分析

IF 5 2区工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

International Journal of Electrical Power & Energy Systems

Pub Date : 2026-03-01 Epub Date: 2026-03-09 DOI: 10.1016/j.ijepes.2026.111710

Xuming Chen, Le Liu, Xiaoning Kang

The transition to a low-carbon transportation system is crucial for urban sustainable development, requiring the integration of electric vehicles, renewable energy, and intelligent transportation systems. The photovoltaic energy storage-charging station (PV-ES-CS), as an effective integration of these key technologies, plays a critical role in the planning of the low-carbon city. This paper considers the dynamic changes in traffic impedance and addresses the issues of siting, sizing of PV-ES-CS and traffic flow distribution in the PTNs, using data mining to assess the impact of multiple uncertainties. First, the dynamic changes in the transportation network are considered by establishing road impedance and user behavior models. The uncertainty of user route choices is incorporated into the traffic flow distribution, and the siting and sizing of PV-ES-CS are determined based on the flow distribution results. Next, a data mining model based on SHAP is proposed, The model identifies the components of the objective function that are most sensitive to uncertainty. Additionally, it extracts the key factors among multiple uncertainty sources. Then, sensitivity analysis is conducted on the impacts of these uncertainties on traffic flow and carbon emission costs. Finally, the proposed model is applied to a coupled network of the IEEE-33 node and a specific traffic network in Xi’an, Shaanxi Province, China. The results show that the proposed model effectively reduces road congestion and carbon emission costs, and the data mining framework can identify key variables, providing a comprehensive solution for low-carbon transportation city planning.

向低碳交通系统过渡对城市可持续发展至关重要，需要将电动汽车、可再生能源和智能交通系统相结合。光伏储能充电站（PV-ES-CS）作为这些关键技术的有效整合，在低碳城市规划中发挥着至关重要的作用。本文考虑了交通阻抗的动态变化，利用数据挖掘的方法，对路网中PV-ES-CS的选址、规模和交通流分布等问题进行了研究。首先，通过建立道路阻抗和用户行为模型，考虑交通网络的动态变化。将用户路径选择的不确定性纳入到交通流分布中，根据流量分布结果确定PV-ES-CS的选址和规模。其次，提出了一种基于SHAP的数据挖掘模型，该模型识别目标函数中对不确定性最敏感的成分。并从多个不确定源中提取关键因素。然后，对这些不确定性对交通流和碳排放成本的影响进行敏感性分析。最后，将该模型应用于中国陕西省西安市的一个IEEE-33节点与特定交通网络的耦合网络。结果表明，该模型有效降低了道路拥堵和碳排放成本，数据挖掘框架能够识别关键变量，为低碳交通城市规划提供了综合解决方案。

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引用次数: 0

Data-feature hybrid optimization for ultra-short-term wind power forecasting 超短期风电预测的数据特征混合优化

IF 5 2区工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

International Journal of Electrical Power & Energy Systems

Pub Date : 2026-03-01 Epub Date: 2026-03-09 DOI: 10.1016/j.ijepes.2026.111742

Pengzhan Xu , Songjie Zhang , Pengxin Luo , Xianbo Wang , Yunfeng Yan , Donglian Qi

Accurate and real-time wind power forecasting can effectively mitigate the impact of wind power grid connection fluctuations on the power system, enhancing the safety, stability, economy, and controllability of wind power generation. However, ultra-short-term wind power forecasting currently faces challenges such as incomplete time-series data, difficult-to-eliminate outliers, low interpretability of prediction features lacking physical meaning, and unstable multi-step predictions. To address these issues, this paper proposes an ultra-short-term wind power forecasting framework based on data-feature-model hybrid optimization. Firstly, the unsupervised clustering silhouette coefficient is used to adaptively determine the optimal parameters of the DBSCAN algorithm within each wind speed interval, enabling the identification and correction of abnormal data. Secondly, three correlation indicators—Pearson correlation coefficient, Grey Relational Analysis (GRA), and Maximum Information Coefficient (MIC)—are weighted and integrated with equal weights to select factors strongly correlated with wind power as model inputs. Finally, the window attention mechanism is combined with Fourier transform to replace the sparse attention module of the original Informer model, constructing an improved prediction model. Experimental results on three real-world datasets (SDWPF, Kaggle, and Penmanshiel) show that compared with state-of-the-art models, the proposed method achieves higher prediction accuracy, better stability, and faster execution efficiency. This method provides reliable data support for the friendly grid connection of large-scale wind power, verifying the effectiveness of synergistic optimization of data quality, feature relevance, and model architecture.

准确、实时的风电功率预测可以有效缓解风电并网波动对电力系统的影响，增强风电的安全性、稳定性、经济性和可控性。然而，超短期风电预测目前面临着时间序列数据不完整、异常值难以消除、预测特征缺乏物理意义可解释性低、多步预测不稳定等挑战。针对这些问题，本文提出了一种基于数据-特征-模型混合优化的超短期风电预测框架。首先，利用无监督聚类廓形系数自适应确定各风速区间内DBSCAN算法的最优参数，实现异常数据的识别和校正；其次，对pearson相关系数、灰色关联分析（GRA）和最大信息系数（MIC）三个相关指标进行加权等权整合，选择与风电关联度强的因素作为模型输入；最后，将窗口注意机制与傅里叶变换相结合，替换原有Informer模型的稀疏注意模块，构建改进的预测模型。在SDWPF、Kaggle和Penmanshiel三个真实数据集上的实验结果表明，与现有模型相比，该方法具有更高的预测精度、更好的稳定性和更快的执行效率。该方法为大规模风电友好并网提供了可靠的数据支持，验证了数据质量、特征相关性、模型架构协同优化的有效性。

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引用次数: 0

Impact of wind speed bias correction methods on reanalysis-based wind power prediction 风速偏置校正方法对再分析风电预测的影响

IF 5 2区工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

International Journal of Electrical Power & Energy Systems

Pub Date : 2026-03-01 Epub Date: 2026-02-25 DOI: 10.1016/j.ijepes.2026.111700

Elizabeth von Zuben, Kristen R. Schell

Compared to other wind speed datasets used to calculate wind power, global reanalyses are the most accessible. However, reanalysis wind speeds are also known to contain biases, which can be compounded when predicting wind power. While several bias correction methods exist, whether these methods improve the downstream task of hourly wind energy prediction has not yet been systematically studied across different terrain. In this work, ERA5 and MERRA2 global reanalyses are compared to observational surface wind speeds from 268 weather stations across Canada. Trends are identified between modelled wind speed and various terrain complexity measures. The impact of bias correction methods for wind speed is studied across 52 wind farms in the provinces of Alberta and Ontario, using both the power curve and machine learning methods. Bias correction methods are found to make effectively no difference to random forest power predictions. When using the power curve method, no single bias correction method is found to improve all evaluation metrics, however linear mixed effects model corrections show the best results. Recommendations are proposed for the use of global reanalysis wind speeds based on terrain features and for the use of bias correction in wind power prediction.

与用于计算风力的其他风速数据集相比，全球再分析是最容易获得的。然而，重新分析风速也有偏差，在预测风力时可能会更加复杂。虽然存在几种偏差校正方法，但这些方法是否能改善逐时风能预测的下游任务尚未在不同地形上进行系统研究。在这项工作中，ERA5和MERRA2全球再分析与加拿大268个气象站的观测地面风速进行了比较。确定了模拟风速与各种地形复杂性度量之间的趋势。在艾伯塔省和安大略省的52个风电场中，使用功率曲线和机器学习方法研究了偏差校正方法对风速的影响。发现偏差校正方法对随机森林功率预测没有有效的影响。当使用功率曲线法时，没有发现单一的偏差校正方法可以改善所有评价指标，而线性混合效应模型校正效果最好。建议使用基于地形特征的全球再分析风速和在风力预测中使用偏差校正。

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引用次数: 0

Fault section identification in single-stage submarine DC power transmission systems via impedance spectrum functionals 基于阻抗谱函数的单级海底直流输电系统故障区段识别

IF 5 2区工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

International Journal of Electrical Power & Energy Systems

Pub Date : 2026-03-01 Epub Date: 2026-03-14 DOI: 10.1016/j.ijepes.2026.111760

Erjia Zhang , Mouduo Yu , Wentao Huang , Nengling Tai

This paper investigates the problem of faulted-section discrimination in island-based DC power supply systems under single-ended measurement conditions. In such systems, measurement nodes are inherently limited, and the input impedance exhibits strong frequency dependence, which disperses and obscures section-related information in terminal responses and makes reliable faulted-section discrimination a critical yet challenging diagnostic task. To address this issue, the input impedance spectrum is modeled from a function-space perspective, where impedance responses under different fault conditions are treated as complex-valued spectral trajectories defined over the frequency axis. Five impedance-spectrum functionals with distinct frequency sensitivities are constructed to map these trajectories into real-valued response vectors, extracting complementary structural features including low-frequency energy levels, mid-frequency phase variations, modal density characteristics, global spectral shifts, and high-frequency attenuation behavior. Correlation analysis and frequency-overlap evaluation are employed to suppress functional redundancy and optimize the embedding dimension. As a result, response vectors form clear cluster structures in the response space, each corresponding to a specific faulted section, and faulted-section discrimination is achieved using a distance-based nearest-cluster assignment rule. The results demonstrate that the proposed functional mapping framework effectively transforms frequency-domain observations into geometrically separable representations, providing an interpretable and computationally tractable pathway for single-ended faulted-section discrimination in island-based DC power supply systems.

研究了单端测量条件下孤岛型直流供电系统的故障段判别问题。在这样的系统中，测量节点本身是有限的，输入阻抗表现出强烈的频率依赖性，这分散和模糊了终端响应中的截面相关信息，使可靠的故障截面判别成为一项关键但具有挑战性的诊断任务。为了解决这个问题，输入阻抗谱从函数空间的角度建模，其中不同故障条件下的阻抗响应被视为在频率轴上定义的复值谱轨迹。构建了五个具有不同频率灵敏度的阻抗谱函数，将这些轨迹映射为实值响应向量，提取互补的结构特征，包括低频能级、中频相位变化、模态密度特征、全局频谱位移和高频衰减行为。采用相关性分析和频率重叠评价来抑制功能冗余，优化嵌入维数。因此，响应向量在响应空间中形成清晰的聚类结构，每个响应向量对应一个特定的断层段，并使用基于距离的最近邻聚类分配规则实现断层段的区分。结果表明，所提出的功能映射框架有效地将频域观测值转换为几何可分表示，为海岛直流供电系统的单端故障段判别提供了可解释和计算可处理的途径。

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引用次数: 0

Virtual power control of electrolyzer system for hydrogen production under unbalanced grid voltage conditions 电网电压不平衡条件下电解槽制氢系统虚功率控制

IF 5 2区工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

International Journal of Electrical Power & Energy Systems

Pub Date : 2026-03-01 Epub Date: 2026-03-12 DOI: 10.1016/j.ijepes.2026.111671

Xinyan Chang, Ning Wang

Grid voltage interface converters play a critical role in supplying power to electrolyzers for hydrogen production. However, the impact of unbalanced grid voltages on the performance of electrolyzers is investigated for the first time in this paper. To ensure high-quality input currents and stable hydrogen production under unbalanced grid voltage conditions, a virtual power control architecture is proposed. The proposed strategy is thoroughly analyzed through theoretical modelling and experimental testes, focusing on its operating principles and dynamic characteristics, and is compared against conventional control methods. Experimental results confirm that the proposed method achieves more balanced and sinusoidal input currents. Compared to traditional strategies—which often result in distorted input currents and unstable output performance—the proposed approach reduces total harmonic distortion on the input side below 5%, thereby improving current quality and enabling more stable hydrogen production.

电网电压接口变换器在向电解槽供氢方面起着至关重要的作用。然而，本文首次研究了电网电压不平衡对电解槽性能的影响。为了在电网电压不平衡的情况下保证高质量的输入电流和稳定的产氢，提出了一种虚拟功率控制架构。通过理论建模和实验分析，重点分析了该策略的工作原理和动态特性，并与传统控制方法进行了比较。实验结果表明，该方法可以实现更平衡的正弦输入电流。传统的策略通常会导致输入电流失真和输出性能不稳定，与之相比，该方法将输入端的总谐波失真降低到5%以下，从而提高了电流质量，实现了更稳定的氢气生产。

引用次数: 0

Enhancing resilient distribution networks through proactive topology optimization and robotaxi dispatch coordination 通过主动拓扑优化和机器人出租车调度协调增强配电网络的弹性

IF 5 2区工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

International Journal of Electrical Power & Energy Systems

Pub Date : 2026-03-01 Epub Date: 2026-02-14 DOI: 10.1016/j.ijepes.2026.111682

Yushen Gong , Tao Yu , Ziyao Wang , Yufeng Wu , Zhenning Pan , Wencong Xiao

Robotaxi (RT), a recently emerging class of autonomous ride-hailing electric taxis, offer significant potential to enhance power distribution network (PDN) resilience through coordinated operation, leveraging their fully dispatchable nature. The charging behavior of electric vehicles (EVs) naturally couples PDN with the road network (RN). However, some studies on PDN resilience overlook the spatial correlation of disaster impacts on both the RN and PDN, which may lead to suboptimal strategies by limiting accurate infrastructure condition assessment and effective coordination between transportation and power systems. Thus, this paper first develops an infrastructure performance degradation model (IPDM) to quantify the joint impact of extreme disasters on both RN and PDN, further analyzing their influence on charging demand variation. Building on this, a coordination framework is proposed to enhance PDN resilience under extreme disasters. This framework innovatively leverages RT fleets as mobile flexibility resources and integrates charging management with proactive PDN topology optimization, including placement of soft open points (SOPs) and circuit breakers (CBs) and PDN reconfiguration. Case studies demonstrate that the proposed framework not only improves PDN resilience but also reduces associated economic cost. Furthermore, sensitivity analysis of varying RT penetration levels on resilience strategies is systematically examined, highlighting the potential of RTs in supporting switch planning.

机器人出租车（RT）是最近兴起的一类自动叫车电动出租车，通过协调运营，利用其完全可调度的性质，为增强配电网络（PDN）的弹性提供了巨大的潜力。电动汽车的充电行为自然地将PDN与路网耦合在一起。然而，一些关于PDN恢复力的研究忽视了灾害影响对RN和PDN的空间相关性，这可能会限制基础设施状况的准确评估和交通与电力系统之间的有效协调，从而导致策略的次优。为此，本文首先建立基础设施性能退化模型（IPDM），量化极端灾害对RN和PDN的联合影响，进一步分析其对充电需求变化的影响。在此基础上，提出了一个协调框架，以增强PDN在极端灾害下的恢复能力。该框架创新性地利用RT车队作为移动灵活性资源，并将充电管理与主动PDN拓扑优化集成在一起，包括软开放点（sop）和断路器（CBs）的放置以及PDN重构。案例研究表明，该框架不仅提高了PDN的弹性，而且降低了相关的经济成本。此外，系统地研究了不同RT渗透水平对弹性策略的敏感性分析，强调了RT在支持交换机规划方面的潜力。

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引用次数: 0

Prognostic analysis of power transformers using high frequency models and Complex-Valued neural networks 基于高频模型和复值神经网络的电力变压器预测分析

IF 5 2区工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

International Journal of Electrical Power & Energy Systems

Pub Date : 2026-03-01 Epub Date: 2026-02-18 DOI: 10.1016/j.ijepes.2026.111701

Marco Bindi, Matteo Intravaia, Lorenzo Becchi, Giosuè Saragoni, Carlo Carobbi, Antonio Luchetta, Gabriele Maria Lozito, Francesco Grasso

This paper proposes a prognostic approach capable of identifying different failure mechanisms in electrical power transformers starting from their high-frequency equivalent circuits. The main objective is to define a general procedure for detecting malfunctions in power transformers before they can lead to catastrophic consequences. Indeed, these malfunction conditions are characterized by variations in the components of the equivalent lumped-parameter model, and this paper shows that they can be correctly recognized through a complex-valued neural network. Therefore, the main contributions of the work consist of the experimental characterization of a power transformer in the 20 Hz – 1 MHz range considering the effects of ambient temperature variations, the parametric analysis of malfunctions and the definition of a procedure based on Power Line Communication technologies and machine learning to detect anomalous situations. The prognostic analysis is organized into four successive phases: experimental measurements, calculation of lumped parameters, simulation of the equivalent circuit and prognostic analysis using complex-valued neural networks. The measurements are performed on a low-voltage isolation transformer in Δ/Y configuration and this allows the overall procedure to be extended to medium voltage transformers used in distribution networks. The results show that malfunctions can be recognized with an average accuracy level of 97%.

本文提出了一种预测方法，能够从电力变压器的高频等效电路开始识别不同的故障机制。主要目的是定义一种在电力变压器故障可能导致灾难性后果之前检测故障的一般程序。实际上，这些故障条件的特征是等效集总参数模型的分量变化，本文表明，它们可以通过复值神经网络正确识别。因此，该工作的主要贡献包括考虑环境温度变化的影响，在20 Hz - 1 MHz范围内对电力变压器进行实验表征，故障的参数分析以及基于电力线通信技术和机器学习检测异常情况的程序定义。预测分析分为四个连续的阶段：实验测量、集总参数计算、等效电路仿真和使用复值神经网络进行预测分析。测量是在Δ/Y配置的低压隔离变压器上进行的，这使得整个过程可以扩展到配电网络中使用的中压变压器。结果表明，故障识别的平均准确率达到97%。

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引用次数: 0

Adaptive fractional-order sliding mode control for robust frequency regulation in islanded renewable microgrids 孤岛可再生微电网鲁棒频率调节的自适应分数阶滑模控制

IF 5 2区工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

International Journal of Electrical Power & Energy Systems

Pub Date : 2026-03-01 Epub Date: 2026-02-18 DOI: 10.1016/j.ijepes.2026.111702

Qing Han , Mohammadreza Askari Sepestanaki , Saleh Mobayen , Van Tinh Nguyen , Paweł Skruch

Frequency regulation in islanded microgrids with high penetration of renewable energy sources is challenging due to low inertia, stochastic generation, and uncertainties associated with inverter-based resources. Conventional controller methods often exhibit limited robustness, excessive chattering, or conservative tuning under such conditions. This paper investigates how robust and smooth frequency stabilization can be achieved in islanded renewable microgrids while explicitly accounting for memory effects and time-varying disturbances. An adaptive finite-time fractional-order sliding mode control strategy is proposed for microgrid frequency regulation. A fractional-order switching surface is designed to capture memory and multi-time-scale dynamics, ensuring finite-time convergence of frequency deviations. An online adaptive law dynamically estimates unknown disturbance bounds, effectively reducing chattering without requiring large switching gains. Moreover, an offline genetic algorithm is employed to optimize the initial controller parameters and enhance transient performance. The main novelty of this work lies in the unified integration of fractional-order dynamics, finite-time sliding mode control, adaptive disturbance estimation, and evolutionary optimization within a single control framework. Extensive MATLAB/Simulink simulations and hardware-in-the-loop experiments validate the proposed approach. Comparative results demonstrate noticeable reductions in frequency overshoot and undershoot, over 80% chattering suppression compared with conventional and existing fractional-order controllers, and improved control smoothness with competitive stabilization times. These results confirm the effectiveness and practical feasibility of the proposed strategy for reliable operation of islanded renewable microgrids under severe disturbances.

由于低惯性、随机发电和与逆变器资源相关的不确定性，具有高可再生能源渗透率的孤岛微电网的频率调节具有挑战性。在这种情况下，传统的控制器方法通常表现出有限的鲁棒性，过度抖振或保守调谐。本文研究了如何在孤岛可再生微电网中实现鲁棒和平滑的频率稳定，同时明确考虑记忆效应和时变干扰。提出了一种微电网频率自适应有限时间分数阶滑模控制策略。设计了分数阶开关曲面来捕获记忆和多时间尺度动态，确保频率偏差的有限时间收敛。在线自适应律动态估计未知干扰边界，在不需要大开关增益的情况下有效地减少抖振。采用离线遗传算法优化控制器初始参数，提高系统暂态性能。这项工作的主要新颖之处在于在单一控制框架内统一集成分数阶动力学，有限时间滑模控制，自适应干扰估计和进化优化。广泛的MATLAB/Simulink仿真和硬件在环实验验证了所提出的方法。对比结果表明，与传统和现有的分数阶控制器相比，该控制器显著降低了频率超调和过调，抑制了80%以上的抖振，并在稳定时间上提高了控制的平稳性。这些结果证实了孤岛式可再生微电网在严重干扰下可靠运行策略的有效性和现实可行性。

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引用次数: 0